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Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall

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  • O. Scaillet

Abstract

We consider a nonparametric method to estimate the expected shortfall—that is, the expected loss on a portfolio of financial assets knowing that the loss is larger than a given quantile. We derive the asymptotic properties of the kernel estimators of the expected shortfall and its first‐order derivative with respect to portfolio allocation in the context of a stationary process satisfying strong mixing conditions. An empirical illustration is given for a portfolio of stocks. Another empirical illustration deals with data on fire insurance losses.

Suggested Citation

  • O. Scaillet, 2004. "Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall," Mathematical Finance, Wiley Blackwell, vol. 14(1), pages 115-129, January.
    • Handle: RePEc:bla:mathfi:v:14:y:2004:i:1:p:115-129
    DOI: 10.1111/j.0960-1627.2004.00184.x
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