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Stefan Mittnik

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Davide Lauria & W. Brent Lindquist & Stefan Mittnik & Svetlozar T. Rachev, 2022. "ESG-Valued Portfolio Optimization and Dynamic Asset Pricing," Papers 2206.02854, arXiv.org.

    Cited by:

    1. Nancy Asare Nyarko & Bhathiya Divelgama & Jagdish Gnawali & Blessing Omotade & Svetlozar T. Rachev & Peter Yegon, 2023. "Exploring Dynamic Asset Pricing within Bachelier’s Market Model," JRFM, MDPI, vol. 16(8), pages 1-18, July.
    2. Nancy Asare Nyarko & Bhathiya Divelgama & Jagdish Gnawali & Blessing Omotade & Svetlozar Rachev & Peter Yegon, 2023. "Exploring Dynamic Asset Pricing within Bachelier Market Model," Papers 2307.04059, arXiv.org.
    3. Yuan Hu & W. Brent Lindquist & Svetlozar T. Rachev & Frank J. Fabozzi, 2023. "Option pricing using a skew random walk pricing tree," Papers 2303.17014, arXiv.org.

  2. Jason R. Bailey & Davide Lauria & W. Brent Lindquist & Stefan Mittnik & Svetlozar T. Rachev, 2022. "Hedonic Models of Real Estate Prices: GAM and Environmental Factors," Papers 2210.14266, arXiv.org.

    Cited by:

    1. Jason R. Bailey & W. Brent Lindquist & Svetlozar T. Rachev, 2024. "Hedonic Models Incorporating ESG Factors for Time Series of Average Annual Home Prices," Papers 2404.07132, arXiv.org.
    2. Jason R. Bailey & W. Brent Lindquist & Svetlozar T. Rachev, 2024. "Hedonic Models Incorporating Environmental, Social, and Governance Factors for Time Series of Average Annual Home Prices," JRFM, MDPI, vol. 17(8), pages 1-17, August.

  3. Abootaleb Shirvani & Stefan Mittnik & W. Brent Lindquist & Svetlozar T. Rachev, 2021. "Bitcoin Volatility and Intrinsic Time Using Double Subordinated Levy Processes," Papers 2109.15051, arXiv.org, revised Aug 2023.

    Cited by:

    1. Yifan He & Abootaleb Shirvani & Barret Shao & Svetlozar Rachev & Frank Fabozzi, 2024. "Beyond the Bid-Ask: Strategic Insights into Spread Prediction and the Global Mid-Price Phenomenon," Papers 2404.11722, arXiv.org, revised Oct 2024.

  4. Cheng Peng & Young Shin Kim & Stefan Mittnik, 2020. "Portfolio Optimization on Multivariate Regime Switching GARCH Model with Normal Tempered Stable Innovation," Papers 2009.11367, arXiv.org, revised Feb 2023.

    Cited by:

    1. Li, Chenxing, 2022. "A multivariate GARCH model with an infinite hidden Markov mixture," MPRA Paper 112792, University Library of Munich, Germany.

  5. Stefan Mittnik & Willi Semmler & Alexander Haider, 2019. "Climate Disaster Risks – Empirics and a Multi-Phase Dynamic Model," IMF Working Papers 2019/145, International Monetary Fund.

    Cited by:

    1. Ar'anzazu de Juan & Pilar Poncela & Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2022. "Economic activity and climate change," Papers 2206.03187, arXiv.org, revised Jun 2022.
    2. Andreas Breitenfellner & Wolfgang Pointner & Helene Schuberth, 2019. "The Potential Contribution of Central Banks to Green Finance," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 88(2), pages 55-71.

  6. Svetlozar Rachev & Stoyan Stoyanov & Stefan Mittnik & Frank J. Fabozzi & Abootaleb Shirvani, 2017. "Behavioral Finance -- Asset Prices Predictability, Equity Premium Puzzle, Volatility Puzzle: The Rational Finance Approach," Papers 1710.03211, arXiv.org, revised Feb 2020.

    Cited by:

    1. Binghui Wu & Tingting Duan, 2019. "Nonlinear Dynamics Characteristic of Risk Contagion in Financial Market Based on Agent Modeling and Complex Network," Complexity, Hindawi, vol. 2019, pages 1-12, June.

  7. Svetlozar T. Rachev & Stefan Mittnik & Frank J. Fabozzi, 2016. "Pricing Derivatives in Hermite Markets," Papers 1612.07016, arXiv.org, revised Dec 2016.

    Cited by:

    1. Daw, Lara & Kerchev, George, 2023. "Fractal dimensions of the Rosenblatt process," Stochastic Processes and their Applications, Elsevier, vol. 161(C), pages 544-571.

  8. Mittnik, Stefan & Semmler, Willi, 2014. "Overleveraging, financial fragility and the banking-macro link: Theory and empirical evidence," ZEW Discussion Papers 14-110, ZEW - Leibniz Centre for European Economic Research.

    Cited by:

    1. Julia M. Puaschunder, 2020. "Monetary Systems," Proceedings of the 16th International RAIS Conference, March 30-31, 2020 001jm1, Research Association for Interdisciplinary Studies.
    2. Nyambuu, Unurjargal & Semmler, Willi, 2017. "Emerging markets’ resource booms and busts, borrowing risk and regime change," Structural Change and Economic Dynamics, Elsevier, vol. 41(C), pages 29-42.
    3. Chen, Pu & Semmler, Willi, 2024. "Wage – price dynamics and financial market in a disequilibrium macro model: A Keynes – Kaldor – Minsky modeling of recession and inflation using VECM," Journal of Economic Behavior & Organization, Elsevier, vol. 220(C), pages 433-452.
    4. Lucidi, Francesco Simone & Semmler, Willi, 2023. "Long-run scarring effects of meltdowns in a small-scale nonlinear quadratic model," Journal of Macroeconomics, Elsevier, vol. 75(C).
    5. Willi Semmler & Brigitte Young, 2024. "Threats of sovereign debt overhang in the EU, the new fiscal rules and the perils of policy drift," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 41(2), pages 565-595, July.
    6. Willi Semmler & Fabio Della Rossa & Giuseppe Orlando & Gabriel R. Padro Rosario & Levent Kockesen, 2023. "Endogenous Economic Resilience, Loss of Resilience, Persistent Cycles, Multiple Attractors, and Disruptive Contractions," Working Papers 2309, New School for Social Research, Department of Economics.
    7. Donal Smith, 2016. "The International Impact of Financial Shocks: A Global VAR and Connectedness Measures Approach," Discussion Papers 16/07, Department of Economics, University of York.
    8. Kanzari, Dalel & Nakhli, Mohamed Sahbi & Gaies, Brahim & Sahut, Jean-Michel, 2023. "Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks," Research in International Business and Finance, Elsevier, vol. 65(C).
    9. Faulwasser Timm & Gross Marco & Semmler Willi & Loungani Prakash, 2020. "Unconventional monetary policy in a nonlinear quadratic model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-19, December.
    10. Issa, Samar & Gevorkyan, Aleksandr V., 2022. "Optimal corporate leverage and speculative cycles: an empirical estimation," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 478-491.

  9. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "The Micro Dynamics of Macro Announcements," CESifo Working Paper Series 4421, CESifo.

    Cited by:

    1. Stefan Sauer & Klaus Wohlrabe, 2018. "The New ifo Business Climate Index for Germany," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 19(02), pages 59-64, July.
    2. Stefan Sauer & Klaus Wohlrabe, 2018. "Das neue ifo Geschäftsklima Deutschland," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(07), pages 54-60, April.
    3. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "Was bewegt den DAX?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(23), pages 32-36, December.
    4. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.

  10. Mittnik, Stefan, 2013. "VaR-implied tail-correlation matrices," CFS Working Paper Series 2013/05, Center for Financial Studies (CFS).

    Cited by:

    1. Mittnik, Stefan, 2014. "VaR-implied tail-correlation matrices," Economics Letters, Elsevier, vol. 122(1), pages 69-73.
    2. Kim, Young Shin & Lee, Jaesung & Mittnik, Stefan & Park, Jiho, 2015. "Quanto option pricing in the presence of fat tails and asymmetric dependence," Journal of Econometrics, Elsevier, vol. 187(2), pages 512-520.
    3. Paulusch, Joachim & Schlütter, Sebastian, 2022. "Sensitivity-implied tail-correlation matrices," Journal of Banking & Finance, Elsevier, vol. 134(C).
    4. Joachim Paulusch, 2017. "The Solvency II Standard Formula, Linear Geometry, and Diversification," JRFM, MDPI, vol. 10(2), pages 1-12, May.
    5. Paulusch, Joachim & Schlütter, Sebastian, 2021. "Sensitivity-implied tail-correlation matrices," ICIR Working Paper Series 33/19, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR), revised 2021.
    6. Aigner, Philipp, 2023. "Identifying scenarios for the own risk and solvency assessment of insurance companies," ICIR Working Paper Series 48/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    7. Haas, Markus & Liu, Ji-Chun, 2015. "Theory for a Multivariate Markov--switching GARCH Model with an Application to Stock Markets," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112855, Verein für Socialpolitik / German Economic Association.

  11. Mittnik, Stefan & Semmler, Willi, 2013. "The real consequences of financial stress," SFB 649 Discussion Papers 2013-011, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Pu Chen & Willi Semmler, 2018. "Short and Long Effects of Productivity on Unemployment," Open Economies Review, Springer, vol. 29(4), pages 853-878, September.
    2. Mittnik, Stefan & Semmler, Willi, 2013. "The real consequences of financial stress," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1479-1499.
    3. Yao, Xiaoyang & Le, Wei & Sun, Xiaolei & Li, Jianping, 2020. "Financial stress dynamics in China: An interconnectedness perspective," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 217-238.
    4. Ubilava, David, 2014. "On the Relationship between Financial Instability and Economic Performance: Stressing the Business of Nonlinear Modelling," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170222, Agricultural and Applied Economics Association.
    5. Frauke Schleer & Willi Semmler, 2014. "Financial Sector and Output Dynamics in the Euro Area: Non-linearities Reconsidered," SCEPA working paper series. 2014-5, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    6. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-1.
    7. Makrelov, Konstantin & Arndt, Channing & Davies, Rob & Harris, Laurence, 2020. "Balance sheet changes and the impact of financial sector risk-taking on fiscal multipliers," Economic Modelling, Elsevier, vol. 87(C), pages 322-343.
    8. Dindo, Pietro & Modena, Andrea & Pelizzon, Loriana, 2022. "Risk pooling, intermediation efficiency, and the business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    9. Francesco Simone Lucidi, 2019. "Real-time signals anticipating credit booms in Euro Area countries," Working Papers in Public Economics 189, Department of Economics and Law, Sapienza University of Roma.
    10. Mittnik, Stefan & Semmler, Willi, 2018. "Overleveraging, Financial Fragility, And The Banking–Macro Link: Theory And Empirical Evidence," Macroeconomic Dynamics, Cambridge University Press, vol. 22(1), pages 4-32, January.
    11. Erhan Uluceviz & Kamil Yilmaz, 2018. "Measuring Real-Financial Connectedness in the U.S. Economy," Koç University-TUSIAD Economic Research Forum Working Papers 1812, Koc University-TUSIAD Economic Research Forum.
    12. Christian Proano & Christian Schoder & Willi Semmler, 2013. "The Role of Financial Stress in Debt and Recovery," SCEPA policy note series. 2012-02, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    13. Tommaso Ferraresi & Andrea Roventini & Willi Semmler, 2016. "Macroeconomic regimes, technological shocks and employment dynamics," Documents de Travail de l'OFCE 2016-19, Observatoire Francais des Conjonctures Economiques (OFCE).
    14. Brunnermeier, Markus & Sannikov, Yuliy, 2016. "Macro, Money and Finance: A Continuous Time Approach," CEPR Discussion Papers 11329, C.E.P.R. Discussion Papers.
    15. Fischer, Henning & Stolper, Oscar, 2019. "The nonlinear dynamics of corporate bond spreads: Regime-dependent effects of their determinants," Discussion Papers 08/2019, Deutsche Bundesbank.
    16. Schleer, Frauke & Semmler, Willi, 2014. "Financial sector-output dynamics in the euro area: Non-linearities reconsidered," ZEW Discussion Papers 13-068 [rev.], ZEW - Leibniz Centre for European Economic Research.
    17. Brana, Sophie & Prat, Stéphanie, 2016. "The effects of global excess liquidity on emerging stock market returns: Evidence from a panel threshold model," Economic Modelling, Elsevier, vol. 52(PA), pages 26-34.
    18. Carrillo Julio A. & García Ana Laura, 2021. "The COVID-19 Economic Crisis in Mexico through the Lens of a Financial Conditions Index," Working Papers 2021-23, Banco de México.
    19. Haddou, Samira, 2022. "International financial stress spillovers to bank lending: Do internal characteristics matter?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    20. Serena Merrino, 2021. "Statedependent fiscal multipliers and financial dynamics An impulse response analysis by local projections for South Africa," Working Papers 11015, South African Reserve Bank.
    21. Poeschel, Friedrich, 2012. "Assortative matching through signals," IAB-Discussion Paper 201215, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    22. George Apostolakis & Athanasios P. Papadopoulos, 2019. "Financial Stability, Monetary Stability and Growth: a PVAR Analysis," Open Economies Review, Springer, vol. 30(1), pages 157-178, February.
    23. Ernst, Ekkehard & Semmler, Willi & Haider, Alexander, 2017. "Debt-deflation, financial market stress and regime change – Evidence from Europe using MRVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 81(C), pages 115-139.
    24. Giorgio Fagiolo & Andrea Roventini, 2016. "Macroeconomic Policy in DGSE and Agent-Based Models Redux," Working Papers hal-03459348, HAL.
    25. Chen, Louisa & Verousis, Thanos & Wang, Kai & Zhou, Zhiping, 2023. "Financial stress and commodity price volatility," Energy Economics, Elsevier, vol. 125(C).
    26. Stolbov, Mikhail & Shchepeleva, Maria & Karminsky, Alexander, 2022. "When central bank research meets Google search: A sentiment index of global financial stress," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    27. Schleer Frauke & Semmler Willi, 2016. "Banking Overleveraging and Macro Instability: A Model and VSTAR Estimations," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(6), pages 609-638, December.
    28. Mikhail Stolbov & Maria Shchepeleva, 2021. "Macrofinancial linkages in Europe: Evidence from quantile local projections," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5557-5569, October.
    29. Stona, Filipe & Morais, Igor A.C. & Triches, Divanildo, 2018. "Economic dynamics during periods of financial stress: Evidences from Brazil," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 130-144.
    30. Proaño, Christian R. & Schoder, Christian & Semmler, Willi, 2014. "Financial Stress, Sovereign Debt and Economic Activity in Industrialized Countries: Evidence from Dynamic Threshold Regressions," Department of Economics Working Paper Series 167, WU Vienna University of Economics and Business.
    31. Konstantin Makrelov & Channing Arndt & Rob Davies & Laurence Harris, 2018. "Fiscal multipliers in South Africa: The importance of financial sector dynamics," WIDER Working Paper Series wp-2018-6, World Institute for Development Economic Research (UNU-WIDER).
    32. Stephan B. Bruns & David I. Stern, 2015. "Meta-Granger causality testing," CAMA Working Papers 2015-22, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    33. Huang, Yu-Fan, 2015. "Time variation in U.S. monetary policy and credit spreads," Journal of Macroeconomics, Elsevier, vol. 43(C), pages 205-215.
    34. Tihana Skrinjaric, 2022. "Macroeconomic effects of systemic stress: a rolling spillover index approach," Public Sector Economics, Institute of Public Finance, vol. 46(1), pages 109-140.
    35. Hong, Yanran & Li, Pan & Wang, Lu & Zhang, Yaojie, 2023. "New evidence of extreme risk transmission between financial stress and international crude oil markets," Research in International Business and Finance, Elsevier, vol. 64(C).
    36. Dovern, Jonas & van Roye, Björn, 2013. "International transmission of financial stress: Evidence from a GVAR," Kiel Working Papers 1844, Kiel Institute for the World Economy (IfW Kiel).
    37. Gross, Marco & Henry, Jerome & Semmler, Willi, 2018. "Destabilizing Effects Of Bank Overleveraging On Real Activity—An Analysis Based On A Threshold Mcs-Gvar," Macroeconomic Dynamics, Cambridge University Press, vol. 22(7), pages 1750-1768, October.
    38. Gardini, Laura & Radi, Davide & Schmitt, Noemi & Sushko, Iryna & Westerhoff, Frank, 2023. "Sentiment-driven business cycle dynamics: An elementary macroeconomic model with animal spirits," Journal of Economic Behavior & Organization, Elsevier, vol. 210(C), pages 342-359.
    39. Evgenidis, Anastasios & Tsagkanos, Athanasios, 2017. "Asymmetric effects of the international transmission of US financial stress. A threshold-VAR approach," International Review of Financial Analysis, Elsevier, vol. 51(C), pages 69-81.
    40. Goldberg, Andrew & Romalis, John, 2015. "Public Debt and Growth in U.S. States," Working Papers 2015-10, University of Sydney, School of Economics.
    41. Ekkehard Ernst & Stefan Mittnik & Willi Semmler, 2016. "Interaction of Labour and Credit Market in Growth Regimes: A Theoretical and Empirical Analysis," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 45(3), pages 393-422, November.
    42. Samar Issa, 2022. "Financial Crises and Business Cycle Implications for Islamic and Non-Islamic Bank Lending in Indonesia," JRFM, MDPI, vol. 15(7), pages 1-32, June.
    43. Semmler, Willi & Haider, Alexander, 2015. "The perils of debt deflation in the euro area: A multi regime model," ZEW Discussion Papers 15-071, ZEW - Leibniz Centre for European Economic Research.
    44. Samar Issa, 2020. "Life after Debt: The Effects of Overleveraging on Conventional and Islamic Banks," JRFM, MDPI, vol. 13(6), pages 1-46, June.
    45. José Pedro Bastos Neves & Willi Semmler, 2022. "Credit, output and financial stress: A non‐linear LVSTAR application to Brazil," Metroeconomica, Wiley Blackwell, vol. 73(3), pages 900-923, July.
    46. Marina Yu. Malkina & Rodion V. Balakin, 2023. "The Relation of Financial and Industrial Stresses to Monetary Policy Parameters in the Russian Economy," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 3, pages 104-121, June.
    47. Mikhail Stolbov & Maria Shchepeleva, 2018. "Systemic risk in Europe: deciphering leading measures, common patterns and real effects," Annals of Finance, Springer, vol. 14(1), pages 49-91, February.
    48. Samar Issa & Gulhan Bizel & Sharath Kumar Jagannathan & Sri Sarat Chaitanya Gollapalli, 2024. "A Comprehensive Approach to Bankruptcy Risk Evaluation in the Financial Industry," JRFM, MDPI, vol. 17(1), pages 1-21, January.
    49. Dovern, Jonas & van Roye, Björn, 2014. "International transmission and business-cycle effects of financial stress," Journal of Financial Stability, Elsevier, vol. 13(C), pages 1-17.
    50. Mao, Jie & Shen, Guanxiong & Yan, Jingzhou, 2023. "A continuous-time macro-finance model with Knightian uncertainty," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    51. Phil Armstrong, 2020. "Can Heterodox Economics Make a Difference?," Books, Edward Elgar Publishing, number 19964.
    52. Kappler, Marcus & Schleer, Frauke, 2017. "A financially stressed euro area," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-37.
    53. Ishmael Radikoko & Shadreck A. Mutobo & Mphoeng Mphoeng, 2019. "The Impact of Stock Market Development on Economic Growth: The Case of Botswana," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(12), pages 149-149, December.
    54. Brunnermeier, M.K. & Sannikov, Y., 2016. "Macro, Money, and Finance," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1497-1545, Elsevier.
    55. Manfred Kremer, 2016. "Macroeconomic effects of financial stress and the role of monetary policy: a VAR analysis for the euro area," International Economics and Economic Policy, Springer, vol. 13(1), pages 105-138, January.
    56. Caulkins, Jonathan P. & Feichtinger, Gustav & Grass, Dieter & Hartl, Richard F. & Kort, Peter M. & Seidl, Andrea, 2015. "Capital stock management during a recession that freezes credit markets," Journal of Economic Behavior & Organization, Elsevier, vol. 116(C), pages 1-14.
    57. Jayantee SAHOO, 2020. "Financial stress index, growth and price stability in India: Some recent evidence," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(622), S), pages 105-124, Spring.
    58. Kappler, Marcus & Schleer, Frauke, 2013. "How many factors and shocks cause financial stress?," ZEW Discussion Papers 13-100, ZEW - Leibniz Centre for European Economic Research.
    59. Semmler, Willi & Tahri, Ibrahim, 2017. "Current account imbalances: A new approach to assess external debt sustainability," Economic Modelling, Elsevier, vol. 62(C), pages 161-170.
    60. Mohammad Dulal Miah & Muhammad Shafiullah & Md. Samsul Alam, 2024. "The effect of financial stress on renewable energy consumption: evidence from US data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 26623-26646, October.

  12. Mittnik, Stefan & Semmler, Willi, 2013. "The real consequences of financial stress," SFB 649 Discussion Papers 2013-011, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Pu Chen & Willi Semmler, 2018. "Short and Long Effects of Productivity on Unemployment," Open Economies Review, Springer, vol. 29(4), pages 853-878, September.
    2. Mittnik, Stefan & Semmler, Willi, 2013. "The real consequences of financial stress," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1479-1499.
    3. Yao, Xiaoyang & Le, Wei & Sun, Xiaolei & Li, Jianping, 2020. "Financial stress dynamics in China: An interconnectedness perspective," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 217-238.
    4. Ubilava, David, 2014. "On the Relationship between Financial Instability and Economic Performance: Stressing the Business of Nonlinear Modelling," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170222, Agricultural and Applied Economics Association.
    5. Frauke Schleer & Willi Semmler, 2014. "Financial Sector and Output Dynamics in the Euro Area: Non-linearities Reconsidered," SCEPA working paper series. 2014-5, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    6. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-1.
    7. Makrelov, Konstantin & Arndt, Channing & Davies, Rob & Harris, Laurence, 2020. "Balance sheet changes and the impact of financial sector risk-taking on fiscal multipliers," Economic Modelling, Elsevier, vol. 87(C), pages 322-343.
    8. Dindo, Pietro & Modena, Andrea & Pelizzon, Loriana, 2022. "Risk pooling, intermediation efficiency, and the business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    9. Francesco Simone Lucidi, 2019. "Real-time signals anticipating credit booms in Euro Area countries," Working Papers in Public Economics 189, Department of Economics and Law, Sapienza University of Roma.
    10. Mittnik, Stefan & Semmler, Willi, 2018. "Overleveraging, Financial Fragility, And The Banking–Macro Link: Theory And Empirical Evidence," Macroeconomic Dynamics, Cambridge University Press, vol. 22(1), pages 4-32, January.
    11. Erhan Uluceviz & Kamil Yilmaz, 2018. "Measuring Real-Financial Connectedness in the U.S. Economy," Koç University-TUSIAD Economic Research Forum Working Papers 1812, Koc University-TUSIAD Economic Research Forum.
    12. Christian Proano & Christian Schoder & Willi Semmler, 2013. "The Role of Financial Stress in Debt and Recovery," SCEPA policy note series. 2012-02, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    13. Tommaso Ferraresi & Andrea Roventini & Willi Semmler, 2016. "Macroeconomic regimes, technological shocks and employment dynamics," Documents de Travail de l'OFCE 2016-19, Observatoire Francais des Conjonctures Economiques (OFCE).
    14. Brunnermeier, Markus & Sannikov, Yuliy, 2016. "Macro, Money and Finance: A Continuous Time Approach," CEPR Discussion Papers 11329, C.E.P.R. Discussion Papers.
    15. Fischer, Henning & Stolper, Oscar, 2019. "The nonlinear dynamics of corporate bond spreads: Regime-dependent effects of their determinants," Discussion Papers 08/2019, Deutsche Bundesbank.
    16. Schleer, Frauke & Semmler, Willi, 2014. "Financial sector-output dynamics in the euro area: Non-linearities reconsidered," ZEW Discussion Papers 13-068 [rev.], ZEW - Leibniz Centre for European Economic Research.
    17. Brana, Sophie & Prat, Stéphanie, 2016. "The effects of global excess liquidity on emerging stock market returns: Evidence from a panel threshold model," Economic Modelling, Elsevier, vol. 52(PA), pages 26-34.
    18. Carrillo Julio A. & García Ana Laura, 2021. "The COVID-19 Economic Crisis in Mexico through the Lens of a Financial Conditions Index," Working Papers 2021-23, Banco de México.
    19. Haddou, Samira, 2022. "International financial stress spillovers to bank lending: Do internal characteristics matter?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    20. Serena Merrino, 2021. "Statedependent fiscal multipliers and financial dynamics An impulse response analysis by local projections for South Africa," Working Papers 11015, South African Reserve Bank.
    21. Poeschel, Friedrich, 2012. "Assortative matching through signals," IAB-Discussion Paper 201215, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    22. George Apostolakis & Athanasios P. Papadopoulos, 2019. "Financial Stability, Monetary Stability and Growth: a PVAR Analysis," Open Economies Review, Springer, vol. 30(1), pages 157-178, February.
    23. Ernst, Ekkehard & Semmler, Willi & Haider, Alexander, 2017. "Debt-deflation, financial market stress and regime change – Evidence from Europe using MRVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 81(C), pages 115-139.
    24. Giorgio Fagiolo & Andrea Roventini, 2016. "Macroeconomic Policy in DGSE and Agent-Based Models Redux," Working Papers hal-03459348, HAL.
    25. Chen, Louisa & Verousis, Thanos & Wang, Kai & Zhou, Zhiping, 2023. "Financial stress and commodity price volatility," Energy Economics, Elsevier, vol. 125(C).
    26. Stolbov, Mikhail & Shchepeleva, Maria & Karminsky, Alexander, 2022. "When central bank research meets Google search: A sentiment index of global financial stress," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    27. Schleer Frauke & Semmler Willi, 2016. "Banking Overleveraging and Macro Instability: A Model and VSTAR Estimations," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(6), pages 609-638, December.
    28. Mikhail Stolbov & Maria Shchepeleva, 2021. "Macrofinancial linkages in Europe: Evidence from quantile local projections," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5557-5569, October.
    29. Stona, Filipe & Morais, Igor A.C. & Triches, Divanildo, 2018. "Economic dynamics during periods of financial stress: Evidences from Brazil," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 130-144.
    30. Proaño, Christian R. & Schoder, Christian & Semmler, Willi, 2014. "Financial Stress, Sovereign Debt and Economic Activity in Industrialized Countries: Evidence from Dynamic Threshold Regressions," Department of Economics Working Paper Series 167, WU Vienna University of Economics and Business.
    31. Konstantin Makrelov & Channing Arndt & Rob Davies & Laurence Harris, 2018. "Fiscal multipliers in South Africa: The importance of financial sector dynamics," WIDER Working Paper Series wp-2018-6, World Institute for Development Economic Research (UNU-WIDER).
    32. Stephan B. Bruns & David I. Stern, 2015. "Meta-Granger causality testing," CAMA Working Papers 2015-22, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    33. Huang, Yu-Fan, 2015. "Time variation in U.S. monetary policy and credit spreads," Journal of Macroeconomics, Elsevier, vol. 43(C), pages 205-215.
    34. Tihana Skrinjaric, 2022. "Macroeconomic effects of systemic stress: a rolling spillover index approach," Public Sector Economics, Institute of Public Finance, vol. 46(1), pages 109-140.
    35. Hong, Yanran & Li, Pan & Wang, Lu & Zhang, Yaojie, 2023. "New evidence of extreme risk transmission between financial stress and international crude oil markets," Research in International Business and Finance, Elsevier, vol. 64(C).
    36. Dovern, Jonas & van Roye, Björn, 2013. "International transmission of financial stress: Evidence from a GVAR," Kiel Working Papers 1844, Kiel Institute for the World Economy (IfW Kiel).
    37. Gross, Marco & Henry, Jerome & Semmler, Willi, 2018. "Destabilizing Effects Of Bank Overleveraging On Real Activity—An Analysis Based On A Threshold Mcs-Gvar," Macroeconomic Dynamics, Cambridge University Press, vol. 22(7), pages 1750-1768, October.
    38. Gardini, Laura & Radi, Davide & Schmitt, Noemi & Sushko, Iryna & Westerhoff, Frank, 2023. "Sentiment-driven business cycle dynamics: An elementary macroeconomic model with animal spirits," Journal of Economic Behavior & Organization, Elsevier, vol. 210(C), pages 342-359.
    39. Evgenidis, Anastasios & Tsagkanos, Athanasios, 2017. "Asymmetric effects of the international transmission of US financial stress. A threshold-VAR approach," International Review of Financial Analysis, Elsevier, vol. 51(C), pages 69-81.
    40. Goldberg, Andrew & Romalis, John, 2015. "Public Debt and Growth in U.S. States," Working Papers 2015-10, University of Sydney, School of Economics.
    41. Ekkehard Ernst & Stefan Mittnik & Willi Semmler, 2016. "Interaction of Labour and Credit Market in Growth Regimes: A Theoretical and Empirical Analysis," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 45(3), pages 393-422, November.
    42. Samar Issa, 2022. "Financial Crises and Business Cycle Implications for Islamic and Non-Islamic Bank Lending in Indonesia," JRFM, MDPI, vol. 15(7), pages 1-32, June.
    43. Semmler, Willi & Haider, Alexander, 2015. "The perils of debt deflation in the euro area: A multi regime model," ZEW Discussion Papers 15-071, ZEW - Leibniz Centre for European Economic Research.
    44. Samar Issa, 2020. "Life after Debt: The Effects of Overleveraging on Conventional and Islamic Banks," JRFM, MDPI, vol. 13(6), pages 1-46, June.
    45. José Pedro Bastos Neves & Willi Semmler, 2022. "Credit, output and financial stress: A non‐linear LVSTAR application to Brazil," Metroeconomica, Wiley Blackwell, vol. 73(3), pages 900-923, July.
    46. Marina Yu. Malkina & Rodion V. Balakin, 2023. "The Relation of Financial and Industrial Stresses to Monetary Policy Parameters in the Russian Economy," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 3, pages 104-121, June.
    47. Mikhail Stolbov & Maria Shchepeleva, 2018. "Systemic risk in Europe: deciphering leading measures, common patterns and real effects," Annals of Finance, Springer, vol. 14(1), pages 49-91, February.
    48. Samar Issa & Gulhan Bizel & Sharath Kumar Jagannathan & Sri Sarat Chaitanya Gollapalli, 2024. "A Comprehensive Approach to Bankruptcy Risk Evaluation in the Financial Industry," JRFM, MDPI, vol. 17(1), pages 1-21, January.
    49. Dovern, Jonas & van Roye, Björn, 2014. "International transmission and business-cycle effects of financial stress," Journal of Financial Stability, Elsevier, vol. 13(C), pages 1-17.
    50. Mao, Jie & Shen, Guanxiong & Yan, Jingzhou, 2023. "A continuous-time macro-finance model with Knightian uncertainty," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    51. Phil Armstrong, 2020. "Can Heterodox Economics Make a Difference?," Books, Edward Elgar Publishing, number 19964.
    52. Kappler, Marcus & Schleer, Frauke, 2017. "A financially stressed euro area," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-37.
    53. Ishmael Radikoko & Shadreck A. Mutobo & Mphoeng Mphoeng, 2019. "The Impact of Stock Market Development on Economic Growth: The Case of Botswana," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(12), pages 149-149, December.
    54. Brunnermeier, M.K. & Sannikov, Y., 2016. "Macro, Money, and Finance," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1497-1545, Elsevier.
    55. Manfred Kremer, 2016. "Macroeconomic effects of financial stress and the role of monetary policy: a VAR analysis for the euro area," International Economics and Economic Policy, Springer, vol. 13(1), pages 105-138, January.
    56. Caulkins, Jonathan P. & Feichtinger, Gustav & Grass, Dieter & Hartl, Richard F. & Kort, Peter M. & Seidl, Andrea, 2015. "Capital stock management during a recession that freezes credit markets," Journal of Economic Behavior & Organization, Elsevier, vol. 116(C), pages 1-14.
    57. Jayantee SAHOO, 2020. "Financial stress index, growth and price stability in India: Some recent evidence," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(622), S), pages 105-124, Spring.
    58. Kappler, Marcus & Schleer, Frauke, 2013. "How many factors and shocks cause financial stress?," ZEW Discussion Papers 13-100, ZEW - Leibniz Centre for European Economic Research.
    59. Semmler, Willi & Tahri, Ibrahim, 2017. "Current account imbalances: A new approach to assess external debt sustainability," Economic Modelling, Elsevier, vol. 62(C), pages 161-170.
    60. Mohammad Dulal Miah & Muhammad Shafiullah & Md. Samsul Alam, 2024. "The effect of financial stress on renewable energy consumption: evidence from US data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 26623-26646, October.

