By: Peter Urbani

Peter Urbani — Mon, 13 Apr 2009 21:31:40 +0000

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.

Comments on: A novel approach to monitoring daily HF returns when they don’t actually exist

By: Peter Urbani