Robust Assessment of Hedge Fund Performance through Nonparametric Discounting

This paper evaluates the performance of hedge funds through a new nonlinear risk adjustment of returns. The risk adjustment is such that it prices exactly the usual set of risk factors considered in the hedge fund literature. This nonlinear risk adjustment goes beyond the usual linear regression methodology used in many hedge fund performance papers, including nonlinear exposures based on option-like features. The approach proposed in this paper overcomes two important limitations of the linear methodology: it captures the nonlinear exposure of a hedge fund strategy to several risk factors, and it is not limited to nonlinear shapes resembling standard option payoff patterns. This methodology is applied to various hedge fund indices as well as to individual hedge funds, considering a set of risk factors including equities, bonds, credit, currencies and commodities. The main message that emerges from the analysis on the performance of hedge fund strategies is that exposure to higher-moment risks on the various factors matters. Analysing the performance of HFRI indices on primary strategies and sub-classes of primary strategies, the paper reports sizeable differences in performance, between the linear and the nonlinear risk adjustment.