  13. Willi Semmler & Stefan Mittnik, 2012. "Estimating a Banking-Macro Model for Europe Using a Multi-Regime VAR," EcoMod2012 4122, EcoMod.

    Cited by:

    1. Baglioni, Angelo & Cherubini, Umberto, 2013. "Within and between systemic country risk. Theory and evidence from the sovereign crisis in Europe," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1581-1597.
    2. Proaño, Christian R. & Schoder, Christian & Semmler, Willi, 2014. "Financial Stress, Sovereign Debt and Economic Activity in Industrialized Countries: Evidence from Dynamic Threshold Regressions," Department of Economics Working Paper Series 167, WU Vienna University of Economics and Business.

  14. Stefan Mittnik & Sandra Paterlini & Tina Yener, 2011. "Operational–risk Dependencies and the Determination of Risk Capital," Center for Economic Research (RECent) 070, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".

    Cited by:

    1. Kley, Oliver & Klüppelberg, Claudia & Paterlini, Sandra, 2020. "Modelling extremal dependence for operational risk by a bipartite graph," Journal of Banking & Finance, Elsevier, vol. 117(C).
    2. Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.
    3. Brechmann, Eike & Czado, Claudia & Paterlini, Sandra, 2014. "Flexible dependence modeling of operational risk losses and its impact on total capital requirements," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 271-285.

  15. Stefan Mittnik & Willi Semmler, 2011. "The Instability of the Banking Sector and Macrodynamics: Theory and Empirics," DEGIT Conference Papers c016_080, DEGIT, Dynamics, Economic Growth, and International Trade.

    Cited by:

    1. Shrestha, Prakash Kumar, 2012. "Banking systems, central banks and international reserve accumulation in East Asian economies," Economics Discussion Papers 2012-48, Kiel Institute for the World Economy (IfW Kiel).
    2. Willi Semmler & Christian R. Proaño, 2015. "Escape Routes from Sovereign Default Risk in the Euro Area," International Symposia in Economic Theory and Econometrics, in: Monetary Policy in the Context of the Financial Crisis: New Challenges and Lessons, volume 24, pages 163-193, Emerald Group Publishing Limited.
    3. Issa, Samar & Gevorkyan, Aleksandr V., 2022. "Optimal corporate leverage and speculative cycles: an empirical estimation," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 478-491.

  16. Thiemo Krink & Stefan Mittnik & Sandra Paterlini, 2009. "Differential Evolution and Combinatorial Search for Constrained Index Tracking," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0016, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".

    Cited by:

    1. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    2. Chiara Pederzoli & Costanza Torricelli, 2013. "Efficiency and unbiasedness of corn futures markets: New evidence across the financial crisis," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0040, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    3. Andriosopoulos, Kostas & Nomikos, Nikos, 2014. "Performance replication of the Spot Energy Index with optimal equity portfolio selection: Evidence from the UK, US and Brazilian markets," European Journal of Operational Research, Elsevier, vol. 234(2), pages 571-582.
    4. Gnägi, M. & Strub, O., 2020. "Tracking and outperforming large stock-market indices," Omega, Elsevier, vol. 90(C).
    5. Chen, Wei, 2015. "Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 125-139.
    6. Elisabetta Gualandri & Mario Noera, 2014. "Towards A Macroprudential Policy In The Eu: Main Issues," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0049, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    7. Andriosopoulos, Kostas & Doumpos, Michael & Papapostolou, Nikos C. & Pouliasis, Panos K., 2013. "Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 52(C), pages 16-34.
    8. Jules Clement Mba & Sutene Mwambi, 2020. "A Markov-switching COGARCH approach to cryptocurrency portfolio selection and optimization," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 199-214, June.
    9. Stelios Tsafarakis & Charalampos Saridakis & Nikolaos Matsatsinis & George Baltas, 2016. "Private labels and retail assortment planning: a differential evolution approach," Annals of Operations Research, Springer, vol. 247(2), pages 677-692, December.
    10. Elena Giarda & Gloria Moroni, 2015. "‘It’s a trap!’ The degree of poverty persistence in Italy and Europe," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0055, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    11. Beatrice Bertelli & Gianna Boero & Costanza Torricelli, 2021. "The market price of greenness A factor pricing approach for Green Bonds," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0083, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    12. Marianna Brunetti & Roberta De Luca, 2023. "Pre-selection in cointegration-based pairs trading," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1611-1640, December.
    13. Andrea Scozzari & Fabio Tardella & Sandra Paterlini & Thiemo Krink, 2012. "Exact and heuristic approaches for the index tracking problem with UCITS constraints," Center for Economic Research (RECent) 081, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    14. Liang-chuan Wu & I-chan Tsai, 2014. "Three fuzzy goal programming models for index portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(8), pages 1155-1169, August.
    15. Dean Altshuler & Carlo Alberto Magni, 2015. "Introducing Aggregate Return on Investment as a Solution to the Contradiction Between Some PME Metrics and IRR," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0056, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    16. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    17. Mahdi Moeini, 2022. "Solving the index tracking problem: a continuous optimization approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(2), pages 807-835, June.
    18. Costanza Torricelli & Eleonora Pellati, 2022. "Social Bonds and the “Social Premiumâ€," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0085, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    19. Doering, Jana & Kizys, Renatas & Juan, Angel A. & Fitó, Àngels & Polat, Onur, 2019. "Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends," Operations Research Perspectives, Elsevier, vol. 6(C).
    20. Bj�rn Fastrich & Sandra Paterlini & Peter Winker, 2014. "Cardinality versus q -norm constraints for index tracking," Quantitative Finance, Taylor & Francis Journals, vol. 14(11), pages 2019-2032, November.
    21. Ardia, David & Boudt, Kris & Carl, Peter & Mullen, Katharine M. & Peterson, Brian, 2010. "Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization," MPRA Paper 22135, University Library of Munich, Germany.
    22. Thiemo Krink & Sandra Paterlini, 2011. "Multiobjective optimization using differential evolution for real-world portfolio optimization," Computational Management Science, Springer, vol. 8(1), pages 157-179, April.
    23. Stefano Cosma & Elisabetta Gualandri, 2013. "The sovereign debt crisis: the impact on the intermediation model of Italian banks," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0042, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    24. Elisabetta Gualandri & Valeria Venturelli, 2013. "The financing of Italian firms and the credit crunch: findings and exit strategies," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0041, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    25. Meihua Wang & Chengxian Xu & Fengmin Xu & Hongang Xue, 2012. "A mixed 0–1 LP for index tracking problem with CVaR risk constraints," Annals of Operations Research, Springer, vol. 196(1), pages 591-609, July.
    26. Billio, Monica & Caporin, Massimiliano & Costola, Michele, 2015. "Backward/forward optimal combination of performance measures for equity screening," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 63-83.
    27. Massimo Baldini & Giovanni Gallo & Costanza Torricelli, 2017. "Past Income Scarcity and Current Perception of Financial Fragility," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0064, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    28. Chiara Pederzoli & Costanza Torricelli, 2019. "The impact of the Fundamental Review of the Trading Book: A preliminary assessment on a stylized portfolio," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0075, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    29. H Mezali & J E Beasley, 2013. "Quantile regression for index tracking and enhanced indexation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(11), pages 1676-1692, November.
    30. Carlo Alberto Magni, 2015. "Pseudo-naïve approaches to investment performance measurement," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0051, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    31. Jules Clement Mba & Edson Pindza & Ur Koumba, 2018. "A differential evolution copula-based approach for a multi-period cryptocurrency portfolio optimization," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(4), pages 399-418, November.
    32. Elisabetta Gualandri, 2011. "Basel 3, Pillar 2: the role of banks’ internal governance and control function," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0027, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    33. Emilio Ricardo Carvalhais & Antonio Marcos Duarte Júnior, 2015. "Indexation of Fixed-Income Portfolios to the IMA-B," Brazilian Business Review, Fucape Business School, vol. 12(3), pages 116-142, May.
    34. Costanza Torricelli & Fabio Ferrari, 2022. "Climate Stress Test: bad (or good) news for the market? An Event Study Analysis on Euro Zone Banks," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0086, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    35. Elisabetta Gualandri & Mario Noera, 2014. "Monitoring Systemic Risk: A Survey Of The Available Macroprudential Toolkit," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0050, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    36. Gabriele Torri & Rosella Giacometti & Sandra Paterlini, 2024. "Penalized enhanced portfolio replication with asymmetric deviation measures," Annals of Operations Research, Springer, vol. 332(1), pages 481-531, January.
    37. Björn Fastrich & Peter Winker, 2014. "Combining Forecasts with Missing Data: Making Use of Portfolio Theory," Computational Economics, Springer;Society for Computational Economics, vol. 44(2), pages 127-152, August.
    38. Sant’Anna, Leonardo R. & Filomena, Tiago P. & Caldeira, João F., 2017. "Index tracking and enhanced indexing using cointegration and correlation with endogenous portfolio selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 146-157.
    39. Leonardo Riegel Sant’Anna & Tiago Pascoal Filomena & Pablo Cristini Guedes & Denis Borenstein, 2017. "Index tracking with controlled number of assets using a hybrid heuristic combining genetic algorithm and non-linear programming," Annals of Operations Research, Springer, vol. 258(2), pages 849-867, November.
    40. Chiara Pederzoli & Costanza Torricelli, 2010. "A parsimonious default prediction model for Italian SMEs," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0022, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    41. Enrico Rubaltelli & Sergio Agnoli & Michela Rancan & Tiziana Pozzoli, 2015. "Emotional Intelligence and risk taking in investment decision-making," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0053, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    42. Costanza Torricelli & Beatrice Bertelli, 2022. "ESG compliant optimal portfolios: The impact of ESG constraints on portfolio optimization in a sample of European stocks," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0088, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    43. Francesca Arnaboldi, Francesca Gioia, 2019. "Portfolio choice: Evidence from new-borns," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0078, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    44. Stefano Cosma & Francesca Pancotto & Paola Vezzani, 2018. "Customer Complaining and Probability of Default in Consumer Credit," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0068, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".

  17. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).

    Cited by:

    1. Philippe Charlot & Vêlayoudom Marimoutou, 2014. "On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree," Working Papers hal-00980125, HAL.
    2. Charlot, Philippe & Darné, Olivier & Moussa, Zakaria, 2016. "Commodity returns co-movements: Fundamentals or “style” effect?," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 130-160.
    3. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    4. Daniel King & Ferdi Botha, 2014. "Modelling Stock Return Volatility Dynamics in Selected African Markets," Working Papers 410, Economic Research Southern Africa.
    5. Su, EnDer, 2013. "Stock index hedge using trend and volatility regime switch model considering hedging cost," MPRA Paper 49190, University Library of Munich, Germany.
    6. Jin, Xin & Maheu, John M., 2016. "Modeling covariance breakdowns in multivariate GARCH," Journal of Econometrics, Elsevier, vol. 194(1), pages 1-23.
    7. Su, EnDer, 2017. "Stock index hedging using a trend and volatility regime-switching model involving hedging cost," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 233-254.

  18. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).

    Cited by:

    1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    2. Francq, Christian & Jiménez Gamero, Maria Dolores & Meintanis, Simos, 2015. "Tests for sphericity in multivariate garch models," MPRA Paper 67411, University Library of Munich, Germany.
    3. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    4. Gambacciani, Marco & Paolella, Marc S., 2017. "Robust normal mixtures for financial portfolio allocation," Econometrics and Statistics, Elsevier, vol. 3(C), pages 91-111.
    5. Augustyniak, Maciej, 2014. "Maximum likelihood estimation of the Markov-switching GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 61-75.
    6. Krishnakumar, Jaya & Kabili, Andi & Roko, Ilir, 2012. "Estimation of SEM with GARCH errors," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3153-3181.
    7. Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    8. Sang‐Kuck Chung, 2009. "Out‐of‐sample Hedge Performances for Risk Management in China Commodity Futures Markets," Asian Economic Journal, East Asian Economic Association, vol. 23(3), pages 349-372, September.
    9. Thomas Chuffart, 2013. "Selection Criteria in Regime Switching Conditional Volatility Models," Working Papers halshs-00844413, HAL.
    10. Alp, Tansel & Demetrescu, Matei, 2010. "Joint forecasts of Dow Jones stocks under general multivariate loss function," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2360-2371, November.
    11. E. Otranto, 2008. "Identifying Financial Time Series with Similar Dynamic Conditional Correlation," Working Paper CRENoS 200817, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    12. Andrey A. Gnidchenko & Vladimir A. Salnikov, 2015. "Net Comparative Advantage Index: Overcoming the Drawbacks of the Existing Indices," HSE Working papers WP BRP 119/EC/2015, National Research University Higher School of Economics.
    13. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    14. Carla Mascarenhas & Anderson Rei Galvão & Carla Susana Marques, 2022. "How Perceived Organizational Support, Identification with Organization and Work Engagement Influence Job Satisfaction: A Gender-Based Perspective," Administrative Sciences, MDPI, vol. 12(2), pages 1-15, May.
    15. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.
    16. Achraf Ghorbel & Ahmed Jeribi, 2021. "Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 449-467, September.
    17. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    18. Francq, C. & Jiménez-Gamero, M.D. & Meintanis, S.G., 2017. "Tests for conditional ellipticity in multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 196(2), pages 305-319.
    19. Marc S. Paolella, 2017. "The Univariate Collapsing Method for Portfolio Optimization," Econometrics, MDPI, vol. 5(2), pages 1-33, May.
    20. Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Time-varying mixture GARCH models and asymmetric volatility," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 602-623.
    21. Arismendi, J.C., 2013. "Multivariate truncated moments," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 41-75.
    22. Santos, André A.P. & Moura, Guilherme V., 2014. "Dynamic factor multivariate GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 606-617.

  19. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).

    Cited by:

    1. Philippe Charlot & Vêlayoudom Marimoutou, 2014. "On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree," Working Papers hal-00980125, HAL.
    2. Charlot, Philippe & Darné, Olivier & Moussa, Zakaria, 2016. "Commodity returns co-movements: Fundamentals or “style” effect?," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 130-160.
    3. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    4. Daniel King & Ferdi Botha, 2014. "Modelling Stock Return Volatility Dynamics in Selected African Markets," Working Papers 410, Economic Research Southern Africa.
    5. Su, EnDer, 2013. "Stock index hedge using trend and volatility regime switch model considering hedging cost," MPRA Paper 49190, University Library of Munich, Germany.
    6. Jin, Xin & Maheu, John M., 2016. "Modeling covariance breakdowns in multivariate GARCH," Journal of Econometrics, Elsevier, vol. 194(1), pages 1-23.
    7. Su, EnDer, 2017. "Stock index hedging using a trend and volatility regime-switching model involving hedging cost," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 233-254.

  20. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2006. "Multivariate normal mixture GARCH," CFS Working Paper Series 2006/09, Center for Financial Studies (CFS).

    Cited by:

    1. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).
    2. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).
    3. Giorgio Canarella & Stephen M. Miller & Stephen K. Pollard, 2009. "Dynamic Stock Market Interactions between the Canadian, Mexican, and the United States Markets: The NAFTA Experience," Working Papers 0905, University of Nevada, Las Vegas , Department of Economics.
    4. Chung, Sang-Kuck, 2009. "Bivariate mixed normal GARCH models and out-of-sample hedge performances," Finance Research Letters, Elsevier, vol. 6(3), pages 130-137, September.

  21. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2006. "Multivariate normal mixture GARCH," CFS Working Paper Series 2006/09, Center for Financial Studies (CFS).

    Cited by:

    1. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).
    2. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).
    3. Giorgio Canarella & Stephen M. Miller & Stephen K. Pollard, 2009. "Dynamic Stock Market Interactions between the Canadian, Mexican, and the United States Markets: The NAFTA Experience," Working Papers 0905, University of Nevada, Las Vegas , Department of Economics.
    4. Chung, Sang-Kuck, 2009. "Bivariate mixed normal GARCH models and out-of-sample hedge performances," Finance Research Letters, Elsevier, vol. 6(3), pages 130-137, September.

  22. Doganoglu, Toker & Hartz, Christoph & Mittnik, Stefan, 2006. "Portfolio optimization when risk factors are conditionally varying and heavy tailed," CFS Working Paper Series 2006/24, Center for Financial Studies (CFS).

    Cited by:

    1. Thiemo Krink & Sandra Paterlini, 2008. "Differential Evolution for Multiobjective Portfolio Optimization," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0007, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    2. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    3. Mainik, Georg & Mitov, Georgi & Rüschendorf, Ludger, 2015. "Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 115-134.
    4. Clara Calvo & Carlos Ivorra & Vicente Liern & Blanca Pérez-Gladish, 2021. "Grading Investment Diversification Options in Presence of Non-Historical Financial Information," Mathematics, MDPI, vol. 9(6), pages 1-11, March.
    5. Calzolari, Giorgio & Halbleib, Roxana, 2018. "Estimating stable latent factor models by indirect inference," Journal of Econometrics, Elsevier, vol. 205(1), pages 280-301.
    6. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    7. Georg Mainik & Georgi Mitov & Ludger Ruschendorf, 2015. "Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz," Papers 1505.04045, arXiv.org.
    8. K. Liagkouras & K. Metaxiotis, 2019. "Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem," Annals of Operations Research, Springer, vol. 272(1), pages 119-137, January.
    9. Margherita Giuzio & Sandra Paterlini, 2019. "Un-diversifying during crises: Is it a good idea?," Computational Management Science, Springer, vol. 16(3), pages 401-432, July.
    10. Matteo Bonato, 2012. "Modeling fat tails in stock returns: a multivariate stable-GARCH approach," Computational Statistics, Springer, vol. 27(3), pages 499-521, September.
    11. Salhi, Khaled & Deaconu, Madalina & Lejay, Antoine & Champagnat, Nicolas & Navet, Nicolas, 2016. "Regime switching model for financial data: Empirical risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 148-157.

  23. Doganoglu, Toker & Hartz, Christoph & Mittnik, Stefan, 2006. "Portfolio optimization when risk factors are conditionally varying and heavy tailed," CFS Working Paper Series 2006/24, Center for Financial Studies (CFS).

    Cited by:

    1. Thiemo Krink & Sandra Paterlini, 2008. "Differential Evolution for Multiobjective Portfolio Optimization," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0007, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    2. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    3. Mainik, Georg & Mitov, Georgi & Rüschendorf, Ludger, 2015. "Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 115-134.
    4. Clara Calvo & Carlos Ivorra & Vicente Liern & Blanca Pérez-Gladish, 2021. "Grading Investment Diversification Options in Presence of Non-Historical Financial Information," Mathematics, MDPI, vol. 9(6), pages 1-11, March.
    5. Calzolari, Giorgio & Halbleib, Roxana, 2018. "Estimating stable latent factor models by indirect inference," Journal of Econometrics, Elsevier, vol. 205(1), pages 280-301.
    6. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    7. Georg Mainik & Georgi Mitov & Ludger Ruschendorf, 2015. "Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz," Papers 1505.04045, arXiv.org.
    8. K. Liagkouras & K. Metaxiotis, 2019. "Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem," Annals of Operations Research, Springer, vol. 272(1), pages 119-137, January.
    9. Margherita Giuzio & Sandra Paterlini, 2019. "Un-diversifying during crises: Is it a good idea?," Computational Management Science, Springer, vol. 16(3), pages 401-432, July.
    10. Matteo Bonato, 2012. "Modeling fat tails in stock returns: a multivariate stable-GARCH approach," Computational Statistics, Springer, vol. 27(3), pages 499-521, September.
    11. Salhi, Khaled & Deaconu, Madalina & Lejay, Antoine & Champagnat, Nicolas & Navet, Nicolas, 2016. "Regime switching model for financial data: Empirical risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 148-157.

  24. Corsi, Fulvio & Kretschmer, Uta & Mittnik, Stefan & Pigorsch, Christian, 2005. "The volatility of realized volatility," CFS Working Paper Series 2005/33, Center for Financial Studies (CFS).

    Cited by:

