In this paper we provide a detailed critical analysis of various methodologies involved in the so-called passive replication of hedge fund returns, a subject that has sparked renewed interest following recent initiatives by major investment banks such as Merrill Lynch and Goldman Sachs. In particular, we examine from both a theoretical and an empirical standpoint the respective benefits and limits of the two different and somewhat competing approaches to hedge fund replication, which are respectively known as "factor-based replication," and "payoff distribution replication." On the one hand, we argue that standard implementation efforts of the factor-based approach, arguably the most natural and straightforward way to tackle the hedge fund replication problem, have mostly failed in thorough empirical tests to produce satisfactory results on an out-of-sample basis.
We also argue that the payoff distribution approach, on the other hand, while insightful and found to generate (relatively) satisfying results on an out-of-sample basis, unfortunately cannot be regarded as a method suitable for performing hedge fund replication, at least not in a sense likely to meet investors' expectations, due to its documented failure to match a number of relevant time-series properties of hedge fund returns. In conclusion, hedge fund replication, while obviously a powerful and attractive concept, is still, at least in terms of successful implementation, very much a work-in-progress. Our analysis suggests that it is only through the introduction of novel adapted econometric techniques allowing for a parsimonious statistical estimation of the dynamic and/or non-linear functions relating underlying factors to hedge fund returns that hedge fund replication could be turned from an attractive concept into a workable investment solution, and we discuss several possible directions for future research.

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Les opinions exprimées sont celles de l'auteur et n'engagent pas la responsabilité de l'EDHEC.