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Roxana Halbleib (Chiriac)

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. Matteo Barigozzi & Roxana Halbleib & David Veredas, 2012. "Which model to match?," Working Papers 1229, Banco de España.

    Cited by:

    1. Yves Dominicy & Hiroaki Ogata & David Veredas, 2013. "Inference for vast dimensional elliptical distributions," Computational Statistics, Springer, vol. 28(4), pages 1853-1880, August.

  2. Giorgio Calzolari & Roxana Halbleib & Alessandro Parrini, 2012. "Indirect Estimation of α-Stable Garch Models," Working Paper Series of the Department of Economics, University of Konstanz 2012-31, Department of Economics, University of Konstanz.

    Cited by:

    1. Yanlin Shi & Lingbing Feng & Tong Fu, 2020. "Markov Regime-Switching in-Mean Model with Tempered Stable Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1275-1299, April.
    2. Sampaio, Jhames M. & Morettin, Pedro A., 2020. "Stable Randomized Generalized Autoregressive Conditional Heteroskedastic Models," Econometrics and Statistics, Elsevier, vol. 15(C), pages 67-83.
    3. Shi, Yanlin & Feng, Lingbing, 2016. "A discussion on the innovation distribution of the Markov regime-switching GARCH model," Economic Modelling, Elsevier, vol. 53(C), pages 278-288.
    4. Calzolari, Giorgio & Halbleib, Roxana & Parrini, Alessandro, 2014. "Estimating GARCH-type models with symmetric stable innovations: Indirect inference versus maximum likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 158-171.
    5. Lingbing Feng & Yanlin Shi, 2017. "A simulation study on the distributions of disturbances in the GARCH model," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1355503-135, January.
    6. Calzolari, Giorgio & Halbleib, Roxana, 2018. "Estimating stable latent factor models by indirect inference," Journal of Econometrics, Elsevier, vol. 205(1), pages 280-301.
    7. Li, Dong & Tao, Yuxin & Yang, Yaxing & Zhang, Rongmao, 2023. "Maximum likelihood estimation for α-stable double autoregressive models," Journal of Econometrics, Elsevier, vol. 236(1).
    8. Feng Lingbing & Shi Yanlin, 2020. "Markov regime-switching autoregressive model with tempered stable distribution: simulation evidence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-27, February.
    9. Tong Liu & Yanlin Shi, 2022. "Innovation of the Component GARCH Model: Simulation Evidence and Application on the Chinese Stock Market," Mathematics, MDPI, vol. 10(11), pages 1-18, June.

  3. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168222, Verein für Socialpolitik / German Economic Association.
    2. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    3. Vander Elst, Harry & Veredas, David, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Kevin Sheppard & Wen Xu, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.
    5. Hautsch, Nikolaus & Kyj, Lada M. & Malec, Peter, 2011. "The merit of high-frequency data in portfolio allocation," SFB 649 Discussion Papers 2011-059, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
    7. Bannouh, K. & Martens, M.P.E. & Oomen, R.C.A. & van Dijk, D.J.C., 2012. "Realized mixed-frequency factor models for vast dimensional covariance estimation," ERIM Report Series Research in Management ERS-2012-017-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Harry Vander Elst & David Veredas, 2017. "Smoothing it Out: Empirical and Simulation Results for Disentangled Realized Covariances," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 106-138.

  4. Roxana Halbleib & Valerie Voev, 2010. "Forecasting Multivariate Volatility Using the VARFIMA Model on Realized Covariance Cholesky Factors," Working Papers ECARES ECARES 2010-041, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    3. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
    4. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

  5. Roxana Chiriac & Winfried Pohlmeier, 2010. "How Risky Is the Value at Risk?," Working Paper series 07_10, Rimini Centre for Economic Analysis.

    Cited by:

    1. McAleer, M.J. & Jiménez-Martín, J.A. & Pérez-Amaral, T., 2012. "Has the Basel Accord Improved Risk Management During the Global Financial Crisis?," Econometric Institute Research Papers EI 2012-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Fatemeh Fattahi & Ali Hadi & Mohammad Afzalinejad & Farhad Hosseinzadeh Lotfi, 2024. "A Modified Network DEA Model for Bank Efficiency Analysis Considering Risk Factors," SN Operations Research Forum, Springer, vol. 5(4), pages 1-24, December.
    3. Shcherba, Alexandr, 2012. "Market risk valuation modeling for the European countries at the financial crisis of 2008," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 20-35.

