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Burak Saltoğlu
(Burak Saltoglu)

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.

    Mentioned in:

    1. MIDAS Regression is Now in EViews
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2016-03-26 00:30:00

Working papers

  1. Mehmet Y. Gürdal & Tolga U. Kuzubaþ & Burak Saltoðlu, 2016. "Measures of Individual Risk Attitudes and Portfolio Choice: Evidence from Pension Participants," Working Papers 2016/02, Bogazici University, Department of Economics.

    Cited by:

    1. Hiroyuki Yamada & Yuki Kanayama & Kanako Yoshikawa & Kyaw Wai Aung, 2023. "Risk attitude, risky behaviour and price determination in the sex market: A case study of Yangon, Myanmar," Pacific Economic Review, Wiley Blackwell, vol. 28(5), pages 665-691, December.
    2. Krčál, Ondřej & Staněk, Rostislav & Slanicay, Martin, 2019. "Made for the job or by the job? A lab-in-the-field experiment with firefighters," Research in Economics, Elsevier, vol. 73(4), pages 271-276.
    3. Shumiao Ouyang & Hayong Yun & Xingjian Zheng, 2024. "How Ethical Should AI Be? How AI Alignment Shapes the Risk Preferences of LLMs," Papers 2406.01168, arXiv.org, revised Aug 2024.
    4. Safdar Ullah Khan & Satyanarayana Ramella & Habib Ur Rahman & Zulfiqar Hyder, 2022. "Household Portfolio Allocations: Evidence on Risk Preferences from the Household, Income, and Labour Dynamics in Australia (HILDA) Survey Using Tobit Models," JRFM, MDPI, vol. 15(4), pages 1-13, April.
    5. Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024. "Survey-based measures of risk attitudes and portfolio risk: Evidence from pension participants," Journal of Behavioral and Experimental Finance, Elsevier, vol. 43(C).
    6. Blake, David & Duffield, Mel & Tonks, Ian & Haig, Alistair & Blower, Dean & MacPhee, Laura, 2022. "Smart defaults: Determining the number of default funds in a pension scheme," The British Accounting Review, Elsevier, vol. 54(4).

  2. Tolga Umut Kuzubas & Burak Saltoglu & Can Sever, 2014. "Systemic Risk and Heterogeneous Leverage in Banking Network: Implications for Banking Regulation," Working Papers 2014/01, Bogazici University, Department of Economics.

    Cited by:

    1. De Caux, Robert & McGroarty, Frank & Brede, Markus, 2017. "The evolution of risk and bailout strategy in banking systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 109-118.

  3. Tolga Umut Kuzubas & Inci Omercikoglu & Burak Saltoglu, 2013. "Network Centrality Measures and Systemic Risk: An Application to the Turkish Financial Crisis," Working Papers 2013/12, Bogazici University, Department of Economics.

    Cited by:

