Can HF return autocorrelation actually predict how easy it will be to eventually redeem?
Aug 4th, 2009 | Filed under: Performance, Analytics & Metrics, Today's Post
If one word could sum up the multi-dimensional train wreck we’ve seen in financial markets over the past few years, it might be liquidity. Illiquidity-driven death spirals have affected individual hedge funds, liquidity “mismatches” have hurt funds of funds and university endowments, which had long benefited from an “illiquidity premium” recently came crashing back to earth (see this recent Vanity Fair article for a great example).
Several experts have contemplated the value of liquidity on these pages. Some have even suggested that hedge funds owe much of their “alpha” returns to illiquidity, not skill.
But what exactly is this illiquidity premium and how do you even begin to measure “illiquidity”? You could, for example, look at the bid/ask spread. Or, you could look at the “metrics such as the “percent of day’s-trading-volume”. Or in the case of managed or structured vehicles, you could simply find out the redemption terms.
Each of these techniques presents complexities when applied to hedge funds, however. But academics use a technique that might save a lot of time for those trying to measure the illiquidity of hedge funds: return autocorrelation. The serial correlation between monthly returns can reveal a lot about the actual illiquidity of a fund according to a study by MIT’s Amir Khandani and the absurdly prolific Andrew Lo, his MIT colleague.
In “Illiquidity Premia in Asset Returns: An Empirical Analysis of Hedge Funds, Mutual Funds, and U.S. Equity Portfolio“, Lo and Khandani prove that hedge fund return autocorrelation is a proxy for actual redemption terms. In their words: More…
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