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Evaluating the precision of estimators of quantile-based risk measures

Author

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  • Kevin Dowd
  • John Cotter

Abstract

This paper examines the precision of estimators of Quantile-Based Risk Measures (Value at Risk, Expected Shortfall, Spectral Risk Measures). It first addresses the question of how to estimate the precision of these estimators, and proposes a Monte Carlo method that is free of some of the limitations of existing approaches. It then investigates the distribution of risk estimators, and presents simulation results suggesting that the common practice of relying on asymptotic normality results might be unreliable with the sample sizes commonly available to them. Finally, it investigates the relationship between the precision of different risk estimators and the distribution of underlying losses (or returns), and yields a number of useful conclusions.

Suggested Citation

  • Kevin Dowd & John Cotter, 2007. "Evaluating the precision of estimators of quantile-based risk measures," Centre for Financial Markets Working Papers 10197/1191, Research Repository, University College Dublin.
    • Handle: RePEc:rru:cfmwps:10197/1191
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    File URL: http://hdl.handle.net/10197/1191
    File Function: First version, 2007
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    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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