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Performance Analysis of VaR and ES Based on Extreme Value Theory
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 Title & Authors
Performance Analysis of VaR and ES Based on Extreme Value Theory
Yeo, Sung-Chil;
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Extreme value theory has been used widely in many areas of science and engineering to deal with the assessment of extreme events which are rare but have catastrophic consequences. The potential of extreme value theory has only been recognized recently in finance area. In this paper, we provide an overview of extreme value theory for estimating and assessing value at risk and expected shortfall which are the methods for modelling and measuring the extreme financial risks. We illustrate that the approach based on extreme value theory is very useful for estimating tail related risk measures through backtesting of an empirical data.
Extreme Vaue Theory;Value at Risk;Expected Shortfall;Backtesting;
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Artzner, P., Delbaen, J.M., Eber, J.M. and Heath, D. (1997). Thing coherently. Risk, Vol. 10, 68-71

Artzner, P., Delbaen, J.M., Eber, J.M. and Heath, D. (1999). Coherent measures of risk. Mathematical finance, Vol. 9, 203-228 crossref(new window)

Balkema, A.A. and de Haan, L. (1974). Residual lifetime at great age. Annals of Probability, Vol. 2, 792-804 crossref(new window)

Berkowitz, J. and O'Brien, J. (2002). How accurate are Value-at-Risk models at commercial banks? Journal of Finance, Vol. 57, 1093-1112 crossref(new window)

Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer, London

Danielsson, J. and de Vries, C.G. (1997a). Tail index and quantile estimation with very high frequency data. Journal of Empirical Finance, Vol. 4, 241-257 crossref(new window)

Danielsson, J. and de Vries, C.G. (1997b). Value at Risk and extreme returns. In Extremes and Integrated Risk Management (ed. Embrechts, P.), 85-106. Risk Waters Group, London

Duffie, D. and Pan, J. (1997). An overview of Value at Risk. Journal of Derivatives, Vol. 4, 7-49 crossref(new window)

Embrechts, P., Klupppelberg, C. and Mikosh, T. (1997). Modelling Extremal Events for Insurance and Finance. Springer, Berlin

Embrechts, P., Kluppelberg, C. and Straumann, D. (2002). Correlation and dependency in risk management: properties and pitfalls. In Risk Management: Value at Risk and Beyond (ed, Dempster, M.). 176-223, Cambridge University Press

Embrechts, P., Kaufmann, R. and Patie, P. (2005). Strategic long-term financial risks: single risk factors, Computational Optimization and Applications, Vol. 32, 61-90 crossref(new window)

Fisher, R.A. and Tippett, L.H.C. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. Proceedings of the Cambridge Philosophical Society, Vol. 24, 180-190

Gnedenko, B.V. (1943). Sur la distribution limite du terme maximum d'une serie aleatoire. Annals of Mathematics. Vol. 44, 423-453 crossref(new window)

Hosking, J.R.M. and Wallis, J.R. (1987). Parameter and quantile estimation for the generalized Pareto distribution. Technometrics, Vol. 29, 339-349 crossref(new window)

Jarque, C.M. and Bera, A.K. (1987). A test for normality of observations and regression residuals. International Statistical Review, Vol. 55, 163-172

Jenkinson, A.F. (1955). The frequency distribution of the annual maximum (or minimum) values of meteorological events. Quarterly Journal of the Royal Meteorological Society, Vol. 81, 158-172 crossref(new window)

Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman & Hall, London

Iorion, P. (2001). Value at Risk: The New Benchmark for Managing Financial Risk, 2nd edition. McGraw-Hill, New York

Kupiec, P. (1995). Techniques for verifying the accuracy of risk management models. Joumal of Derivatives, Vol..2, 73-84 crossref(new window)

Longin, F.M. (1996). The asymptotic distribution of extreme stock market returns. Journal of Business, Vol. 69, 383-408 crossref(new window)

Longin, F.M. (2000). From Value at Risk to stress testing: The extreme value approach. The Journal of Banking and Finance, Vol. 24, 1097-1130 crossref(new window)

McNeil, A.J. and Frey, R. (2000). Estimation of tail-related risk for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, Vol. 7, 271-300 crossref(new window)

Nelson, R.B. (1999). An Introduction to Copulas. Springer, New York

Pickands, J. (1975). Statistical inference using extreme order statistics. Annals of Statistics, Vol. 3, 119-131 crossref(new window)

Reiss, R.D. and Thomas, M. (2001). Statistical Analysis of Extreme Values, 2nd edition. Birkhauser Verlag, Basel

von Mises, R. (1936). La distribution de la plus grande de n valeurs. Rev. Math Union Interbalcanique, Vol. 1, 142-160 Reproduced in Selected Papers of Richard von Mises. American Mathematical Society (1964), Vol. 2, 271-294