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Ranking of VaR and ES Models: Performance in Developed and Emerging Markets

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Abstract

There is an inherent problem with comparing and ranking competing Value at Risk (VaR) and Expected shortfall (ES) models since we are measuring only a single realization of the underlying data generation process. The question is whether there is any significant statistical difference in the performance of different models. It all comes down to whether something that we subjectively perceive as different is actually statistically different. We introduce a new methodology for ranking the performance of VaR and ES models based on a nonparametric test. The relative performance of models is analysed using returns for sixteen stock market indices (eight each from developed and emerging markets) prior to and during the global financial crisis. Results show that for a large number of models there is no statistically significant difference. The top performers are conditional extreme value GARCH model and models based on volatility updating. ES results are similar to VaR results with the models being even more closely matched. The same models that were the top performers in VaR comparison also perform significantly better in ES estimation.

Suggested Citation

  • Saša ŽIKOVIÆ & Randall K. FILER, 2013. "Ranking of VaR and ES Models: Performance in Developed and Emerging Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(4), pages 327-359, August.
  • Handle: RePEc:fau:fauart:v:63:y:2013:i:4:p:327-359
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    1. Zikovic, Sasa & Aktan, Bora, 2011. "Decay factor optimisation in time weighted simulation -- Evaluating VaR performance," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1147-1159, October.
    2. Gencay, Ramazan & Selcuk, Faruk & Ulugulyagci, Abdurrahman, 2003. "High volatility, thick tails and extreme value theory in value-at-risk estimation," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 337-356, October.
    3. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    4. West, Kenneth D, 2001. "Tests for Forecast Encompassing When Forecasts Depend on Estimated Regression Parameters," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 29-33, January.
    5. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    6. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    7. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    8. Timotheos Angelidis & Stavros Degiannakis, 2007. "Backtesting VaR Models: An Expected Shortfall Approach," Working Papers 0701, University of Crete, Department of Economics.
    9. Song Xi Chen, 2008. "Nonparametric Estimation of Expected Shortfall," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(1), pages 87-107, Winter.
    10. Gencay, Ramazan & Selcuk, Faruk, 2004. "Extreme value theory and Value-at-Risk: Relative performance in emerging markets," International Journal of Forecasting, Elsevier, vol. 20(2), pages 287-303.
    11. Yamai, Yasuhiro & Yoshiba, Toshinao, 2005. "Value-at-risk versus expected shortfall: A practical perspective," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 997-1015, April.
    12. Mc Cracken, Michael W., 2000. "Robust out-of-sample inference," Journal of Econometrics, Elsevier, vol. 99(2), pages 195-223, December.
    13. Sullivan, Ryan & Timmermann, Allan & White, Halbert, 2003. "Forecast evaluation with shared data sets," International Journal of Forecasting, Elsevier, vol. 19(2), pages 217-227.
    14. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    15. Aktham I. Maghyereh & Haitham A. Al-Zoubi, 2006. "Value-at-risk under extreme values: the relative performance in MENA emerging stock markets," International Journal of Managerial Finance, Emerald Group Publishing, vol. 2(2), pages 154-172, July.
    16. John Cotter, 2004. "Downside risk for European equity markets," Applied Financial Economics, Taylor & Francis Journals, vol. 14(10), pages 707-716.
    17. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
    18. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    19. Inui, Koji & Kijima, Masaaki, 2005. "On the significance of expected shortfall as a coherent risk measure," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 853-864, April.
    20. David Harvey & Paul Newbold, 2000. "Tests for multiple forecast encompassing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 471-482.
    21. West, Kenneth D & McCracken, Michael W, 1998. "Regression-Based Tests of Predictive Ability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 817-840, November.
    22. Imre Kondor & Istvan Varga-Haszonits, 2008. "Feasibility of Portfolio Optimization under Coherent Risk Measures," Papers 0803.2283, arXiv.org, revised Apr 2008.
    23. Cotter, John, 2007. "Extreme risk in Asian equity markets," MPRA Paper 3536, University Library of Munich, Germany.
    24. Song Xi Chen, 2005. "Nonparametric Inference of Value-at-Risk for Dependent Financial Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(2), pages 227-255.
    25. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    26. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin: The Journal of the International Actuarial Association, Cambridge University Press, vol. 27(01), pages 117-137, May.
    27. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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    Cited by:

    1. Chaker Aloui & Hela BEN HAMIDA, 2015. "Estimation and Performance Assessment of Value-at-Risk and Expected Shortfall Based on Long-Memory GARCH-Class Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(1), pages 30-54, January.
    2. Sasa Zikovic & Rafal Weron & Ivana Tomas Zikovic, 2014. "Evaluating the performance of VaR models in energy markets," HSC Research Reports HSC/14/12, Hugo Steinhaus Center, Wroclaw University of Technology.

    More about this item

    Keywords

    ranking; Value at Risk; Expected shortfall; extreme value theory;

    JEL classification:

    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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