    1. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    2. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," JRFM, MDPI, vol. 7(2), pages 1-30, June.
    3. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
    4. Yin Liao & Heather M. Anderson & Farshid Vahid, 2010. "Do Jumps Matter? Forecasting Multivariate Realized Volatility allowing for Common Jumps," Monash Econometrics and Business Statistics Working Papers 11/10, Monash University, Department of Econometrics and Business Statistics.
    5. Ciarreta, Aitor & Zarraga, Ainhoa, 2016. "Modeling realized volatility on the Spanish intra-day electricity market," Energy Economics, Elsevier, vol. 58(C), pages 152-163.
    6. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    7. Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
    8. Meng, Xiaochun & Taylor, James W., 2018. "An approximate long-memory range-based approach for value at risk estimation," International Journal of Forecasting, Elsevier, vol. 34(3), pages 377-388.
    9. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    10. Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, January.
    11. Michael McAleer & Marcelo Cunha Medeiros, 2010. "Forecasting Realized Volatility with Linear and Nonlinear Models," Textos para discussão 568, Department of Economics PUC-Rio (Brazil).
    12. Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
    13. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    14. Paul Wohlfarth, 2018. "Measuring the Impact of Monetary Policy Attention on Global Asset Volatility Using Search Data," Birkbeck Working Papers in Economics and Finance 1803, Birkbeck, Department of Economics, Mathematics & Statistics.
    15. Perera, Indeewara & Silvapulle, Mervyn J., 2021. "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, vol. 221(1), pages 1-24.
    16. Anne Opschoor & André Lucas, 2019. "Time-varying tail behavior for realized kernels," Tinbergen Institute Discussion Papers 19-051/IV, Tinbergen Institute.
    17. Yin Liao, 2012. "Does Modeling Jumps Help? A Comparison of Realized Volatility Models for Risk Prediction," CAMA Working Papers 2012-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Tim Bollerslev & Uta Kretschmer & Christian Pigorsch & George Tauchen, 2010. "A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects," Working Papers 10-06, Duke University, Department of Economics.
    19. Uwe Hassler & Paulo M.M. Rodrigues & Antonio Rubia, 2016. "Quantile Regression for Long Memory Testing: A Case of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 693-724.
    20. Vít Bubák & Evžen Kocenda & Filip Zikes, 2010. "Volatility Transmission in Emerging European Foreign Exchange Markets," CESifo Working Paper Series 3063, CESifo.
    21. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
    22. Zeng, Yayun & Wang, Jun & Xu, Kaixuan, 2017. "Complexity and multifractal behaviors of multiscale-continuum percolation financial system for Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 364-376.
    23. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 185-226.
    24. Fabrizio Cipollini & Giampiero M. Gallo & Edoardo Otranto, 2019. "Realized Volatility Forecasting: Robustness to Measurement Errors," Econometrics Working Papers Archive 2019_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    25. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Pigorsch, Uta, 2008. "Measuring and modeling risk using high-frequency data," SFB 649 Discussion Papers 2008-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    26. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    27. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2017. "A vector heterogeneous autoregressive index model for realized volatility measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 337-344.
    28. Jia, Linlu & Ke, Jinchuan & Wang, Jun, 2019. "Volatility aggregation intensity energy futures series on stochastic finite-range exclusion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 370-383.
    29. Rossi, Eduardo & Santucci de Magistris, Paolo, 2013. "Long memory and tail dependence in trading volume and volatility," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 94-112.
    30. B. Zhang & J. Wang & W. Zhang & G. C. Wang, 2020. "Nonlinear Scaling Behavior of Visible Volatility Duration for Financial Statistical Physics Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 373-389, August.
    31. Chevallier, Julien & Sévi, Benoît, 2012. "On the volatility–volume relationship in energy futures markets using intraday data," Energy Economics, Elsevier, vol. 34(6), pages 1896-1909.
    32. Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2014. "Precious Metals Under the Microscope: A High-Frequency Analysis," Working Papers on Finance 1409, University of St. Gallen, School of Finance.
    33. Catania, Leopoldo & Proietti, Tommaso, 2020. "Forecasting volatility with time-varying leverage and volatility of volatility effects," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1301-1317.
    34. Zhang, Bo & Wang, Guochao & Wang, Yiduan & Zhang, Wei & Wang, Jun, 2019. "Multiscale statistical behaviors for Ising financial dynamics with continuum percolation jump," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1012-1025.
    35. Lazar, Emese & Xue, Xiaohan, 2020. "Forecasting risk measures using intraday data in a generalized autoregressive score framework," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1057-1072.
    36. Christian Conrad, 2007. "Non-negativity Conditions for the Hyperbolic GARCH Model," KOF Working papers 07-162, KOF Swiss Economic Institute, ETH Zurich.
    37. Massimiliano Caporin & Gabriel G. Velo, 2011. "Modeling and forecasting realized range volatility," "Marco Fanno" Working Papers 0128, Dipartimento di Scienze Economiche "Marco Fanno".
    38. Zheng, Tingguo & Zuo, Haomiao, 2013. "Reexamining the time-varying volatility spillover effects: A Markov switching causality approach," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 643-662.
    39. Isao Ishida & Toshiaki Watanabe, 2009. "Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model," CARF F-Series CARF-F-145, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    40. Hadhri, Sinda & Ftiti, Zied, 2019. "Commonality in liquidity among Middle East and North Africa emerging stock markets: Does it really matter?," Economic Systems, Elsevier, vol. 43(3).
    41. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    42. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    43. Roxana Chiriac & Valeri Voev, 2011. "Modelling and forecasting multivariate realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 922-947, September.
    44. Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2006. "Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility," The Warwick Economics Research Paper Series (TWERPS) 777, University of Warwick, Department of Economics.
    45. Wen Cheong Chin & Min Cherng Lee, 2018. "S&P500 volatility analysis using high-frequency multipower variation volatility proxies," Empirical Economics, Springer, vol. 54(3), pages 1297-1318, May.
    46. Gustavo Fruet Dias & Karsten Schweiker, 2024. "Integrated Variance Estimation for Assets Traded in Multiple Venues," University of East Anglia School of Economics Working Paper Series 2024-04, School of Economics, University of East Anglia, Norwich, UK..
    47. Li, Zhinan & Pei, Shan & Li, Ting & Wang, Yu, 2023. "Risk spillover network in the supply chain system during the COVID-19 crisis: Evidence from China," Economic Modelling, Elsevier, vol. 126(C).
    48. David E. Allen & Michael McAleer & Marcel Scharth, 2010. "Realized Volatility Risk," KIER Working Papers 753, Kyoto University, Institute of Economic Research.
    49. Ait-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2005. "Ultra high frequency volatility estimation with dependent microstructure noise," Discussion Paper Series 1: Economic Studies 2005,30, Deutsche Bundesbank.
    50. Richard D. F. Harris & Anh T. H. Nguyen, 2017. "Dynamic factor long memory volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1205-1221, August.
    51. Julien Chevallier & Benoît Sévi, 2009. "On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting," Working Papers 2009.113, Fondazione Eni Enrico Mattei.
    52. Štefan Lyócsa & Peter Molnár, 2016. "Volatility forecasting of strategically linked commodity ETFs: gold-silver," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1809-1822, December.
    53. Oleg Sokolinskiy & Dick van Dijk, 2011. "Forecasting Volatility with Copula-Based Time Series Models," Tinbergen Institute Discussion Papers 11-125/4, Tinbergen Institute.
    54. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 326-360, Summer.
    55. Ding, Yashuang (Dexter), 2023. "A simple joint model for returns, volatility and volatility of volatility," Journal of Econometrics, Elsevier, vol. 232(2), pages 521-543.
    56. Caporin, Massimiliano & Velo, Gabriel G., 2015. "Realized range volatility forecasting: Dynamic features and predictive variables," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 98-112.
    57. Weber, Enzo, 2013. "Simultaneous stochastic volatility transmission across American equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(1), pages 53-60.
    58. Liu, Chun & Maheu, John M., 2012. "Intraday dynamics of volatility and duration: Evidence from Chinese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 20(3), pages 329-348.
    59. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    60. Stavros Degiannakis, 2008. "ARFIMAX and ARFIMAX-TARCH realized volatility modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1169-1180.
    61. Aitor Ciarreta & Peru Muniain & Ainhoa Zarraga, 2020. "Realized volatility and jump testing in the Japanese electricity spot market," Empirical Economics, Springer, vol. 58(3), pages 1143-1166, March.
    62. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    63. Xiangjin B. Chen & Jiti Gao & Degui Li & Param Silvapulle, 2013. "Nonparametric Estimation and Parametric Calibration of Time-Varying Coefficient Realized Volatility Models," Monash Econometrics and Business Statistics Working Papers 21/13, Monash University, Department of Econometrics and Business Statistics.
    64. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.
    65. Neda Todorova & Michael Soucek & Eduardo Roca, 2015. "Volatility spillovers from international commodity markets to the Australian equity market," Discussion Papers in Finance finance:201505, Griffith University, Department of Accounting, Finance and Economics.
    66. Jeremy Large, 2007. "Estimating Quadratic Variation When Quoted Prices Change by a Constant Increment," Economics Series Working Papers 340, University of Oxford, Department of Economics.
    67. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    68. Souček, Michael & Todorova, Neda, 2013. "Realized volatility transmission between crude oil and equity futures markets: A multivariate HAR approach," Energy Economics, Elsevier, vol. 40(C), pages 586-597.
    69. Ma, Feng & Liu, Jing & Huang, Dengshi & Chen, Wang, 2017. "Forecasting the oil futures price volatility: A new approach," Economic Modelling, Elsevier, vol. 64(C), pages 560-566.
    70. Fulvio Corsi & Francesco Audrino, 2012. "Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 591-616, September.
    71. Aitor Ciarreta & Peru Muniainy & Ainhoa Zarraga, 2017. "Modelling Realized Volatility in Electricity Spot Prices: New insights and Application to the Japanese Electricity Market," ISER Discussion Paper 0991, Institute of Social and Economic Research, Osaka University.
    72. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    73. Allen, David & Lazarov, Zdravetz & McAleer, Michael & Peiris, Shelton, 2009. "Comparison of alternative ACD models via density and interval forecasts: Evidence from the Australian stock market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2535-2555.
    74. Yanhui Chen & Kin Lai & Jiangze Du, 2014. "Modeling and forecasting Hang Seng index volatility with day-of-week effect, spillover effect based on ARIMA and HAR," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(2), pages 113-132, December.
    75. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 339-358.
    76. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.
    77. D. Delpini & G. Bormetti, 2015. "Stochastic volatility with heterogeneous time scales," Quantitative Finance, Taylor & Francis Journals, vol. 15(10), pages 1597-1608, October.
    78. Ahoniemi, Katja & Lanne, Markku, 2010. "Realized volatility and overnight returns," Bank of Finland Research Discussion Papers 19/2010, Bank of Finland.
    79. Zhiyuan Pan & Jun Zhang & Yudong Wang & Juan Huang, 2024. "Modeling and forecasting stock return volatility using the HARGARCH model with VIX information," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(8), pages 1383-1403, August.
    80. Imma Valentina Curato, 2012. "Asymptotics for the Fourier estimators of the volatility of volatility and the leverage," Working Papers - Mathematical Economics 2012-11, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    81. Giampiero M. Gallo & Edoardo Otranto, 2014. "Forecasting Realized Volatility with Changes of Regimes," Econometrics Working Papers Archive 2014_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    82. Luo, Jiawen & Ji, Qiang, 2018. "High-frequency volatility connectedness between the US crude oil market and China's agricultural commodity markets," Energy Economics, Elsevier, vol. 76(C), pages 424-438.
    83. Maria Socorro Gochoco-Bautista & Jianxin Wang & Minxian Yang, 2014. "Commodity Price, Carry Trade, and the Volatility and Liquidity of Asian Currencies," The World Economy, Wiley Blackwell, vol. 37(6), pages 811-833, June.
    84. Zhouwei Wang & Qicheng Zhao & Min Zhu & Tao Pang, 2020. "Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    85. Tingguo Zheng & Tao Song, 2014. "A Realized Stochastic Volatility Model With Box-Cox Transformation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 593-605, October.
    86. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2018. "Forecasting global stock market implied volatility indices," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 111-129.
    87. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.
    88. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    89. Reboredo, Juan C., 2014. "Volatility spillovers between the oil market and the European Union carbon emission market," Economic Modelling, Elsevier, vol. 36(C), pages 229-234.
    90. Audrino, Francesco & Corsi, Fulvio, 2010. "Modeling tick-by-tick realized correlations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
    91. Gaurav Raizada & Vartika Srivastava & S. V. D. Nageswara Rao, 2020. "Shall One Sit “Longer” for a Free Lunch? Impact of Trading Durations on the Realized Variances and Volatility Spillovers," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(1), pages 1-28, March.
    92. Hassan, Aminu & Ibrahim, Masud Usman & Bala, Ahmed Jinjiri, 2024. "Vulnerability of a developing stock market to openness: One-way return and volatility transmissions," International Review of Financial Analysis, Elsevier, vol. 93(C).
    93. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Papers 2011-W01, Economics Group, Nuffield College, University of Oxford.
    94. J. Eduardo Vera-Vald'es, 2017. "On Long Memory Origins and Forecast Horizons," Papers 1712.08057, arXiv.org.
    95. Ehouman, Yao Axel, 2020. "Volatility transmission between oil prices and banks' stock prices as a new source of instability: Lessons from the United States experience," Economic Modelling, Elsevier, vol. 91(C), pages 198-217.
    96. Zhang, Wei & Wang, Jun, 2017. "Nonlinear stochastic exclusion financial dynamics modeling and time-dependent intrinsic detrended cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 29-41.
    97. Ji‐Eun Choi & Dong Wan Shin, 2018. "Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 691-704, September.
    98. Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    99. Gkillas, Konstantinos & Konstantatos, Christoforos & Floros, Christos & Tsagkanos, Athanasios, 2021. "Realized volatility spillovers between US spot and futures during ECB news: Evidence from the European sovereign debt crisis," International Review of Financial Analysis, Elsevier, vol. 74(C).
    100. Pham, Son Duy & Nguyen, Thao Thac Thanh & Do, Hung Xuan & Vo, Xuan Vinh, 2023. "Portfolio diversification during the COVID-19 pandemic: Do vaccinations matter?," Journal of Financial Stability, Elsevier, vol. 65(C).
    101. Song, Junmo & Baek, Changryong, 2019. "Detecting structural breaks in realized volatility," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 58-75.
    102. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    103. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01442618, HAL.
    104. Chuxuan Xiao & Winifred Huang & David P. Newton, 2024. "Predicting expected idiosyncratic volatility: Empirical evidence from ARFIMA, HAR, and EGARCH models," Review of Quantitative Finance and Accounting, Springer, vol. 63(3), pages 979-1006, October.
    105. Warshaw, Evan, 2020. "Asymmetric volatility spillover between European equity and foreign exchange markets: Evidence from the frequency domain," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 1-14.
    106. Charles S. Bos & Pawel Janus, 2013. "A Quantile-based Realized Measure of Variation: New Tests for Outlying Observations in Financial Data," Tinbergen Institute Discussion Papers 13-155/III, Tinbergen Institute.
    107. Dimos Kambouroudis & David McMillan & Katerina Tsakou, 2019. "Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility," Working Papers 2019-03, Swansea University, School of Management.
    108. Payzan-LeNestour, Elise & Pradier, Lionnel & Putniņš, Tālis J., 2023. "Biased risk perceptions: Evidence from the laboratory and financial markets," Journal of Banking & Finance, Elsevier, vol. 154(C).
    109. Eric Hillebrand & Marcelo C. Medeiros, 2012. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," CREATES Research Papers 2012-30, Department of Economics and Business Economics, Aarhus University.
    110. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    111. Th'eophile Griveau-Billion & Ben Calderhead, 2019. "A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour," Papers 1904.08153, arXiv.org, revised Jan 2020.
    112. Opschoor, Anne & Lucas, André, 2023. "Time-varying variance and skewness in realized volatility measures," International Journal of Forecasting, Elsevier, vol. 39(2), pages 827-840.
    113. Moawia Alghalith, 2022. "Methods in Econophysics: Estimating the Probability Density and Volatility," Papers 2301.10178, arXiv.org.
    114. Jean-Pierre Zigrand & Hyun Song Shin & Jon Danielsson, 2010. "Risk Appetite and Endogenous Risk," FMG Discussion Papers dp647, Financial Markets Group.
    115. Seul-Ki Park & Ji-Eun Choi & Dong Wan Shin, 2017. "Value at risk forecasting for volatility index," Applied Economics Letters, Taylor & Francis Journals, vol. 24(21), pages 1613-1620, December.
    116. Chen, Ying & Härdle, Wolfgang Karl & Pigorsch, Uta, 2010. "Localized Realized Volatility Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1376-1393.
    117. Hwang, Eunju & Hong, Won-Tak, 2021. "A multivariate HAR-RV model with heteroscedastic errors and its WLS estimation," Economics Letters, Elsevier, vol. 203(C).
    118. A Clements & D Preve, 2019. "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series 120, National Centre for Econometric Research.
    119. Zhimin Wu & Guanghui Cai, 2024. "Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1956-1974, September.
    120. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    121. Degiannakis, Stavros, 2017. "The one-trading-day-ahead forecast errors of intra-day realized volatility," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1298-1314.
    122. Stavroula Yfanti & Georgios Chortareas & Menelaos Karanasos & Emmanouil Noikokyris, 2022. "A three‐dimensional asymmetric power HEAVY model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2737-2761, July.
    123. Giovanni Bonaccolto & Massimiliano Caporin, 2016. "The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective," JRFM, MDPI, vol. 9(3), pages 1-25, July.
    124. Julien Chevallier & Benoît Sévi, 2011. "On the volatility-volume relationship in energy futures markets using intraday data," Working Papers hal-04140997, HAL.
    125. Hiroyuki Kawakatsu, 2022. "Modeling Realized Variance with Realized Quarticity," Stats, MDPI, vol. 5(3), pages 1-25, September.
    126. Tingguo Zheng & Han Xiao & Rong Chen, 2022. "Generalized autoregressive moving average models with GARCH errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 125-146, January.
    127. Lanne, Markku & Ahoniemi, Katja, 2008. "Implied Volatility with Time-Varying Regime Probabilities," MPRA Paper 23721, University Library of Munich, Germany.
    128. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2016. "Volatility Jumps and Their Economic Determinants," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 29-80.
    129. Degiannakis, Stavros, 2018. "Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts," MPRA Paper 96272, University Library of Munich, Germany.
    130. Liang, Chao & Ma, Feng & Li, Ziyang & Li, Yan, 2020. "Which types of commodity price information are more useful for predicting US stock market volatility?," Economic Modelling, Elsevier, vol. 93(C), pages 642-650.
    131. Ding, Y., 2021. "Conditional Heteroskedasticity in the Volatility of Asset Returns," Cambridge Working Papers in Economics 2179, Faculty of Economics, University of Cambridge.
    132. Onno Kleen, 2024. "Scaling and measurement error sensitivity of scoring rules for distribution forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 833-849, August.
    133. Eunju Hwang, 2021. "Limit Theory for Stationary Autoregression with Heavy-Tailed Augmented GARCH Innovations," Mathematics, MDPI, vol. 9(8), pages 1-10, April.
    134. Qu, Hui & Duan, Qingling & Niu, Mengyi, 2018. "Modeling the volatility of realized volatility to improve volatility forecasts in electricity markets," Energy Economics, Elsevier, vol. 74(C), pages 767-776.
    135. Zhu, Xuehong & Zhang, Hongwei & Zhong, Meirui, 2017. "Volatility forecasting using high frequency data: The role of after-hours information and leverage effects," Resources Policy, Elsevier, vol. 54(C), pages 58-70.
    136. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    137. Chen, Wang & Ma, Feng & Wei, Yu & Liu, Jing, 2020. "Forecasting oil price volatility using high-frequency data: New evidence," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 1-12.
    138. Xu, Yongdeng, 2022. "The Exponential HEAVY Model: An Improved Approach to Volatility Modeling and Forecasting," Cardiff Economics Working Papers E2022/5, Cardiff University, Cardiff Business School, Economics Section.
    139. Jian Zhou, 2017. "Forecasting REIT volatility with high-frequency data: a comparison of alternative methods," Applied Economics, Taylor & Francis Journals, vol. 49(26), pages 2590-2605, June.
    140. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2021. "Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1618-1639, October.
    141. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, Department of Economics and Business Economics, Aarhus University.
    142. Todorova, Neda & Worthington, Andrew & Souček, Michael, 2014. "Realized volatility spillovers in the non-ferrous metal futures market," Resources Policy, Elsevier, vol. 39(C), pages 21-31.
    143. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach," Papers 1708.02073, arXiv.org.
    144. Yusaku Nishimura & Xuyi Dong & Bianxia Sun, 2021. "Trump's tweets: Sentiment, stock market volatility, and jumps," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(3), pages 497-512, September.
    145. Piotr Fiszeder & Grzegorz Perczak, 2013. "A new look at variance estimation based on low, high and closing prices taking into account the drift," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 456-481, November.
    146. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    147. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun, 2023. "The effect of uncertainty on stock market volatility and correlation," Journal of Banking & Finance, Elsevier, vol. 154(C).
    148. Charalampos Stasinakis & Georgios Sermpinis & Ioannis Psaradellis & Thanos Verousis, 2016. "Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1901-1915, December.
    149. Jung, R.C. & Maderitsch, R., 2014. "Structural breaks in volatility spillovers between international financial markets: Contagion or mere interdependence?," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 331-342.
    150. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    151. Jean-François Carpantier & Arnaud Dufays, 2014. "Specific Markov-switching behaviour for ARMA parameters," Working Papers hal-01821134, HAL.
    152. Yarovaya, Larisa & Matkovskyy, Roman & Jalan, Akanksha, 2021. "The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    153. Arnaud Dufays & Maciej Augustyniak & Luc Bauwens, 2016. "A new approach to volatility modeling: the High-Dimensional Markov model," Cahiers de recherche 1609, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    154. Markopoulou, Chrysi E. & Skintzi, Vasiliki D. & Refenes, Apostolos-Paul N., 2016. "Realized hedge ratio: Predictability and hedging performance," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 121-133.
    155. Cui, Jinxin & Maghyereh, Aktham & Goh, Mark & Zou, Huiwen, 2022. "Risk spillovers and time-varying links between international oil and China’s commodity futures markets: Fresh evidence from the higher-order moments," Energy, Elsevier, vol. 238(PB).
    156. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena.
    157. Yao Axel Ehouman, 2020. "Volatility transmission between oil prices and banks’ stock prices as a new source of instability: Lessons from the United States experience," Post-Print hal-02960571, HAL.
    158. Chen, Wang & Lu, Xinjie & Wang, Jiqian, 2022. "Modeling and managing stock market volatility using MRS-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 625-635.
    159. Antonio Díaz & Francisco Jareño & Eliseo Navarro, 2020. "Yield curves from different bond data sets," Review of Derivatives Research, Springer, vol. 23(2), pages 191-226, July.
    160. Roxana Halbleib & Valeri Voev, 2011. "Forecasting multivariate volatility using the VARFIMA model on realized covariance cholesky factors," ULB Institutional Repository 2013/195065, ULB -- Universite Libre de Bruxelles.
    161. Ole E. Barndorff-Nielsen & Almut E. D. Veraart, 2009. "Stochastic volatility of volatility in continuous time," CREATES Research Papers 2009-25, Department of Economics and Business Economics, Aarhus University.
    162. Nikita Medvedev & Zhiguang Wang, 2022. "Multistep forecast of the implied volatility surface using deep learning," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 645-667, April.
    163. Zhang, Bo & Wang, Jun & Fang, Wen, 2015. "Volatility behavior of visibility graph EMD financial time series from Ising interacting system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 301-314.
    164. Pham, Son Duy & Nguyen, Thao Thac Thanh & Do, Hung Xuan, 2022. "Dynamic volatility connectedness between thermal coal futures and major cryptocurrencies: Evidence from China," Energy Economics, Elsevier, vol. 112(C).
    165. Wang, Xunxiao & Wu, Chongfeng & Xu, Weidong, 2015. "Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects," International Journal of Forecasting, Elsevier, vol. 31(3), pages 609-619.
    166. Chao Liang & Yongan Xu & Zhonglu Chen & Xiafei Li, 2023. "Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3689-3699, October.
    167. Chai, Edwina F.L. & Lee, Adrian D. & Wang, Jianxin, 2015. "Global information distribution in the gold OTC markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 206-217.
    168. Benoît Sévi, 2013. "An empirical analysis of the downside risk-return trade-off at daily frequency," Post-Print hal-01500860, HAL.
    169. Chun Liu & John M Maheu, 2010. "Intraday Dynamics of Volatility and Duration: Evidence from the Chinese Stock Market," Working Papers tecipa-401, University of Toronto, Department of Economics.
    170. Díaz, Antonio & Jareño, Francisco & Navarro, Eliseo, 2018. "Zero-coupon interest rates: Evaluating three alternative datasets," Economics Discussion Papers 2018-67, Kiel Institute for the World Economy (IfW Kiel).
    171. Todorova, Neda & Souček, Michael, 2014. "Overnight information flow and realized volatility forecasting," Finance Research Letters, Elsevier, vol. 11(4), pages 420-428.
    172. Ceylan, Ozcan, 2012. "Time-Varying Volatility Asymmetry: A Conditioned HAR-RV(CJ) EGARCH-M Model," GIAM Working Papers 12-4, Galatasaray University Economic Research Center.
    173. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
    174. Sergii Pypko, 2015. "Volatility Forecast in Crises and Expansions," JRFM, MDPI, vol. 8(3), pages 1-26, August.
    175. Todorova, Neda, 2015. "The course of realized volatility in the LME non-ferrous metal market," Economic Modelling, Elsevier, vol. 51(C), pages 1-12.
    176. Manh Cuong Dong & Cathy W. S. Chen & Manabu Asai, 2023. "Bayesian non‐linear quantile effects on modelling realized kernels," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 981-995, January.
    177. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
    178. Ke Yang & Langnan Chen, 2014. "Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day-of-the-Week Effect," International Review of Finance, International Review of Finance Ltd., vol. 14(3), pages 345-392, September.
    179. Xuan Yao & Xiaofeng Hui & Kaican Kang, 2021. "Can night trading sessions improve forecasting performance of gold futures' volatility in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 849-860, August.
    180. Moawia Alghalith & Christos Floros & Konstantinos Gkillas, 2020. "Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility," Risks, MDPI, vol. 8(2), pages 1-15, April.
    181. Vít Bubák & Filip Žikeš, 2009. "Distribution and Dynamics of Central-European Exchange Rates: Evidence from Intraday Data," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(4), pages 334-359, Oktober.
    182. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    183. Leopoldo Catania & Mads Sandholdt, 2019. "Bitcoin at High Frequency," JRFM, MDPI, vol. 12(1), pages 1-20, February.
    184. Nicholas Taylor, 2015. "Realized volatility forecasting in an international context," Applied Economics Letters, Taylor & Francis Journals, vol. 22(6), pages 503-509, April.
    185. Dimitrios P. Louzis & Spyros Xanthopoulos - Sissinis & Apostolos P. Refenes, 2012. "Stock index Value-at-Risk forecasting: A realized volatility extreme value theory approach," Economics Bulletin, AccessEcon, vol. 32(1), pages 981-991.
    186. Ding, Y., 2021. "Conditional Heteroskedasticity in the Volatility of Asset Returns," Janeway Institute Working Papers 2111, Faculty of Economics, University of Cambridge.
    187. Philip L. H. Yu & W. K. Li & F. C. Ng, 2017. "The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 513-527, October.
    188. Aboura, Sofiane & Wagner, Niklas, 2016. "Extreme asymmetric volatility: Stress and aggregate asset prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 47-59.
    189. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Chasing volatility - A persistent multiplicative error model with jumps," CREATES Research Papers 2014-29, Department of Economics and Business Economics, Aarhus University.
    190. Jonathan R. Stroud & Michael S. Johannes, 2014. "Bayesian Modeling and Forecasting of 24-Hour High-Frequency Volatility," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1368-1384, December.
    191. Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
    192. Ding, Y., 2021. "Augmented Real-Time GARCH: A Joint Model for Returns, Volatility and Volatility of Volatility," Cambridge Working Papers in Economics 2112, Faculty of Economics, University of Cambridge.
    193. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    194. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.
    195. Naimoli, Antonio & Gerlach, Richard & Storti, Giuseppe, 2022. "Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators," Economic Modelling, Elsevier, vol. 107(C).
    196. Dimitrios Louzis & Spyros Xanthopoulos-Sisinis & Apostolos Refenes, 2011. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Post-Print hal-00709559, HAL.
    197. Luo, Jiawen & Wang, Shengquan, 2019. "The asymmetric high-frequency volatility transmission across international stock markets," Finance Research Letters, Elsevier, vol. 31(C), pages 104-109.
    198. Heinen, Florian & Willert, Juliane, 2011. "Monitoring a change in persistence of a long range dependent time series," Hannover Economic Papers (HEP) dp-479, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    199. Fulvio Corsi & Francesco Audrino, 2007. "Realized Correlation Tick-by-Tick," University of St. Gallen Department of Economics working paper series 2007 2007-02, Department of Economics, University of St. Gallen.
    200. Cui, Jing & Zhao, Hua, 2015. "Intraday jumps in China's Treasury bond market and macro news announcements," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 211-223.
    201. Mykland, Per A. & Zhang, Lan, 2016. "Between data cleaning and inference: Pre-averaging and robust estimators of the efficient price," Journal of Econometrics, Elsevier, vol. 194(2), pages 242-262.
    202. Dimitrios Vortelinos & Dimitrios Thomakos, 2009. "Realized Volatility and Jumps in the Athens Stock Exchange," Working Papers 00044, University of Peloponnese, Department of Economics.
    203. Julien Chevallier & Benoît Sévi, 2009. "On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting," Working Papers hal-04140871, HAL.
    204. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    205. Xin Du & Kai Moriyama & Kumiko Tanaka-Ishii, 2023. "Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation," Papers 2310.14536, arXiv.org.
    206. Chin Wen CHEONG & Lee Min CHERNG & Grace Lee Ching YAP, 2016. "Heterogeneous Market Hypothesis Evaluations using Various Jump-Robust Realized Volatility," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 50-64, December.
    207. Mike Buckle & Jing Chen & Julian Williams, 2014. "How Predictable Are Equity Covariance Matrices? Evidence from High‐Frequency Data for Four Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(7), pages 542-557, November.
    208. Chen, Zhonglu & Liang, Chao & Umar, Muhammad, 2021. "Is investor sentiment stronger than VIX and uncertainty indices in predicting energy volatility?," Resources Policy, Elsevier, vol. 74(C).
    209. Wang, Jianxin, 2013. "Liquidity commonality among Asian equity markets," Pacific-Basin Finance Journal, Elsevier, vol. 21(1), pages 1209-1231.
    210. M. Karanasos & S. Yfanti & A. Christopoulos, 2021. "The long memory HEAVY process: modeling and forecasting financial volatility," Annals of Operations Research, Springer, vol. 306(1), pages 111-130, November.
    211. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    212. Czado, Claudia & Ivanov, Eugen & Okhrin, Yarema, 2019. "Modelling temporal dependence of realized variances with vines," Econometrics and Statistics, Elsevier, vol. 12(C), pages 198-216.
    213. Xiangjin B. Chen & Jiti Gao & Degui Li & Param Silvapulle, 2018. "Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 88-100, January.
    214. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    215. Huayun Jiang & Neda Todorova & Eduardo Roca & Jen-Je Su, 2017. "Dynamics of volatility transmission between the U.S. and the Chinese agricultural futures markets," Applied Economics, Taylor & Francis Journals, vol. 49(34), pages 3435-3452, July.
    216. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2019. "Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility," CESifo Working Paper Series 8000, CESifo.
    217. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    218. Yuping Song & Bolin Lei & Xiaolong Tang & Chen Li, 2024. "Volatility forecasting for stock market index based on complex network and hybrid deep learning model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 544-566, April.
    219. Massimiliano Frezza & Sergio Bianchi & Augusto Pianese, 2022. "Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process," Computational Management Science, Springer, vol. 19(1), pages 99-132, January.
    220. Haugom, Erik & Westgaard, Sjur & Solibakke, Per Bjarte & Lien, Gudbrand, 2011. "Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data," Energy Economics, Elsevier, vol. 33(6), pages 1206-1215.
    221. Barbaglia, Luca & Croux, Christophe & Wilms, Ines, 2020. "Volatility spillovers in commodity markets: A large t-vector autoregressive approach," Energy Economics, Elsevier, vol. 85(C).
    222. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.

  25. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2005. "Modeling and predicting market risk with Laplace-Gaussian mixture distributions," CFS Working Paper Series 2005/11, Center for Financial Studies (CFS).

    Cited by:

    1. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    2. BOUADDI, Mohammed & ROMBOUTS, Jeroen V.K., 2007. "Mixed exponential power asymmetric conditional heteroskedasticity," LIDAM Discussion Papers CORE 2007097, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Stoyanov, Stoyan V. & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "CVaR sensitivity with respect to tail thickness," Working Paper Series in Economics 29, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    4. Saissi Hassani, Samir & Dionne, Georges, 2021. "The New International Regulation of Market Risk: Roles of VaR and CVaR in Model Validation," Working Papers 21-1, HEC Montreal, Canada Research Chair in Risk Management.
    5. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    6. Gel, Yulia R., 2010. "Test of fit for a Laplace distribution against heavier tailed alternatives," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 958-965, April.
    7. Kaldasch, Joachim, 2014. "Evolutionary model of stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 449-462.
    8. Leopoldo Catania, 2016. "Dynamic Adaptive Mixture Models," Papers 1603.01308, arXiv.org, revised Jan 2023.
    9. Mahmood Ul Hassan & Pär Stockhammar, 2016. "Fitting probability distributions to economic growth: a maximum likelihood approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1583-1603, July.
    10. Yining Chen, 2015. "Semiparametric Time Series Models with Log-concave Innovations: Maximum Likelihood Estimation and its Consistency," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 1-31, March.
    11. John Douglas (J.D.) Opdyke, 2007. "Comparing Sharpe ratios: So where are the p-values?," Journal of Asset Management, Palgrave Macmillan, vol. 8(5), pages 308-336, December.

  26. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2005. "Modeling and predicting market risk with Laplace-Gaussian mixture distributions," CFS Working Paper Series 2005/11, Center for Financial Studies (CFS).

    Cited by:

    1. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    2. BOUADDI, Mohammed & ROMBOUTS, Jeroen V.K., 2007. "Mixed exponential power asymmetric conditional heteroskedasticity," LIDAM Discussion Papers CORE 2007097, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Stoyanov, Stoyan V. & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "CVaR sensitivity with respect to tail thickness," Working Paper Series in Economics 29, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    4. Saissi Hassani, Samir & Dionne, Georges, 2021. "The New International Regulation of Market Risk: Roles of VaR and CVaR in Model Validation," Working Papers 21-1, HEC Montreal, Canada Research Chair in Risk Management.
    5. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    6. Gel, Yulia R., 2010. "Test of fit for a Laplace distribution against heavier tailed alternatives," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 958-965, April.
    7. Kaldasch, Joachim, 2014. "Evolutionary model of stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 449-462.
    8. Leopoldo Catania, 2016. "Dynamic Adaptive Mixture Models," Papers 1603.01308, arXiv.org, revised Jan 2023.
    9. Mahmood Ul Hassan & Pär Stockhammar, 2016. "Fitting probability distributions to economic growth: a maximum likelihood approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1583-1603, July.
    10. Yining Chen, 2015. "Semiparametric Time Series Models with Log-concave Innovations: Maximum Likelihood Estimation and its Consistency," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 1-31, March.
    11. John Douglas (J.D.) Opdyke, 2007. "Comparing Sharpe ratios: So where are the p-values?," Journal of Asset Management, Palgrave Macmillan, vol. 8(5), pages 308-336, December.

  27. Markus Haas & Stefan Mittnik & Bruce Mizrach, 2004. "Assessing Central Bank Credibility During the EMS Crises: Comparing Option and Spot Market-Based Forecasts," Departmental Working Papers 200424, Rutgers University, Department of Economics.

    Cited by:

    1. Chen Yu-Fu & Funke Michael & Glanemann Nicole, 2013. "Off-the-record target zones: theory with an application to Hong Kong’s currency board," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 373-393, September.
    2. Li, Liuling & Mizrach, Bruce, 2010. "Tail return analysis of Bear Stearns' credit default swaps," Economic Modelling, Elsevier, vol. 27(6), pages 1529-1536, November.
    3. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).
    4. Michael Funke & Julius Loermann & Richhild Moessner, 2017. "The discontinuation of the EUR/CHF minimum exchange rate in January 2015: was it expected?," BIS Working Papers 652, Bank for International Settlements.
    5. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).
    6. Bruce Mizrach, 2007. "Recovering Probabilistic Information From Options Prices and the Underlying," Departmental Working Papers 200702, Rutgers University, Department of Economics.
    7. Peter Christoffersen & Kris Jacobs & Bo Young Chang, 2011. "Forecasting with Option Implied Information," CREATES Research Papers 2011-46, Department of Economics and Business Economics, Aarhus University.
    8. Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Time-varying mixture GARCH models and asymmetric volatility," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 602-623.
    9. Bruce Mizrach, 2006. "The Enron Bankruptcy: When did the options market in Enron lose it’s smirk?," Review of Quantitative Finance and Accounting, Springer, vol. 27(4), pages 365-382, December.

  28. Stefan Mittnik & Peter A. Zadrozny, 2004. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Conditions Data," CESifo Working Paper Series 1203, CESifo.

