Hedge Fund Indexation And Replication

October 23, 2013

 

Replication products may also play a complementary role in portfolio management. The tactical portfolio management application involves keeping a certain portion of an alternative portfolio liquid in order to make funds readily available to the portfolio manager to make new investments without the time lag involved in redeeming most alternative investments. While one can solve this problem simply by holding cash, a liquid replication product that generates returns typical of alternative assets potentially enables managers to maintain higher total exposure to alternatives with enhanced liquidity.

Finally, replication products may enhance balance sheet liquidity by preserving exposure to alternative asset classes without the reduced liquidity of many alternative investment funds. Institutional investors that must meet liquidity targets or standards may find that their monitors9 tolerate greater overall exposure to alternative assets when they can liquidate them with the same ease as they do conventional holdings of stocks and bonds.

What Makes A Factor-Based Replication Strategy Successful?10
The inimitable nature of hedge fund alpha means that the success of a factor-based replication effort depends completely on two factors: the magnitude of the hedge fund index's beta signal; and the serial correlation of the index itself. Regressing a time series of hedge fund returns attributes 100 percent of the returns to the three sources identified above,11 but factor-based replicators can only attempt to reproduce the portion classified as hedge fund beta. As a result, the coefficient of determination or R2 of the regression that measures the portion of hedge fund returns explained by the hedge fund beta indicates ex ante how successful a replication effort may be. The R2 itself depends on the homogeneity of the index constituents. Because replicators must extract risk factor sensitivities from the indexes they seek to replicate, they require indexes that comprise hedge funds driven by the same risk factors. A good index, then, should comprise hedge funds responsive to the same risk factors. The choice of hedge funds included in an index determines the strength of the risk factor sensitivities, i.e., the "betas" a replicator seeks to capture.

In general, hedge fund indexes fall into two categories: single strategy and multistrategy. Assigning funds to single-strategy classifications, while simple in principle, becomes complicated when one encounters funds whose investments span two or three related strategies. For example, many convertible arbitrage funds overlap significantly with credit funds focused primarily on high-yield corporate debt. Similarly, some long/short equity funds have much in common with some event-driven funds. Other event-driven funds have much in common with distressed investment funds. Multistrategy indexes, while useful as measures of overall hedge fund performance, are statistically poor choices for replication strategies because they tend to mask factor sensitivities. Hedge fund replicators face, therefore, a "garbage in/garbage out" problem. An index comprising funds that do not share factor sensitivities cannot produce a reliable replication strategy.

Of course an accurate explanation of past performance does not guarantee future success. That only occurs when the future resembles the past, a phenomenon known in statistics as "serial correlation." Put simply, from a statistical standpoint, if the past performance of a hedge fund index turns out to be a good predictor of its future performance—a hypothesis that statisticians can test by regressing a time series against a lagged version of itself—then a strategy based on a replication of an index's past performance may perform reasonably well as a predictor of its future. Conversely, if a lagged time series of an index does not explain a nonlagged series of itself, then a regression-based replication of it will probably not work well either.

 

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