    1. Michel Alexandre & Kau^e Lopes de Moraes & Francisco Aparecido Rodrigues, 2021. "Risk-dependent centrality in the Brazilian stock market," Papers 2103.09059, arXiv.org.
    2. Thiago Christiano Silva & Sergio Rubens Stancato de Souza & Benjamin Miranda Tabak, 2016. "Structure and Dynamics of the Global Financial Network," Working Papers Series 439, Central Bank of Brazil, Research Department.
    3. Solange Maria Guerra & Benjamin Miranda Tabak & Rodrigo Andrés De Souza Penaloza & Rodrigo César De Castro Mirand, 2014. "Systemic Risk Measures," Anais do XLI Encontro Nacional de Economia [Proceedings of the 41st Brazilian Economics Meeting] 124, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    4. Baumöhl, Eduard & Kočenda, Evžen & Lyócsa, Štefan & Výrost, Tomáš, 2018. "Networks of volatility spillovers among stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1555-1574.
    5. Alexandre, Michel & Silva, Thiago Christiano & Tabak, Benjamin Miranda, 2024. "The labor market channel of systemic risk," Journal of Economic Dynamics and Control, Elsevier, vol. 168(C).
    6. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2019. "Financial systemic risk measurement based on causal network connectedness analysis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 290-307.
    7. Song, Jae Wook & Ko, Bonggyun & Cho, Poongjin & Chang, Woojin, 2016. "Time-varying causal network of the Korean financial system based on firm-specific risk premiums," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 287-302.
    8. Xu, Qifa & Li, Mengting & Jiang, Cuixia & He, Yaoyao, 2019. "Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    9. Kuzubaş, Tolga Umut & Saltoğlu, Burak & Sever, Can, 2016. "Systemic risk and heterogeneous leverage in banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 358-375.
    10. Andrea Barón & María Victoria Landaberry & Rodrigo Lluberas & Jorge Ponce, 2020. "Commercial and banking credit network in Uruguay," Documentos de trabajo 2020006, Banco Central del Uruguay.
    11. Yun, Tae-Sub & Jeong, Deokjong & Park, Sunyoung, 2019. "“Too central to fail” systemic risk measure using PageRank algorithm," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 251-272.
    12. Onur Polat, 2021. "Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 7(2), pages 47-59, December.
    13. Lai, Yujie & Hu, Yibo, 2021. "A study of systemic risk of global stock markets under COVID-19 based on complex financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    14. Silva, Thiago Christiano & Guerra, Solange Maria & Tabak, Benjamin Miranda & de Castro Miranda, Rodrigo Cesar, 2016. "Financial networks, bank efficiency and risk-taking," Journal of Financial Stability, Elsevier, vol. 25(C), pages 247-257.
    15. Michel Alexandre & Thiago Christiano Silva & Francisco Aparecido Rodrigues, 2024. "Critical Edges in Financial Networks," Working Papers Series 594, Central Bank of Brazil, Research Department.
    16. Michel Alexandre & Felipe Jordão Xavier & Thiago Christiano Silva & Francisco A. Rodrigues, 2022. "Nestedness in the Brazilian Financial System," Working Papers Series 566, Central Bank of Brazil, Research Department.
    17. Giulia Masi & Giorgio Ricchiuti, 2020. "From FDI network topology to macroeconomic instability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 133-158, January.
    18. Silva, Thiago Christiano & Dias, Felipe A.M. & dos Reis, Vinicius E. & Tabak, Benjamin M., 2022. "The role of network topology in competition and ticket pricing in air transportation: Evidence from Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 601(C).
    19. Barroso, João Barata Ribeiro Blanco & Silva, Thiago Christiano & Souza, Sergio Rubens Stancato de, 2018. "Identifying systemic risk drivers in financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 650-674.
    20. Michel Alexandre & Thiago Christiano Silva & Colm Connaughton & Francisco A. Rodrigues, 2021. "The Role of (non-)Topological Features as Drivers of Systemic Risk: a machine learning approach," Working Papers Series 556, Central Bank of Brazil, Research Department.
    21. Seabrook, Isobel & Caccioli, Fabio & Aste, Tomaso, 2022. "Quantifying impact and response in markets using information filtering networks," LSE Research Online Documents on Economics 115308, London School of Economics and Political Science, LSE Library.
    22. Alexandre, Michel & Silva, Thiago Christiano & Connaughton, Colm & Rodrigues, Francisco A., 2021. "The drivers of systemic risk in financial networks: a data-driven machine learning analysis," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    23. Deev, Oleg & Lyócsa, Štefan, 2020. "Connectedness of financial institutions in Europe: A network approach across quantiles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    24. Qin, Xiao & Wang, Ze, 2023. "Share pledge financing network and systemic risks: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 152(C).
    25. Yao, Yanzhen & Li, Jianping & Zhu, Xiaoqian & Wei, Lu, 2017. "Expected default based score for identifying systemically important banks," Economic Modelling, Elsevier, vol. 64(C), pages 589-600.
    26. Hossein Dastkhan & Naser Shams Gharneh, 2016. "Determination of Systemically Important Companies with Cross-Shareholding Network Analysis: A Case Study from an Emerging Market," IJFS, MDPI, vol. 4(3), pages 1-17, June.
    27. Saidane, Dhafer & Sène, Babacar & Désiré Kanga, Kouamé, 2021. "Pan-African banks, banking interconnectivity: A new systemic risk measure in the WAEMU," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    28. Fiedor, Paweł, 2014. "Sector strength and efficiency on developed and emerging financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 180-188.
    29. Nasirian, Farzaneh & Mahdavi Pajouh, Foad & Balasundaram, Balabhaskar, 2020. "Detecting a most closeness-central clique in complex networks," European Journal of Operational Research, Elsevier, vol. 283(2), pages 461-475.
    30. Frank Emmert-Streib & Aliyu Musa & Kestutis Baltakys & Juho Kanniainen & Shailesh Tripathi & Olli Yli-Harja & Herbert Jodlbauer & Matthias Dehmer, 2017. "Computational Analysis of the structural properties of Economic and Financial Networks," Papers 1710.04455, arXiv.org.
    31. Ardekani, Aref Mahdavi & Distinguin, Isabelle & Tarazi, Amine, 2020. "Do banks change their liquidity ratios based on network characteristics?," European Journal of Operational Research, Elsevier, vol. 285(2), pages 789-803.