    Cited by:

    1. Cecilia Frale, Serena Teobaldo, Marco Cacciotti, Alessandra Caretta, 2013. "A Quarterly Measure Of Potential Output In The New European Fiscal Framework," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 181-197, April-Jun.
    2. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    3. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
    4. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    5. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    6. Sieds, 2013. "Complete Volume LXVII n.2 2013," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 1-197, April-Jun.
    7. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sektorale Prognosen im Verarbeitenden Gewerbe," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    8. Katja Heinisch & Rolf Scheufele, 2019. "Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 170-200, November.
    9. Franco, Ray John Gabriel & Mapa, Dennis S., 2014. "The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach," MPRA Paper 55858, University Library of Munich, Germany.
    10. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    11. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    12. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    13. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    14. Klaus Abberger & Gebhard Flaig & Wolfgang Nierhaus, 2007. "ifo Konjunkturumfragen und Konjunkturanalyse : ausgewählte methodische Aufsätze aus dem ifo Schnelldienst," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 33, September.
    15. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," IREA Working Papers 201801, University of Barcelona, Research Institute of Applied Economics, revised Jan 2018.
    16. Konstantin A. Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2007. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Discussion Papers of DIW Berlin 664, DIW Berlin, German Institute for Economic Research.
    17. Klaus Abberger, 2007. "Forecasting Quarter-on-Quarter Changes of German GDP with Monthly Business Tendency Survey Results," ifo Working Paper Series 40, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    18. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    19. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming”," AQR Working Papers 201706, University of Barcelona, Regional Quantitative Analysis Group, revised May 2017.
    20. Seong, Byeongchan, 2020. "Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models," Economic Modelling, Elsevier, vol. 91(C), pages 463-468.
    21. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo.
    22. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    23. Klaus Abberger, 2005. "Qualitative Business Surveys and the Assessment of Employment A Case Study for Germany," ifo Working Paper Series 11, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    24. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    25. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    26. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    27. Ojogho, Osaihiomwan & Egware, Robert Awotu, 2015. "Price Generating Process And Volatility In Nigerian Agricultural Commodities Market," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 3(4), pages 1-10, October.
    28. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
    29. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    30. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72, September.
    31. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    32. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    33. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    34. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    35. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    36. Daniel Roash & Tanya Suhoy, 2019. "Sentiment Indicators Based on a Short Business Tendency Survey," Bank of Israel Working Papers 2019.11, Bank of Israel.
    37. Neville Francis & Eric Ghysels & Michael T. Owyang, 2011. "The low-frequency impact of daily monetary policy shocks," Working Papers 2011-009, Federal Reserve Bank of St. Louis.
    38. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    39. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    40. Chen, Pu, 2009. "A Note on Updating Forecasts When New Information Arrives between Two Periods," Economics Discussion Papers 2009-22, Kiel Institute for the World Economy (IfW Kiel).
    41. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    42. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    43. Vermeulen, Philip, 2014. "An evaluation of business survey indices for short-term forecasting: Balance method versus Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 30(4), pages 882-897.
    44. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    45. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.

  29. Mittnik, Stefan & Paolella, Marc S., 2003. "Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions," CFS Working Paper Series 2003/04, Center for Financial Studies (CFS).

    Cited by:

    1. Xing Yu, 2012. "The optimal portfolio model based on multivariate t distribution with linear weighted sum method," E3 Journal of Business Management and Economics., E3 Journals, vol. 3(1), pages 044-047.
    2. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    3. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    4. Paolella, Marc S. & Taschini, Luca, 2008. "An econometric analysis of emission allowance prices," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2022-2032, October.
    5. Victoria Zinde-Walsh & Dongming Zhu, 2007. "Properties And Estimation Of Asymmetric Exponential Power Distribution," Departmental Working Papers 2007-11, McGill University, Department of Economics.
    6. Sung Ik Kim, 2022. "ARMA–GARCH model with fractional generalized hyperbolic innovations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    7. Stoyanov, Stoyan V. & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "CVaR sensitivity with respect to tail thickness," Working Paper Series in Economics 29, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    8. Vacca, Gianmarco & Zoia, Maria Grazia & Bagnato, Luca, 2022. "Forecasting in GARCH models with polynomially modified innovations," International Journal of Forecasting, Elsevier, vol. 38(1), pages 117-141.
    9. José Curto & José Pinto & Gonçalo Tavares, 2009. "Modeling stock markets’ volatility using GARCH models with Normal, Student’s t and stable Paretian distributions," Statistical Papers, Springer, vol. 50(2), pages 311-321, March.
    10. Klar, B. & Lindner, F. & Meintanis, S.G., 2012. "Specification tests for the error distribution in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3587-3598.
    11. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    12. Dongming Zhu & John W. Galbraith, 2009. "Forecasting Expected Shortfall with a Generalized Asymmetric Student-t Distribution," CIRANO Working Papers 2009s-24, CIRANO.
    13. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2002. "Stationarity of stable power-GARCH processes," Journal of Econometrics, Elsevier, vol. 106(1), pages 97-107, January.
    14. Dongming Zhu & John W. Galbraith, 2009. "A Generalized Asymmetric Student-t Distribution with Application to Financial Econometrics," CIRANO Working Papers 2009s-13, CIRANO.
    15. Calzolari, Giorgio & Halbleib, Roxana, 2018. "Estimating stable latent factor models by indirect inference," Journal of Econometrics, Elsevier, vol. 205(1), pages 280-301.
    16. Pacicco, Fausto & Vena, Luigi & Venegoni, Andrea, 2020. "Communication and financial supervision: How does disclosure affect market stability?," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 1-15.
    17. Lampros Kalyvas & Athanasios Sfetsos, 2006. "Does The Application Of Innovative Internal Models Diminish Regulatory Capital?," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 217-226.
    18. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    19. Fausto Pacicco & Luigi Vena & Andrea Venegoni, 2017. "Full disclosure and financial stability: how does the market digest the transparency shock?," LIUC Papers in Economics 305, Cattaneo University (LIUC).
    20. Emilio Cardona & Andrés Mora-Valencia & Daniel Velásquez-Gaviria, 2019. "Testing expected shortfall: an application to emerging market stock indices," Risk Management, Palgrave Macmillan, vol. 21(3), pages 153-182, September.
    21. Bentes, Sónia R., 2014. "Measuring persistence in stock market volatility using the FIGARCH approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 190-197.
    22. Slim, Skander & Koubaa, Yosra & BenSaïda, Ahmed, 2017. "Value-at-Risk under Lévy GARCH models: Evidence from global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 30-53.
    23. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.

  30. Mittnik, Stefan & Paolella, Marc S., 2003. "Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions," CFS Working Paper Series 2003/04, Center for Financial Studies (CFS).

    Cited by:

    1. Xing Yu, 2012. "The optimal portfolio model based on multivariate t distribution with linear weighted sum method," E3 Journal of Business Management and Economics., E3 Journals, vol. 3(1), pages 044-047.
    2. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    3. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    4. Paolella, Marc S. & Taschini, Luca, 2008. "An econometric analysis of emission allowance prices," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2022-2032, October.
    5. Victoria Zinde-Walsh & Dongming Zhu, 2007. "Properties And Estimation Of Asymmetric Exponential Power Distribution," Departmental Working Papers 2007-11, McGill University, Department of Economics.
    6. Sung Ik Kim, 2022. "ARMA–GARCH model with fractional generalized hyperbolic innovations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    7. Stoyanov, Stoyan V. & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "CVaR sensitivity with respect to tail thickness," Working Paper Series in Economics 29, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    8. Vacca, Gianmarco & Zoia, Maria Grazia & Bagnato, Luca, 2022. "Forecasting in GARCH models with polynomially modified innovations," International Journal of Forecasting, Elsevier, vol. 38(1), pages 117-141.
    9. José Curto & José Pinto & Gonçalo Tavares, 2009. "Modeling stock markets’ volatility using GARCH models with Normal, Student’s t and stable Paretian distributions," Statistical Papers, Springer, vol. 50(2), pages 311-321, March.
    10. Klar, B. & Lindner, F. & Meintanis, S.G., 2012. "Specification tests for the error distribution in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3587-3598.
    11. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    12. Dongming Zhu & John W. Galbraith, 2009. "Forecasting Expected Shortfall with a Generalized Asymmetric Student-t Distribution," CIRANO Working Papers 2009s-24, CIRANO.
    13. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2002. "Stationarity of stable power-GARCH processes," Journal of Econometrics, Elsevier, vol. 106(1), pages 97-107, January.
    14. Dongming Zhu & John W. Galbraith, 2009. "A Generalized Asymmetric Student-t Distribution with Application to Financial Econometrics," CIRANO Working Papers 2009s-13, CIRANO.
    15. Calzolari, Giorgio & Halbleib, Roxana, 2018. "Estimating stable latent factor models by indirect inference," Journal of Econometrics, Elsevier, vol. 205(1), pages 280-301.
    16. Pacicco, Fausto & Vena, Luigi & Venegoni, Andrea, 2020. "Communication and financial supervision: How does disclosure affect market stability?," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 1-15.
    17. Lampros Kalyvas & Athanasios Sfetsos, 2006. "Does The Application Of Innovative Internal Models Diminish Regulatory Capital?," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 217-226.
    18. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    19. Fausto Pacicco & Luigi Vena & Andrea Venegoni, 2017. "Full disclosure and financial stability: how does the market digest the transparency shock?," LIUC Papers in Economics 305, Cattaneo University (LIUC).
    20. Emilio Cardona & Andrés Mora-Valencia & Daniel Velásquez-Gaviria, 2019. "Testing expected shortfall: an application to emerging market stock indices," Risk Management, Palgrave Macmillan, vol. 21(3), pages 153-182, September.
    21. Bentes, Sónia R., 2014. "Measuring persistence in stock market volatility using the FIGARCH approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 190-197.
    22. Slim, Skander & Koubaa, Yosra & BenSaïda, Ahmed, 2017. "Value-at-Risk under Lévy GARCH models: Evidence from global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 30-53.
    23. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.

  31. Claessen, Holger & Mittnik, Stefan, 2002. "Forecasting stock market volatility and the informational efficiency of the DAX-index options market," CFS Working Paper Series 2002/04, Center for Financial Studies (CFS).

    Cited by:

    1. Leonardo Quero Virla, 2023. "An empirical characterization of volatility in the German stock market," SN Business & Economics, Springer, vol. 3(7), pages 1-19, July.
    2. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2019. "Forecasting the KOSPI200 spot volatility using various volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 156-166.
    3. Markus Haas & Stefan Mittnik & Bruce Mizrach, 2004. "Assessing Central Bank Credibility During the EMS Crises: Comparing Option and Spot Market-Based Forecasts," Departmental Working Papers 200424, Rutgers University, Department of Economics.
    4. Imlak Shaikh & Puja Padhi, 2014. "The forecasting performance of implied volatility index: evidence from India VIX," Economic Change and Restructuring, Springer, vol. 47(4), pages 251-274, November.
    5. GIOT, Pierre, 2003. "The Asian financial crisis : the start of a regime switch in volatility," LIDAM Discussion Papers CORE 2003078, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Ewa Ratuszny, 2015. "Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 129-156.
    7. Peng, Qing & Li, Jie & Zhao, Yu & Wu, Han, 2021. "The informational content of implied volatility: Application to the USD/JPY exchange rates," Journal of Asian Economics, Elsevier, vol. 76(C).
    8. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    9. GIOT, Pierre, 2003. "The information content of implied volatility indexes for forecasting volatility and market risk," LIDAM Discussion Papers CORE 2003027, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Tanuj Nandan & Puja Agrawal, 2016. "Pricing Efficiency in CNX Nifty Index Options Using the Black–Scholes Model: A Comparative Study of Alternate Volatility Measures," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 10(2), pages 281-304, May.
    11. Bruce Burton & Satish Kumar & Nitesh Pandey, 2020. "Twenty-five years of The European Journal of Finance (EJF): a retrospective analysis," The European Journal of Finance, Taylor & Francis Journals, vol. 26(18), pages 1817-1841, December.
    12. Wilkens, Sascha & Roder, Klaus, 2006. "The informational content of option-implied distributions: Evidence from the Eurex index and interest rate futures options market," Global Finance Journal, Elsevier, vol. 17(1), pages 50-74, September.
    13. Kaufmann Sylvia & Scheicher Martin, 2006. "A Switching ARCH Model for the German DAX Index," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(4), pages 1-37, December.
    14. Chao Liang & Yi Zhang & Yaojie Zhang, 2022. "Forecasting the volatility of the German stock market: New evidence," Applied Economics, Taylor & Francis Journals, vol. 54(9), pages 1055-1070, February.
    15. Emmanouil Karakostas, 2023. "The Macroeconomic Determinants of the Stock Market Index Performance: The Case of DAX Index," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 21-38.
    16. Virla, Leonardo Quero, 2021. "An empirical characterization of volatility dynamics in the DAX," IPE Working Papers 167/2021, Berlin School of Economics and Law, Institute for International Political Economy (IPE).
    17. Wagner, Niklas & Szimayer, Alexander, 2004. "Local and spillover shocks in implied market volatility: evidence for the U.S. and Germany," Research in International Business and Finance, Elsevier, vol. 18(3), pages 237-251, September.
    18. Chan, Chia-Ying & de Peretti, Christian & Qiao, Zhuo & Wong, Wing-Keung, 2012. "Empirical test of the efficiency of the UK covered warrants market: Stochastic dominance and likelihood ratio test approach," Journal of Empirical Finance, Elsevier, vol. 19(1), pages 162-174.
    19. Yu, Wayne W. & Lui, Evans C.K. & Wang, Jacqueline W., 2010. "The predictive power of the implied volatility of options traded OTC and on exchanges," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 1-11, January.
    20. André Schöne, 2010. "Zum Informationsgehalt der Volatilitätsindizes VDAX und VDAX-New der Deutsche Börse AG," Schmalenbach Journal of Business Research, Springer, vol. 62(6), pages 625-661, September.

  32. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2002. "Mixed normal conditional heteroskedasticity," CFS Working Paper Series 2002/10, Center for Financial Studies (CFS).

    Cited by:

    1. Bauwens, Luc & Dufays, Arnaud & Rombouts, Jeroen V.K., 2014. "Marginal likelihood for Markov-switching and change-point GARCH models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 508-522.
    2. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    3. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).
    4. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    5. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    6. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    7. Mittnik, Stefan, 2014. "VaR-implied tail-correlation matrices," Economics Letters, Elsevier, vol. 122(1), pages 69-73.
    8. Yang Minxian, 2011. "Volatility Feedback and Risk Premium in GARCH Models with Generalized Hyperbolic Distributions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-21, May.
    9. L. Bauwens & J.V.K. Rombouts, 2007. "Bayesian inference for the mixed conditional heteroskedasticity model," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 408-425, July.
    10. Paolella, Marc S. & Taschini, Luca, 2008. "An econometric analysis of emission allowance prices," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2022-2032, October.
    11. Maria Eugenia Sanin & Francesco Violante & Maria Mansanet-Bataller, 2015. "Understanding volatility dynamics in the EU-ETS market," Post-Print hal-02878047, HAL.
    12. Jeroen V.K. Rombouts & Lars Stentoft, 2009. "Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models," CREATES Research Papers 2009-07, Department of Economics and Business Economics, Aarhus University.
    13. Luc Bauwens & Arie Preminger & Jeroen V.K. Rombouts, 2006. "Regime switching GARCH models," Cahiers de recherche 06-08, HEC Montréal, Institut d'économie appliquée.
    14. BOUADDI, Mohammed & ROMBOUTS, Jeroen V.K., 2007. "Mixed exponential power asymmetric conditional heteroskedasticity," LIDAM Discussion Papers CORE 2007097, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Nankervis, John C. & Savin, N. E., 2010. "Testing for Serial Correlation: Generalized Andrews–Ploberger Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 246-255.
    16. Markus Haas & Stefan Mittnik & Bruce Mizrach, 2004. "Assessing Central Bank Credibility During the EMS Crises: Comparing Option and Spot Market-Based Forecasts," Departmental Working Papers 200424, Rutgers University, Department of Economics.
    17. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).
    18. Xi, Yanhui & Peng, Hui & Qin, Yemei & Xie, Wenbiao & Chen, Xiaohong, 2015. "Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 117(C), pages 141-153.
    19. Eduardo Ramos-Pérez & Pablo J. Alonso-González & José Javier Núñez-Velázquez, 2021. "Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility," Mathematics, MDPI, vol. 9(15), pages 1-18, July.
    20. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2005. "Modeling and predicting market risk with Laplace-Gaussian mixture distributions," CFS Working Paper Series 2005/11, Center for Financial Studies (CFS).
    21. Dinghai Xu & Tony S. Wirjanto, 2008. "An Empirical Characteristic Function Approach to VaR under a Mixture of Normal Distribution with Time-Varying Volatility," Working Papers 08008, University of Waterloo, Department of Economics.
    22. Emese Lazar & Carol Alexander, 2006. "Normal mixture GARCH(1,1): applications to exchange rate modelling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 307-336.
    23. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.
    24. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    25. Kuang-Liang Chang, 2011. "The optimal value-at-risk hedging strategy under bivariate regime switching ARCH framework," Applied Economics, Taylor & Francis Journals, vol. 43(21), pages 2627-2640.
    26. Bauwens, L. & Hafner, C.M. & Rombouts, J.V.K., 2007. "Multivariate mixed normal conditional heteroskedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3551-3566, April.
    27. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    28. Rui Albuquerque, 2012. "Skewness in Stock Returns: Reconciling the Evidence on Firm Versus Aggregate Returns," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1630-1673.
    29. Ausin, Maria Concepcion & Galeano, Pedro, 2007. "Bayesian estimation of the Gaussian mixture GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2636-2652, February.
    30. Antonio Diez de los Rios, 2007. "Exchange Rate Regimes, Globalisation, and the Cost of Capital in Emerging Markets," Staff Working Papers 07-29, Bank of Canada.
    31. Wang, Hui & Pan, Jiazhu, 2014. "Normal mixture quasi maximum likelihood estimation for non-stationary TGARCH(1,1) models," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 117-123.
    32. Saïd Souam & Faycal Hamdi, 2018. "Mixture Periodic GARCH Models: Theory and Applications," Post-Print hal-01589209, HAL.
    33. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    34. Oscar V. De la Torre-Torres & Francisco Venegas-Martínez & Mᵃ Isabel Martínez-Torre-Enciso, 2021. "Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models," Mathematics, MDPI, vol. 9(2), pages 1-22, January.
    35. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    36. Mohamed Saidane & Christian Lavergne, 2009. "Optimal Prediction with Conditionally Heteroskedastic Factor Analysed Hidden Markov Models," Computational Economics, Springer;Society for Computational Economics, vol. 34(4), pages 323-364, November.
    37. BAUWENS, Luc & DUFAYS, Arnaud & DE BACKER, Bruno, 2011. "Estimating and forecasting structural breaks in financial time series," LIDAM Discussion Papers CORE 2011055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    38. Dias, Alexandra, 2014. "Semiparametric estimation of multi-asset portfolio tail risk," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 398-408.
    39. ROMBOUTS, Jeroen V. K. & STENTOFT, Lars, 2010. "Option pricing with asymmetric heteroskedastic normal mixture models," LIDAM Discussion Papers CORE 2010049, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    40. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
    41. Abdelhakim Aknouche & Nadia Rabehi, 2010. "On an independent and identically distributed mixture bilinear time‐series model," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(2), pages 113-131, March.
    42. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2021. "Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility," Papers 2109.12621, arXiv.org.
    43. Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    44. Cheung, Yin-Wong & Chung, Sang-Kuck, 2009. "A Long Memory Model with Mixed Normal GARCH for US Inflation Data," Santa Cruz Department of Economics, Working Paper Series qt2202s99q, Department of Economics, UC Santa Cruz.
    45. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    46. Naeem, Muhammad & Tiwari, Aviral Kumar & Mubashra, Sana & Shahbaz, Muhammad, 2019. "Modeling volatility of precious metals markets by using regime-switching GARCH models," Resources Policy, Elsevier, vol. 64(C).
    47. Philippe Charlot & Vêlayoudom Marimoutou, 2008. "Hierarchical hidden Markov structure for dynamic correlations: the hierarchical RSDC model," Working Papers halshs-00285866, HAL.
    48. Dinghai Xu, 2009. "The Applications of Mixtures of Normal Distributions in Empirical Finance: A Selected Survey," Working Papers 0904, University of Waterloo, Department of Economics, revised Sep 2009.
    49. Albuquerque, Rui, 2009. "Skewness in Stock Returns, Periodic Cash Payouts, and Investor Heterogeneity," CEPR Discussion Papers 7573, C.E.P.R. Discussion Papers.
    50. Jochen Krause & Marc S. Paolella, 2014. "A Fast, Accurate Method for Value-at-Risk and Expected Shortfall," Econometrics, MDPI, vol. 2(2), pages 1-25, June.
    51. BAUWENS, Luc & HAFNER, Christian M. & PIERRET, Diane, 2013. "Multivariate volatility modeling of electricity futures," LIDAM Reprints CORE 2526, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    52. Markku Lanne, 2006. "A Mixture Multiplicative Error Model for Realized Volatility," Economics Working Papers ECO2006/3, European University Institute.
    53. Alizadeh, Amir H. & Nomikos, Nikos K. & Pouliasis, Panos K., 2008. "A Markov regime switching approach for hedging energy commodities," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1970-1983, September.
    54. Hodoshima, Jiro & Yamawake, Toshiyuki, 2019. "Comparison of utility indifference pricing and mean-variance approach under a normal mixture distribution with time-varying volatility," Finance Research Letters, Elsevier, vol. 28(C), pages 74-81.
    55. Carol Alexander & Emese Lazar & Silvia Stanescu, 2010. "Analytic Moments for GARCH Processes," ICMA Centre Discussion Papers in Finance icma-dp2011-07, Henley Business School, University of Reading, revised Apr 2011.
    56. Jiro Hodoshima & Toshiyuki Yamawake, 2022. "Comparing Dynamic and Static Performance Indexes in the Stock Market: Evidence From Japan," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(2), pages 171-193, June.
    57. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    58. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    59. Nick James & Roman Marchant & Richard Gerlach & Sally Cripps, 2019. "Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series," Papers 1902.03350, arXiv.org.
    60. Bertholon, H. & Monfort, A. & Pegoraro, F., 2007. "Pricing and Inference with Mixtures of Conditionally Normal Processes," Working papers 188, Banque de France.
    61. Rubing Liang & Binbin Qin & Qiang Xia, 2024. "Bayesian Inference for Mixed Gaussian GARCH-Type Model by Hamiltonian Monte Carlo Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 193-220, January.
    62. Pouliasis, Panos K. & Papapostolou, Nikos C. & Kyriakou, Ioannis & Visvikis, Ilias D., 2018. "Shipping equity risk behavior and portfolio management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 178-200.
    63. Emiliano Delfau, 2018. "Indice de turbulencia financiera para Argentina mediante un modelo SWARCH," CEMA Working Papers: Serie Documentos de Trabajo. 656, Universidad del CEMA.
    64. Grammig, Joachim G. & Peter, Franziska J., 2008. "International price discovery in the presence of market microstructure effects," CFR Working Papers 08-10, University of Cologne, Centre for Financial Research (CFR).
    65. Donghang Luo & Ke Zhu & Huan Gong & Dong Li, 2020. "Testing error distribution by kernelized Stein discrepancy in multivariate time series models," Papers 2008.00747, arXiv.org.
    66. Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Time-varying mixture GARCH models and asymmetric volatility," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 602-623.
    67. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    68. Hotta, Luiz Koodi & Trucíos Maza, Carlos César & Pereira, Pedro L. Valls & Zevallos Herencia, Mauricio Henrique, 2024. "Forecasting VaR and ES through Markov-switching GARCH models: does the specication matter?," Textos para discussão 567, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    69. Ha, Jeongcheol & Lee, Taewook, 2011. "NM-QELE for ARMA-GARCH models with non-Gaussian innovations," Statistics & Probability Letters, Elsevier, vol. 81(6), pages 694-703, June.
    70. Paolella, Marc S., 2017. "Asymmetric stable Paretian distribution testing," Econometrics and Statistics, Elsevier, vol. 1(C), pages 19-39.
    71. Taewook Lee & Sangyeol Lee, 2009. "Normal Mixture Quasi‐maximum Likelihood Estimator for GARCH Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 157-170, March.
    72. Wu, C.C. & Lee, Jack C., 2007. "Estimation of a utility-based asset pricing model using normal mixture GARCH(1,1)," Economic Modelling, Elsevier, vol. 24(2), pages 329-349, March.
    73. Yang Zhang & Yidong Peng & Xiuli Qu & Jing Shi & Ergin Erdem, 2021. "A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications," Energies, MDPI, vol. 14(9), pages 1-22, April.
    74. Xu, Jiahua, 2019. "Semiparametric Value-At-Risk Estimation of Portfolios. A replication study of Dias (Journal of Banking & Finance, 2014)," International Journal for Re-Views in Empirical Economics (IREE), ZBW - Leibniz Information Centre for Economics, vol. 3(2019-6), pages 1-20.
    75. León, Ángel & Ñíguez, Trino-Manuel, 2021. "The transformed Gram Charlier distribution: Parametric properties and financial risk applications," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 323-349.
    76. Pilar Abad Romero & Sonia Benito Muela & Miguel Angel Sánchez Granero & Carmen López, 2013. "Evaluating the performance of the skewed distributions to forecast Value at Risk in the Global Financial Crisis," Documentos de Trabajo del ICAE 2013-40, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    77. Slim, Skander & Koubaa, Yosra & BenSaïda, Ahmed, 2017. "Value-at-Risk under Lévy GARCH models: Evidence from global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 30-53.
    78. Kim, Yujin & Hwang, Eunju, 2018. "A dynamic Markov regime-switching GARCH model and its cumulative impulse response function," Statistics & Probability Letters, Elsevier, vol. 139(C), pages 20-30.
    79. Pedro Correia S. Bezerra & Pedro Henrique M. Albuquerque, 2017. "Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels," Computational Management Science, Springer, vol. 14(2), pages 179-196, April.
    80. Panos Pouliasis & Ioannis Kyriakou & Nikos Papapostolou, 2017. "On equity risk prediction and tail spillovers," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(4), pages 379-393, October.
    81. Giannikis, D. & Vrontos, I.D. & Dellaportas, P., 2008. "Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1549-1571, January.
    82. Jiro Hodoshima & Toshiyuki Yamawake, 2020. "The Aumann–Serrano Performance Index for Multi-Period Gambles in Stock Data," JRFM, MDPI, vol. 13(11), pages 1-18, November.
    83. Chung, Sang-Kuck, 2009. "Bivariate mixed normal GARCH models and out-of-sample hedge performances," Finance Research Letters, Elsevier, vol. 6(3), pages 130-137, September.

Articles

  1. Cheng Peng & Young Shin Kim & Stefan Mittnik, 2022. "Portfolio Optimization on Multivariate Regime-Switching GARCH Model with Normal Tempered Stable Innovation," JRFM, MDPI, vol. 15(5), pages 1-23, May.
    See citations under working paper version above.
  2. Stefan Mittnik & Willi Semmler & Alexander Haider, 2020. "Climate Disaster Risks—Empirics and a Multi-Phase Dynamic Model," Econometrics, MDPI, vol. 8(3), pages 1-27, August.
    See citations under working paper version above.
  3. Stoyan V. Stoyanov & Svetlozar T. Rachev & Stefan Mittnik & Frank J. Fabozzi, 2019. "Pricing Derivatives In Hermite Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(06), pages 1-27, September.
    See citations under working paper version above.
  4. Mittnik, Stefan & Semmler, Willi, 2018. "Overleveraging, Financial Fragility, And The Banking–Macro Link: Theory And Empirical Evidence," Macroeconomic Dynamics, Cambridge University Press, vol. 22(1), pages 4-32, January.
    See citations under working paper version above.
  5. Ekkehard Ernst & Stefan Mittnik & Willi Semmler, 2016. "Interaction of Labour and Credit Market in Growth Regimes: A Theoretical and Empirical Analysis," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 45(3), pages 393-422, November.

    Cited by:

    1. Pu Chen & Willi Semmler, 2018. "Short and Long Effects of Productivity on Unemployment," Open Economies Review, Springer, vol. 29(4), pages 853-878, September.
    2. Ernst, Ekkehard & Semmler, Willi & Haider, Alexander, 2017. "Debt-deflation, financial market stress and regime change – Evidence from Europe using MRVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 81(C), pages 115-139.
    3. Ekkehard Ernst, 2019. "Finance and Jobs: How Financial Markets and Prudential Regulation Shape Unemployment Dynamics," JRFM, MDPI, vol. 12(1), pages 1-30, January.

  6. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.

