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 Mall. In particular, we observed the highest prediction accuracy for the predictions of 1% VaR. We also find that the goodness of E'S prediction models is primarily influenced by the distribution model rather than the mean/variance specification. (C) 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:244-269, 2009