EVT and tail-risk modelling: Evidence from market indices and volatility series
Value-at-Risk (VaR) has become the universally accepted risk metric adopted internationally under the Basel Accords for banking industry internal control, capital adequacy and regulatory reporting. The recent extreme financial market events such as the Global Financial Crisis (GFC) commencing in 2007 and the following developments in European markets mean that there is a great deal of attention paid to risk measurement and risk hedging. In particular, to risk indices and attached derivatives as hedges for equity market risk. The techniques used to model tail risk such as VaR have attracted criticism for their inability to model extreme market conditions. In this paper we discuss tail specific distribution based Extreme Value Theory (EVT) and evaluate different methods that may be used to calculate VaR ranging from well known econometrics models of GARCH and its variants to EVT based models which focus specifically on the tails of the distribution. We apply Univariate Extreme Value Theory to model extreme market risk for the FTSE100 UK Index and S&P-500 US markets indices plus their volatility indices. We show with empirical evidence that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and also daily VaR and ES using a GARCH(1,1) and EVT based dynamic approach to these various indices. The behaviour of these indices in their tails have implications for hedging strategies in extreme market conditions.
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Volume (Year): 26 (2013)
Issue (Month): C ()
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- Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 0075, European Central Bank.
- Loretan, Mico & Phillips, Peter C. B., 1994.
"Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets,"
Journal of Empirical Finance,
Elsevier, vol. 1(2), pages 211-248, January.
- Loretan, M. & Phillips, P.C.B., 1992. "Testing the Covariance Stationarity of Heavy-Tailed Time Series: An Overview of the Theory with Applications to Several Financial Datasets," Working papers 9208, Wisconsin Madison - Social Systems.
- ROCKINGER, Michael & JONDEAU, Eric, 1999. "The Tail Behavior of Stock Returns: Emerging versus Mature Markets," Les Cahiers de Recherche 668, HEC Paris.
- Jondeau, E. & Rockinger, M., 1999. "The Tail Behavior of Sotck Returns: Emerging Versus Mature Markets," Working papers 66, Banque de France.
- Ibrahim Onour, "undated". "Extreme Risk and Fat-tails Distribution Model:Empirical Analysis," API-Working Paper Series 0911, Arab Planning Institute - Kuwait, Information Center.
- Onour, Ibrahim, 2009. "Extreme Risk and Fat-tails Distribution Model:Empirical Analysis," MPRA Paper 17736, University Library of Munich, Germany, revised 20 Sep 2009.
- Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
- Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
- Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
- repec:adr:anecst:y:2000:i:60:p:10 is not listed on IDEAS
- Frederick C. Mills, 1927. "The Behavior of Prices," NBER Books, National Bureau of Economic Research, Inc, number mill27-1, November.
- Sheedy, Elizabeth, 2009. "Can risk modeling work?," Journal of Financial Transformation, Capco Institute, vol. 27, pages 82-87.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Longin, Francois M, 1996. "The Asymptotic Distribution of Extreme Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 69(3), pages 383-408, July.
- Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
- Elias G. Carayannis & Aris Kaloudis & åge Mariussen, 2008. "Introduction," Chapters,in: Diversity in the Knowledge Economy and Society, chapter 1 Edward Elgar Publishing.
- de Jesús, Raúl & Ortiz, Edgar & Cabello, Alejandra, 2013. "Long run peso/dollar exchange rates and extreme value behavior: Value at Risk modeling," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 139-152. Full references (including those not matched with items on IDEAS)