A Comparison of Alternative Approaches for Determining the Downside Risk of Hedge Fund Strategies

摘要

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.