Portfolio Value-at-Risk with Heavy-Tailed Risk Factors

31 Pages Posted: 16 Jan 2003

See all articles by Paul Glasserman

Paul Glasserman

Columbia Business School

Philip Heidelberger

IBM Research Division

Perwez Shahabuddin

Columbia University - Department of Industrial Engineering and Operations Research (IEOR)

Abstract

This paper develops efficient methods for computing portfolio value-at-risk (VAR) when the underlying risk factors have a heavy-tailed distribution. In modeling heavy tails, we focus on multivariate t distributions and some extensions thereof. We develop two methods for VAR calculation that exploit a quadratic approximation to the portfolio loss, such as the delta-gamma approximation. In the first method, we derive the characteristic function of the quadratic approximation and then use numerical transform inversion to approximate the portfolio loss distribution. Because the quadratic approximation may not always yield accurate VAR estimates, we also develop a low variance Monte Carlo method. This method uses the quadratic approximation to guide the selection of an effective importance sampling distribution that samples risk factors so that large losses occur more often. Variance is further reduced by combining the importance sampling with stratified sampling. Numerical results on a variety of test portfolios indicate that large variance reductions are typically obtained. Both methods developed in this paper overcome difficulties associated with VAR calculation with heavy-tailed risk factors. The Monte Carlo method also extends to the problem of estimating the conditional excess, sometimes known as the conditional VAR.

Suggested Citation

Glasserman, Paul and Heidelberger, Philip and Shahabuddin, Perwez, Portfolio Value-at-Risk with Heavy-Tailed Risk Factors. Mathematical Finance, Vol. 12, pp. 239-269, 2002. Available at SSRN: https://ssrn.com/abstract=316207

Paul Glasserman (Contact Author)

Columbia Business School ( email )

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Philip Heidelberger

IBM Research Division ( email )

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Perwez Shahabuddin

Columbia University - Department of Industrial Engineering and Operations Research (IEOR) ( email )

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New York, NY 10027
United States

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