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Kernel estimation of quantile sensitivities

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  • Guangwu Liu
  • Liu Jeff Hong

Abstract

Quantiles, also known as value‐at‐risks in the financial industry, are important measures of random performances. Quantile sensitivities provide information on how changes in input parameters affect output quantiles. They are very useful in risk management. In this article, we study the estimation of quantile sensitivities using stochastic simulation. We propose a kernel estimator and prove that it is consistent and asymptotically normally distributed for outputs from both terminating and steady‐state simulations. The theoretical analysis and numerical experiments both show that the kernel estimator is more efficient than the batching estimator of Hong 9. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009

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  • Guangwu Liu & Liu Jeff Hong, 2009. "Kernel estimation of quantile sensitivities," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(6), pages 511-525, September.
    • Handle: RePEc:wly:navres:v:56:y:2009:i:6:p:511-525
    DOI: 10.1002/nav.20358
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    References listed on IDEAS

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