Comment: The Problem of “Missing Factors” in Hedge Fund Replication
| Nov 9th, 2008 | Filed under: Academic Research, Alternative Beta & Hedge Fund Replication, Guest Posts, Today's Post | By: Alpha Male |
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The Fall issue of the Journal of Alternative Investments contains a great 75 page section on hedge fund replication. Articles cover the latest developments in the two major techniques used to approximate hedge fund returns (factor and distributional replication), performance characteristics of actual hedge fund replication programs, and practical hurdles to implementing these programs.
These articles have begun to attract interest from the hedge fund and broader financial communities. One paper by Jean-Francois Bacmann, Ryan Held, Pierre Jeanneret and Stefan Scholz called “The impact of missing factors on replication quality” has caught the eye of AllAboutAlpha.com contributor Pierre Laroche, head of R&D and Innocap, a joint venture between Canada’s National Bank and BNP Paribas (related post). Below, Laroche examines the delicate balance between adding too many factors and too few factors in a factor-replication model.
Special to AllAboutAlpha.com by: Pierre Laroche, Managing Director, R&D, Innocap Investment Management.
The issue of “missing factors” was raised soon after several major financial institutions launched their HF index replicators last year. The use of traditional regressions by these products raised some questions about the number of factors required to fully capture the nuances of HF returns. Specifically, the more factors one adds, the more likely those factors are to be collinear (correlated), thus lowering the regressors’ efficiency. This property of regression-based HF replicators (along with other properties such as their inability to track abrupt changes in weights) pushed financial institutions to look for more appropriate tracking models. One such model is the “Kalman Filter” (KF).
KFs can contribute greatly to hedge fund replication models for at least two reasons:
- Their tracking algorithm explicitly takes into account that exposure to return-generating factors are dynamic (they vary through time).
- The quality of the estimated weights is impacted much less by the presence of highly correlated factors.
In other words, KFs are influenced less by using a small number of highly correlated factors. Unfortunately, however, they do not settle the central question of the ideal number of factors to use when trying to “replicate” HF returns. More…
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