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Conditional VaR using EVT - Towards a planned margin scheme

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  • Bhattacharyya, Malay
  • Ritolia, Gopal

Abstract

This paper constructs a robust Value-at-Risk (VaR) measure for the Indian stock markets by combining two well-known facts about equity return time series -- dynamic volatility resulting in the well-recognized phenomenon of volatility clustering, and non-normality giving rise to fat tails of the return distribution. While the phenomenon of volatility dynamics has been extensively studied using GARCH model and its many relatives, the application of Extreme Value Theory (EVT) is relatively recent in tracking extreme losses in the study of risk measurement. There are recent applications of Extreme Value Theory to estimate the unexpected losses due to extreme events and hence modify the current methodology of VaR. Extreme value theory (EVT) has been used to analyze financial data showing clear non-normal behavior. We combine the two methodologies to come up with a robust model with much enhanced predictive abilities. A robust model would obviate the need for imposing special ad hoc margins by the regulator in times of extreme volatility. A rule based margin system would increase efficiency of the price discovery process and also the market integrity with the regulator no longer seen as managing volatility.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:finana:v:17:y:2008:i:2:p:382-395
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    References listed on IDEAS

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    1. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    2. 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.
    3. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    4. 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.
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    Cited by:

    1. repec:eee:intfor:v:34:y:2018:i:3:p:497-506 is not listed on IDEAS
    2. 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.
    3. Uribe Gil, Jorge Mario & Ulloa Villegas, Inés Maria, 2012. "La medición del riesgo en eventos extremos. Una revisión metodológica en contexto," REVISTA LECTURAS DE ECONOMÍA, UNIVERSIDAD DE ANTIOQUIA - CIE, June.
    4. Bhattacharyya, Malay & Madhav R, Siddarth, 2012. "A Comparison of VaR Estimation Procedures for Leptokurtic Equity Index Returns," MPRA Paper 54189, University Library of Munich, Germany.

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