  6. Roxana Chiriac & Valeri Voev, 2008. "Modelling and Forecasting Multivariate Realized Volatility," CREATES Research Papers 2008-39, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Han, Chulwoo & Park, Frank C., 2022. "A geometric framework for covariance dynamics," Journal of Banking & Finance, Elsevier, vol. 134(C).
    2. Márcio Gomes Pinto Garcia & Marcelo Cunha Medeiros & Francisco Eduardo de Luna e Almeida Santos, 2014. "Economic gains of realized volatility in the Brazilian stock market," Brazilian Review of Finance, Brazilian Society of Finance, vol. 12(3), pages 319-349.
    3. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    4. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    5. Hung Do & Rabindra Nepal & Tooraj Jamasb, 2020. "Electricity market integration, decarbonisation and security of supply: Dynamic volatility connectedness in the Irish and Great Britain markets," CAMA Working Papers 2020-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    8. Kawakatsu Hiroyuki, 2021. "Simple Multivariate Conditional Covariance Dynamics Using Hyperbolically Weighted Moving Averages," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 33-52, January.
    9. Alketa Bejko & Etleva Peta & Belinda Xarba, 2015. "The Evaluation of the Drafting Process of Regional’s Development Strategies in Albania. the Research on Gjirokastra’s Region," European Journal of Interdisciplinary Studies Articles, Revistia Research and Publishing, vol. 1, ejis_v1_i.
    10. Dong Hwan Oh & Andrew J. Patton, 2015. "High-Dimensional Copula-Based Distributions with Mixed Frequency Data," Finance and Economics Discussion Series 2015-50, Board of Governors of the Federal Reserve System (U.S.).
    11. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
    12. Robinson Kruse & Christian Leschinski & Michael Will, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," CREATES Research Papers 2016-17, Department of Economics and Business Economics, Aarhus University.
    13. ROMBOUTS, Jeroen V. K. & STENTOFT, Lars & VIOLANTE, Francesco, 2012. "The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options," LIDAM Discussion Papers CORE 2012003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Manabu Asai & Michael McAleer, 2017. "The impact of jumps and leverage in forecasting covolatility," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 638-650, October.
    15. Bonato, Mateo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Risk Spillovers in International Equity Portfolios," Working Papers on Finance 1214, University of St. Gallen, School of Finance.
    16. Ralf Becker & Adam Clements & Robert O'Neill, 2010. "A Kernel Technique for Forecasting the Variance-Covariance Matrix," Centre for Growth and Business Cycle Research Discussion Paper Series 151, Economics, The University of Manchester.
    17. Sibbertsen, Philipp & Leschinski, Christian & Busch, Marie, 2018. "A multivariate test against spurious long memory," Journal of Econometrics, Elsevier, vol. 203(1), pages 33-49.
    18. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    19. Rasmus Tangsgaard Varneskov, 2011. "Flat-Top Realized Kernel Estimation of Quadratic Covariation with Non-Synchronous and Noisy Asset Prices," CREATES Research Papers 2011-35, Department of Economics and Business Economics, Aarhus University.
    20. Xu, Jiawen & Perron, Pierre, 2014. "Forecasting return volatility: Level shifts with varying jump probability and mean reversion," International Journal of Forecasting, Elsevier, vol. 30(3), pages 449-463.
    21. Bastian Gribisch, 2018. "A latent dynamic factor approach to forecasting multivariate stock market volatility," Empirical Economics, Springer, vol. 55(2), pages 621-651, September.
    22. Li, Dan & Drovandi, Christopher & Clements, Adam, 2024. "Outlier-robust methods for forecasting realized covariance matrices," International Journal of Forecasting, Elsevier, vol. 40(1), pages 392-408.
    23. Chatziantoniou, Ioannis & Degiannakis, Stavros & Filis, George, 2019. "Futures-based forecasts: How useful are they for oil price volatility forecasting?," Energy Economics, Elsevier, vol. 81(C), pages 639-649.
    24. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    25. Tobias Hartl & Roland Weigand, 2018. "Multivariate Fractional Components Analysis," Papers 1812.09149, arXiv.org, revised Jan 2019.
    26. Fengler, Matthias R. & Gisler, Katja I. M., 2014. "A variance spillover analysis without covariances: what do we miss?," Economics Working Paper Series 1409, University of St. Gallen, School of Economics and Political Science.
    27. Jiayuan Zhou & Feiyu Jiang & Ke Zhu & Wai Keung Li, 2019. "Time series models for realized covariance matrices based on the matrix-F distribution," Papers 1903.12077, arXiv.org, revised Jul 2020.
    28. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Tinbergen Institute Discussion Papers 14-037/III, Tinbergen Institute.
    29. Anne Opschoor & André Lucas & István Barra & Dick van Dijk, 2021. "Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1066-1079, October.
    30. Dark, Jonathan, 2024. "An adaptive long memory conditional correlation model," Journal of Empirical Finance, Elsevier, vol. 75(C).
    31. 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.
