In 2007, Thierry Roncalli and Guillaume Weisang8 presented a framework for hedge fund replication using Bayesian filters. An important outgrowth of their research is that by creating a reliable model, it became possible to estimate the proportion of returns due to alpha and to beta. When analyzing the returns of the Hedge Fund Research Index (HFRI), they wrote “… a large part of the HF [hedge fund] returns are not explained by the traditional alpha but by the alternative beta. For the entire period [1994-2008], the alternative alpha explains about 23% of the HF returns whereas the alternative beta explains about 77%.” This result supported the notion of a core/satellite approach, using hedge fund replication as a core component that could “… still be supplemented by other illiquid instruments to capture and reproduce more efficiently the risk profile of the hedge fund industry.”
In 2009, Noel Amenc, Lionel Martellini and others at EDHEC9 published a paper titled “Passive Hedge Fund Replication—Beyond the Linear Case.” The paper made several important contributions to the growing field of hedge fund replication by extending the paper of Hasanhodzic and Lo. Amenc et al. examined different approaches to hedge fund replication. They wrote, “We find that going beyond the linear case does not necessarily enhance the replication power. On the other hand, we find that selecting the factors on the basis of an economic analysis allows for a substantial improvement in the out-of-sample replication quality, whatever the underlying form of the factor model.” This was an important piece of research because it documented the importance of factor selection in the investment process. Amenc et al. also wrote, “[W]e confirm the findings in Hasanhodzic and Lo that the performance of the replicating strategies is systematically inferior to that of actual hedge funds.” In other words, hedge funds returns still offer alpha even after identifying and capturing the beta. This conclusion confirmed the research of Roncalli and Weisang.
One of the most recent papers to be published added an interesting wrinkle to the analysis of hedge fund returns. All of the previous papers looked at performance using reported hedge fund returns. Adam Aiken, Christopher Clifford and Jesse Ellis10 sought to determine if hedge fund alpha truly existed after controlling for biases introduced by the self-selective nature of hedge fund reporting to commercial databases. They found “evidence that most of the average fund’s alpha can be explained by its decision to voluntarily report its performance to a database; 95 percent of a typical fund manager’s measured skill can be explained by whether they report to a database.” This is an important contribution to the field because it calls into question whether investors are actually benefiting from the returns purported to be achieved by hedge funds. This bias in reported returns effectively raises the bar for hedge fund replication strategies, as they are being compared to an artificially high benchmark. To the extent that a hedge fund replication product can produce returns that are very close to reported hedge fund returns at a lower cost and without the negative characteristics of limited transparency and liquidity, the benefit of the replication strategy becomes more apparent.
Not All Hedge Fund Replication Strategies Are The Same
Despite the fact that many of the current hedge fund replication strategies are based on the solid principles established by academic researchers, significant differences can be seen in the products based upon the investment process. In this section, we discuss some of the key decisions that need to be made and use the IndexIQ methodology when necessary as an illustrative example.
Hedge Fund Return Providers
The two major providers of hedge fund returns are Dow Jones Credit Suisse (DJCS) and Hedge Fund Research (HFR). While there are other providers such as Barclay Hedge and MSCI Barra, it is generally acknowledged in the industry that DJCS and HFR are the dominant providers. Both provide returns for individual strategies as well as for broad-based composites. Additionally, both provide returns for investable (open for new investors) and noninvestable (closed to new investors) hedge funds. DJCS and HFR both include funds that have at least $50 million in assets. While HFR requires assets greater than $50 million or 12 months of trading history, DJCS requires assets greater than $50 million and 12 months of trading.
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