Conditional Properties Of Hedge Funds PDF Download

Using daily returns on a set of hedge fund indices, we study (i) the properties of the indices' conditional density functions, (ii) the presence of asymmetries in conditional correlations between hedge fund indices and other investments and between hedge indices themselves, and (iii) the presence of market timing skills in the indices. 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 also 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 do not find strong evidence in support of asymmetric correlations between hedge funds and other investments, while we find evidence in support of asymmetric correlations between some hedge fund indices. Finally, we find strong evidence supporting the presence of market timing skills in these indices. However, we argue that our findings are more likely to be because of long-volatility positions held by the managers rather than because of their market timing abilities. Further, we claim that the presence of option-type returns as explanatory variables are not likely to correct for this problem. We use market timing results concerning returns on a simulated portfolio to support this conclusion.