  4. Saltoglu, Burak & Yenilmez, Taylan, 2010. "Analyzing Systemic Risk with Financial Networks An Application During a Financial Crash," MPRA Paper 26684, University Library of Munich, Germany.

    Cited by:

    1. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    2. Martinez-Jaramillo, Serafin & Alexandrova-Kabadjova, Biliana & Bravo-Benitez, Bernardo & Solórzano-Margain, Juan Pablo, 2014. "An empirical study of the Mexican banking system’s network and its implications for systemic risk," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 242-265.
    3. Tolga Umut Kuzubas & Burak Saltoglu & Can Sever, 2014. "Systemic Risk and Heterogeneous Leverage in Banking Network: Implications for Banking Regulation," Working Papers 2014/01, Bogazici University, Department of Economics.

  5. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.

    Cited by:

    1. Bharat Kumar Meher & Iqbal Thonse Hawaldar & Mathew Thomas Gil & Deebom Zorle Dum, 2021. "Measuring Leverage Effect of Covid 19 on Stock Price Volatility of Energy Companies Using High Frequency Data," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 489-502.
    2. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
    3. Kumar SANTOSH & Meher Kumar BHARAT & Ramona BIRAU & Mircea Laurentiu SIMION & Anand ABHISHEK & Singh MANOHAR, 2023. "Quantifying Long-Term Volatility for Developed Stock Markets: An Empirical Case Study Using PGARCH Model on Toronto Stock Exchange (TSX)," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-68.

  6. Saltoglu, Burak & Yazgan, Ege, 2009. "The role of Regime Shifts in the Term Structure of Interest Rates: Further evidence from an Emerging Market," MPRA Paper 18741, University Library of Munich, Germany.

    Cited by:

    1. Kannan S. Thuraisamy, 2015. "Volatility Dynamics in the Term Structure of Latin American Sovereign International Bonds," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(5), pages 859-866, September.
    2. Kang, Bo Soo & Ryu, Doojin & Ryu, Doowon, 2014. "Phase-shifting behaviour revisited: An alternative measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 167-173.

  7. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.

    Cited by:

    1. 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.
    2. Afees A. Salisu & Umar B. Ndako & Idris Adediran, 2018. "Forecasting GDP of OPEC: The role of oil price," Working Papers 044, Centre for Econometric and Allied Research, University of Ibadan.
    3. Yunxu Wang & Chi-Wei Su & Yuchen Zhang & Oana-Ramona Lobonţ & Qin Meng, 2023. "Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product," Mathematics, MDPI, vol. 11(19), pages 1-14, September.
    4. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    5. Biswas, Anindya, 2014. "The output gap and expected security returns," Review of Financial Economics, Elsevier, vol. 23(3), pages 131-140.
    6. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    7. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    8. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Improving the Predictive ability of oil for inflation: An ADL-MIDAS Approach," Working Papers 025, Centre for Econometric and Allied Research, University of Ibadan.
    9. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
    10. Jennie Bai & Eric Ghysels & Jonathan H. Wright, 2013. "State Space Models and MIDAS Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 32(7), pages 779-813, October.
    11. Chan-Guk Huh & Jie Wu, 2015. "Linkage between US monetary policy and emerging economies: the case of Korea?s financial market and monetary policy," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 4(3), pages 1-18, September.
    12. Julián Alonso Cárdenas-Cárdenas & Edgar Caicedo-García & Eliana R. González Molano, 2020. "Estimación de la variación del precio de los alimentos con modelos de frecuencias mixtas," Borradores de Economia 1109, Banco de la Republica de Colombia.
    13. Anindya Biswas, 2014. "The output gap and expected security returns," Review of Financial Economics, John Wiley & Sons, vol. 23(3), pages 131-140, September.
    14. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    15. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    16. Asgharian, Hossein & Hou, Ai Jun & Javed, Farrukh, 2013. "Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach," Knut Wicksell Working Paper Series 2013/4, Lund University, Knut Wicksell Centre for Financial Studies.

