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Abstract "This paper compares 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 completely model-free methods, as well as through mean/variance and distribution model-based methods. Among the models considered certain specifications can technically address autocorrelation, asymmetry, fat tails, and time-varying variances which are typical characteristics of hedge fund returns. We find that conditional mean/variance models coupled with appropriate distributional assumptions improve our ability to predict VaR, 1% VaR in particular. We also find that the goodness of ES prediction models is primarily influenced by the distribution model rather than the mean/variance specification."

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