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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
  • 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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    Cited by:

    1. 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.
    2. Axel A. Araneda, 2021. "Asset volatility forecasting:The optimal decay parameter in the EWMA model," Papers 2105.14382, arXiv.org.
    3. Mesut BALLIBEY & Serpil T RKYILMAZ, 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.
    4. Nikola Radivojević & Nikola V. Ćurčić & Djurdjica Dj. Vukajlović, 2017. "Hull-White’s value at risk model: case study of Baltic equities market," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(5), pages 1023-1041, September.
    5. 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.

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    Keywords

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