    32. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    33. Abderrazak Ben Maatoug & Rim Lamouchi & Russell Davidson & Ibrahim Fatnassi, 2018. "Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks," Post-Print hal-01982032, HAL.
    34. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg & Orimar Sauri, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Post-Print hal-01505775, HAL.
    35. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    36. Kim, Alisa & Trimborn, Simon & Härdle, Wolfgang Karl, 2021. "VCRIX — A volatility index for crypto-currencies," International Review of Financial Analysis, Elsevier, vol. 78(C).
    37. Ilya Archakov & Peter Reinhard Hansen, 2020. "A New Parametrization of Correlation Matrices," Papers 2012.02395, arXiv.org.
    38. Rasmus Tangsgaard Varneskov & Pierre Perron, 2011. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," CREATES Research Papers 2011-26, Department of Economics and Business Economics, Aarhus University.
    39. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," CREATES Research Papers 2011-37, Department of Economics and Business Economics, Aarhus University.
    40. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
    41. M. Shabani & M. Magris & George Tzagkarakis & J. Kanniainen & A. Iosifidis, 2023. "Predicting the state of synchronization of financial time series using cross recurrence plots," Post-Print hal-04415269, HAL.
    42. Gribisch, Bastian, 2013. "A latent dynamic factor approach to forecasting multivariate stock market volatility," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79823, Verein für Socialpolitik / German Economic Association.
    43. 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.
    44. J. Eduardo Vera-Vald'es, 2017. "On Long Memory Origins and Forecast Horizons," Papers 1712.08057, arXiv.org.
    45. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2010. "The conditional autoregressive wishart model for multivariate stock market volatility," Economics Working Papers 2010-07, Christian-Albrechts-University of Kiel, Department of Economics.
    46. Vassallo, Danilo & Buccheri, Giuseppe & Corsi, Fulvio, 2021. "A DCC-type approach for realized covariance modeling with score-driven dynamics," International Journal of Forecasting, Elsevier, vol. 37(2), pages 569-586.
    47. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    48. Yaojie Zhang & Yu Wei & Li Liu, 2019. "Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1425-1438, September.
    49. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
    50. Ubukata, Masato & Watanabe, Toshiaki, 2015. "Evaluating the performance of futures hedging using multivariate realized volatility," Journal of the Japanese and International Economies, Elsevier, vol. 38(C), pages 148-171.
    51. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    52. Nick Taylor, 2017. "Risk Control: Who Cares?," European Financial Management, European Financial Management Association, vol. 23(1), pages 153-179, January.
    53. Hautsch, Nikolaus & Kyj, Lada. M. & Malec, Peter, 2013. "Do high-frequency data improve high-dimensional portfolio allocations?," SFB 649 Discussion Papers 2013-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    54. Manabu Asai & Mike K. P. So, 2021. "Quasi‐maximum likelihood estimation of conditional autoregressive Wishart models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 271-294, May.
    55. A Clements & D Preve, 2019. "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series 120, National Centre for Econometric Research.
    56. Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.
    57. Philip Bertram & Robinson Kruse & Philipp Sibbertsen, 2013. "Fractional integration versus level shifts: the case of realized asset correlations," Statistical Papers, Springer, vol. 54(4), pages 977-991, November.
    58. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    59. 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.
    60. Ilya Archakov & Peter Reinhard Hansen & Asger Lunde, 2020. "A Multivariate Realized GARCH Model," Papers 2012.02708, arXiv.org, revised May 2024.
    61. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    62. Gaoxiu Qiao & Yangli Cao & Feng Ma & Weiping Li, 2023. "Liquidity and realized covariance forecasting: a hybrid method with model uncertainty," Empirical Economics, Springer, vol. 64(1), pages 437-463, January.
    63. Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • 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).
    64. Andre Lucas & Anne Opschoor, 2016. "Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns," Tinbergen Institute Discussion Papers 16-069/IV, Tinbergen Institute, revised 07 Jul 2017.
    65. Wei Kuang, 2021. "Conditional covariance matrix forecast using the hybrid exponentially weighted moving average approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1398-1419, December.
    66. Fabrizio Cipollini & Giampiero M. Gallo & Alessandro Palandri, 2020. "A dynamic conditional approach to portfolio weights forecasting," Papers 2004.12400, arXiv.org.
    67. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    68. 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.
    69. 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.
    70. Mostafa Shabani & Martin Magris & George Tzagkarakis & Juho Kanniainen & Alexandros Iosifidis, 2022. "Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots," Papers 2210.14605, arXiv.org, revised Nov 2022.
