A novel approach to monitoring daily HF returns when they don’t actually exist
| Apr 12th, 2009 | Filed under: Academic Research, Alternative Beta & Hedge Fund Replication, Today's Post | By: Alpha Male |
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In just about every action movie and TV show these days there is at least one scene where the hero asks one of his or her techies to “sharpen” a satellite image. Suddenly, what looked like a fuzzy bunch of pixelated squares takes on the form of someone’s face, a car, or some kind of mobile rocket launcher.  We’re not graphic imaging specialists. But to us, it looks kind of outlandish that someone could take a very small amount of information (a few pixels) and divine the underlying image in fantastic detail.
But in a way, that’s exactly what Daniel Li & Michael Markov (of quantitative investment software vendor Markov Processes) and Russ Wermers of the University of Maryland have done in a paper released last month called “Monitoring Daily Hedge Fund Performance When Only Monthly Data is Available.” Their trick is to leverage another kind of technology: hedge fund replication.
As we have reported extensively, “linear factor replication” aims to predict the performance of hedge funds based on a multiple regression of their historical returns on a number of variables such as equities, Fama/French factors, and several more “exotic” risk factors. More…
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The Markov Process software is one of the best style analysis packages on the market. The statistical backfill engine in our own Infinti Analytics Suite also allows the forward filling of one or more data points using potentially relevant benchmarks or factors. However the problem with all such ‘linear regression’ or Ordinary Least Squares methods is that they may not accurately capture some of the embedded ‘non-linearities’ common to hedge funds. In essence such returns based style analysis based on the method by Sharpe are essentially a form of reverse optimisation. Like all mean variance optimisation the fact the you are attempting to explain the variance of the original return series in terms of the possibly explanatory variables or factors is subject to all of the same sort of problems as traditional mean variance optimisation most notably sensitivity to change. This manifests itself as weights which change too frequently through time when you use a rolling window period rather than a single period. More significantly it is the inappropriate use of variance as the ‘risk measure’ that causes the biggest problem because it is a symmetrical measure that is incapable of capturing non-linear properties that make hedge funds desirable in the first place. Only more sophisticated methods such as those using kalman filters or better yet distributional modeling are able to capture this embedded optionality that represents the ‘hedge’ in a decent hedge fund.