Improving Value-at-Risk Estimates by Combining Kernel Estimation with Historical Simulation

Posted: 26 Jun 1998

See all articles by Barry Schachter

Barry Schachter

affiliation not provided to SSRN

J.S. Butler

Syracuse University - Department of Economics; University of Kentucky

Date Written: April 1996

Abstract

In this paper we develop a means to improve the performance of one of the more popular methods for Value-at-Risk measurement, the historical simulation approach. The procedure we employ is the following: First, the density of the return on a portfolio is estimated using a non- parametric method, called a Gaussian kernel. Second, we derive an expression for the density of any order statistic of the return distribution. Finally, because the density is not analytic, we employ Gauss-Legendre integration to obtain the moments of the density of the order statistic, the mean being our Value-at-Risk estimate, and the standard deviation providing us with the unique ability to construct a confidence interval around the estimate. We apply this method to trading portfolios provided by a financial institution.

JEL Classification: G21, G11

Suggested Citation

Schachter, Barry and Butler, J.S., Improving Value-at-Risk Estimates by Combining Kernel Estimation with Historical Simulation (April 1996 ). Available at SSRN: https://ssrn.com/abstract=7408

Barry Schachter (Contact Author)

affiliation not provided to SSRN

J.S. Butler

Syracuse University - Department of Economics ( email )

Syracuse, NY 13244-1020
United States
315-443-4589 (Phone)
315-443-1081 (Fax)

University of Kentucky ( email )

Lexington, KY 40546
United States

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