    71. Tsunehiro Ishihara & Yasuhiro Omori & Manabu Asai, 2014. "Matrix Exponential Stochastic Volatility with Cross Leverage," CIRJE F-Series CIRJE-F-932, CIRJE, Faculty of Economics, University of Tokyo.
    72. 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).
    73. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    74. Manabu Asai & Chia-Lin Chang & Michael McAleer, 2016. "Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers," Documentos de Trabajo del ICAE 2016-15, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    75. Behrendt, Simon & Schmidt, Alexander, 2018. "The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 355-367.
    76. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2019. "Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations," Papers 1912.09002, arXiv.org, revised Jun 2021.
    77. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2018. "A simple test on structural change in long-memory time series," Economics Letters, Elsevier, vol. 163(C), pages 90-94.
    78. Jin, Xin & Maheu, John M & Yang, Qiao, 2017. "Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices," MPRA Paper 81920, University Library of Munich, Germany.
    79. Adam E Clements & Ayesha Scott & Annastiina Silvennoinen, 2012. "Forecasting multivariate volatility in larger dimensions: some practical issues," NCER Working Paper Series 80, National Centre for Econometric Research.
    80. Libo Yin & Jing Nie & Liyan Han, 2021. "Intermediary capital risk and commodity futures volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(5), pages 577-640, May.
    81. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2022. "Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers," Journal of Econometrics, Elsevier, vol. 227(1), pages 285-304.
    82. Hartkopf, Jan Patrick & Reh, Laura, 2023. "Challenging golden standards in EWMA smoothing parameter calibration based on realized covariance measures," Finance Research Letters, Elsevier, vol. 56(C).
    83. Pawel Janus & André Lucas & Anne Opschoor & Dick J.C. van Dijk, 2014. "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels," Tinbergen Institute Discussion Papers 14-073/IV, Tinbergen Institute, revised 19 Aug 2015.
    84. Nicholas Taylor, 2014. "The Economic Value of Volatility Forecasts: A Conditional Approach," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 433-478.
    85. Mustafayeva, Konul & Wang, Weining, 2020. "Non-Parametric Estimation of Spot Covariance Matrix with High-Frequency Data," IRTG 1792 Discussion Papers 2020-025, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    86. Rim Ammar Lamouchi & Ruba Khalid Shira, 2023. "Heterogeneous Behavior and Volatility Transmission in the Forex Market using High-Frequency Data," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(3), pages 1-3.
    87. Asai Manabu & So Mike K. P., 2023. "Realized BEKK-CAW Models," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 49-77, January.
    88. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "Multiplicative Conditional Correlation Models for Realized Covariance Matrices," LIDAM Discussion Papers CORE 2016041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    89. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Forecasting Realized (Co)Variances with a Bloc Structure Wishart Autoregressive Model," Working Papers on Finance 1211, University of St. Gallen, School of Finance.
    90. Pop, Raluca Elena, 2012. "Herd behavior towards the market index: evidence from Romanian stock exchange," MPRA Paper 51595, University Library of Munich, Germany.
    91. Bauwens, Luc & Dzuverovic, Emilija & Hafner, Christian, 2024. "Asymmetric Models for Realized Covariances," LIDAM Discussion Papers ISBA 2024022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    92. Hung Do & Rabindra Nepal & Russell Smyth, 2020. "Interconnectedness in the Australian national electricity market: A higher moment analysis," CAMA Working Papers 2020-49, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    93. Kleppe, Tore Selland & Liesenfeld, Roman, 2011. "Efficient high-dimensional importance sampling in mixture frameworks," Economics Working Papers 2011-11, Christian-Albrechts-University of Kiel, Department of Economics.
    94. Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
    95. Kleppe, Tore Selland & Liesenfeld, Roman, 2014. "Efficient importance sampling in mixture frameworks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 449-463.
    96. Stefan Lyocsa & Peter Molnar & Igor Fedorko, 2016. "Forecasting Exchange Rate Volatility: The Case of the Czech Republic, Hungary and Poland," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(5), pages 453-475, October.
    97. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
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    106. Andre Lucas & Anne Opschoor & Luca Rossini, 2021. "Tail Heterogeneity for Dynamic Covariance Matrices: the F-Riesz Distribution," Tinbergen Institute Discussion Papers 21-010/III, Tinbergen Institute, revised 11 Jul 2023.
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    114. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
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Articles

