Kernel estimation for quantile sensitivities

Authors:
Guangwu Liu;L. Jeff Hong
Affiliations:
The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China;The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
Venue:
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Year:
2007

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Abstract

Quantiles, also known as value-at-risk in financial applications, are important measures of random performance. Quantile sensitivities provide information on how changes in the input parameters affect the output quantiles. In this paper, we study the estimation of quantile sensitivities using simulation. We propose a new estimator by employing kernel method and show its consistency and asymptotic normality for i.i.d. data. Numerical results show that our estimator works well for the test problems.