Nonparametric Assessment of Hedge Fund Performance

Abstract

We propose a new class of performance measures for Hedge Fund (HF) returns based on a family of empirically identiable 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 a full asymptotic theory for our SDF estimators to test for the statistical signicance 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 signicant 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 dierent fund exposure to higherorder moments of the benchmark factors, highlighting the potential heterogeneity across investors in evaluating performance.

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Replaced by

Caio Almeida, Kim Ardison, and René Garcia, “Nonparametric Assessment of Hedge Fund Performance”, Journal of Econometrics, vol. 214, n. 2, February 2020, pp. 349–378.

Reference

Caio Almeida, Kim Ardison, and René Garcia, “Nonparametric Assessment of Hedge Fund Performance”, TSE Working Paper, n. 19-1024, July 2019.