Portfolio Optimization in Practice 
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 meanvariance framework (the standard deviation) to measure risks. Since the meanvariance methodology is hardly appropriate for hedge funds due to their nonnormality, other, more advanced measures of risks should be used. However, using the more advanced risk metrics (a classic VaR or Omega, for example) may result in nonconvex objective functions, which, in turn, leads to multiextreme optimization.
Employing alternative metrics like CVaR, LPM or MVaR does not require multiextreme 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 the given range, while taking into account a broad set of constraints.
Despite the Quant Optimum framework incorporates one of the most powerful genetic optimization routines capable of optimizing multiextreme objective functions with virtually unlimited constraints, we strongly recommend analyzing the generated portfolio allocations thoroughly by applying additional techniques, ex. factor analysis or style analysis. Often, the best results are obtained by combining purely machinegenerated baskets with a heuristic approach.
Instead of chasing tails and trying to construct a truly optimal portfolio, we recommend constructing quasioptimal portfolios with riskreturn profiles in close proximity to Efficient Frontiers. This way we may also include instruments based on other considerations rather than an optimization output only.
Quant Platform Portfolio Optimization FeaturesObjective Functions
Constraints
Typical Tasks
Features
Optimization engines
Objective Functions (examples)