  1. Calzolari, Giorgio & Halbleib, Roxana, 2018. "Estimating stable latent factor models by indirect inference," Journal of Econometrics, Elsevier, vol. 205(1), pages 280-301.

    Cited by:

    1. Shuquan Yang & Nengxiang Ling & Yulin Gong, 2022. "Robust estimation of the number of factors for the pair-elliptical factor models," Computational Statistics, Springer, vol. 37(3), pages 1495-1522, July.
    2. Creal, Drew & Koopman, Siem Jan & Lucas, André & Zamojski, Marcin, 2024. "Observation-driven filtering of time-varying parameters using moment conditions," Journal of Econometrics, Elsevier, vol. 238(2).
    3. Cosimo Magazzino & Marco Mele, 2022. "A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(2), pages 289-338, July.
    4. Wan, Runzhe & Li, Yingying & Lu, Wenbin & Song, Rui, 2024. "Mining the factor zoo: Estimation of latent factor models with sufficient proxies," Journal of Econometrics, Elsevier, vol. 239(2).
    5. Giorgio Calzolari & Roxana Halbleib & Christian Mucher, 2023. "Sequential Estimation of Multivariate Factor Stochastic Volatility Models," Papers 2302.07052, arXiv.org.
    6. Yu, Long & He, Yong & Zhang, Xinsheng, 2019. "Robust factor number specification for large-dimensional elliptical factor model," Journal of Multivariate Analysis, Elsevier, vol. 174(C).

  2. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    2. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168222, Verein für Socialpolitik / German Economic Association.
    3. Kevin Sheppard & Wen Xu, 2019. "Factor High-Frequency-Based Volatility (HEAVY) Models," Journal of Financial Econometrics, Oxford University Press, vol. 17(1), pages 33-65.
    4. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.

  3. Calzolari, Giorgio & Halbleib, Roxana & Parrini, Alessandro, 2014. "Estimating GARCH-type models with symmetric stable innovations: Indirect inference versus maximum likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 158-171.
    See citations under working paper version above.
  4. 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.

    Cited by:

    1. Großmaß Lidan, 2014. "Liquidity and the Value at Risk," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(5), pages 572-602, October.
    2. 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.
    3. Bogdan ZUGRAVU & Dumitru Cristian OANEA & Victoria Gabriela ANGHELACHE, 2013. "Analysis Based on the Risk Metrics Model," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(2), pages 145-154, May.
    4. Evangelos Vasileiou, 2022. "Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1155-1171, March.
    5. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    6. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    7. Theo Berger & Jana Koubová, 2024. "Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2904-2916, November.
    8. 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.
    9. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
    10. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    11. Będowska-Sójka, Barbara & Kliber, Agata, 2021. "Information content of liquidity and volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    12. Berger, Theo, 2016. "On the isolated impact of copulas on risk measurement: Asimulation study," Economic Modelling, Elsevier, vol. 58(C), pages 475-481.
    13. Dias, Alexandra, 2013. "Market capitalization and Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5248-5260.
    14. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    15. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
    16. Timo Dimitriadis & Julie Schnaitmann, 2019. "Forecast Encompassing Tests for the Expected Shortfall," Papers 1908.04569, arXiv.org, revised Aug 2020.
    17. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2016. "Measuring risks in the extreme tail: The extreme VaR and its confidence interval," Documents de travail du Centre d'Economie de la Sorbonne 16034rr, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jan 2017.
    18. David Happersberger & Harald Lohre & Ingmar Nolte, 2020. "Estimating portfolio risk for tail risk protection strategies," European Financial Management, European Financial Management Association, vol. 26(4), pages 1107-1146, September.
    19. Dominique Guegan & Bertrand Hassani & Kehan Li, 2017. "Measuring risks in the extreme tail: The extreme VaR and its confidence interval," Post-Print halshs-01317391, HAL.
    20. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    21. Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Adaptive combinations of tail-risk forecasts," Papers 2406.06235, arXiv.org.
    27. Timo Dimitriadis & Sebastian Bayer, 2017. "A Joint Quantile and Expected Shortfall Regression Framework," Papers 1704.02213, arXiv.org, revised Aug 2017.
    28. Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
    29. Javier Población & Gregorio Serna, 2021. "Measuring bulk shipping prices risk," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 291-309, June.

  5. Halbleib Roxana & Voev Valeri, 2011. "Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 134-152, February.
    See citations under working paper version above.
  6. 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.
    See citations under working paper version above.
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