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Varying the VaR for Unconditional and Conditional Environments

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  • John Cotter

Abstract

Accurate forecasting of risk is the key to successful risk management techniques. Using the largest stock index futures from twelve European bourses, this paper presents VaR measures based on their unconditional and conditional distributions for single and multi-period settings. These measures underpinned by extreme value theory are statistically robust explicitly allowing for fat-tailed densities. Conditional tail estimates are obtained by adjusting the unconditional extreme value procedure with GARCH filtered returns. The conditional modelling results in iid returns allowing for the use of a simple and efficient multi-period extreme value scaling law. The paper examines the properties of these distinct conditional and unconditional trading models. The paper finds that the biases inherent in unconditional single and multi-period estimates assuming normality extend to the conditional setting.

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Paper provided by arXiv.org in its series Papers with number 1103.5649.

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Date of creation: Mar 2011
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Handle: RePEc:arx:papers:1103.5649

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  1. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-16, April.
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  14. Cotter, John, 2001. "Margin exceedences for European stock index futures using extreme value theory," Journal of Banking & Finance, Elsevier, vol. 25(8), pages 1475-1502, August.
  15. 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.
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Cited by:
  1. Christian Francq & Jean-Michel Zakoïan, 2011. "Estimating the Marginal Law of a Time Series with Applications to Heavy Tailed Distributions," Working Papers 2011-30, Centre de Recherche en Economie et Statistique.
  2. Karmakar, Madhusudan, 2013. "Estimation of tail-related risk measures in the Indian stock market: An extreme value approach," Review of Financial Economics, Elsevier, vol. 22(3), pages 79-85.
  3. Wyn Morgan & John Cotter & Kevin Dowd, 2012. "Extreme Measures of Agricultural Financial Risk," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(1), pages 65-82, 02.
  4. john cotter & kevin dowd, 2011. "The tail risks of FX return distributions: a comparison of the returns associated with limit orders and market orders," Papers 1103.5661, arXiv.org.
  5. Kevin Dowd & John Cotter, 2011. "Intra-Day Seasonality in Foreign Market Transactions," Working Papers 200746, Geary Institute, University College Dublin.
  6. John Cotter & Kevin Dowd, 2011. "Spectral Risk Measures with an Application to Futures Clearinghouse Variation Margin Requirements," Working Papers 200616, Geary Institute, University College Dublin.
  7. Trenca Ioan & Zoicas-Ienciu Adrian, 2010. "The Correlation Between The Market Risk And The Liquidity Risk In The Romanian Banking Sector," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 437-442, July.
  8. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.

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