A comparison of alternative approaches for determining the downside risk of hedge fund strategies

In this study, we compare a number of different approaches for determining the Value at Risk (VaR) and Expected Shortfall (ES) of hedge fund investment strategies. We compute VaR and ES through both model‐free and mean/variance and distribution model‐based methods. Certain specifications of the models that we considered can technically address the typical characteristics of hedge fund returns such as autocorrelation, asymmetry, fat tails, and time‐varying variances. We find that conditional mean/variance models coupled with appropriate assumptions on the empirical distribution can improve the prediction accuracy of VaR. In particular, we observed the highest prediction accuracy for the predictions of 1% VaR. We also find that the goodness of ES prediction models is primarily influenced by the distribution model rather than the mean/variance specification. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:244–269, 2009