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Value-at-risk and extreme returns

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  • Danielsson, Jon
  • Vries, Casper

Abstract

Accurate prediction of extreme events are of primary importance in many financial applications. The properties of historical simulation and RiskMetrics techniques for computing Value-at-Risk (VaR) are compared with a method which involves modelling the tails of financial returns explicitly with a tail estimator. The methods are compared using a sample of U. S. stock returns. For predictions of low probability worst outcomes, RiskMetrics type analysis underpredicts while historical simulation overpredicts. However, the estimates obtained from applying the tail estimator are more accurate in the VaR prediction. This implies that capital requirements can be lower by doing VaR with the tail estimator.

Suggested Citation

  • Danielsson, Jon & Vries, Casper, 1997. "Value-at-risk and extreme returns," LSE Research Online Documents on Economics 119166, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:119166
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    References listed on IDEAS

    as
    1. de Haan, Laurens & Resnick, Sidney I. & Rootzén, Holger & de Vries, Casper G., 1989. "Extremal behaviour of solutions to a stochastic difference equation with applications to arch processes," Stochastic Processes and their Applications, Elsevier, vol. 32(2), pages 213-224, August.
    2. J. S. Butler & Barry Schachter, 1996. "Improving Value-At-Risk Estimates By Combining Kernel Estimation With Historical Simulation," Finance 9605001, University Library of Munich, Germany.
    3. J. S. Butler & Barry Schachter, 1996. "Improving value-at-risk estimates by combining kernel estimation," Proceedings 513, Federal Reserve Bank of Chicago.
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    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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