Measuring Funds of Hedge Funds Performance Using Quantile Regressions: Do Experience and Size Matter?

Abstract

This article is the first to use quantile regression to analyze the impact of experience and size of funds of hedge funds (FHFs) on performance. In comparison to OLS regression, quantile regression provides a more detailed picture of the influence of size and experience on FHF return patterns. Hence, it allows one to study the relevance of these factors for various return and risk levels instead of average return and risk, as is the case with OLS regression. Because FHF size and age (as a proxy for experience) are available in a panel setting, one can perform estimations in an unbalanced stacked panel framework. This study analyzes time series and descriptive variables of 649 FHFs drawn from the Lipper TASS Hedge Fund database for the time period January 1996 to August 2007. The empirical results suggest that experience and size have a negative effect on performance, with a positive curvature at the higher quantiles. At the lower quantiles, however, size has a positive effect with a negative curvature. Both factors show no significant effect at the median.