FoF Portfolio Optimization in Practice ^Featured

Hedge fund portfolio optimization drastically differs from that of conventional asset classes. Applying the common asset optimization framework to hedge funds, usually leads to highly questionable results that only mislead an inexperienced investor. The main aspects of hedge fund portfolio optimization could be outlined as follows:

• The conventional quadratic optimization methods are applicable only for convex single extreme objective functions typically incorporating the mean-variance framework (the standard deviation as the objective function) to measure risks. Since the mean-variance methodology is hardly appropriate for hedge funds due to non-normal distributions of their returns, other, more advanced measures of risks should be applied. However, using the more advanced risk metrics (a classic VaR or Omega, for example) may result in non-convex objective functions, which, in turn, leads to multi-extreme optimization.
• Employing alternative metrics like CVaR, LPM or MVaR does not require multi-extreme optimization, however, inherits all the pitfalls and drawbacks of these metrics (see the Quant knowledge base for more information). Nevertheless, these metrics can provide a better approximation yet offering faster optimization routines.
• Portfolio optimization itself, even if a right methodology applied, could be difficult or impossible to implement in practice due to liquidity restrictions or imposed strategy concentration limits. Therefore, a more sensible approach implies finding an acceptable risk/return extreme within a given range, while taking into account a broad set of constraints.
• Though the Risk Shell optimization framework incorporates one of the most powerful genetic optimization routines capable of optimizing multi-extreme objective functions with virtually unlimited constraints, we strongly recommend analyzing the generated portfolio allocations thoroughly by applying additional techniques, e.g. factor analysis or style analysis. Often, the best results are obtained by combining purely machine-generated baskets with a heuristic approach.
• Instead of chasing tails and trying to construct a truly optimal portfolio, we recommend constructing quasi-optimal portfolios with risk-return profiles in close proximity to the Efficient Frontiers. This way we may also include instruments based on other considerations rather than relying on an optimization output only.

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