  8. Danielsson, Jon & Saltoglu, Burak, 2003. "Anatomy of a market crash: a market microstructure analysis of the Turkish overnight liquidity crisis," LSE Research Online Documents on Economics 24855, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    2. Başak Tanyeri, 2010. "Financial Transparency and Sources of Hidden Capital in Turkish Banks," Journal of Financial Services Research, Springer;Western Finance Association, vol. 37(1), pages 25-43, February.
    3. Saltoglu, Burak & Yenilmez, Taylan, 2010. "Analyzing Systemic Risk with Financial Networks An Application During a Financial Crash," MPRA Paper 26684, University Library of Munich, Germany.
    4. Nikola Gradojevic, 2007. "A market microstructure analysis of the Canadian dollar depreciation episodes in the 1990s," Applied Financial Economics, Taylor & Francis Journals, vol. 17(17), pages 1377-1387.
    5. Kathy Yuan & Emre Ozdenoren & Itay Goldstein, 2008. "Learning and Complementarities: Implications for Speculative Attacks," 2008 Meeting Papers 276, Society for Economic Dynamics.
    6. Burak Saltoğlu, 2013. "Turkish Banking Sector Current Status and the Future Challenges," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 41(1), pages 75-86, March.
    7. Andrea Eross & Andrew Urquhart & Simon Wolfe, 2019. "Investigating risk contagion initiated by endogenous liquidity shocks: evidence from the US and eurozone interbank markets," The European Journal of Finance, Taylor & Francis Journals, vol. 25(1), pages 35-53, January.
    8. Eross, Andrea & Urquhart, Andrew & Wolfe, Simon, 2016. "Liquidity risk contagion in the interbank market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 45(C), pages 142-155.
    9. Raphael Solomon, 2004. "When Bad Things Happen to Good Banks: Contagious Bank Runs and Currency Crises," Staff Working Papers 04-18, Bank of Canada.

Articles

  1. Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024. "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, vol. 67(PB).

    Cited by:

    1. Dongyan Fan & Sicen Lai & Hai Sun & Yuqing Yang & Can Yang & Nianyang Fan & Minhui Wang, 2025. "Review of Machine Learning Methods for Steady State Capacity and Transient Production Forecasting in Oil and Gas Reservoir," Energies, MDPI, vol. 18(4), pages 1-25, February.

  2. Gürdal, Mehmet Y. & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2017. "Measures of individual risk attitudes and portfolio choice: Evidence from pension participants," Journal of Economic Psychology, Elsevier, vol. 62(C), pages 186-203.
    See citations under working paper version above.
  3. Kuzubaş, Tolga Umut & Saltoğlu, Burak & Sever, Can, 2016. "Systemic risk and heterogeneous leverage in banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 358-375.

    Cited by:

    1. Hikmet Akyol & Selim Basar, 2024. "Empirical Analysis of Turkish Banking Sector Institutional and Macroeconomic Determinants of Risks," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 73(74-1), pages 59-98, June.
    2. Morteza Alaeddini & Philippe Madiès & Paul J. Reaidy & Julie Dugdale, 2023. "Interbank money market concerns and actors’ strategies—A systematic review of 21st century literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 573-654, April.
    3. Jiang, Shanshan & Fan, Hong, 2018. "Credit risk contagion coupling with sentiment contagion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 186-202.
    4. Aida Barkauskaite & Ausrine Lakstutiene & Justyna Witkowska, 2018. "Measurement of Systemic Risk in a Common European Union Risk-Based Deposit Insurance System: Formal Necessity or Value-Adding Process?," Risks, MDPI, vol. 6(4), pages 1-21, December.
    5. Atasoy, Burak Sencer & Özkan, İbrahim & Erden, Lütfi, 2024. "The determinants of systemic risk contagion," Economic Modelling, Elsevier, vol. 130(C).

