Variance reduction techniques for value-at-risk with heavy-tailed risk factors

The calculation of value-at-risk (VAR) for large portfolios of complex instruments is among the most demanding and widespread computational challenges facing the financial industry. Current methods for calculating VAR include comparatively fast numerical approximations--especially the linear and quadratic (delta-gamma) approximations--and more robust but more computationally demanding Monte Carlo simulation. The linear and delta-gamma methods typically rely on an assumption that the underlying market risk factors have a Gaussian distribution over the VAR horizon. But there is ample empirical evidence that market data is more accurately described by heavy-tailed distributions. Capturing heavy tails in VAR calculations has to date required highly time-consuming Monte Carlo simulation. We describe two methods for computationally efficient calculation of VAR in the presence of heavy-tailed risk factors, specifically when risk factors have a multivariate t distribution. The first method us...

• Type: Conference
• Year: 2000
• Where: WSC
• Authors: Paul Glasserman, Philip Heidelberger, Perwez Shahabuddin
• Keywords: Modeling And Simulation, Monte Carlo, Monte Carlo Simulation, Risk Factors, WSC 2000