    Cited by:

    1. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
    2. Dai, Zhifeng & Chang, Xiaoming, 2021. "Forecasting stock market volatility: Can the risk aversion measure exert an important role?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    3. Torben G. Andersen & Rasmus T. Varneskov, 2018. "Consistent Inference for Predictive Regressions in Persistent VAR Economies," CREATES Research Papers 2018-09, Department of Economics and Business Economics, Aarhus University.
    4. Bolin Lei & Yuping Song, 2024. "Volatility forecasting for stock market incorporating media reports, investors' sentiment, and attention based on MTGNN model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1706-1730, August.
    5. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2021. "Investor Confidence and Forecastability of US Stock Market Realized Volatility : Evidence from Machine Learning," Working Papers 202118, University of Pretoria, Department of Economics.
    6. Ballinari, Daniele & Audrino, Francesco & Sigrist, Fabio, 2022. "When does attention matter? The effect of investor attention on stock market volatility around news releases," International Review of Financial Analysis, Elsevier, vol. 82(C).
    7. Ye Luo & Martin Spindler, 2017. "$L_2$Boosting for Economic Applications," Papers 1702.03244, arXiv.org.
    8. Zhang, Lili & Zhong, Juandan, 2024. "Transportation sector and Chinese stock volatility forecasting: Evidence from freight and passenger traffic," Finance Research Letters, Elsevier, vol. 60(C).
    9. Caporale, Guglielmo Maria & Kyriacou, Kyriacos & Spagnolo, Nicola, 2023. "Aggregate insider trading and stock market volatility in the UK," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    10. Caglayan, Mustafa Onur & Xue, Wenjun & Zhang, Liwen, 2020. "Global investigation on the country-level idiosyncratic volatility and its determinants," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 143-160.
    11. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    12. Libo Yin, 2022. "The role of intermediary capital risk in predicting oil volatility," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 401-416, January.
    13. Souropanis, Ioannis & Vivian, Andrew, 2023. "Forecasting realized volatility with wavelet decomposition," Journal of Empirical Finance, Elsevier, vol. 74(C).
    14. Bai, Lan & Wei, Yu & Wei, Guiwu & Li, Xiafei & Zhang, Songyun, 2021. "Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective," Finance Research Letters, Elsevier, vol. 40(C).
    15. Fang, Libing & Yu, Honghai & Xiao, Wen, 2018. "Forecasting gold futures market volatility using macroeconomic variables in the United States," Economic Modelling, Elsevier, vol. 72(C), pages 249-259.
    16. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2024. "Machine-learning stock market volatility: Predictability, drivers, and economic value," International Review of Financial Analysis, Elsevier, vol. 94(C).
    17. Vo, Xuan Vinh, 2016. "Does institutional ownership increase stock return volatility? Evidence from Vietnam," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 54-61.
    18. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    19. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Consistent Inference for Predictive Regressions in Persistent Economic Systems," NBER Working Papers 28568, National Bureau of Economic Research, Inc.
    20. Emrich Eike & Pierdzioch Christian, 2016. "Public Goods, Private Consumption, and Human Capital: Using Boosted Regression Trees to Model Volunteer Labour Supply," Review of Economics, De Gruyter, vol. 67(3), pages 263-283, December.
    21. Liang, Chao & Ma, Feng & Li, Ziyang & Li, Yan, 2020. "Which types of commodity price information are more useful for predicting US stock market volatility?," Economic Modelling, Elsevier, vol. 93(C), pages 642-650.
    22. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    23. Lu, Fei & Ma, Feng & Guo, Qiang, 2023. "Less is more? New evidence from stock market volatility predictability," International Review of Financial Analysis, Elsevier, vol. 89(C).
    24. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions With Boosted Regression Trees," Working Papers 2015-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    25. Crimmel, Jeremy & Elyasiani, Elyas, 2021. "The association between financial market volatility and banking market structure," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 335-349.
    26. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2020. "Economic indicators and stock market volatility in an emerging economy," Economic Systems, Elsevier, vol. 44(2).
    27. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun, 2023. "The effect of uncertainty on stock market volatility and correlation," Journal of Banking & Finance, Elsevier, vol. 154(C).
    28. Amit K. Sinha, 2021. "The reliability of geometric Brownian motion forecasts of S&P500 index values," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1444-1462, December.
    29. Osman Dou{g}an & Raffaele Mattera & Philipp Otto & Suleyman Tac{s}p{i}nar, 2024. "A Dynamic Spatiotemporal and Network ARCH Model with Common Factors," Papers 2410.16526, arXiv.org.
    30. Nan Hu & Jian Li & Alexis Meyer-Cirkel, 2019. "Completing the Market: Generating Shadow CDS Spreads by Machine Learning," IMF Working Papers 2019/292, International Monetary Fund.
    31. Theu Dinh & Stéphane Goutte & Khuong Nguyen & Thomas Walther, 2022. "Economic drivers of volatility and correlation in precious metal markets," Working Papers halshs-03672469, HAL.
    32. Albaity, Mohamed & Shah, Syed Faisal & Al-Tamimi, Hussein A.Hassan & Rahman, Mahfuzur & Thangavelu, Shanmugam, 2023. "Country risk and bank returns: Evidence from MENA countries," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    33. Thampanya, Natthinee & Wu, Junjie & Nasir, Muhammad Ali & Liu, Jia, 2020. "Fundamental and behavioural determinants of stock return volatility in ASEAN-5 countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    34. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.
    35. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
    36. Constandina Koki & Loukia Meligkotsidou & Ioannis Vrontos, 2020. "Forecasting under model uncertainty: Non‐homogeneous hidden Markov models with Pòlya‐Gamma data augmentation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 580-598, July.
    37. M. Karanasos & S. Yfanti & J. Hunter, 2022. "Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises," Annals of Operations Research, Springer, vol. 313(2), pages 1077-1116, June.
    38. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, University of Hamburg, Department of Socioeconomics.
    39. Liang, Chao & Luo, Qin & Li, Yan & Huynh, Luu Duc Toan, 2023. "Global financial stress index and long-term volatility forecast for international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    40. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    41. Emrich, Eike & Pierdzioch, Christian, 2015. "Public goods, private consumption, and human-capital formation: On the economics of volunteer labour supply," Working Papers of the European Institute for Socioeconomics 14, European Institute for Socioeconomics (EIS), Saarbrücken.
    42. Feng He & Libo Yin, 2021. "Shocks to the equity capital ratio of financial intermediaries and the predictability of stock return volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 945-962, September.
    43. Conrad, Christian & Glas, Alexander, 2018. "‘Déjà vol’ revisited: Survey forecasts of macroeconomic variables predict volatility in the cross-section of industry portfolios," Working Papers 0655, University of Heidelberg, Department of Economics.
    44. Li Liu & Yudong Wang, 2021. "Forecasting aggregate market volatility: The role of good and bad uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 40-61, January.

  7. Kim, Young Shin & Lee, Jaesung & Mittnik, Stefan & Park, Jiho, 2015. "Quanto option pricing in the presence of fat tails and asymmetric dependence," Journal of Econometrics, Elsevier, vol. 187(2), pages 512-520.

    Cited by:

    1. Young Shin Kim, 2019. "Tempered stable process, first passage time, and path-dependent option pricing," Computational Management Science, Springer, vol. 16(1), pages 187-215, February.
    2. Tetsuo Kurosaki & Young Shin Kim, 2020. "Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk," Papers 2010.08900, arXiv.org.
    3. Young Shin Kim & Frank J. Fabozzi, 2024. "Portfolio optimization with relative tail risk," Annals of Operations Research, Springer, vol. 341(2), pages 1023-1055, October.
    4. Holger Fink & Stefan Mittnik, 2021. "Quanto Pricing beyond Black–Scholes," JRFM, MDPI, vol. 14(3), pages 1-27, March.
    5. Young Shin Kim & Kum-Hwan Roh & Raphael Douady, 2020. "Tempered Stable Processes with Time Varying Exponential Tails," Papers 2006.07669, arXiv.org, revised Aug 2020.
    6. Chia-Lin Chang & Michael McAleer, 2014. "Econometric Analysis of Financial Derivatives: An Overview," Working Papers in Economics 14/29, University of Canterbury, Department of Economics and Finance.
    7. Lin, Lisha & Li, Yaqiong & Gao, Rui & Wu, Jianhong, 2021. "The numerical simulation of Quanto option prices using Bayesian statistical methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    8. Kim, Sung Ik, 2023. "A comparative study of firm value models: Default risk of corporate bonds," Finance Research Letters, Elsevier, vol. 56(C).
    9. Tiantian Li & Young Shin Kim & Qi Fan & Fumin Zhu, 2021. "Aumann–Serrano index of risk in portfolio optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 94(2), pages 197-217, October.
    10. Bošnjak Mile & Novak Ivan & Vlajčić Davor, 2021. "Market Efficiency of Euro Exchange Rates and Trading Strategies," Naše gospodarstvo/Our economy, Sciendo, vol. 67(2), pages 10-19, June.
    11. Gong, Xiaoli & Zhuang, Xintian, 2017. "American option valuation under time changed tempered stable Lévy processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 57-68.
    12. Battauz, Anna & De Donno, Marzia & Sbuelz, Alessandro, 2022. "On the exercise of American quanto options," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    13. Chang, C-L. & McAleer, M.J., 2014. "Econometric Analysis of Financial Derivatives," Econometric Institute Research Papers EI 2015-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Young Shin Kim, 2022. "Portfolio optimization and marginal contribution to risk on multivariate normal tempered stable model," Annals of Operations Research, Springer, vol. 312(2), pages 853-881, May.
    15. Sung Ik Kim & Young Shin Kim, 2018. "Tempered stable structural model in pricing credit spread and credit default swap," Review of Derivatives Research, Springer, vol. 21(1), pages 119-148, April.
    16. Kurosaki, Tetsuo & Kim, Young Shin, 2022. "Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk," Finance Research Letters, Elsevier, vol. 45(C).
    17. Young Shin Kim & Hyun-Gyoon Kim, 2024. "Quanto Option Pricing on a Multivariate Levy Process Model with a Generative Artificial Intelligence," Papers 2402.17919, arXiv.org, revised Mar 2024.
    18. Hasan A. Fallahgoul & Young S. Kim & Frank J. Fabozzi & Jiho Park, 2019. "Quanto Option Pricing with Lévy Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1279-1308, March.
    19. Young Shin Kim, 2020. "Portfolio Optimization on the Dispersion Risk and the Asymmetric Tail Risk," Papers 2007.13972, arXiv.org, revised Sep 2020.
    20. Lisha Lin & Yaqiong Li & Rui Gao & Jianhong Wu, 2019. "The Numerical Simulation of Quanto Option Prices Using Bayesian Statistical Methods," Papers 1910.04075, arXiv.org.
    21. Young Shin Kim, 2023. "Portfolio Optimization with Relative Tail Risk," Papers 2303.12209, arXiv.org, revised Mar 2023.
    22. Li, Zhe & Zhang, Wei-Guo & Liu, Yong-Jun, 2018. "European quanto option pricing in presence of liquidity risk," The North American Journal of Economics and Finance, Elsevier, vol. 45(C), pages 230-244.

  8. Mittnik, Stefan, 2014. "VaR-implied tail-correlation matrices," Economics Letters, Elsevier, vol. 122(1), pages 69-73.
    See citations under working paper version above.
  9. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "Was bewegt den DAX?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(23), pages 32-36, December.

    Cited by:

    1. Sauer,Stefan & Klaus Wohlrabe & Stefan Sauer, 2019. "CEO or Intern − Who Actually Answers the Questionnaires in the ifo Business Survey?," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 20(02), pages 29-31, July.
    2. Stefan Sauer & Klaus Wohlrabe, 2019. "Chef oder Praktikant – wer beantwortet eigentlich die Fragebögen in den ifo Konjunkturumfragen?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(03), pages 30-32, February.
    3. Stefan Sauer & Klaus Wohlrabe, 2018. "Das neue ifo Geschäftsklima Deutschland," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(07), pages 54-60, April.

  10. Mittnik, Stefan & Semmler, Willi, 2013. "The real consequences of financial stress," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1479-1499.
    See citations under working paper version above.
  11. Mittnik, Stefan & Semmler, Willi, 2012. "Regime dependence of the fiscal multiplier," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 502-522.

    Cited by:

    1. Mario Alloza, 2017. "Is fiscal policy more effective in uncertain times or during recessions?," Working Papers 1730, Banco de España.
    2. Pu Chen & Willi Semmler, 2018. "Short and Long Effects of Productivity on Unemployment," Open Economies Review, Springer, vol. 29(4), pages 853-878, September.
    3. Mittnik, Stefan & Semmler, Willi, 2013. "The real consequences of financial stress," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1479-1499.
    4. Julien Albertini & Stéphane Auray & Hafedh Bouakez & Aurélien Eyquem, 2019. "Taking off into the Wind: Unemployment Risk and State-Dependent Government Spending Multipliers," Post-Print halshs-02503455, HAL.
    5. Piotr Krajewski & Agata Szymanska, 2019. "The effectiveness of fiscal policy within business cycle-Ricardians vs. non-Ricardians approach," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 19(2), pages 195-215.
    6. Kim, Hyeongwoo, 2018. "Fiscal Policy, Wages, and Jobs in the U.S," MPRA Paper 89763, University Library of Munich, Germany.
    7. Frauke Schleer & Willi Semmler, 2014. "Financial Sector and Output Dynamics in the Euro Area: Non-linearities Reconsidered," SCEPA working paper series. 2014-5, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    8. Hyeongwoo Kim & Shuwei Zhang, 2022. "Policy Coordination and the Effectiveness of Fiscal Stimulus," Auburn Economics Working Paper Series auwp2022-01, Department of Economics, Auburn University.
    9. Chacko George & Florian Kuhn, 2019. "Business Cycle Implications of Capacity Constraints under Demand Shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 32, pages 94-121, April.
    10. Sims, Eric & Wolff, Jonathan, 2018. "The state-dependent effects of tax shocks," European Economic Review, Elsevier, vol. 107(C), pages 57-85.
    11. Giovanni Caggiano & Efrem Castelnuovo & Olivier Damette & Antoine Parent & Giovanni Pellegrino, 2017. "Liquidity traps and large-scale financial crises," Post-Print halshs-01675562, HAL.
    12. Yifei Lyu & Eul Noh, 2022. "Cyclical variation in US government spending multipliers," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 831-846, April.
    13. Hyeongwoo Kim & Bijie Jia, 2017. "Government Spending Shocks and Private Activity: The Role of Sentiments," Auburn Economics Working Paper Series auwp2017-08, Department of Economics, Auburn University.
    14. Tommaso Ferraresi & Andrea Roventini & Willi Semmler, 2016. "Macroeconomic regimes, technological shocks and employment dynamics," Documents de Travail de l'OFCE 2016-19, Observatoire Francais des Conjonctures Economiques (OFCE).
    15. Efrem Castelnuovo & Guay Lim, 2019. "What Do We Know About the Macroeconomic Effects of Fiscal Policy? A Brief Survey of the Literature on Fiscal Multipliers," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 52(1), pages 78-93, March.
    16. António Afonso & Jaromír Baxa & Michal Slavík, 2011. "Fiscal developments and financial stress: a threshold VAR analysis," Working Papers IES 2011/16, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Aug 2011.
    17. Jerow, Sam & Wolff, Jonathan, 2022. "Fiscal policy and uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 145(C).
    18. Hyeongwoo Kim & Shuwei Zhang, 2018. "Understanding Why Fiscal Stimulus Can Fail through the Lens of the Survey of Professional Forecasters," Auburn Economics Working Paper Series auwp2018-04, Department of Economics, Auburn University.
    19. Schleer, Frauke & Semmler, Willi, 2014. "Financial sector-output dynamics in the euro area: Non-linearities reconsidered," ZEW Discussion Papers 13-068 [rev.], ZEW - Leibniz Centre for European Economic Research.
    20. Sangyup Choi & Junhyeok Shin, 2020. "Household Indebtedness and the Macroeconomic Effects of Tax Changes," Working papers 2020rwp-178, Yonsei University, Yonsei Economics Research Institute.
    21. Hory, Marie-Pierre, 2016. "Fiscal multipliers in Emerging Market Economies: Can we learn something from Advanced Economies?," International Economics, Elsevier, vol. 146(C), pages 59-84.
    22. Christophe Blot & Marion Cochard & Jérôme Creel & Bruno Ducoudre & Danielle Schweisguth & Xavier Timbeau, 2014. "Fiscal consolidation in times of crisis: is the sooner really the better?," SciencePo Working papers Main hal-00980392, HAL.
    23. Steven Fazzari & James Morley & Irina Panovska, 2014. "State-Dependent Effects of Fiscal Policy," Discussion Papers 2012-27C, School of Economics, The University of New South Wales.
    24. Eric Sims & Jonathan Wolff, 2018. "The Output And Welfare Effects Of Government Spending Shocks Over The Business Cycle," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(3), pages 1403-1435, August.
    25. Willi Semmler & Lucas Bernard, 2011. "Boom-Bust Cycles: Leveraging, Complex Securities, and Asset Prices," DEGIT Conference Papers c016_034, DEGIT, Dynamics, Economic Growth, and International Trade.
    26. Julia M. Puaschunder, 2020. "Heterodox Economic Cycles Theories," Proceedings of the 20th International RAIS Conference, December 6-7, 2020 019jp, Research Association for Interdisciplinary Studies.
    27. Ernst, Ekkehard & Semmler, Willi & Haider, Alexander, 2017. "Debt-deflation, financial market stress and regime change – Evidence from Europe using MRVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 81(C), pages 115-139.
    28. Sylvérie Herbert, 2014. "Econometric analysis of regime switches and of fiscal multipliers," Documents de Travail de l'OFCE 2014-01, Observatoire Francais des Conjonctures Economiques (OFCE).
    29. Eduardo Garzón Espinosa & Bibiana Medialdea García & Esteban Cruz Hidalgo, 2021. "Fiscal Policy Approaches: An Inquiring Look From The Modern Monetary Theory," Journal of Economic Issues, Taylor & Francis Journals, vol. 55(4), pages 999-1022, October.
    30. Gustav A. Horn & Sebastian Gechert & Katja Rietzler & Kai D. Schmid, 2014. "Streitfall Fiskalpolitik," IMK Report 92-2014, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    31. Sofia São Marcos & Sofia Vale, 2024. "Is there a nonlinear relationship between public investment and private investment? Evidence from 21 Organization for Economic Cooperation and Development countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 887-902, January.
    32. Proaño, Christian R. & Schoder, Christian & Semmler, Willi, 2014. "Financial Stress, Sovereign Debt and Economic Activity in Industrialized Countries: Evidence from Dynamic Threshold Regressions," Department of Economics Working Paper Series 167, WU Vienna University of Economics and Business.
    33. Gardini, Laura & Radi, Davide & Schmitt, Noemi & Sushko, Iryna & Westerhoff, Frank, 2023. "Sentiment-driven business cycle dynamics: An elementary macroeconomic model with animal spirits," Journal of Economic Behavior & Organization, Elsevier, vol. 210(C), pages 342-359.
    34. Willi Semmler & Brigitte Young, 2017. "Re-Booting Europe: What kind of Fiscal Union - What kind of Social Union?," Working Papers 1713, New School for Social Research, Department of Economics.
    35. Goldberg, Andrew & Romalis, John, 2015. "Public Debt and Growth in U.S. States," Working Papers 2015-10, University of Sydney, School of Economics.
    36. Willi Semmler & Brigitte Young, 2024. "Threats of sovereign debt overhang in the EU, the new fiscal rules and the perils of policy drift," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 41(2), pages 565-595, July.
    37. Salvatore Perdichizzi, 2017. "Estimating Fiscal multipliers in the Eurozone. A Nonlinear Panel Data Approach," DISCE - Working Papers del Dipartimento di Economia e Finanza def058, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    38. Kunzmann Vanessa, 2022. "Effects of Cross Country Fiscal Interdependence on Multipliers within a Monetary Union," Working Papers 216, Bavarian Graduate Program in Economics (BGPE).
    39. Samar Issa, 2022. "Financial Crises and Business Cycle Implications for Islamic and Non-Islamic Bank Lending in Indonesia," JRFM, MDPI, vol. 15(7), pages 1-32, June.
    40. Semmler, Willi & Haider, Alexander, 2015. "The perils of debt deflation in the euro area: A multi regime model," ZEW Discussion Papers 15-071, ZEW - Leibniz Centre for European Economic Research.
    41. Samar Issa, 2020. "Life after Debt: The Effects of Overleveraging on Conventional and Islamic Banks," JRFM, MDPI, vol. 13(6), pages 1-46, June.
    42. José Pedro Bastos Neves & Willi Semmler, 2022. "Credit, output and financial stress: A non‐linear LVSTAR application to Brazil," Metroeconomica, Wiley Blackwell, vol. 73(3), pages 900-923, July.
    43. Mario Alloza, 2022. "Is Fiscal Policy More Effective During Recessions?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(3), pages 1271-1292, August.
    44. Rabia Rafique & Asad Nisar & Syed Sadaqat Ali Shah, 2024. "Testing the effects of fiscal policy shocks on output growth in recession and expansion: empirical evidence from developing countries," Economic Change and Restructuring, Springer, vol. 57(3), pages 1-26, June.
    45. Andrea Boitani & Salvatore Perdichizzi, 2018. "Public Expenditure Multipliers in recessions. Evidence from the Eurozone," DISCE - Working Papers del Dipartimento di Economia e Finanza def068, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    46. Gilles Dufrénot & Aurélia Jambois & Laurine Jambois & Guillaume Khayat, 2016. "Regime-Dependent Fiscal Multipliers in the United States," Post-Print hal-01447865, HAL.
    47. Bogdan Muraraşu & Cristina Anghelescu & Robert Adrian Grecu, 2023. "Assessing fiscal multipliers in times of crisis: evidence from selected CEE countries," Empirical Economics, Springer, vol. 65(4), pages 1627-1654, October.
    48. Mao, Jie & Shen, Guanxiong & Yan, Jingzhou, 2023. "A continuous-time macro-finance model with Knightian uncertainty," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    49. Jerome Creel & Paul Hubert & Francesco Saraceno, 2012. "An assessment of Stability and Growth Pact Reform Proposals in a Small-Scale Macro Framework," Documents de Travail de l'OFCE 2012-04, Observatoire Francais des Conjonctures Economiques (OFCE).
    50. Kamalyan, Hayk, 2021. "Phase-Dependent Monetary and Fiscal Policy," MPRA Paper 110341, University Library of Munich, Germany.
    51. Steven Fazzari & James Morley & Irina Panovska, 2013. "State-Dependent Effects of Fiscal Policy," Discussion Papers 2012-27B, School of Economics, The University of New South Wales.
    52. Mark Setterfield, 2015. "Time variation in the size of the multiplier: a Kalecki-Harrod approach," Working Papers 1522, New School for Social Research, Department of Economics, revised Jan 2017.
    53. Agata Szymańska, 2018. "Wpływ polityki fiskalnej na PKB w krajach Unii Europejskiej spoza strefy euro," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 49-74.
    54. Demirel, Ufuk Devrim, 2021. "The short-term effects of tax changes: The role of state dependence," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 918-934.
    55. Barbu Cristian - Marian, 2017. "In Romania, the Buzzing Economy is Unable to Attenuate the Uproar Caused by Economic Backslides," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 150-155, December.

  12. Stefan Mittnik & Irina Starobinskaya, 2010. "Modeling Dependencies in Operational Risk with Hybrid Bayesian Networks," Methodology and Computing in Applied Probability, Springer, vol. 12(3), pages 379-390, September.

    Cited by:

    1. Cornwell, Nikki & Bilson, Christopher & Gepp, Adrian & Stern, Steven & Vanstone, Bruce J., 2023. "Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    2. Kaghazchi, Afsaneh & Hashemy Shahdany, S. Mehdy & Roozbahani, Abbas, 2021. "Simulation and evaluation of agricultural water distribution and delivery systems with a Hybrid Bayesian network model," Agricultural Water Management, Elsevier, vol. 245(C).
    3. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    4. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.
    5. Yash Daultani & Mohit Goswami & Omkarprasad S. Vaidya & Sushil Kumar, 2019. "Inclusive risk modeling for manufacturing firms: a Bayesian network approach," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2789-2803, December.

  13. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2009. "Asymmetric multivariate normal mixture GARCH," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2129-2154, April.
    See citations under working paper version above.
  14. Thiemo Krink & Stefan Mittnik & Sandra Paterlini, 2009. "Differential evolution and combinatorial search for constrained index-tracking," Annals of Operations Research, Springer, vol. 172(1), pages 153-176, November.
    See citations under working paper version above.
  15. Fulvio Corsi & Stefan Mittnik & Christian Pigorsch & Uta Pigorsch, 2008. "The Volatility of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 46-78.
    See citations under working paper version above.
  16. Toker Doganoglu & Christoph Hartz & Stefan Mittnik, 2007. "Portfolio optimization when risk factors are conditionally varying and heavy tailed," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 333-354, May.
    See citations under working paper version above.
  17. Haas, Markus & Mittnik, Stefan & Mizrach, Bruce, 2006. "Assessing central bank credibility during the ERM crises: Comparing option and spot market-based forecasts," Journal of Financial Stability, Elsevier, vol. 2(1), pages 28-54, April.
    See citations under working paper version above.
  18. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 53-89.

    Cited by:

