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Conditional Properties of Hedge Funds: Evidence from Daily Returns*


  • We would like to thank Bing Liang, the editor, an anonymous referee, and participants in finance seminar at the University of Massachusetts and the 2006 meeting of European Financial Management for their helpful comments. We remain responsible for any errors. Corresponding Author: Ying Li, School of Business and Economics, Indiana University South Bend, 1700 Mishawaka Avenue, South Bend, IN 46634.


Using daily returns on a set of hedge fund indices, we study (i) the properties of the indices' conditional density functions and (ii) the presence of asymmetries in conditional correlations between hedge fund indices and other investments and between hedge fund indices themselves. We use the SNP approach to obtain estimates of conditional densities of hedge fund returns and then proceed to examine their properties. In general, a nonparametric GARCH(1,1) model appears to provide the best fit for all strategies. We find that the conditional third and fourth moments are significantly affected by changes in the current volatility of returns on hedge fund indices. We examine changes in the conditional probability of tail events and report significant changes in the probability of extreme events when the conditioning information changes. These results have important implications for models of hedge fund risk that rely on probability of tail events. We formally test for the presence of asymmetries in conditional correlations to determine if there is contagion between hedge funds and other investments and between various hedge fund indices in extreme down markets versus extreme up markets. We generally do not find strong evidence in support of asymmetric correlations.