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Dynamic VaR models and the Peaks over Threshold method for market risk measurement: an empirical investigation during a financial crisis

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  • Marco Bee

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  • Fabrizio Miorelli

Abstract

This paper presents a backtesting exercise involving several VaR models for measuring market risk in a dynamic context. The focus is on the comparison of standard dynamic VaR models, ad hoc fat-tailed models and the dynamic Peaks over Threshold (POT) procedure for VaR estimation with different volatility specifications. We introduce three different stochastic processes for the losses: two of them are of the GARCH-type and one is of the EWMA-type. In order to assess the performance of the models, we implement a backtesting procedure using the log-losses of a diversified sample of 15 financial assets. The backtesting analysis covers the period March 2004 - May 2009, thus including the turmoil period corresponding to the subprime crisis. The results show that the POT approach and a Dynamic Historical Simulation method, both combined with the EWMA volatility specification, are particularly effective at high VaR coverage probabilities and outperform the other models under consideration. Moreover, VaR measures estimated with these models react quickly to the turmoil of the last part of the backtesting period, so that they seem to be efficient in high-risk periods as well.

Suggested Citation

  • Marco Bee & Fabrizio Miorelli, 2010. "Dynamic VaR models and the Peaks over Threshold method for market risk measurement: an empirical investigation during a financial crisis," Department of Economics Working Papers 1009, Department of Economics, University of Trento, Italia.
  • Handle: RePEc:trn:utwpde:1009
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    References listed on IDEAS

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    1. Bhattacharyya, Malay & Ritolia, Gopal, 2008. "Conditional VaR using EVT - Towards a planned margin scheme," International Review of Financial Analysis, Elsevier, vol. 17(2), pages 382-395.
    2. Gil-Alana, L.A., 2006. "Fractional integration in daily stock market indexes," Review of Financial Economics, Elsevier, vol. 15(1), pages 28-48.
    3. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2010. "The Use of GARCH Models in VaR Estimation," Working Papers 0048, University of Peloponnese, Department of Economics.
    4. Robert F. Engle & Simone Manganelli, 1999. "CAViaR: Conditional Value at Risk by Quantile Regression," NBER Working Papers 7341, National Bureau of Economic Research, Inc.
    5. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    6. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
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    Cited by:

    1. Song, Wenjuan & Sun, Lixin, 2014. "The Measurement of the Long-Term and Short-Term Risks of Chinese Listed Banks," MPRA Paper 70007, University Library of Munich, Germany, revised Jul 2014.
    2. Mesut BALLIBEY & Serpil TÜRKYILMAZ, 2014. "Value-at-Risk Analysis in the Presence of Asymmetry and Long Memory: The Case of Turkish Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 4(4), pages 836-848.

    More about this item

    Keywords

    Market risk; Extreme Value Theory; Peaks over Threshold; Value at Risk; Fat tails;

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