Nonparametric Assessment of Hedge Fund Performance
62 Pages Posted: 8 Aug 2013 Last revised: 1 Jun 2021
Date Written: August 12, 2019
We propose a new class of performance measures for Hedge Fund (HF) returns based on a family of empirically identifiable stochastic discount factors (SDFs). The SDF-based measures incorporate no-arbitrage pricing restrictions and naturally embed information about higher-order mixed moments between HF and benchmark factors returns. We provide full asymptotic theory for our SDF estimators to test for the statistical significance of each fund's performance and for the relevance of individual benchmark factors within each proposed measure. We apply our methodology to a panel of 4815 individual hedge funds. Our empirical analysis reveals that fewer funds have a statistically significant positive alpha compared to the Jensen's alpha obtained by the traditional linear regression approach. Moreover, the funds' rankings vary considerably between the two approaches. Performance also varies between the members of our family because of a different fund exposure to higher-order moments of the benchmark factors, highlighting the potential heterogeneity across investors in evaluating performance.
Keywords: Stochastic Discount Factors, Performance Measurement, Minimum Discrepancy measures, Nonparametric discounting
JEL Classification: C1, C5, G1
Suggested Citation: Suggested Citation