    1. Herrera, Rodrigo & Schipp, Bernhard, 2013. "Value at risk forecasts by extreme value models in a conditional duration framework," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 33-47.
    2. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    3. Chan, Ngai Hang & Sit, Tony, 2016. "Artifactual unit root behavior of Value at risk (VaR)," Statistics & Probability Letters, Elsevier, vol. 116(C), pages 88-93.
    4. Grigory Franguridi, 2014. "Higher order conditional moment dynamics and forecasting value-at-risk (in Russian)," Quantile, Quantile, issue 12, pages 69-82, February.
    5. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    6. Marcin Faldzinski, 2009. "Application of Modified POT Method with Volatility Model for Estimation of Risk Measures," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 119-128.
    7. Hammoudeh, S.M. & Malik, F. & McAleer, M.J., 2010. "Risk management of precious metals," Econometric Institute Research Papers EI 2010-48, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Kulp-Tåg, Sofie, 2007. "An Empirical Investigation of Value-at-Risk in Long and Short Trading Positions," Working Papers 526, Hanken School of Economics.
    9. Antonio Cosma & antonio.cosma@uni.lu & Michel Beine & Robert Vermeulen, 2009. "The Dark Side of Global Integration: Increasing Tail Dependence," LSF Research Working Paper Series 09-05, Luxembourg School of Finance, University of Luxembourg.
    10. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    11. Wang, Guochang & Zhu, Ke & Li, Guodong & Li, Wai Keung, 2022. "Hybrid quantile estimation for asymmetric power GARCH models," Journal of Econometrics, Elsevier, vol. 227(1), pages 264-284.
    12. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    13. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    14. Stavros Degiannakis & Pamela Dent & Christos Floros, 2014. "A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification," Manchester School, University of Manchester, vol. 82(1), pages 71-102, January.
    15. Mittnik, Stefan, 2014. "VaR-implied tail-correlation matrices," Economics Letters, Elsevier, vol. 122(1), pages 69-73.
    16. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    17. Iulia Lupu & Ana Barbara Bobirca & Paul Gabriel Miclaus & Tudor Ciumara, 2020. "Risk Management of Companies Included in the EURO STOXX Sustainability Index. An Investors' Perception," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 22(55), pages 707-707, August.
    18. Fernanda Maria Müller & Marcelo Brutti Righi, 2024. "Comparison of Value at Risk (VaR) Multivariate Forecast Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 75-110, January.
    19. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
    20. Asai, M. & McAleer, M.J. & Medeiros, M.C., 2010. "Asymmetry and Long Memory in Volatility Modelling," Econometric Institute Research Papers EI 2010-60, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    21. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    22. Herrera, Rodrigo & González, Nicolás, 2014. "The modeling and forecasting of extreme events in electricity spot markets," International Journal of Forecasting, Elsevier, vol. 30(3), pages 477-490.
    23. Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," KIER Working Papers 775, Kyoto University, Institute of Economic Research.
    24. Oliver Ledoit & Michael Wolf, 2008. "Robust Performance Hypothesis Testing with the Sharpe Ratio," IEW - Working Papers 320, Institute for Empirical Research in Economics - University of Zurich.
    25. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    26. Hamidi, Benjamin & Maillet, Bertrand & Prigent, Jean-Luc, 2014. "A dynamic autoregressive expectile for time-invariant portfolio protection strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 1-29.
    27. Cai, Yi & Tang, Zhenpeng & Chen, Kaijie & Liu, Dinggao, 2023. "Quantifying the international stock market risk spillover: An analysis based on G-expectation upper variances," Finance Research Letters, Elsevier, vol. 58(PA).
    28. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
    29. Hammoudeh, Shawkat & Araújo Santos, Paulo & Al-Hassan, Abdullah, 2013. "Downside risk management and VaR-based optimal portfolios for precious metals, oil and stocks," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 318-334.
    30. Schaumburg, Julia, 2012. "Predicting extreme value at risk: Nonparametric quantile regression with refinements from extreme value theory," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4081-4096.
    31. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    32. Laura Garcia‐Jorcano & Alfonso Novales, 2021. "Volatility specifications versus probability distributions in VaR forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 189-212, March.
    33. Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    34. Loriano Mancini & Fabio Trojani, 2011. "Robust Value at Risk Prediction," Journal of Financial Econometrics, Oxford University Press, vol. 9(2), pages 281-313, Spring.
    35. Olivier Ledoit & Michael Wolf, 2018. "Robust performance hypothesis testing with smooth functions of population moments," ECON - Working Papers 305, Department of Economics - University of Zurich.
    36. Marita Kuhlmann, 2022. "Eine empirische Analyse der Skalierung von Value-at-Risk Schaetzungen," Papers 2205.02123, arXiv.org.
    37. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "VaR performance during the subprime and sovereign debt crises: An application to emerging markets," Emerging Markets Review, Elsevier, vol. 20(C), pages 23-41.
    38. Oliver Linton & Dajing Shang & Yang Yan, 2012. "Efficient estimation of conditional risk measures in a semiparametric GARCH model," CeMMAP working papers CWP25/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    39. BOUADDI, Mohammed & ROMBOUTS, Jeroen V.K., 2007. "Mixed exponential power asymmetric conditional heteroskedasticity," LIDAM Discussion Papers CORE 2007097, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    40. Wentao Hu, 2019. "calculation worst-case Value-at-Risk prediction using empirical data under model uncertainty," Papers 1908.00982, arXiv.org.
    41. Stoyanov, Stoyan V. & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "CVaR sensitivity with respect to tail thickness," Working Paper Series in Economics 29, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    42. Jimenez-Martin, Juan-Angel & McAleer, Michael & Pérez-Amaral, Teodosio & Santos, Paulo Araújo, 2013. "GFC-robust risk management under the Basel Accord using extreme value methodologies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 223-237.
    43. Chrétien, Stéphane & Coggins, Frank, 2010. "Performance and conservatism of monthly FHS VaR: An international investigation," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 323-333, December.
    44. Fei, Fei & Fuertes, Ana-Maria & Kalotychou, Elena, 2017. "Dependence in credit default swap and equity markets: Dynamic copula with Markov-switching," International Journal of Forecasting, Elsevier, vol. 33(3), pages 662-678.
    45. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 515-526, November.
    46. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    47. Haugom, Erik & Ray, Rina & Ullrich, Carl J. & Veka, Steinar & Westgaard, Sjur, 2016. "A parsimonious quantile regression model to forecast day-ahead value-at-risk," Finance Research Letters, Elsevier, vol. 16(C), pages 196-207.
    48. Kubitza, Christian & Gründl, Helmut, 2016. "Systemic risk: Time-lags and persistence," ICIR Working Paper Series 20/16, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    49. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    50. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, September.
    51. Asai, Manabu & Brugal, Ivan, 2013. "Forecasting volatility via stock return, range, trading volume and spillover effects: The case of Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 202-213.
    52. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    53. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    54. Mawuli Segnon & Mark Trede, 2017. "Forecasting Market Risk of Portfolios: Copula-Markov Switching Multifractal Approach," CQE Working Papers 6617, Center for Quantitative Economics (CQE), University of Muenster.
    55. Alejandro Bernales & Diether W. Beuermann & Gonzalo Cortazar, 2014. "Thinly traded securities and risk management," Estudios de Economia, University of Chile, Department of Economics, vol. 41(1 Year 20), pages 5-48, June.
    56. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    57. Shcherba, Alexandr, 2011. "Comparison of VaR estimation methods for different forecasting samples for Russian stocks," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 24(4), pages 58-70.
    58. Marius Lux & Wolfgang Karl Hardle & Stefan Lessmann, 2020. "Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid," Papers 2009.06910, arXiv.org.
    59. Krzysztof Echaust & Małgorzata Just, 2020. "Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection," Mathematics, MDPI, vol. 8(1), pages 1-24, January.
    60. Hotta, Luiz & Trucíos, Carlos, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
    61. Zhen-Hua Feng & Yi-Ming Wei & Kai Wang, 2011. "Estimating risk for the carbon market via extreme value theory: An empirical analysis of the EU ETS," CEEP-BIT Working Papers 19, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    62. DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge.
    63. Daniel Mariño Ustacara & Luis Fernando Melo Velandia, 2016. "Regresión Cuantílica Dinámica para la Medición del Valor en Riesgo: una Aplicación a Datos Colombianos," Borradores de Economia 939, Banco de la Republica de Colombia.
    64. Asai, Manabu & McAleer, Michael, 2008. "A Portfolio Index GARCH model," International Journal of Forecasting, Elsevier, vol. 24(3), pages 449-461.
    65. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    66. Angelidis, Timotheos & Degiannakis, Stavros, 2007. "Backtesting VaR Models: A Τwo-Stage Procedure," MPRA Paper 96327, University Library of Munich, Germany.
    67. Mohamed El Ghourabi & Christian Francq & Fedya Telmoudi, 2016. "Consistent Estimation of the Value at Risk When the Error Distribution of the Volatility Model is Misspecified," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 46-76, January.
    68. F. Acebes & J. M. González-Varona & A. López-Paredes & J. Pajares, 2024. "Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    69. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    70. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    71. Edimilson Costa Lucas & Wesley Mendes Da Silva & Gustavo Silva Araujo, 2017. "Does Extreme Rainfall Lead to Heavy Economic Losses in the Food Industry?," Working Papers Series 462, Central Bank of Brazil, Research Department.
    72. Escanciano, J. C. & Olmo, J., 2007. "Estimation risk effects on backtesting for parametric value-at-risk models," Working Papers 07/11, Department of Economics, City University London.
    73. O’Brien, James & Szerszeń, Paweł J., 2017. "An evaluation of bank measures for market risk before, during and after the financial crisis," Journal of Banking & Finance, Elsevier, vol. 80(C), pages 215-234.
    74. Santos, André A. P. & Nogales, Francisco J., 2009. "Comparing univariate and multivariate models to forecast portfolio value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws097222, Universidad Carlos III de Madrid. Departamento de Estadística.
    75. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    76. Brent C Smith & Kenneth N. Daniels, 2018. "Unintended Consequences of Risk Based Pricing: Racial Differences in Mortgage Costs," Journal of Financial Services Research, Springer;Western Finance Association, vol. 54(3), pages 323-343, December.
    77. Kevin Sheppard & Andrew J. Patton, 2008. "Evaluating Volatility and Correlation Forecasts," Economics Series Working Papers 2008fe22, University of Oxford, Department of Economics.
    78. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2011. "Financial network systemic risk contributions," SFB 649 Discussion Papers 2011-072, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    79. Hoga, Yannick, 2021. "The uncertainty in extreme risk forecasts from covariate-augmented volatility models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 675-686.
    80. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
    81. Huang, Alex YiHou, 2010. "An optimization process in Value-at-Risk estimation," Review of Financial Economics, Elsevier, vol. 19(3), pages 109-116, August.
    82. Oliver Linton & Dajing Shang & Yang Yan, 2012. "Efficient estimation of conditional risk measures in a semiparametric GARCH model," CeMMAP working papers 25/12, Institute for Fiscal Studies.
    83. Danielsson, Jon & James, Kevin R. & Valenzuela, Marcela & Zer, Ilknur, 2016. "Model risk of risk models," LSE Research Online Documents on Economics 66365, London School of Economics and Political Science, LSE Library.
    84. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    85. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
    86. Jian Zhou & Randy Anderson, 2012. "Extreme Risk Measures for International REIT Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 45(1), pages 152-170, June.
    87. Francq, Christian & Zakoian, Jean-Michel, 2015. "Looking for efficient qml estimation of conditional value-at-risk at multiple risk levels," MPRA Paper 67195, University Library of Munich, Germany.
    88. Braione, Manuela & Scholtes, Nicolas K., 2014. "Construction of value-at-risk forecasts under different distributional assumptions within a BEKK framework," LIDAM Discussion Papers CORE 2014059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    89. Tjeerd de Vries, 2021. "A Tale of Two Tails: A Model-free Approach to Estimating Disaster Risk Premia and Testing Asset Pricing Models," Papers 2105.08208, arXiv.org, revised Oct 2023.
    90. Samet Günay, 2016. "Alteration of Risk in Asian Bond Markets during and after Mortgage Crisis: Evidence from Value at Risk (VaR) Analysis," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 12(Suppl. 1), pages 159–182-1.
    91. Shen, Yifan & Shi, Xunpeng & Variam, Hari Malamakkavu Padinjare, 2018. "Risk transmission mechanism between energy markets: A VAR for VaR approach," Energy Economics, Elsevier, vol. 75(C), pages 377-388.
    92. Makushkin, Mikhail & Lapshin, Victor, 2020. "Modelling tail dependencies between Russian and foreign stock markets: Application for market risk valuation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 30-52.
    93. Marius Lux & Wolfgang Karl Härdle & Stefan Lessmann, 2020. "Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid," Computational Statistics, Springer, vol. 35(3), pages 947-981, September.
    94. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    95. Davide Ferrari & Sandra Paterlini, 2007. "The Maximum Lq-Likelihood Method: an Application to Extreme Quantile Estimation in Finance," Department of Economics 555, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    96. Şener, Emrah & Baronyan, Sayad & Ali Mengütürk, Levent, 2012. "Ranking the predictive performances of value-at-risk estimation methods," International Journal of Forecasting, Elsevier, vol. 28(4), pages 849-873.
    97. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," International Journal of Forecasting, Elsevier, vol. 33(4), pages 958-969.
    98. Bi, Jia & Zhu, Yifeng, 2020. "Value at risk, cross-sectional returns and the role of investor sentiment," Journal of Empirical Finance, Elsevier, vol. 56(C), pages 1-18.
    99. Halbleib, Roxana & Pohlmeier, Winfried, 2012. "Improving the value at risk forecasts: Theory and evidence from the financial crisis," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1212-1228.
    100. Degiannakis, Stavros & Kiohos, Apostolos, 2014. "Multivariate modelling of 10-day-ahead VaR and dynamic correlation for worldwide real estate and stock indices," MPRA Paper 80438, University Library of Munich, Germany.
    101. James W. Taylor & Keming Yu, 2016. "Using auto-regressive logit models to forecast the exceedance probability for financial risk management," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 1069-1092, October.
    102. Escanciano, J. Carlos & Olmo, Jose, 2010. "Backtesting Parametric Value-at-Risk With Estimation Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
    103. Yesol Huh, 2014. "Machines vs. Machines: High Frequency Trading and Hard Information," Finance and Economics Discussion Series 2014-33, Board of Governors of the Federal Reserve System (U.S.).
    104. Stanislav Anatolyev & Stanislav Khrapov, 2015. "Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting," Econometrics, MDPI, vol. 3(3), pages 1-23, August.
    105. Jian, Zhihong & Lu, Haisong & Zhu, Zhican & Xu, Huiling, 2023. "Frequency heterogeneity of tail connectedness: Evidence from global stock markets," Economic Modelling, Elsevier, vol. 125(C).
    106. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    107. Benjamin Mögel & Benjamin R. Auer, 2018. "How accurate are modern Value-at-Risk estimators derived from extreme value theory?," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 979-1030, May.
    108. Bakshi, Gurdip & Panayotov, George, 2010. "First-passage probability, jump models, and intra-horizon risk," Journal of Financial Economics, Elsevier, vol. 95(1), pages 20-40, January.
    109. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    110. Schaumburg, Julia, 2010. "Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory," SFB 649 Discussion Papers 2010-009, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    111. Giannopoulos, Kostas, 2008. "Nonparametric, conditional pricing of higher order multivariate contingent claims," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1907-1915, September.
    112. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
    113. James M. O'Brien & Pawel J. Szerszen, 2014. "An Evaluation of Bank VaR Measures for Market Risk During and Before the Financial Crisis," Finance and Economics Discussion Series 2014-21, Board of Governors of the Federal Reserve System (U.S.).
    114. Mohammed Berkhouch & Fernanda Maria Müller & Ghizlane Lakhnati & Marcelo Brutti Righi, 2022. "Deviation-Based Model Risk Measures," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 527-547, February.
    115. Adam Misiorek & Rafal Weron, 2010. "Heavy-tailed distributions in VaR calculations," HSC Research Reports HSC/10/05, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    116. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    117. Fries, Christian P. & Nigbur, Tobias & Seeger, Norman, 2017. "Displaced relative changes in historical simulation: Application to risk measures of interest rates with phases of negative rates," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 175-198.
    118. Ivana Komunjer, 2007. "Asymmetric power distribution: Theory and applications to risk measurement," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 891-921.
    119. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," Bank of England working papers 660, Bank of England.
    120. Felipe de Oliveira & Sinézio Fernandes Maia, 2017. "Volatility Forecasting before the Subprime Crisis," EcoMod2017 10376, EcoMod.
    121. Fernanda Maria Müller & Marcelo Brutti Righi, 2018. "Numerical comparison of multivariate models to forecasting risk measures," Risk Management, Palgrave Macmillan, vol. 20(1), pages 29-50, February.
    122. Ozun, Alper & Cifter, Atilla & Yilmazer, Sait, 2007. "Filtered Extreme Value Theory for Value-At-Risk Estimation," MPRA Paper 3302, University Library of Munich, Germany.
    123. Shige Peng & Shuzhen Yang, 2020. "Distributional uncertainty of the financial time series measured by G-expectation," Papers 2011.09226, arXiv.org, revised Jul 2021.
    124. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    125. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    126. Ramona Rupeika-Apoga & Roberts Nedovis, 2015. "The Foreign Exchange Exposure of Non-Financial Companies in Eurozone: Myth or Reality?," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 54-66.
    127. Marcelo Brutti Righi & Paulo Sergio Ceretta, 2015. "Shortfall Deviation Risk: An alternative to risk measurement," Papers 1501.02007, arXiv.org, revised May 2016.
    128. Sobreira, Nuno & Louro, Rui, 2020. "Evaluation of volatility models for forecasting Value-at-Risk and Expected Shortfall in the Portuguese stock market," Finance Research Letters, Elsevier, vol. 32(C).
    129. Lin, Chu-Hsiung & Changchien, Chang-Cheng & Kao, Tzu-Chuan & Kao, Wei-Shun, 2014. "High-order moments and extreme value approach for value-at-risk," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 421-434.
    130. Shojai, Shahin & Feiger, George & Kumar, Rajesh, 2010. "Economists’ hubris — the case of equity asset management," Journal of Financial Transformation, Capco Institute, vol. 29, pages 9-16.
    131. Harris, Richard D.F. & Nguyen, Linh H. & Stoja, Evarist, 2019. "Systematic extreme downside risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 128-142.
    132. Chesney, Marc & Reshetar, Ganna & Karaman, Mustafa, 2011. "The impact of terrorism on financial markets: An empirical study," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 253-267, February.
    133. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2024. "Forecasting the effect of extreme sea-level rise on financial market risk," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 1-27.
    134. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2007. "A robust VaR model under different time periods and weighting schemes," Review of Quantitative Finance and Accounting, Springer, vol. 28(2), pages 187-201, February.
    135. Zeno Adams & Roland Füss & Felix Schindler, 2015. "The Sources of Risk Spillovers among U.S. REITs: Financial Characteristics and Regional Proximity," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(1), pages 67-100, March.
    136. Hood, Matthew & Malik, Farooq, 2018. "Estimating downside risk in stock returns under structural breaks," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 102-112.
    137. Codrut Florin Ivascu & Daniela Serban, 2023. "Value at Risk Estimation for Non-Gaussian Distributions," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 15(2), pages 181-190, December.
    138. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.
    139. Peng, Wei & Hu, Shichao & Chen, Wang & Zeng, Yu-feng & Yang, Lu, 2019. "Modeling the joint dynamic value at risk of the volatility index, oil price, and exchange rate," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 137-149.
    140. Wei Sun & Svetlozar Rachev & Frank J. Fabozzi, 2009. "A New Approach for Using Lévy Processes for Determining High‐Frequency Value‐at‐Risk Predictions," European Financial Management, European Financial Management Association, vol. 15(2), pages 340-361, March.
    141. Yannick Hoga, 2023. "The Estimation Risk in Extreme Systemic Risk Forecasts," Papers 2304.10349, arXiv.org.
    142. Mark H. A. Davis, 2014. "Verification of internal risk measure estimates," Papers 1410.4382, arXiv.org, revised Nov 2015.
    143. Xun Lu & Liangjun Su, 2014. "Jackknife Model Averaging for Quantile Regressions," Working Papers 11-2014, Singapore Management University, School of Economics.
    144. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.
    145. Nieto, María Rosa, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    146. Ñíguez, Trino-Manuel & Perote, Javier, 2017. "Moments expansion densities for quantifying financial risk," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 53-69.
    147. Holger Fink & Andreas Fuest & Henry Port, 2018. "The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates," Risks, MDPI, vol. 6(3), pages 1-19, August.
    148. Martin Waltz & Abhay Kumar Singh & Ostap Okhrin, 2022. "Vulnerability-CoVaR: Investigating the Crypto-market," Papers 2203.10777, arXiv.org.
    149. Kiani, Khurshid M., 2011. "Relationship between portfolio diversification and value at risk: Empirical evidence," Emerging Markets Review, Elsevier, vol. 12(4), pages 443-459.
    150. Harris, Richard & Stoja, Evarist & Nguyen, Linh, 2016. "Systematic tail risk," Bank of England working papers 637, Bank of England.
    151. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2015. "A comparison of Expected Shortfall estimation models," Journal of Economics and Business, Elsevier, vol. 78(C), pages 14-47.
    152. Shen, Yifan, 2018. "International risk transmission of stock market movements," Economic Modelling, Elsevier, vol. 69(C), pages 220-236.
    153. Borak, Szymon & Misiorek, Adam & Weron, Rafal, 2010. "Models for Heavy-tailed Asset Returns," MPRA Paper 25494, University Library of Munich, Germany.
    154. Christian Francq & Jean-Michel Zakoian, 2014. "Multi-level Conditional VaR Estimation in Dynamic Models," Working Papers 2014-01, Center for Research in Economics and Statistics.
    155. Sinha, Pankaj & Agnihotri, Shalini, 2014. "Sensitivity of Value at Risk estimation to NonNormality of returns and Market capitalization," MPRA Paper 56307, University Library of Munich, Germany, revised 26 May 2014.
    156. Harris, Richard D. F. & Nguyen, Linh H & Stoja, Evarist, 2015. "Extreme downside risk and financial crises," Bank of England working papers 547, Bank of England.
    157. Spelta, A. & Flori, A. & Pecora, N. & Pammolli, F., 2021. "Financial crises: Uncovering self-organized patterns and predicting stock markets instability," Journal of Business Research, Elsevier, vol. 129(C), pages 736-756.
    158. Rossignolo, Adrian F. & Fethi, Meryem Duygun & Shaban, Mohamed, 2012. "Value-at-Risk models and Basel capital charges," Journal of Financial Stability, Elsevier, vol. 8(4), pages 303-319.
    159. Gao, Chun-Ting & Zhou, Xiao-Hua, 2016. "Forecasting VaR and ES using dynamic conditional score models and skew Student distribution," Economic Modelling, Elsevier, vol. 53(C), pages 216-223.
    160. Meriem Rjiba, Meriem & Tsagris, Michail & Mhalla, Hedi, 2015. "Bootstrap for Value at Risk Prediction," MPRA Paper 68842, University Library of Munich, Germany.
    161. Jungsik Noh & Sangyeol Lee, 2016. "Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 700-720, September.
    162. Yannick Hoga & Matei Demetrescu, 2023. "Monitoring Value-at-Risk and Expected Shortfall Forecasts," Management Science, INFORMS, vol. 69(5), pages 2954-2971, May.
    163. Araújo Santos, Paulo & Fraga Alves, Isabel & Hammoudeh, Shawkat, 2013. "High quantiles estimation with Quasi-PORT and DPOT: An application to value-at-risk for financial variables," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 487-496.
    164. Cheng Cheng & Xiaohang Ren & Zhen Wang & Yukun Shi, 2018. "The Impacts of Non-Fossil Energy, Economic Growth, Energy Consumption, and Oil Price on Carbon Intensity: Evidence from a Panel Quantile Regression Analysis of EU 28," Sustainability, MDPI, vol. 10(11), pages 1-20, November.
    165. Broda, Simon & Paolella, Marc S., 2007. "Saddlepoint approximations for the doubly noncentral t distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2907-2918, March.
    166. Gabriele Canna & Francesca Centrone & Emanuela Rosazza Gianin, 2021. "Capital Allocation Rules and the No-Undercut Property," Mathematics, MDPI, vol. 9(2), pages 1-13, January.
    167. Guochang Wang & Ke Zhu & Guodong Li & Wai Keung Li, 2019. "Hybrid quantile estimation for asymmetric power GARCH models," Papers 1911.09343, arXiv.org.
    168. Choi, Insu & Kim, Woo Chang, 2023. "Estimating Historical Downside Risks of Global Financial Market Indices via Inflation Rate-Adjusted Dependence Graphs," Research in International Business and Finance, Elsevier, vol. 66(C).
    169. Wang, Jying-Nan & Du, Jiangze & Hsu, Yuan-Teng, 2018. "Measuring long-term tail risk: Evaluating the performance of the square-root-of-time rule," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 120-138.
    170. Marc S. Paolella, 2017. "The Univariate Collapsing Method for Portfolio Optimization," Econometrics, MDPI, vol. 5(2), pages 1-33, May.
    171. Dias, Alexandra, 2013. "Market capitalization and Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5248-5260.
    172. Chen, Ning & Li, Shaofang & Lu, Shuai, 2023. "The extreme risk connectedness of the global financial system: G7 and BRICS evidence," Journal of Multinational Financial Management, Elsevier, vol. 69(C).
    173. Emrah ALTUN & Morad ALIZADEH & Gamze OZEL & Hüseyin TATLIDIL & Najmieh MAKSAYI, 2017. "Forecasting Value-At-Risk With Two-Step Method: Garch-Exponentiated Odd Log-Logistic Normal Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 97-115, December.
    174. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2024. "Climate Risks and Forecastability of US Inflation: Evidence from Dynamic Quantile Model Averaging," Working Papers 202420, University of Pretoria, Department of Economics.
    175. Araújo Santos, P. & Fraga Alves, M.I., 2012. "A new class of independence tests for interval forecasts evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3366-3380.
    176. Richard Gerlach & Zudi Lu & Hai Huang, 2013. "Exponentially Smoothing the Skewed Laplace Distribution for Value‐at‐Risk Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 534-550, September.
    177. Fracasso, Laís Martins & Müller, Fernanda Maria & Ramos, Henrique Pinto & Righi, Marcelo Brutti, 2023. "Is there a risk premium? Evidence from thirteen measures," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 182-199.
    178. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
    179. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
    180. Roland Füss & Zeno Adams & Dieter G Kaiser, 2010. "The predictive power of value-at-risk models in commodity futures markets," Journal of Asset Management, Palgrave Macmillan, vol. 11(4), pages 261-285, October.
    181. Christos Agiakloglou & Charalampos Agiropoulos, 2011. "The sensitivity of Value-at-Risk estimates using Monte Carlo approach," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 61(1-2), pages 7-12, January -.
    182. Shojai, Shahin & Feiger, George, 2010. "Economists’ hubris – the case of risk management," Journal of Financial Transformation, Capco Institute, vol. 28, pages 27-35.
    183. Paolella, Marc S. & Polak, Paweł, 2015. "ALRIGHT: Asymmetric LaRge-scale (I)GARCH with Hetero-Tails," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 282-297.
    184. John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace & Adam Metzler, 2021. "Measuring Financial Advice: aligning client elicited and revealed risk," Papers 2105.11892, arXiv.org.
    185. Manuela Braione & Nicolas K. Scholtes, 2016. "Forecasting Value-at-Risk under Different Distributional Assumptions," Econometrics, MDPI, vol. 4(1), pages 1-27, January.
    186. Derek Bunn, Arne Andresen, Dipeng Chen, Sjur Westgaard, 2016. "Analysis and Forecasting of Electricty Price Risks with Quantile Factor Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    187. Marcin Fałdziński & Magdalena Osińska & Tomasz Zdanowicz, 2012. "Detecting Risk Transfer in Financial Markets using Different Risk Measures," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(1), pages 45-64, March.
    188. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2017. "Risk quantification in turmoil markets," Risk Management, Palgrave Macmillan, vol. 19(3), pages 202-224, August.
    189. Christian T. Brownlees & Giampiero Gallo, 2008. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2008_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    190. Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Time-varying mixture GARCH models and asymmetric volatility," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 602-623.
    191. Mitrodima, Gelly & Oberoi, Jaideep, 2024. "CAViaR models for Value-at-Risk and Expected Shortfall with long range dependency features," LSE Research Online Documents on Economics 120880, London School of Economics and Political Science, LSE Library.
    192. Zishu Zhan & Yang Li & Yuhong Yang & Cunjie Lin, 2023. "Model averaging for semiparametric varying coefficient quantile regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(4), pages 649-681, August.
    193. Adams, Zeno & Gerner, Mathias, 2012. "Cross hedging jet-fuel price exposure," Energy Economics, Elsevier, vol. 34(5), pages 1301-1309.
    194. Georg Keilbar & Weining Wang, 2022. "Modelling systemic risk using neural network quantile regression," Empirical Economics, Springer, vol. 62(1), pages 93-118, January.
    195. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    196. Marina Brogi & Valentina Lagasio & Luca Riccetti, 2021. "Systemic risk measurement: bucketing global systemically important banks," Annals of Finance, Springer, vol. 17(3), pages 319-351, September.
    197. Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013. "Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence," International Review of Financial Analysis, Elsevier, vol. 27(C), pages 21-33.
    198. Marius Galabe Sampid & Haslifah M Hasim & Hongsheng Dai, 2018. "Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-33, June.
    199. Chiu, Yen-Chen & Chuang, I-Yuan, 2016. "The performance of the switching forecast model of value-at-risk in the Asian stock markets," Finance Research Letters, Elsevier, vol. 18(C), pages 43-51.
    200. Kostas Andriosopoulos & Nikos Nomikos, 2012. "Risk management in the energy markets and Value-at-Risk modelling: a Hybrid approach," RSCAS Working Papers 2012/47, European University Institute.
    201. Fiala, Tomas & Havranek, Tomas, 2017. "The sources of contagion risk in a banking sector with foreign ownership," Economic Modelling, Elsevier, vol. 60(C), pages 108-121.
    202. Berger, Theo & Gençay, Ramazan, 2018. "Improving daily Value-at-Risk forecasts: The relevance of short-run volatility for regulatory quality assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 30-46.
    203. Huang, Alex YiHou & Peng, Sheng-Pen & Li, Fangjhy & Ke, Ching-Jie, 2011. "Volatility forecasting of exchange rate by quantile regression," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 591-606, October.
    204. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
    205. Giovanni Barone Adesi, 2016. "VaR and CVaR Implied in Option Prices," JRFM, MDPI, vol. 9(1), pages 1-6, February.
    206. Harvey, Andrew & Oryshchenko, Vitaliy, 2012. "Kernel density estimation for time series data," International Journal of Forecasting, Elsevier, vol. 28(1), pages 3-14.
    207. Emrah Altun & Huseyin Tatlidil & Gamze Ozel & Saralees Nadarajah, 2018. "Does the Assumption on Innovation Process Play an Important Role for Filtered Historical Simulation Model?," JRFM, MDPI, vol. 11(1), pages 1-13, January.
    208. Georgios Fatouros & Georgios Makridis & Dimitrios Kotios & John Soldatos & Michael Filippakis & Dimosthenis Kyriazis, 2023. "DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks," Digital Finance, Springer, vol. 5(1), pages 29-56, March.
    209. Salhi, Khaled & Deaconu, Madalina & Lejay, Antoine & Champagnat, Nicolas & Navet, Nicolas, 2016. "Regime switching model for financial data: Empirical risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 148-157.
    210. Shige Peng & Shuzhen Yang & Jianfeng Yao, 2018. "Improving Value-at-Risk prediction under model uncertainty," Papers 1805.03890, arXiv.org, revised Jun 2020.
    211. Cortazar, Gonzalo & Beuermann, Diether & Bernales, Alejandro, 2013. "Risk Management with Thinly Traded Securities: Methodology and Implementation," IDB Publications (Working Papers) 4647, Inter-American Development Bank.
    212. Marcelo Brutti Righi & Fernanda Maria Muller & Marlon Ruoso Moresco, 2022. "A risk measurement approach from risk-averse stochastic optimization of score functions," Papers 2208.14809, arXiv.org, revised May 2023.
    213. Stanislav Anatolyev & Jozef Barunik, 2017. "Forecasting dynamic return distributions based on ordered binary choice," Papers 1711.05681, arXiv.org, revised Jan 2019.
    214. Henryk Gurgul & Artur Machno, 2014. "The optimal portfolio under VaR and ES," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 24(2), pages 59-79.
    215. Shuzhen Yang, 2021. "Compensatory model for quantile estimation and application to VaR," Papers 2112.07278, arXiv.org.
    216. Bams, Dennis & Blanchard, Gildas & Lehnert, Thorsten, 2017. "Volatility measures and Value-at-Risk," International Journal of Forecasting, Elsevier, vol. 33(4), pages 848-863.
    217. Chan Jennifer So Kuen & Ng Kok-Haur & Nitithumbundit Thanakorn & Peiris Shelton, 2019. "Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-22, April.
    218. Shojai, Shahin & Feiger, George, 2011. "Economists’ Hubris – The Case of Award Winning Finance Literature," Journal of Financial Transformation, Capco Institute, vol. 31, pages 9-17.
    219. Kavussanos, Manolis G. & Dimitrakopoulos, Dimitris N., 2011. "Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 258-268.
    220. Pablo Cristini Guedes & Fernanda Maria Müller & Marcelo Brutti Righi, 2023. "Risk measures-based cluster methods for finance," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-56, March.
    221. Lesedi Mabitsela & Eben Maré & Rodwell Kufakunesu, 2015. "Quantification of VaR: A Note on VaR Valuation in the South African Equity Market," JRFM, MDPI, vol. 8(1), pages 1-24, February.
    222. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    223. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    224. Liu, Ruipeng & Lux, Thomas, 2010. "Flexible and robust modelling of volatility comovements: a comparison of two multifractal models," Kiel Working Papers 1594, Kiel Institute for the World Economy (IfW Kiel).
    225. Pilar Abad Romero & Sonia Benito Muela & Miguel Angel Sánchez Granero & Carmen López, 2013. "Evaluating the performance of the skewed distributions to forecast Value at Risk in the Global Financial Crisis," Documentos de Trabajo del ICAE 2013-40, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    226. Zongwu Cai & Ying Fang & Dingshi Tian, 2024. "CAViaR Model Selection Via Adaptive Lasso," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202403, University of Kansas, Department of Economics, revised Jan 2024.
    227. Ramona Rupeika-Apoga & Roberts Nedovis, 2016. "The Foreign Exchange Exposure of Domestic Companies in Eurozone: Case of the Baltic States," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 165-178.
    228. Slim, Skander & Koubaa, Yosra & BenSaïda, Ahmed, 2017. "Value-at-Risk under Lévy GARCH models: Evidence from global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 30-53.
    229. Lidia Sanchis-Marco & Antonio Rubia Serrano, 2011. "On downside risk predictability through liquidity and trading activity: a quantile regression approach," Working Papers. Serie AD 2011-14, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    230. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Stanley, H. Eugene, 2016. "Extreme risk spillover effects in world gold markets and the global financial crisis," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 55-77.
    231. Timotheos Angelidis & Stavros Degiannakis, 2007. "Backtesting VaR Models: An Expected Shortfall Approach," Working Papers 0701, University of Crete, Department of Economics.
    232. Rubia, Antonio & Sanchis-Marco, Lidia, 2013. "On downside risk predictability through liquidity and trading activity: A dynamic quantile approach," International Journal of Forecasting, Elsevier, vol. 29(1), pages 202-219.
    233. Todor Stoilov & Krasimira Stoilova & Miroslav Vladimirov, 2021. "Explicit Value at Risk Goal Function in Bi-Level Portfolio Problem for Financial Sustainability," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    234. Araújo Santos, P. & Fraga Alves, M.I., 2013. "Forecasting Value-at-Risk with a duration-based POT method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 295-309.
    235. Righi, Marcelo Brutti & Borenstein, Denis, 2018. "A simulation comparison of risk measures for portfolio optimization," Finance Research Letters, Elsevier, vol. 24(C), pages 105-112.
    236. Stanislav Anatolyev, 2013. "Objects of nonstructural time series modeling (in Russian)," Quantile, Quantile, issue 11, pages 1-12, December.
    237. Jooyong Shim & Yongtae Kim & Jangtaek Lee & Changha Hwang, 2012. "Estimating value at risk with semiparametric support vector quantile regression," Computational Statistics, Springer, vol. 27(4), pages 685-700, December.
    238. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    239. Slim, Skander & Dahmene, Meriam & Boughrara, Adel, 2020. "How informative are variance risk premium and implied volatility for Value-at-Risk prediction? International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 22-37.
    240. Svetlana Mira & Nicholas Taylor, 2013. "An International Perspective on Risk Management Quality," European Financial Management, European Financial Management Association, vol. 19(5), pages 935-955, November.
    241. Liu, Xiaochun & Luger, Richard, 2015. "Unfolded GARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 186-217.
    242. Héctor Pérez Saiz & Blair Williams & Gabriel Xerri, 2018. "Tail Risk in a Retail Payment System: An Extreme-Value Approach," Discussion Papers 18-2, Bank of Canada.
    243. Dupuy, Philippe, 2015. "The tail risk premia of the carry trades," Journal of International Money and Finance, Elsevier, vol. 59(C), pages 123-145.
    244. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.
    245. Kamila Sommer, 2014. "Fertility Choice in a Life Cycle Model with Idiosyncratic Uninsurable Earnings Risk," Finance and Economics Discussion Series 2014-32, Board of Governors of the Federal Reserve System (U.S.).
    246. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    247. Hammadi Zouari, 2022. "On the Effectiveness of Stock Index Futures for Tail Risk Protection," International Journal of Economics and Financial Issues, Econjournals, vol. 12(3), pages 38-52, May.

  19. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.