  4. Ahmet Faruk Aysan & Huseyin Ozturk & Ali Yavuz Polat & Burak Saltoğlu, 2016. "Macroeconomic Drivers of Loan Quality in Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(1), pages 98-109, January.

    Cited by:

    1. Segun Thompson Bolarinwa & Olawale Akinyele & Xuan Vinh Vo, 2021. "Determinants of nonperforming loans after recapitalization in the Nigerian banking industry: Does efficiency matter?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1509-1524, September.
    2. Ahmet Faruk Aysan & Dilek Demirbaş & José Luis Alberto Delgado, 2022. "Old But Resilient Story: Impact Of Decentralization On Social Welfare," Working Papers hal-03866662, HAL.
    3. Alberto Delgado, José Luis & Demirbaş, Dilek & Aysan, Ahmet Faruk, 2022. "Old But Resilient Story: Impact Of Decentralization On Social Welfare," MPRA Paper 115432, University Library of Munich, Germany.

  5. Burak Saltoglu & Taylan Yenilmez, 2015. "When does low interconnectivity cause systemic risk?," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1933-1942, December.

    Cited by:

    1. Cem Iskender Aydin & Begum Ozkaynak & Beatriz Rodríguez-Labajos & Taylan Yenilmez, 2017. "Network effects in environmental justice struggles: An investigation of conflicts between mining companies and civil society organizations from a network perspective," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-20, July.
    2. Kuzubaş, Tolga Umut & Saltoğlu, Burak & Sever, Can, 2016. "Systemic risk and heterogeneous leverage in banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 358-375.

  6. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    See citations under working paper version above.
  7. Burak Saltoğlu, 2013. "Turkish Banking Sector Current Status and the Future Challenges," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 41(1), pages 75-86, March.

    Cited by:

    1. Simone Auer & Emidio Cocozza & Andrea COlabella, 2016. "The financial systems in Russia and Turkey: recent developments and challenges," Questioni di Economia e Finanza (Occasional Papers) 358, Bank of Italy, Economic Research and International Relations Area.

  8. Burak Saltoglu & M. Ege Yazgan, 2012. "The Role of Regime Shifts in the Term Structure of Interest Rates: Further Evidence from an Emerging Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(S5), pages 48-63, November.
    See citations under working paper version above.
  9. Emre Alper, C. & Fendoglu, Salih & Saltoglu, Burak, 2012. "MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets," Economics Letters, Elsevier, vol. 117(2), pages 528-532.

    Cited by:

    1. Yan, Xiang & Bai, Jiancheng & Li, Xiafei & Chen, Zhonglu, 2022. "Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?," Resources Policy, Elsevier, vol. 75(C).
    2. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Wang, Jianqiong, 2020. "Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models," Energy, Elsevier, vol. 212(C).
    3. Alper Gormus, N., 2016. "Do different time-horizons in volatility have any significance for the emerging markets?," Economics Letters, Elsevier, vol. 145(C), pages 29-32.
    4. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
    5. Liu, Min, 2022. "The driving forces of green bond market volatility and the response of the market to the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 288-309.
    6. Murat Körs & Mehmet Baha Karan, 2023. "Stock exchange volatility forecasting under market stress with MIDAS regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 295-306, January.
    7. Xinjie Lu & Feng Ma & Jiqian Wang & Jing Liu, 2022. "Forecasting oil futures realized range‐based volatility with jumps, leverage effect, and regime switching: New evidence from MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 853-868, July.
    8. Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022. "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, vol. 108(C).
    9. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    10. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.

  10. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003;," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.

