Risk Management for Hedge Fund Portfolios

This article applies formal risk management methodologies to optimization of a portfolio of hedge funds (fund of funds). We compare recently developed risk management methodologies: conditional value-at-risk and conditional drawdown-at-risk with more established mean-absolute deviation, maximum loss, and market neutrality approaches. The common property of considered risk management techniques is that they admit the formulation of a portfolio optimization model as a linear programming (LP) problem. LP formulations allow for implementing efficient and robust portfolio allocation algorithms, which can successfully handle optimization problems with thousands of instruments and scenarios. The performance of various risk constraints is investigated and discussed in detail for in-sample and out-of-sample testing of the algorithm. The numerical experiments show that imposing risk constraints may improve the “real” performance of a portfolio rebalancing strategy in out-of-sample runs. It is beneficial to combine several types of risk constraints that control different sources of risk.