    Cited by:

    1. Wynand Smit & Gary van Vuuren and Paul Styger, 2011. "Economic capital for credit risk in the trading book," South African Journal of Economic and Management Sciences, University of Pretoria, Faculty of Economic and Management Sciences, vol. 14(2), pages 138-152, June.
    2. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, August.
    3. Stavros Degiannakis & Pamela Dent & Christos Floros, 2014. "A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification," Manchester School, University of Manchester, vol. 82(1), pages 71-102, January.
    4. Fernanda Maria Müller & Marcelo Brutti Righi, 2024. "Comparison of Value at Risk (VaR) Multivariate Forecast Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 75-110, January.
    5. Alicia Pérez Alonso, 2006. "A Bootstrap Approach To Test The Conditional Symmetry In Time Series Models," Working Papers. Serie AD 2006-18, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    6. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2024. "A residual bootstrap for conditional Value-at-Risk," Journal of Econometrics, Elsevier, vol. 238(2).
    7. Weronika Ormaniec & Marcin Pitera & Sajad Safarveisi & Thorsten Schmidt, 2022. "Estimating value at risk: LSTM vs. GARCH," Papers 2207.10539, arXiv.org.
    8. Hotta, Luiz & Trucíos, Carlos, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Mahsa Gorji & Rasoul Sajjad, 2017. "Improving Value-at-Risk Estimation from the Normal EGARCH Model," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 11(1), March.
    10. Carol Alexander & Jose Maria Sarabia, 2010. "Endogenizing Model Risk to Quantile Estimates," ICMA Centre Discussion Papers in Finance icma-dp2010-07, Henley Business School, University of Reading.
    11. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    12. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    13. Taewook Lee & Moosup Kim & Changryong Baek, 2015. "Tests for Volatility Shifts in Garch Against Long-Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 127-153, March.
    14. Fresoli, Diego Eduardo, 2014. "The uncertainty of conditional returns, volatilities and correlations in DCC models," DES - Working Papers. Statistics and Econometrics. WS ws140202, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Hussein Khraibani & Bilal Nehme & Olivier Strauss, 2018. "Interval Estimation of Value-at-Risk Based on Nonparametric Models," Econometrics, MDPI, vol. 6(4), pages 1-30, December.
    16. Müller, Fernanda Maria & Santos, Samuel Solgon & Righi, Marcelo Brutti, 2023. "A description of the COVID-19 outbreak role in financial risk forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    17. Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024. "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, vol. 92(C).
    18. Zili Zhang & Saralees Nadarajah, 2021. "A Statistical Analysis of the Colombo Stock Returns," Global Business Review, International Management Institute, vol. 22(1), pages 101-118, February.
    19. Emese Lazar & Ning Zhang, 2017. "Model Risk of Expected Shortfall," ICMA Centre Discussion Papers in Finance icma-dp2017-10, Henley Business School, University of Reading.
    20. Nieto, María Rosa, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Dilip Kumar & S. Maheswaran, 2013. "Return, Volatility and Risk Spillover from Oil Prices and the US Dollar Exchange Rate to the Indian Industrial Sectors," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 7(1), pages 61-91, February.
    22. Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-02312332, HAL.
    23. Meriem Rjiba, Meriem & Tsagris, Michail & Mhalla, Hedi, 2015. "Bootstrap for Value at Risk Prediction," MPRA Paper 68842, University Library of Munich, Germany.
    24. Broda, Simon & Paolella, Marc S., 2007. "Saddlepoint approximations for the doubly noncentral t distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2907-2918, March.
    25. d’Addona, Stefano & Khanom, Najrin, 2022. "Estimating tail-risk using semiparametric conditional variance with an application to meme stocks," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 241-260.
    26. Shimizu Kenichi, 2013. "The bootstrap does not alwayswork for heteroscedasticmodels," Statistics & Risk Modeling, De Gruyter, vol. 30(3), pages 189-204, August.
    27. Tomáš Jeøábek, 2020. "The Efficiency of GARCH Models in Realizing Value at Risk Estimates," ACTA VSFS, University of Finance and Administration, vol. 14(1), pages 32-50.
    28. Nieto, María Rosa, 2010. "Bootstrap prediction intervals for VaR and ES in the context of GARCH models," DES - Working Papers. Statistics and Econometrics. WS ws102814, Universidad Carlos III de Madrid. Departamento de Estadística.
    29. Ian Laker & Chun-Kai Huang & Allan Ernest Clark, 2017. "Dependent bootstrapping for value-at-risk and expected shortfall," Risk Management, Palgrave Macmillan, vol. 19(4), pages 301-322, November.
    30. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    31. Köksal, Bülent & Orhan, Mehmet, 2012. "Market risk of developed and developing countries during the global financial crisis," MPRA Paper 37523, University Library of Munich, Germany.
    32. Fan, Ying & Zhang, Yue-Jun & Tsai, Hsien-Tang & Wei, Yi-Ming, 2008. "Estimating 'Value at Risk' of crude oil price and its spillover effect using the GED-GARCH approach," Energy Economics, Elsevier, vol. 30(6), pages 3156-3171, November.
    33. Giannikis, D. & Vrontos, I.D. & Dellaportas, P., 2008. "Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1549-1571, January.
    34. Lönnbark, Carl, 2008. "A Corrected Value-at-Risk Predictor," Umeå Economic Studies 734, Umeå University, Department of Economics.
    35. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

  20. Stefan Mittnik & Thorsten Neumann, 2003. "Time-Series Evidence on the Nonlinearity Hypothesis for Public Spending," Economic Inquiry, Western Economic Association International, vol. 41(4), pages 565-573, October.

    Cited by:

    1. François Facchini & Mickaël Melki, 2013. "Efficient government size: France in the 20 th century," Post-Print hal-01286723, HAL.
    2. Tamoya Christie, 2014. "The Effect Of Government Spending On Economic Growth: Testing The Non-Linear Hypothesis," Bulletin of Economic Research, Wiley Blackwell, vol. 66(2), pages 183-204, April.
    3. Ayşegül Durucan, 2022. "Testing The Validity Of The Bars Curve For Turkey," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 67(232), pages 153-192, January –.
    4. François Facchini & Mickael Melki, 2014. "Political Ideology And Economic Growth: Evidence From The French Democracy," Economic Inquiry, Western Economic Association International, vol. 52(4), pages 1408-1426, October.
    5. d'Agostino, Giorgio & Daddi, Pierluigi & Pieroni, Luca & Steinbrueck, Eric, 2014. "Does military spending stimulate growth? An empirical investigation in Italy," MPRA Paper 58290, University Library of Munich, Germany.
    6. Rajkumar, Andrew Sunil & Swaroop, Vinaya, 2008. "Public spending and outcomes: Does governance matter?," Journal of Development Economics, Elsevier, vol. 86(1), pages 96-111, April.
    7. d'Agostino, G. & Dunne, J.P. & Pieroni, L., 2011. "Optimal military spending in the US: A time series analysis," Economic Modelling, Elsevier, vol. 28(3), pages 1068-1077, May.
    8. Thibaut Dort & Pierre-Guillaume Méon & Khalid Sekkat, 2013. "Does investment spur growth everywhere? Not where institutions are weak," Working Papers CEB 13-030, ULB -- Universite Libre de Bruxelles.
    9. Iana Paliova & Robert McNown & Grant Nülle, 2019. "Multiple Dimensions of Human Development Index and Public Social Spending for Sustainable Development," IMF Working Papers 2019/204, International Monetary Fund.
    10. Ratbek Dzhumashev, 2014. "The Two-Way Relationship Between Government Spending And Corruption And Its Effects On Economic Growth," Contemporary Economic Policy, Western Economic Association International, vol. 32(2), pages 403-419, April.
    11. Hüseyin Şen & Ayşe Kaya & Ayşegül Durucan, 2023. "New insights into the growth-maximizing size of government: evidence and implications for Turkey," Economic Change and Restructuring, Springer, vol. 56(4), pages 2243-2296, August.
    12. d'Agostino, G. & Dunne, J.P. & Pieroni, L., 2016. "Corruption and growth in Africa," European Journal of Political Economy, Elsevier, vol. 43(C), pages 71-88.
    13. Fiseha Gebregziabher & Miguel Niño-Zarazúa, 2014. "Social Spending and Aggregate Welfare in Developing and Transition Economies," WIDER Working Paper Series wp-2014-082, World Institute for Development Economic Research (UNU-WIDER).
    14. François Facchini & Mickaël Melki, 2011. "Optimal government size and economic growth in France (1871-2008) : An explanation by the State and market failures," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00654363, HAL.
    15. Milad Zarin-Nejadan, 2011. "Government and Growth," IRENE Working Papers 11-02, IRENE Institute of Economic Research.

  21. Holger Claessen & Stefan Mittnik, 2002. "Forecasting stock market volatility and the informational efficiency of the DAX-index options market," The European Journal of Finance, Taylor & Francis Journals, vol. 8(3), pages 302-321.
    See citations under working paper version above.
  22. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2002. "Stationarity of stable power-GARCH processes," Journal of Econometrics, Elsevier, vol. 106(1), pages 97-107, January.

    Cited by:

    1. Xing Yu, 2012. "The optimal portfolio model based on multivariate t distribution with linear weighted sum method," E3 Journal of Business Management and Economics., E3 Journals, vol. 3(1), pages 044-047.
    2. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    3. Budi Setiawan & Marwa Ben Abdallah & Maria Fekete-Farkas & Robert Jeyakumar Nathan & Zoltan Zeman, 2021. "GARCH (1,1) Models and Analysis of Stock Market Turmoil during COVID-19 Outbreak in an Emerging and Developed Economy," JRFM, MDPI, vol. 14(12), pages 1-19, December.
    4. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    5. Paolella, Marc S. & Taschini, Luca, 2008. "An econometric analysis of emission allowance prices," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2022-2032, October.
    6. Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    7. BOUADDI, Mohammed & ROMBOUTS, Jeroen V.K., 2007. "Mixed exponential power asymmetric conditional heteroskedasticity," LIDAM Discussion Papers CORE 2007097, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Stoyanov, Stoyan V. & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "CVaR sensitivity with respect to tail thickness," Working Paper Series in Economics 29, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    9. Dennis Kristensen, 2009. "On stationarity and ergodicity of the bilinear model with applications to GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 125-144, January.
    10. Sun, Wei & Rachev, Svetlozar & Fabozzi, Frank J., 2007. "Fractals or I.I.D.: Evidence of long-range dependence and heavy tailedness from modeling German equity market returns," Journal of Economics and Business, Elsevier, vol. 59(6), pages 575-595.
    11. Huang, Alex YiHou, 2010. "An optimization process in Value-at-Risk estimation," Review of Financial Economics, Elsevier, vol. 19(3), pages 109-116, August.
    12. Ramona Serrano Bautista & Leovardo Mata Mata, 2018. "Estimación del VaR mediante un modelo condicional multivariado bajo la hipótesis α-estable sub-Gaussiana. (A conditional approach to VaR with multivariate α-stable sub-Gaussian distributions)," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(1), pages 43-76, May.
    13. Calzolari, Giorgio & Halbleib, Roxana, 2018. "Estimating stable latent factor models by indirect inference," Journal of Econometrics, Elsevier, vol. 205(1), pages 280-301.
    14. Kirt C. Butler & Katsushi Okada, 2008. "Higher-Order Terms in Bivariate Returns to International Stock Market Indices," Multinational Finance Journal, Multinational Finance Journal, vol. 12(1-2), pages 127-155, March-Jun.
    15. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    16. Nolan, John P., 2018. "Truncated fractional moments of stable laws," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 312-318.
    17. Sio Chong U & Jacky So & Deng Ding & Lihong Liu, 2016. "An efficient Fourier expansion method for the calculation of value-at-risk: Contributions of extra-ordinary risks," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 3(01), pages 1-27, March.
    18. Wei Sun & Svetlozar Rachev & Frank Fabozzi & Petko Kalev, 2009. "A new approach to modeling co-movement of international equity markets: evidence of unconditional copula-based simulation of tail dependence," Empirical Economics, Springer, vol. 36(1), pages 201-229, February.
    19. Giorgio Calzolari & Roxana Halbleib, 2014. "Estimating Stable Factor Models By Indirect Inference," Working Paper Series of the Department of Economics, University of Konstanz 2014-25, Department of Economics, University of Konstanz.
    20. Peter A. Zadrozny, 2005. "Necessary and Sufficient Restrictions for Existence of a Unique Fourth Moment of a Univariate GARCH(p,q) Process," CESifo Working Paper Series 1505, CESifo.
    21. Paolella, Marc S. & Polak, Paweł, 2015. "ALRIGHT: Asymmetric LaRge-scale (I)GARCH with Hetero-Tails," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 282-297.
    22. URAL, Mert & DEMİRELİ, Erhan, 2018. "Modeling Asymmetric Volatility In The Chicago Board Options Exchange Volatility Index," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 22(1), pages 20-31.
    23. Huang, Alex YiHou & Peng, Sheng-Pen & Li, Fangjhy & Ke, Ching-Jie, 2011. "Volatility forecasting of exchange rate by quantile regression," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 591-606, October.
    24. Gonçalves, E. & Leite, J. & Mendes-Lopes, N., 2012. "On the probabilistic structure of power threshold generalized arch stochastic processes," Statistics & Probability Letters, Elsevier, vol. 82(8), pages 1597-1609.
    25. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    26. Bekaert, Geert & Engstrom, Eric & Ermolov, Andrey, 2015. "Bad environments, good environments: A non-Gaussian asymmetric volatility model," Journal of Econometrics, Elsevier, vol. 186(1), pages 258-275.
    27. Bellini, Fabio & Bottolo, Leonardo, 2007. "Stationarity domains for [delta]-power Garch process with heavy tails," Statistics & Probability Letters, Elsevier, vol. 77(13), pages 1418-1427, July.
    28. LOMBARDI, Marco & VEREDAS, David, 2007. "Indirect estimation of elliptical stable distributions," LIDAM Discussion Papers CORE 2007018, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

  23. Chiarella Carl & Semmler Willi & Mittnik Stefan & Zhu Peiyuan, 2002. "Stock Market, Interest Rate and Output: A Model and Estimation for US Time Series Data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(1), pages 1-39, April.

    Cited by:

    1. Lambert, Dayton M. & Lowenberg-DeBoer, James & Malzer, Gary L., 2004. "A Systems Approach For Evaluating On-Farm Site-Specific Management Trials: A Case Study With Variable Rate Manure And Crop Quality Response To Inputs," 2004 Annual meeting, August 1-4, Denver, CO 20091, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Carl Chiarella & Peter Flaschel & Reiner Franke & Willi Semmler, 2000. "Output, Financial Markets and Growth," Working Paper Series 108, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    3. Peter Flaschel & Willi Semmler, 2004. "Real-Financial Interaction: A Reconsideration of the Blanchard Model with a State-of-Market Dependent Reaction Coefficient," International Symposia in Economic Theory and Econometrics, in: Economic Complexity, pages 31-65, Emerald Group Publishing Limited.
    4. Raberto, Marco & Teglio, Andrea & Cincotti, Silvano, 2006. "A dynamic general disequilibrium model of a sequential monetary production economy," Chaos, Solitons & Fractals, Elsevier, vol. 29(3), pages 566-577.
    5. F. Cavalli & A. Naimzada & N. Pecora, 2022. "A stylized macro-model with interacting real, monetary and stock markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(1), pages 225-257, January.
    6. Willi Semmler, 2011. "Asset Prices, Booms and Recessions," Springer Books, Springer, number 978-3-642-20680-1, December.
    7. Harper, David C. & Lambert, Dayton M. & Larson, James A. & Gwathmey, C. Owen, 2012. "Potassium carryover dynamics and optimal application policies in cotton production," Agricultural Systems, Elsevier, vol. 106(1), pages 84-93.
    8. Angelos Kanas & Christos Ioannidis, 2010. "Causality from real stock returns to real activity: evidence of regime-dependence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(2), pages 180-197.
    9. Peter Woehrmann, "undated". "A dynamic model of the financial�real interaction as a model selection criterion for nonparametric stock market prediction," IEW - Working Papers 226, Institute for Empirical Research in Economics - University of Zurich.

  24. Stefan Mittnik & Thorsten Neumann, 2001. "Dynamic effects of public investment: Vector autoregressive evidence from six industrialized countries," Empirical Economics, Springer, vol. 26(2), pages 429-446.

    Cited by:

    1. Mario Alloza & Danilo Leiva-León & Alberto Urtasun, 2022. "The response of private investment to an increase in public investment," Economic Bulletin, Banco de España, issue 2/2022.
    2. Kamps, Christophe, 2004. "The Dynamic Effects of Public Capital: VAR Evidence for 22 OECD Countries," Kiel Working Papers 1224, Kiel Institute for the World Economy (IfW Kiel).
    3. Agenor, Pierre-Richard & Nabli, Mustapha K. & Yousef, Tarik M., 2005. "Public infrastructure and private investment in the Middle East and North Africa," Policy Research Working Paper Series 3661, The World Bank.
    4. Jordi Pons-i-Novell & Ramon Tremosa-i-Balcells, 2005. "Macroeconomic effects of Catalan fiscal deficit with the Spanish state (2002-2010)," Applied Economics, Taylor & Francis Journals, vol. 37(13), pages 1455-1463.
    5. Anca-Stefania Sava & Bogdan-Gabriel Zugravu, 2010. "Analysis of the Correlations Between Public Capital Investments and Economic Development in Romania," Studies and Scientific Researches. Economics Edition, "Vasile Alecsandri" University of Bacau, Faculty of Economic Sciences, issue 15.
    6. Dai, Meixing & Sidiropoulos, Moïse, 2010. "Monetary and fiscal policy interactions with central bank transparency and public investment," MPRA Paper 23704, University Library of Munich, Germany.
    7. Ibrahim, Taofki, 2018. "Does Public Capital Influence Output Growth? Further Evidence from Nigeria," MPRA Paper 88635, University Library of Munich, Germany, revised 22 Jul 2018.
    8. Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung (ed.), 2007. "Staatsverschuldung wirksam begrenzen. Expertise im Auftrag des Bundesministers für Wirtschaft und Technologie," Occasional Reports / Expertisen, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, number 75368.
    9. Ibrahim Ari & Muammer Koc, 2020. "Economic Growth, Public and Private Investment: A Comparative Study of China and the United States," Sustainability, MDPI, vol. 12(6), pages 1-19, March.
    10. Sergio Destefanis & Mario Di Serio & Matteo Fragetta, 2020. "Regional multipliers across the Italian regions," Discussion Paper series in Regional Science & Economic Geography 2020-04, Gran Sasso Science Institute, Social Sciences, revised Jul 2020.
    11. Gadatsch, Niklas & Hauzenberger, Klemens & Stähler, Nikolai, 2015. "German and the rest of euro area fiscal policy during the crisis," Discussion Papers 05/2015, Deutsche Bundesbank.
    12. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2010. "Dynamic Macroeconomic Effects of Public Capital: Evidence from Regional Italian Data," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 69(1), pages 29-66, April.
    13. Oukhallou, Youssef, 2016. "Analyzing economic growth: what role for public investment?," MPRA Paper 69772, University Library of Munich, Germany.
    14. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2012. "The Macroeconomic Impact of Infrastructures: A Literature Review and Empirical Analysis on the Case of Italy," QA - Rivista dell'Associazione Rossi-Doria, Associazione Rossi Doria, issue 1, March.
    15. Christophe Kamps, 2004. "New Estimates of Government Net Capital Stocks for 22 OECD Countries 1960-2001," IMF Working Papers 2004/067, International Monetary Fund.
    16. Batool, Irem & Goldmann, Kathrin, 2021. "The role of public and private transport infrastructure capital in economic growth. Evidence from Pakistan," Research in Transportation Economics, Elsevier, vol. 88(C).
    17. Ward Romp & Jakob De Haan, 2007. "Public Capital and Economic Growth: A Critical Survey," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 8(S1), pages 6-52, April.
    18. João Sousa Andrade & António Portugal Duarte, 2014. "Crowding-in and Crowding-out Effects of Public Investments in the Portuguese Economy," GEMF Working Papers 2014-24, GEMF, Faculty of Economics, University of Coimbra.
    19. Ahmed, Qazi Masood & Ali, Syed Ammad, 2014. "Public investment efficiency and sectoral economic growth in Pakistan:," PSSP working papers 22, International Food Policy Research Institute (IFPRI).
    20. Syed Ammad & Qazi Masood Ahmed, 2014. "Dynamic Effects of Energy Sector Public Investment on Sectoral Economic Growth: Experience from Pakistan Economy," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 53(4), pages 403-421.
    21. Alfredo M. Pereira & Jorge M. Andraz, 2013. "On The Economic Effects Of Public Infrastructure Investment: A Survey Of The International Evidence," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 38(4), pages 1-37, December.
    22. Cheteni, Priviledge, 2013. "Transport Infrastructure Investment and Transport Sector Productivity on Economic Growth in South Africa (1975-2011)," MPRA Paper 53175, University Library of Munich, Germany, revised 18 Jul 2013.
    23. Ozkan, F Gulcin & Ismihan, Mustafa, 2007. "Public investment: a remedy or a curse? Examining the Role of Public Investment for Macroeconomic Performance," CEPR Discussion Papers 6139, C.E.P.R. Discussion Papers.
    24. Federici, Andrea, 2018. "Il rapporto tra capitale pubblico e altre variabili macroeconomiche: un'applicazione empirica [The relationship between public capital and other macroeconomic variables: an empirical application]," MPRA Paper 88516, University Library of Munich, Germany.
    25. Jérôme Creel & Paola Monperrus‐Veroni & Francesco Saraceno, 2009. "On The Long‐Term Effects Of Fiscal Policy In The United Kingdom: The Case For A Golden Rule," Scottish Journal of Political Economy, Scottish Economic Society, vol. 56(5), pages 580-607, November.
    26. António Afonso & Miguel St. Aubyn, 2009. "Macroeconomic Rates Of Return Of Public And Private Investment: Crowding‐In And Crowding‐Out Effects," Manchester School, University of Manchester, vol. 77(s1), pages 21-39, September.
    27. Gadatsch, Niklas & Hauzenberger, Klemens & Stähler, Nikolai, 2016. "Fiscal policy during the crisis: A look on Germany and the Euro area with GEAR," Economic Modelling, Elsevier, vol. 52(PB), pages 997-1016.
    28. Trofimov, Ivan D., 2020. "Public capital and productive economy profits: evidence from OECD economies," MPRA Paper 106848, University Library of Munich, Germany.
    29. Torrisi, Gianpiero, 2009. "Infrastructures and economic performance: a critical comparison across four approaches," MPRA Paper 18688, University Library of Munich, Germany.
    30. Stephanos Papadamou & Eleftherios Spyromitros & Panagiotis Tsintzos, 2017. "Public investment, inflation persistence and central bank independence," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 44(6), pages 976-986, November.
    31. Salwa Trabelsi, 2018. "Public Education Spending and Economic Growth: The Governance Threshold Effect," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 43(1), pages 101-124, March.
    32. Carvelli, Gianni, 2024. "The dynamic effects of public investments on private capital formation: Modelling a heterogeneous asymmetric cointegration with unobserved global factors," International Economics, Elsevier, vol. 177(C).
    33. Olanrewaju Makinde Hassan, 2015. "The Impact of Monetary Policy on Private Capital Formation in Nigeria," Journal of Empirical Economics, Research Academy of Social Sciences, vol. 4(3), pages 138-153.
    34. Luigi Marattin & Simone Salotti, 2014. "Consumption multipliers of different types of public spending: a structural vector error correction analysis for the UK," Empirical Economics, Springer, vol. 46(4), pages 1197-1220, June.
    35. Alexandre Manuel Angelo da Silva & José Oswaldo Cândido Júnior, 2009. "Impactos Macroeconômicos dos Gastos Públicos na América Latina," Discussion Papers 1434, Instituto de Pesquisa Econômica Aplicada - IPEA.
    36. Ms. Anita Tuladhar & Markus Bruckner, 2010. "Public Investment as a Fiscal Stimulus: Evidence from Japan’s Regional Spending During the 1990s," IMF Working Papers 2010/110, International Monetary Fund.
    37. Federici, Andrea, 2018. "Il rapporto tra capitale pubblico e altre variabili macroeconomiche: analisi della letteratura [The relationship between public capital and other macroeconomic variable: a literature review]," MPRA Paper 88515, University Library of Munich, Germany.
    38. Torrisi, Gianpiero, 2009. "Public infrastructure: definition, classification and measurement issues," MPRA Paper 12990, University Library of Munich, Germany.
    39. Baussola, Maurizio & Carvelli, Gianni, 2023. "Public and private investments: Long-run asymmetric effects in France and the US," Finance Research Letters, Elsevier, vol. 58(PA).
    40. Hicham GOUMRHAR & Youssef OUKHALLOU, 2017. "Public Investment and GDP Growth in Developing and Advanced Countries: A Panel Data Analysis," Journal of Economics Bibliography, KSP Journals, vol. 4(1), pages 77-86, March.
    41. Rafiq Sohrab, 2012. "Is Discretionary Fiscal Policy in Japan Effective?," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(1), pages 1-49, August.
    42. Ejaz Ghani & Musleh-Ud Din, 2006. "The Impact of Public Investment on Economic Growth in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 45(1), pages 87-98.
    43. Nusrat Akber & Megha Gupta & Kirtti Ranjan Paltasingh, 2020. "The Crowding-in/ out Debate in Investments in India: Fresh Evidence from NARDL Application," South Asian Journal of Macroeconomics and Public Finance, , vol. 9(2), pages 167-189, December.
    44. Mathias A. Chuba, 2021. "Crowding-Out and Crowding-In Effects of Public Borrowing on Private Domestic Investment in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(11), pages 439-448, November.
    45. Inácia Pimentel & Miguel St.Aubyn & Nuno Ribeiro, 2017. "The impact of investment in Public Private Partnerships on Public, Private investment and GDP in Portugal," Working Papers Department of Economics 2017/13, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.

  25. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2000. "Diagnosing and treating the fat tails in financial returns data," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 389-416, November.

    Cited by:

    1. Cotter, John & Dowd, Kevin, 2007. "The tail risks of FX return distributions: A comparison of the returns associated with limit orders and market orders," Finance Research Letters, Elsevier, vol. 4(3), pages 146-154, September.
    2. Hemei Li & Zhenya Liu & Shixuan Wang, 2022. "Vines climbing higher: Risk management for commodity futures markets using a regular vine copula approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2438-2457, April.
    3. Wolff, Christian & Bams, Dennis & Lehnert, Thorsten, 2002. "An Evaluation Framework for Alternative VaR Models," CEPR Discussion Papers 3403, C.E.P.R. Discussion Papers.
    4. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    5. Munir Khamis & Dalal Aassouli, 2023. "The Eligibility of Green Bonds as Safe Haven Assets: A Systematic Review," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    6. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    7. Sun, Wei & Rachev, Svetlozar & Fabozzi, Frank J., 2007. "Fractals or I.I.D.: Evidence of long-range dependence and heavy tailedness from modeling German equity market returns," Journal of Economics and Business, Elsevier, vol. 59(6), pages 575-595.
    8. Tokat, Yesim & Rachev, Svetlozar T. & Schwartz, Eduardo, 2000. "The Stable non-Gaussian Asset Allocation: A Comparison with the Classical Gaussian Approach," University of California at Santa Barbara, Economics Working Paper Series qt9ph6b5gp, Department of Economics, UC Santa Barbara.
    9. Gong, Xiaoli & Zhuang, Xintian, 2017. "American option valuation under time changed tempered stable Lévy processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 57-68.
    10. Choe, Kwang-il & Choi, Pilsun & Nam, Kiseok & Vahid, Farshid, 2012. "Testing financial contagion on heteroskedastic asset returns in time-varying conditional correlation," Pacific-Basin Finance Journal, Elsevier, vol. 20(2), pages 271-291.
    11. Heitham Al-Hajieh & Hashem AlNemer & Timothy Rodgers & Jacek Niklewski, 2015. "Forecasting the Jordanian stock index: modelling asymmetric volatility and distribution effects within a GARCH framework," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 4(2), pages 9-26.
    12. Kevin Dowd & John Cotter, 2011. "Intra-Day Seasonality in Foreign Market Transactions," Working Papers 200746, Geary Institute, University College Dublin.
    13. Choi, Pilsun & Nam, Kiseok, 2008. "Asymmetric and leptokurtic distribution for heteroscedastic asset returns: The SU-normal distribution," Journal of Empirical Finance, Elsevier, vol. 15(1), pages 41-63, January.
    14. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    15. de Vries, Casper & Hartmann, Philipp & Straetmans, Stefan, 2004. "Fundamentals and Joint Currency Crises," CEPR Discussion Papers 4338, C.E.P.R. Discussion Papers.
    16. Lehnert, Thorsten & Wolff, Christian C. P., 2004. "Scale-consistent Value-at-Risk," Finance Research Letters, Elsevier, vol. 1(2), pages 127-134, June.
    17. J. Baixauli & Susana Alvarez, 2006. "Evaluating effects of excess kurtosis on VaR estimates: Evidence for international stock indices," Review of Quantitative Finance and Accounting, Springer, vol. 27(1), pages 27-46, August.
    18. Zoia, Maria Grazia & Biffi, Paola & Nicolussi, Federica, 2018. "Value at risk and expected shortfall based on Gram-Charlier-like expansions," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 92-104.
    19. Kim, Young Shin & Lee, Jaesung & Mittnik, Stefan & Park, Jiho, 2015. "Quanto option pricing in the presence of fat tails and asymmetric dependence," Journal of Econometrics, Elsevier, vol. 187(2), pages 512-520.
    20. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2007. "A robust VaR model under different time periods and weighting schemes," Review of Quantitative Finance and Accounting, Springer, vol. 28(2), pages 187-201, February.
    21. Talla M Aldeehani, 2019. "Have Stock Markets Become Less Volatile After the Great Recession?," Research in World Economy, Research in World Economy, Sciedu Press, vol. 10(3), pages 10-25, December.
    22. Mittnik, Stefan & Paolella, Marc S., 2003. "Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions," CFS Working Paper Series 2003/04, Center for Financial Studies (CFS).
    23. Gimeno, Ricardo & Gonzalez, Clara I., 2012. "An automatic procedure for the estimation of the tail index," MPRA Paper 37023, University Library of Munich, Germany.
    24. Giorgio Calzolari & Roxana Halbleib, 2014. "Estimating Stable Factor Models By Indirect Inference," Working Paper Series of the Department of Economics, University of Konstanz 2014-25, Department of Economics, University of Konstanz.
    25. Garcia, René & Renault, Eric & Veredas, David, 2011. "Estimation of stable distributions by indirect inference," Journal of Econometrics, Elsevier, vol. 161(2), pages 325-337, April.
    26. Matteo Bonato, 2012. "Modeling fat tails in stock returns: a multivariate stable-GARCH approach," Computational Statistics, Springer, vol. 27(3), pages 499-521, September.
    27. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2002. "Mixed normal conditional heteroskedasticity," CFS Working Paper Series 2002/10, Center for Financial Studies (CFS).
    28. Tokat, Yesim & Rachev, Svetlozar T. & Schwartz, Eduardo S., 2003. "The stable non-Gaussian asset allocation: a comparison with the classical Gaussian approach," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 937-969, April.
    29. Paolella, Marc S., 2017. "Asymmetric stable Paretian distribution testing," Econometrics and Statistics, Elsevier, vol. 1(C), pages 19-39.
    30. Hallin, Marc & Swan, Yvik & Verdebout, Thomas & Veredas, David, 2013. "One-step R-estimation in linear models with stable errors," Journal of Econometrics, Elsevier, vol. 172(2), pages 195-204.
    31. Bonato, Matteo, 2011. "Robust estimation of skewness and kurtosis in distributions with infinite higher moments," Finance Research Letters, Elsevier, vol. 8(2), pages 77-87, June.
    32. Mai, Nhat Chi, 2016. "The Influence Of Macroeconomic Announcements Into Vietnamese Stock Market Volatility," OSF Preprints ydmhx, Center for Open Science.
    33. Anatolyev Stanislav, 2019. "Volatility filtering in estimation of kurtosis (and variance)," Dependence Modeling, De Gruyter, vol. 7(1), pages 1-23, February.
    34. René Garcia & Éric Renault & Georges Tsafack, 2007. "Proper Conditioning for Coherent VaR in Portfolio Management," Management Science, INFORMS, vol. 53(3), pages 483-494, March.
    35. Dominicy, Yves & Veredas, David, 2013. "The method of simulated quantiles," Journal of Econometrics, Elsevier, vol. 172(2), pages 235-247.