    Cited by:

    1. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.
    2. Francesco Ravazzolo & Shaun P. Vahey, 2010. "Forecast densities for economic aggregates from disaggregate ensembles," Working Paper 2010/02, Norges Bank.
    3. 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.
    4. John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics.
    5. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Post-Print hal-00732675, HAL.
    6. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    7. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Post-Print hal-00834423, HAL.
    8. Rompolis, Leonidas S., 2010. "Retrieving risk neutral densities from European option prices based on the principle of maximum entropy," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 918-937, December.
    9. Dr. James Mitchell, 2009. "Measuring Output Gap Uncertainty," National Institute of Economic and Social Research (NIESR) Discussion Papers 342, National Institute of Economic and Social Research.
    10. Francesco Ravazzolo & Shaun P Vahey, 2010. "Measuring Core Inflation in Australia with Disaggregate Ensembles," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    11. Del Brio, Esther B. & Ñíguez, Trino-Manuel & Perote, Javier, 2011. "Multivariate semi-nonparametric distributions with dynamic conditional correlations," International Journal of Forecasting, Elsevier, vol. 27(2), pages 347-364.
    12. Cheng, Xixin & Li, W.K. & Yu, Philip L.H. & Zhou, Xuan & Wang, Chao & Lo, P.H., 2011. "Modeling threshold conditional heteroscedasticity with regime-dependent skewness and kurtosis," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2590-2604, September.
    13. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    14. Li, Yushu & Andersson, Jonas, 2014. "A Likelihood Ratio and Markov Chain Based Method to Evaluate Density Forecasting," Discussion Papers 2014/12, Norwegian School of Economics, Department of Business and Management Science.
    15. 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.
    16. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    17. Kyungchul Song, 2009. "Testing Predictive Ability and Power Robustification," PIER Working Paper Archive 09-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  11. 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.

