Dynamic hedge fund portfolio construction: A semi-parametric approach

Richard D.F. Harris, Murat Mazibas

    Research output: Contribution to journalArticlepeer-review

    27 Citations (Scopus)


    In this article, we evaluate alternative optimization frameworks for constructing portfolios of hedge funds. We compare the standard mean-variance optimization model with models based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investment strategies. In order to implement the CVaR, CDaR and Omega optimization models, we propose a semi-parametric methodology, which is based on extreme value theory, copula and Monte Carlo simulation. We compare the semi-parametric approach with the standard, non-parametric approach, used to compute CVaR, CDaR and Omega, and the benchmark parametric approach, based on both static and dynamic mean-variance optimization. We report two main findings. The first is that the CVaR, CDaR and Omega models offer a significant improvement in terms of risk-adjusted portfolio performance over the parametric mean-variance model. The second is that semi-parametric estimation of the CVaR, CDaR and Omega models offers a very substantial improvement over non-parametric estimation. Our results are robust to the choice of target return, risk limit and estimation sample size. © 2012 Elsevier B.V.
    Original languageEnglish
    Pages (from-to)139-149
    Number of pages11
    JournalJournal of Banking and Finance
    Issue number1
    Early online date29 Aug 2012
    Publication statusPublished - Jan 2013


    • Portfolio optimization
    • Copula
    • Extreme value theory
    • Funds of hedge funds
    • Monte Carlo simulation


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