Bayesian Inference for Hedge Funds with Stable Distribution of Returns

Recently, a large body of academic literature has focused on the area of stable distributions and their application potential for improving our understanding of the risk of hedge funds. At the same time, research has sprung up on standard Bayesian methods applied to hedge fund evaluation. Little or no academic attention has been paid to the combination of these two topics.This paper considers the Bayesian inference for alpha-stable distributions with particular regard to hedge fund performance and risk assessment. We construct Bayesian estimators for alpha-stable distributions in the context of an ARMA-GARCH time series model with stable disturbances. Our risk evaluation and prediction results are compared to the predictions of a battery of conditional and unconditional models estimated in both the frequentist and Bayesian setting. The conditional Bayesian model with stable disturbances is found to have superb risk prediction capability of the abnormally-large losses some hedge funds sustained in the months of September and October 2008 across different hedge fund strategies