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    1. Sofiane Aboura, 2014. "When the U.S. Stock Market Becomes Extreme?," Risks, MDPI, vol. 2(2), pages 1-15, May.
    2. Jian Zhou, 2013. "Extreme risk spillover among international REIT markets," Applied Financial Economics, Taylor & Francis Journals, vol. 23(2), pages 91-103, January.
    3. 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.
    4. Olivier Ledoit & Pedro Santa-Clara & Michael Wolf, 2003. "Flexible Multivariate GARCH Modeling with an Application to International Stock Markets," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 735-747, August.
    5. Ergün, A. Tolga & Jun, Jongbyung, 2010. "Time-varying higher-order conditional moments and forecasting intraday VaR and Expected Shortfall," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 264-272, August.
    6. Tchamyou, Vanessa & Asongu, Simplice, 2017. "Conditional Market Timing in the Mutual Fund Industry," MPRA Paper 82633, University Library of Munich, Germany.
    7. Christina Ziegler, 2009. "Testing Predicitive Ability of Business Cycle Indicators for the Euro Area," ifo Working Paper Series 69, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    8. 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.
    9. Szubzda Filip & Chlebus Marcin, 2019. "Comparison of Block Maxima and Peaks Over Threshold Value-at-Risk models for market risk in various economic conditions," Central European Economic Journal, Sciendo, vol. 6(53), pages 70-85, January.
    10. 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.
    11. 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.
    12. 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.
    13. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2021. "Bayesian estimation for a semiparametric nonlinear volatility model," Economic Modelling, Elsevier, vol. 98(C), pages 361-370.
    14. Shuowen Hu & D.S. Poskitt & Xibin Zhang, 2010. "Bayesian Adaptive Bandwidth Kernel Density Estimation of Irregular Multivariate Distributions," Monash Econometrics and Business Statistics Working Papers 21/10, Monash University, Department of Econometrics and Business Statistics.
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    41. Cao, Guangxi & Zhang, Minjia, 2015. "Extreme values in the Chinese and American stock markets based on detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 25-35.
    42. Santosh Mishra & Gloria Gonzalez-Rivera & Tae-Hwy Lee, 2004. "Jumps in Rank and Expected Returns. Introducing Varying Cross-sectional Risk," Econometric Society 2004 North American Winter Meetings 356, Econometric Society.
    43. 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.
    44. Bekiros, Stelios D. & Georgoutsos, Dimitris A., 2005. "Estimation of Value-at-Risk by extreme value and conventional methods: a comparative evaluation of their predictive performance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(3), pages 209-228, July.
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    46. 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.
    47. Mateusz Buczyński & Marcin Chlebus, 2019. "Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states," Working Papers 2019-12, Faculty of Economic Sciences, University of Warsaw.
    48. 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.
    49. Auerbach, Jonathan & Wan, Phyllis, 2020. "Forecasting the urban skyline with extreme value theory," International Journal of Forecasting, Elsevier, vol. 36(3), pages 814-828.
    50. 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.
    51. Abad, Pilar & Benito, Sonia, 2013. "A detailed comparison of value at risk estimates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 258-276.
    52. 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.
    53. Reber, Beat, 2017. "Does mispricing, liquidity or third-party certification contribute to IPO downside risk?," International Review of Financial Analysis, Elsevier, vol. 51(C), pages 25-53.
    54. Laura Garcia-Jorcano & Alfonso Novales, 2020. "A dominance approach for comparing the performance of VaR forecasting models," Computational Statistics, Springer, vol. 35(3), pages 1411-1448, September.
    55. 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.).
    56. 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.
    57. 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.
    58. Bucevska Vesna, 2013. "An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange," Business Systems Research, Sciendo, vol. 4(1), pages 49-64, March.
    59. Bekiros, Stelios D. & Georgoutsos, Dimitris A., 2008. "The extreme-value dependence of Asia-Pacific equity markets," Journal of Multinational Financial Management, Elsevier, vol. 18(3), pages 197-208, July.
    60. Hu, Yang & Lang, Chunlin & Corbet, Shaen & Hou, Yang (Greg) & Oxley, Les, 2023. "Exploring the dynamic behaviour of commodity market tail risk connectedness during the negative WTI pricing event," Energy Economics, Elsevier, vol. 125(C).
    61. Sang Hoon Kang & Seong-Min Yoon, 2009. "Value-at-Risk Analysis for Asian Emerging Markets: Asymmetry and Fat Tails in Returns Innovation," Korean Economic Review, Korean Economic Association, vol. 25, pages 387-411.
    62. 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.
    63. Filippo Curti & Marco Migueis, 2016. "Predicting Operational Loss Exposure Using Past Losses," Finance and Economics Discussion Series 2016-2, Board of Governors of the Federal Reserve System (U.S.).
    64. 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.
    65. Kiani, Khurshid M., 2011. "Relationship between portfolio diversification and value at risk: Empirical evidence," Emerging Markets Review, Elsevier, vol. 12(4), pages 443-459.
    66. Maghyereh Aktham Issa & Awartani Basel, 2012. "Modeling and Forecasting Value-at-Risk in the UAE Stock Markets: The Role of Long Memory, Fat Tails and Asymmetries in Return Innovations," Review of Middle East Economics and Finance, De Gruyter, vol. 8(1), pages 1-22, August.
    67. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    68. 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.
    69. Degiannakis, Stavros & Floros, Christos & Livada, Alexandra, 2012. "Evaluating Value-at-Risk Models before and after the Financial Crisis of 2008: International Evidence," MPRA Paper 80463, University Library of Munich, Germany.
    70. Wang, David Han-Min & Yu, Tiffany Hui-Kuang & Liu, Hong-Quan, 2013. "Heterogeneous effect of high-tech industrial R&D spending on economic growth," Journal of Business Research, Elsevier, vol. 66(10), pages 1990-1993.
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    72. Audrino, Francesco, 2006. "The impact of general non-parametric volatility functions in multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3032-3052, July.
    73. Alex YiHou Huang, 2009. "A value-at-risk approach with kernel estimator," Applied Financial Economics, Taylor & Francis Journals, vol. 19(5), pages 379-395.
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  12. Nowman, K. Ben & Saltoglu, Burak, 2003. "Continuous time and nonparametric modelling of U.S. interest rate models," International Review of Financial Analysis, Elsevier, vol. 12(1), pages 25-34.