  26. Stefan Mittnik & Sascha Rieken, 2000. "Lower‐boundary violations and market efficiency: Evidence from the German DAX‐index options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(5), pages 405-424, May.

    Cited by:

    1. Debaditya Mohanti & P. K. Priyan, 2014. "An Empirical Test of Market Efficiency of Indian Index Options Market Using the Black–Scholes Model and Dynamic Hedging Strategy," Paradigm, , vol. 18(2), pages 221-237, December.
    2. Martin Husák, 2022. "Do Damodaran's Multiples Value a Company Accurately? Evidence from Germany," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2022(3), pages 5-21.
    3. Zhang, Huiming & Watada, Junzo, 2019. "An analysis of the arbitrage efficiency of the Chinese SSE 50ETF options market," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 474-489.
    4. Yueh-Neng Lin & Shih-Kuo Yeh & Shih-Ching Chuan & Steven J. Jordan, 2008. "The link between intraday signals and call warrant mispricing," The Service Industries Journal, Taylor & Francis Journals, vol. 30(13), pages 2273-2288, November.

  27. Stefan Mittnik & Marc Paolella & Svetlozar Rachev, 1998. "Unconditional and Conditional Distributional Models for the Nikkei Index," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 5(2), pages 99-128, May.

    Cited by:

    1. Corsi, Fulvio & Kretschmer, Uta & Mittnik, Stefan & Pigorsch, Christian, 2005. "The volatility of realized volatility," CFS Working Paper Series 2005/33, Center for Financial Studies (CFS).
    2. Sung Ik Kim, 2022. "ARMA–GARCH model with fractional generalized hyperbolic innovations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    3. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2005. "Modeling and predicting market risk with Laplace-Gaussian mixture distributions," CFS Working Paper Series 2005/11, Center for Financial Studies (CFS).
    4. José Curto & José Pinto & Gonçalo Tavares, 2009. "Modeling stock markets’ volatility using GARCH models with Normal, Student’s t and stable Paretian distributions," Statistical Papers, Springer, vol. 50(2), pages 311-321, March.
    5. José Dias Curto & João Tomaz & José Castro Pinto, 2009. "A new approach to bad news effects on volatility: the multiple-sign-volume sensitive regime EGARCH model (MSV-EGARCH)," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 8(1), pages 23-36, April.
    6. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.
    7. Fischer, Matthias J., 2002. "Skew generalized secant hyperbolic distributions: unconditional and conditional fit to asset returns," Discussion Papers 46/2002, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    8. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    9. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Post-Print hal-00834423, HAL.
    10. Fischer, Matthias J. & Vaughan, David, 2002. "Classes of skew generalized hyperbolic secant distributions," Discussion Papers 45/2002, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    11. Broda, Simon & Paolella, Marc S., 2007. "Saddlepoint approximations for the doubly noncentral t distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2907-2918, March.
    12. Gel, Yulia R., 2010. "Test of fit for a Laplace distribution against heavier tailed alternatives," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 958-965, April.
    13. Peter Bossaerts & Shijie Huang & Nitin Yadav, 2020. "Exploiting Distributional Temporal Difference Learning to Deal with Tail Risk," Risks, MDPI, vol. 8(4), pages 1-20, October.
    14. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2000. "Diagnosing and treating the fat tails in financial returns data," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 389-416, November.
    15. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2002. "Mixed normal conditional heteroskedasticity," CFS Working Paper Series 2002/10, Center for Financial Studies (CFS).
    16. Fischer, Matthias J., 2000. "The folded EGB2 distribution and its application to financial return data," Discussion Papers 32/2000, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    17. Lee, Tae-Hwy & Saltoglu, Burak, 2002. "Assessing the risk forecasts for Japanese stock market," Japan and the World Economy, Elsevier, vol. 14(1), pages 63-85, January.

  28. Mittnik, Stefan & Rachev, Svetlozar T. & Kim, Jeong-Ryeol, 1998. "Chi-Square-Type Distributions For Heavy-Tailed Variates," Econometric Theory, Cambridge University Press, vol. 14(3), pages 339-354, June.

    Cited by:

    1. Hill, Jonathan B. & Aguilar, Mike, 2013. "Moment condition tests for heavy tailed time series," Journal of Econometrics, Elsevier, vol. 172(2), pages 255-274.
    2. Kurz-Kim, Jeong-Ryeol & Loretan, Mico, 2014. "On the properties of the coefficient of determination in regression models with infinite variance variables," Journal of Econometrics, Elsevier, vol. 181(1), pages 15-24.
    3. Hansen, Gerd, 2000. "The German labour market and the unification shock," Economic Modelling, Elsevier, vol. 17(3), pages 439-454, August.
    4. Hansen, Gerd & Kim, Jeong-Ryeol & Mittnik, Stefan, 1998. "Testing cointegrating coefficients in vector autoregressive error correction models," Economics Letters, Elsevier, vol. 58(1), pages 1-5, January.

  29. Hansen, Gerd & Kim, Jeong-Ryeol & Mittnik, Stefan, 1998. "Testing cointegrating coefficients in vector autoregressive error correction models," Economics Letters, Elsevier, vol. 58(1), pages 1-5, January.

    Cited by:

    1. Carstensen, Kai & Hawellek, J., 2003. "Forecasting Inflation from the Term Structure," Munich Reprints in Economics 19949, University of Munich, Department of Economics.
    2. Julián Ramajo Hernández(1) & Montserrat Ferré Carracedo(2), "undated". "Testing For Long-Run Purchasing Power Parity In The Post Bretton Woods Era: Evidence From Old And New Tests," Working Papers 24-05 Classification-JEL , Instituto de Estudios Fiscales.
    3. Joerg Breitung & M. Hashem Pesaran, 2005. "Unit Roots and Cointegration in Panels," CESifo Working Paper Series 1565, CESifo.
    4. Kurita, Takamitsu, 2010. "Effects of a signal-to-noise ratio on finite sample inference for cointegrating vectors," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(10), pages 2033-2039.
    5. Hansen, Gerd, 2000. "The German labour market and the unification shock," Economic Modelling, Elsevier, vol. 17(3), pages 439-454, August.

  30. Kim Jeong-Ryeol & Mittnik Stefan & Rachev Svetlozar T., 1996. "Detecting Asymmetries in Observed Linear Time Series and Unobserved Disturbances," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(3), pages 1-15, October.

    Cited by:

    1. Randal J. Verbrugge, 1998. "A cross-country investigation of macroeconomic asymmetries," Macroeconomics 9809017, University Library of Munich, Germany, revised 30 Sep 1998.
    2. Belaire-Franch Jorge & Peiro Amado, 2003. "Conditional and Unconditional Asymmetry in U.S. Macroeconomic Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(1), pages 1-19, April.
    3. Jagric Timotej, 2003. "A Nonlinear Approach to Forecasting with Leading Economic Indicators," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-20, July.
    4. W A Razzak, 1998. "Business cycle asymmetries and the nominal exchange rate regimes," Reserve Bank of New Zealand Discussion Paper Series G98/4, Reserve Bank of New Zealand.
    5. Luke Hartigan, 2016. "Testing for Symmetry in Weakly Dependent Time Series," Discussion Papers 2016-18, School of Economics, The University of New South Wales.
    6. Dufour, Jean-Marie & Kurz-Kim, Jeong-Ryeol, 2010. "Exact inference and optimal invariant estimation for the stability parameter of symmetric [alpha]-stable distributions," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 180-194, March.
    7. W.A. Razzak, 2001. "Business Cycle Asymmetries: International Evidence," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 4(1), pages 230-243, January.

  31. Mittnik, Stefan & Zadrozny, Peter A, 1993. "Asymptotic Distributions of Impulse Responses, Step Responses, and Variance Decompositions of Estimated Linear Dynamic Models," Econometrica, Econometric Society, vol. 61(4), pages 857-870, July.

    Cited by:

    1. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "The Micro Dynamics of Macro Announcements," CESifo Working Paper Series 4421, CESifo.
    2. Mittnik, Stefan & Semmler, Willi, 2013. "The real consequences of financial stress," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1479-1499.
    3. André Klein & Guy Melard & Toufik Zahaf, 1998. "Computation of the exact information matrix of Gaussian dynamic regression time series models," ULB Institutional Repository 2013/13738, ULB -- Universite Libre de Bruxelles.
    4. André Klein & Guy Melard, 2004. "An algorithm for computing the asymptotic Fisher information matrix for seasonal SISO models," ULB Institutional Repository 2013/13746, ULB -- Universite Libre de Bruxelles.
    5. Mittnik, Stefan & Semmler, Willi, 2018. "Overleveraging, Financial Fragility, And The Banking–Macro Link: Theory And Empirical Evidence," Macroeconomic Dynamics, Cambridge University Press, vol. 22(1), pages 4-32, January.
    6. Chiarella Carl & Semmler Willi & Mittnik Stefan & Zhu Peiyuan, 2002. "Stock Market, Interest Rate and Output: A Model and Estimation for US Time Series Data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(1), pages 1-39, April.
    7. Gabriele Fiorentini & Enrique Sentana, 2020. "Discrete Mixtures of Normals Pseudo Maximum Likelihood Estimators of Structural Vector Autoregressions," Working Papers wp2020_2023, CEMFI.
    8. Atsushi Inoue & Lutz Kilian, 2013. "Inference on Impulse Response Functions in Structural VAR Models," DSSR Discussion Papers 11, Graduate School of Economics and Management, Tohoku University.
    9. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    10. Renata Wróbel-Rotter, 2016. "Impulse Response Functions in the Dynamic Stochastic General Equilibrium Vector Autoregression Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(2), pages 93-114, June.
    11. Mittnik, Stefan & Semmler, Willi, 2012. "Regime dependence of the fiscal multiplier," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 502-522.
    12. Uhlig, Harald, 1999. "What are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure," CEPR Discussion Papers 2137, C.E.P.R. Discussion Papers.
    13. Mounir Ben Mbarek & Samia Nasreen & Rochdi Feki, 2017. "The contribution of nuclear energy to economic growth in France: short and long run," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(1), pages 219-238, January.
    14. Chevillon, Guillaume & Mavroeidis, Sophocles & Zhan, Zhaoguo, 2016. "Robust inference in structural VARs with long-run restrictions," ESSEC Working Papers WP1702, ESSEC Research Center, ESSEC Business School.
    15. Christopher A. Sims & Tao Zha, 1995. "Error bands for impulse responses," FRB Atlanta Working Paper 95-6, Federal Reserve Bank of Atlanta.
    16. Uhlig, H.F.H.V.S., 1999. "What are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure," Other publications TiSEM 2e0fa8dd-ead5-4c6b-97cb-1, Tilburg University, School of Economics and Management.
    17. Kirstin Hubrich & Peter Vlaar, 2004. "Monetary transmission in Germany: Lessons for the Euro area," Empirical Economics, Springer, vol. 29(2), pages 383-414, May.
    18. Chung, Ching-Fan, 2001. "Calculating and analyzing impulse responses for the vector ARFIMA model," Economics Letters, Elsevier, vol. 71(1), pages 17-25, April.
    19. Huh, Hyeon-seung & Kim, David, 2013. "An empirical test of exogenous versus endogenous growth models for the G-7 countries," Economic Modelling, Elsevier, vol. 32(C), pages 262-272.
    20. Ekkehard Ernst & Stefan Mittnik & Willi Semmler, 2016. "Interaction of Labour and Credit Market in Growth Regimes: A Theoretical and Empirical Analysis," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 45(3), pages 393-422, November.
    21. Kirstin Hubrich & Peter J. G. Vlaar, 2000. "Germany and the Euro Area: Differences in the Transmission Process of Monetary Policy," Econometric Society World Congress 2000 Contributed Papers 1802, Econometric Society, revised 08 Nov 2000.
    22. Willi Semmler & Stefan Mittnik, 2012. "Estimating a Banking-Macro Model for Europe Using a Multi-Regime VAR," EcoMod2012 4122, EcoMod.
    23. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "Was bewegt den DAX?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(23), pages 32-36, December.
    24. Stefan Mittnik & Willi Semmler, 2011. "The Instability of the Banking Sector and Macrodynamics: Theory and Empirics," DEGIT Conference Papers c016_080, DEGIT, Dynamics, Economic Growth, and International Trade.
    25. Hyeon-Seung Huh, 2013. "A Monte Carlo test for the identifying assumptions of the Blanchard and Quah (1989) model," Applied Economics Letters, Taylor & Francis Journals, vol. 20(6), pages 601-605, April.
    26. Zhou, Mo & Buongiorno, Joseph, 2005. "Price transmission between products at different stages of manufacturing in forest industries," Journal of Forest Economics, Elsevier, vol. 11(1), pages 5-19, June.

  32. Braun, Phillip A. & Mittnik, Stefan, 1993. "Misspecifications in vector autoregressions and their effects on impulse responses and variance decompositions," Journal of Econometrics, Elsevier, vol. 59(3), pages 319-341, October.

    Cited by:

    1. Thomas Gries & Tim Krieger & Daniel Meierrieks, 2009. "Causal Linkages Between Domestic Terrorism and Economic Growth," Working Papers CIE 20, Paderborn University, CIE Center for International Economics.
    2. Leonardo Nogueira Ferreira & Silvia Miranda-Agrippino & Giovanni Ricco, 2023. "Bayesian Local Projections," Working Papers Series 581, Central Bank of Brazil, Research Department.
    3. Maswana, Jean-Claude, 2006. "An empirical investigation around the finance-growth puzzle in China with a particular focus on causality and efficiency considerations," MPRA Paper 3946, University Library of Munich, Germany, revised Apr 2006.
    4. Dupasquier, Chantal & Guay, Alain & St-Amant, Pierre, 1999. "A Survey of Alternative Methodologies for Estimating Potential Output and the Output Gap," Journal of Macroeconomics, Elsevier, vol. 21(3), pages 577-595, July.
    5. Jean-Sébastien Pentecôte, 2010. "Long-run identifying restrictions on VARs within the AS-AD framework," Post-Print halshs-00554867, HAL.
    6. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2017. "The transmission of monetary policy shocks," Bank of England working papers 657, Bank of England.
    7. Gutierrez, Carlos Enrique Carrasco & Souza, Reinaldo Castro & Guillén, Osmani Teixeira de Carvalho, 2009. "Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 29(1), May.
    8. Shinya Sugawara & Tsunehiro Ishihara & Susumu Kunisawa & Etsu Goto & Yuichi Imanaka, 2024. "A panel vector autoregression analysis for the dynamics of medical and long‐term care expenditures," Health Economics, John Wiley & Sons, Ltd., vol. 33(4), pages 748-763, April.
    9. Silvia Miranda Agrippino & Giovanni Ricco, 2018. "Identification with external instruments in structural VARs under partial invertibility," Working Papers hal-03475454, HAL.
    10. Neri, Stefano, 2023. "Long-term inflation expectations and monetary policy in the euro area before the pandemic," European Economic Review, Elsevier, vol. 154(C).
    11. Benjamin Auer & Frank Schuhmacher, 2013. "RETRACTED ARTICLE: Investor sentiment, stock market valuation and merger activity," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 60(2), pages 245-245, June.
    12. Zernov, Serguei & Zinde-Walsh, Victoria & Galbraith, John W., 2009. "Asymptotics for estimation of quantile regressions with truncated infinite-dimensional processes," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 497-508, March.
    13. Thomas Gries & Manfred Kraft & Daniel Meierrieks, 2011. "Financial deepening, trade openness and economic growth in Latin America and the Caribbean," Applied Economics, Taylor & Francis Journals, vol. 43(30), pages 4729-4739.
    14. Binet, Marie-Estelle & Pentecôte, Jean-Sébastien, 2015. "Macroeconomic idiosyncrasies and European monetary unification: A sceptical long run view," Economic Modelling, Elsevier, vol. 51(C), pages 412-423.
    15. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2023. "Identification with External Instruments in Structural VARs," Journal of Monetary Economics, Elsevier, vol. 135(C), pages 1-19.
    16. Fabio Canova, 2007. "How much structure in empirical models?," Economics Working Papers 1054, Department of Economics and Business, Universitat Pompeu Fabra.
    17. Laing, Andrew R. & Nolan, James F., 2009. "Price Dynamics and Market Structure in Transportation: For-Hire Grain Trucking Along the Alberta- Saskatchewan Border," 50th Annual Transportation Research Forum, Portland, Oregon, March 16-18, 2009 207599, Transportation Research Forum.
    18. Altissimo, F. & Violante, G.L., 1998. "Nonlinear VAR: Some Theory and an Application to the US GNP and Unemployment," Papers 338, Banca Italia - Servizio di Studi.
    19. M. Huchet & Jean-Sébastien Pentecôte, 2008. "Growing too fast? Shock asymmetries and the Euro area enlargement," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 51(1), pages 33-56.
    20. W. Douglas McMillin & Keuk-soo Kim, 2002. "Estimating the Effects of Monetary Policy Shocks: Does Lag Structure Matter?," Departmental Working Papers 2002-04, Department of Economics, Louisiana State University.
    21. Kemal Bagzibagli, 2012. "Monetary Transmission Mechanism and Time Variation in the Euro Area," Discussion Papers 12-12, Department of Economics, University of Birmingham.
    22. YOUNG Ademola Obafemi, 2022. "Non-Oil Sectors, Economic Diversification And Growth In Nigeria: Further Empirical Evidence," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 17(1), pages 290-311, April.
    23. Gries, Thomas & Kraft, Manfred & Meierrieks, Daniel, 2009. "Linkages Between Financial Deepening, Trade Openness, and Economic Development: Causality Evidence from Sub-Saharan Africa," World Development, Elsevier, vol. 37(12), pages 1849-1860, December.
    24. Yasir Riaz & Choudhry T. Shehzad & Zaghum Umar, 2021. "The sovereign yield curve and credit ratings in GIIPS," International Review of Finance, International Review of Finance Ltd., vol. 21(3), pages 895-916, September.
    25. Helmut Lütkepohl, 2012. "Fundamental Problems with Nonfundamental Shocks," Discussion Papers of DIW Berlin 1230, DIW Berlin, German Institute for Economic Research.
    26. Marrouch, Walid & Mourad, Jana, 2019. "Effect of gasoline prices on car fuel efficiency: Evidence from Lebanon," Energy Policy, Elsevier, vol. 135(C).
    27. Horaţiu LOVIN, 2015. "Liquidity Shocks Transmission to Lending Activity in the Romanian Banking System. A VAR Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 48-60, June.
    28. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    29. Ellahie, Atif & Ricco, Giovanni, 2017. "Government Purchases Reloaded : Informational Insufficiency and Heterogeneity in Fiscal VARs," Economic Research Papers 269308, University of Warwick - Department of Economics.
    30. Betül Mutlugün, 2022. "Endogenous income distribution and aggregate demand: Empirical evidence from heterogeneous panel structural vector autoregression," Metroeconomica, Wiley Blackwell, vol. 73(2), pages 583-637, May.
    31. Carrasco Gutierrez, Carlos Enrique & Castro Souza, Reinaldo & Teixeira de Carvalho Guillén, Osmani, 2009. "Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features," MPRA Paper 22550, University Library of Munich, Germany.
    32. Maganya, Mnaku H. & Ndanshau, Michael O. A., 2020. "Money and Output in Tanzania: A Test for Causality," African Journal of Economic Review, African Journal of Economic Review, vol. 8(2), July.
    33. Gan‐Ochir Doojav & Davaasukh Damdinjav, 2023. "The macroeconomic effects of unconventional monetary policies in a commodity‐exporting economy: Evidence from Mongolia," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4627-4654, October.
    34. Pierre St-Amant & David Tessier, 1998. "A Discussion of the Reliability of Results Obtained with Long-Run Identifying Restrictions," Staff Working Papers 98-4, Bank of Canada.
    35. Ronayne, David, 2011. "Which Impulse Response Function?," Economic Research Papers 270753, University of Warwick - Department of Economics.
    36. René Lalonde & Jennifer Page & Pierre St-Amant, 1998. "Une nouvelle méthode d'estimation de l'écart de production et son application aux États-Unis, au Canada et à l'Allemagne," Staff Working Papers 98-21, Bank of Canada.
    37. Kilian, Lutz & Chang, Pao-Li, 2000. "How accurate are confidence intervals for impulse responses in large VAR models?," Economics Letters, Elsevier, vol. 69(3), pages 299-307, December.
    38. Albis, Manuel Leonard F. & Mapa, Dennis S., 2014. "Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models," MPRA Paper 55902, University Library of Munich, Germany.
    39. Deven Bathia & Don Bredin & Dirk Nitzsche, 2016. "International Sentiment Spillovers in Equity Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(4), pages 332-359, October.
    40. OSENI Isiaq Olasunkanmi, 2015. "Fiscal Policy Shocks and Private Consumption in Nigeria: Blanchard-Perotti (2002) Approach," Journal of Economics and Behavioral Studies, AMH International, vol. 7(6), pages 42-60.
    41. Lovcha, Yuliya & Pérez Laborda, Àlex, 2016. "Structural shocks and dinamic elasticities in a long memory model of the US gasoline retail market," Working Papers 2072/261538, Universitat Rovira i Virgili, Department of Economics.
    42. Qianqian Wang & Choi, 2015. "Co-movement of the Chinese and U.S. aggregate stock returns," Applied Economics, Taylor & Francis Journals, vol. 47(50), pages 5337-5353, October.
    43. Serguei Zernov & Victoria Zindle-Walsh & John Galbraith, 2006. "Asymptotics For Estimation Of Truncated Infinite-Dimensional Quantile Regressions," Departmental Working Papers 2006-16, McGill University, Department of Economics.
    44. Stanley Tweyman, 2022. "Hume on The Epistemological Status of Metaphysical Statements," European Journal of Interdisciplinary Studies Articles, Revistia Research and Publishing, vol. 8, ejis_v8_i.
    45. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, June.
    46. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2019. "Identification with External Instruments in Structural VARs under Partial Invertibility," The Warwick Economics Research Paper Series (TWERPS) 1213, University of Warwick, Department of Economics.
    47. René Lalonde, 1998. "Le PIB potentiel des États-Unis et ses déterminants : la productivité de la main-d'oeuvre et le taux d'activité," Staff Working Papers 98-13, Bank of Canada.
    48. Ren, Yunwen & Xiao, Zhiguo & Zhang, Xinsheng, 2013. "Two-step adaptive model selection for vector autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 349-364.
    49. Wei Yao & Weikun Zhang & Wenxiu Li & Penglong Li, 2022. "Measurement and Evaluation of Convergence of Japan’s Marine Fisheries and Marine Tourism," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    50. Omer Ali Ibrahim & Sonal Devesh & Hisham Mohamed Hassan, 2019. "Sensitivity of Fiscal Balances to Oil Price Shocks: Short and Long Term Effects in the Context of Oman," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 146-155.
    51. Nezir Kose & Nuri Ucar, 2006. "Effect of cross correlations in error terms on the model selection criteria for the stationary VAR process," Applied Economics Letters, Taylor & Francis Journals, vol. 13(4), pages 223-228.
    52. Abidemi Abiola & Rasak A. Adefabi, 2022. "Rural Structural Transformation and Agricultural Productivity in Nigeria," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 8(2), pages 119-138, April.
    53. W. Douglas McMillin & Keuk-Soo Kim, 2001. "Symmetric versus Asymmetric Lag Structures in Vector Autoregressive Models: A Monte Carlo Analysis with an Application to Estimating the Effects of Monetary Policy Shocks," Departmental Working Papers 2001-01, Department of Economics, Louisiana State University.
    54. Pierre St-Amant & David Tessier, 1998. "Tendance des dépenses publiques et de l'inflation et évolution comparative du taux de chômage au Canada et aux États-Unis," Staff Working Papers 98-3, Bank of Canada.

  33. Mittnik, Stefan, 1991. "Derivation of the unconditional state-covariance matrix for exact maximum-likelihood estimation of ARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 15(4), pages 731-740, October.

    Cited by:

    1. Guy Melard & Roch Roy & Abdessamad Saidi, 2006. "Exact maximum likelihood estimation of structured or unit root multivariate time series models," ULB Institutional Repository 2013/13754, ULB -- Universite Libre de Bruxelles.

  34. Mittnik, Stefan, 1990. "Macroeconomic forecasting experience with balanced state space models," International Journal of Forecasting, Elsevier, vol. 6(3), pages 337-348, October.

    Cited by:

    1. Mittnik, Stefan, 2014. "VaR-implied tail-correlation matrices," Economics Letters, Elsevier, vol. 122(1), pages 69-73.
    2. Saxen, Henrik & Ostermark, Ralf, 1996. "State realization with exogenous variables - A test on blast furnace data," European Journal of Operational Research, Elsevier, vol. 89(1), pages 34-52, February.
    3. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    4. Donald S. Allen & Meenakshi Pasupathy, 1997. "A state space forecasting model with fiscal and monetary control," Working Papers 1997-017, Federal Reserve Bank of St. Louis.
    5. Najand, Mohammad & Bond, Charlotte, 2000. "Structural models of exchange rate determination," Journal of Multinational Financial Management, Elsevier, vol. 10(1), pages 15-27, January.
    6. Najand, Mohammad & Noronha, Gregory, 1998. "Causal relations among stock returns, inflation, real activity, and interest rates: Evidence from Japan," Global Finance Journal, Elsevier, vol. 9(1), pages 71-80.
    7. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    8. Michael Mosebach & Mohammad Najand, 1999. "Are The Structural Changes In Mutual Funds Investing Driving The U.S. Stock Market To Its Current Levels?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 22(3), pages 317-329, September.
    9. Stefan Mittnik & Peter Zadrozny, 2005. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly Ifo Business Conditions Data," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 19-48, Springer.

  35. Mittnik, Stefan, 1990. "Macroeconomic Forecasting Using Pooled International Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 205-208, April.

    Cited by:

    1. Ash, J. C. K. & Smyth, D. J. & Heravi, S. M., 1997. "The accuracy of OECD forecasts of the international economy: balance of payments," Journal of International Money and Finance, Elsevier, vol. 16(6), pages 969-987, December.
    2. Poncela, Pilar, 2000. "Forecasting with nostationary dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 9959, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Ash, J. C. K. & Smyth, D. J. & Heravi, S. M., 1998. "Are OECD forecasts rational and useful?: a directional analysis," International Journal of Forecasting, Elsevier, vol. 14(3), pages 381-391, September.
    4. Antonio García Ferrer & Juan del Hoyo Bernat & Peter C. Young & Alfonso Novales Cinca, 1993. "Recursive identification, estimation and forecasting of nonstationary economic time series with applications to GNP international data," Documentos de Trabajo del ICAE 9310, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.

  36. Mittnik, Stefan, 1987. "Non-recursive methods for computing the coefficients of the autoregressive and the moving-average representation of mixed ARMA processes," Economics Letters, Elsevier, vol. 23(3), pages 279-284.

    Cited by:

    1. D.S. Poskitt & Wenying Yao, 2012. "VAR Modeling and Business Cycle Analysis: A Taxonomy of Errors," Monash Econometrics and Business Statistics Working Papers 11/12, Monash University, Department of Econometrics and Business Statistics.
    2. Menelaos Karanasos, "undated". "The Covariance Structure of Mixed ARMA Models," Discussion Papers 00/11, Department of Economics, University of York.
    3. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "Was bewegt den DAX?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(23), pages 32-36, December.
    4. Pollock, D. S. G., 2003. "Recursive estimation in econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 37-75, October.

  37. Mittnik, Stefan, 1987. "The determination of the state covariance matrix of moving-average processes without computation," Economics Letters, Elsevier, vol. 23(2), pages 177-179.

    Cited by:

    1. Pollock, D. S. G., 2003. "Recursive estimation in econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 37-75, October.

Chapters

  1. Stefan Mittnik & Willi Semmler, 2014. "Estimating a Banking-Macro Model Using a Multi-regime VAR," Dynamic Modeling and Econometrics in Economics and Finance, in: Frauke Schleer-van Gellecom (ed.), Advances in Non-linear Economic Modeling, edition 127, pages 3-40, Springer.

    Cited by:

    1. Ernst, Ekkehard & Semmler, Willi & Haider, Alexander, 2017. "Debt-deflation, financial market stress and regime change – Evidence from Europe using MRVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 81(C), pages 115-139.
    2. Barrales-Ruiz, Jose & Mohammed, Mikidadu, 2021. "Financial regimes and oil prices," Resources Policy, Elsevier, vol. 74(C).
    3. Halvorsen, Jørn I. & Jacobsen, Dag Henning, 2016. "The bank-lending channel empirically revisited," Journal of Financial Stability, Elsevier, vol. 27(C), pages 95-105.
    4. Gaies, Brahim & Nakhli, Mohamed Sahbi & Ayadi, Rim & Sahut, Jean-Michel, 2022. "Exploring the causal links between investor sentiment and financial instability: A dynamic macro-financial analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 290-303.

  2. Markus Haas & Stefan Mittnik, 2009. "Portfolio Selection with Common Correlation Mixture Models," Contributions to Economics, in: Georg Bol & Svetlozar T. Rachev & Reinhold Würth (ed.), Risk Assessment, pages 47-76, Springer.

    Cited by:

    1. Mittnik, Stefan, 2014. "VaR-implied tail-correlation matrices," Economics Letters, Elsevier, vol. 122(1), pages 69-73.

  3. Stefan Mittnik & Peter Zadrozny, 2005. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly Ifo Business Conditions Data," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 19-48, Springer.
    See citations under working paper version above.
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