COMPARATIVE ANALYSIS OF LINEAR PORTFOLIO
REBALANCING STRATEGIES:
AN APPLICATION TO HEDGE FUNDS*

Abstract. This paper 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.