    Cited by:

    1. Zi‐Yi Guo, 2021. "Out‐of‐sample performance of bias‐corrected estimators for diffusion processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 243-268, March.
    2. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
    3. Nowman, Khalid Ben, 2010. "Modelling the UK and Euro yield curves using the Generalized Vasicek model: Empirical results from panel data for one and two factor models," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 334-341, December.
    4. Charlotte Christiansen, 2007. "Level-ARCH Short Rate Models with Regime Switching: Bivariate Modeling of US and European Short Rates," CREATES Research Papers 2007-05, Department of Economics and Business Economics, Aarhus University.
    5. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    6. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00511979, HAL.
    7. K. Ben Nowman & Burak Saltoglu, 2003. "An empirical comparison of interest rates using an interest rate model and nonparametric methods," Applied Economics Letters, Taylor & Francis Journals, vol. 10(10), pages 643-645.
    8. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," PSE-Ecole d'économie de Paris (Postprint) halshs-00511979, HAL.
    9. Dominique Guegan & Patrick Rakotomarolahy, 2009. "The Multivariate k-Nearest Neighbor Model for Dependent Variables : One-Sided Estimation and Forecasting," Post-Print halshs-00423871, HAL.
    10. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00505165, HAL.

  13. Burak Saltoglu, 2003. "Comparing forecasting ability of parametric and non-parametric methods: an application with Canadian monthly interest rates," Applied Financial Economics, Taylor & Francis Journals, vol. 13(3), pages 169-176.

    Cited by:

    1. Degiannakis, Stavros & Xekalaki, Evdokia, 2007. "Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models," MPRA Paper 96324, University Library of Munich, Germany.
    2. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    3. K. Ben Nowman & Burak Saltoglu, 2003. "An empirical comparison of interest rates using an interest rate model and nonparametric methods," Applied Economics Letters, Taylor & Francis Journals, vol. 10(10), pages 643-645.
    4. Sutthisit Jamdee & Cornelis A. Los, 2005. "Multifractal Modeling of the US Treasury Term Structure and Fed Funds Rate," Finance 0502021, University Library of Munich, Germany.

  14. 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.

    Cited by:

    1. 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.
    2. He, Kaijian & Lai, Kin Keung & Yen, Jerome, 2011. "Value-at-risk estimation of crude oil price using MCA based transient risk modeling approach," Energy Economics, Elsevier, vol. 33(5), pages 903-911, September.
    3. Stephanos Papadamou & George Stephanides, 2004. "Evaluating the style-based risk model for equity mutual funds investing in Europe," Applied Financial Economics, Taylor & Francis Journals, vol. 14(10), pages 751-760.

  15. Burc Kayahan & Thanasis Stengos & Burak Saltoglu, 2002. "Intra-Day Features of Realized Volatility: Evidence from an Emerging Market," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(1), pages 17-24, April.

    Cited by:

    1. Xekalaki, Evdokia & Degiannakis, Stavros, 2005. "Evaluating volatility forecasts in option pricing in the context of a simulated options market," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 611-629, April.
    2. Degiannakis, Stavros, 2004. "Volatility Forecasting: Evidence from a Fractional Integrated Asymmetric Power ARCH Skewed-t Model," MPRA Paper 96330, University Library of Munich, Germany.
    3. Georgios Chortareas & John Nankervis & Ying Jiang, 2007. "Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?," Money Macro and Finance (MMF) Research Group Conference 2006 79, Money Macro and Finance Research Group.
    4. Degiannakis, Stavros, 2004. "Forecasting Realized Intra-day Volatility and Value at Risk: Evidence from a Fractional Integrated Asymmetric Power ARCH Skewed-t Model," MPRA Paper 80488, University Library of Munich, Germany.
    5. Stavros Degiannakis, 2008. "ARFIMAX and ARFIMAX-TARCH realized volatility modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1169-1180.
    6. Balaban, Ercan & Ozgen, Tolga & Karidis, Socrates, 2018. "Intraday and interday distribution of stock returns and their asymmetric conditional volatility: Firm-level evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 905-915.
    7. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    8. Balaban, Ercan & Ozgen, Tolga, 2016. "Trading session effects on stock returns and their conditional volatility: Firm-level evidence from a European Union accession country," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 264-271.

  16. Burak Saltoglu, 1998. "Speed of adjustment to the long-run equilibrium: an application with US Stock Price and Dividend data," Applied Financial Economics, Taylor & Francis Journals, vol. 8(4), pages 367-375.

    Cited by:

    1. Bohl, Martin T., 2003. "Periodically collapsing bubbles in the US stock market?," International Review of Economics & Finance, Elsevier, vol. 12(3), pages 385-397.
    2. Fredj Jawadi, 2008. "Estimating The S&P Fundamental Value Using Star Models," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 2(1), pages 137-146.

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