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Applying a global optimisation algorithm to Fund of Hedge Funds portfolio optimisation

  • Thapar, Rishi
  • Minsky, Bernard
  • Obradovic, M
  • Tang, Qi

Portfolio optimisation for a Fund of Hedge Funds (“FoHF”) has to address the asymmetric, non-Gaussian nature of the underlying returns distributions. Furthermore, the objective functions and constraints are not necessarily convex or even smooth. Therefore traditional portfolio optimisation methods such as mean-variance optimisation are not appropriate for such problems and global search optimisation algorithms could serve better to address such problems. Also, in implementing such an approach the goal is to incorporate information as to the future expected outcomes to determine the optimised portfolio rather than optimise a portfolio on historic performance. In this paper, we consider the suitability of global search optimisation algorithms applied to FoHF portfolios, and using one of these algorithms to construct an optimal portfolio of investable hedge fund indices given forecast views of the future and our confidence in such views.

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File URL: https://mpra.ub.uni-muenchen.de/17099/1/MPRA_paper_17099.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 17099.

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Date of creation: 19 Aug 2009
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Handle: RePEc:pra:mprapa:17099
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  1. Alexei Chekhlov & Stanislav Uryasev & Michael Zabarankin, 2005. "Drawdown Measure In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 13-58.
  2. Manfred Gilli & Evis Këllezi & Hilda Hysi, . "A Data-Driven Optimization Heuristic for Downside Risk Minimization," Swiss Finance Institute Research Paper Series 06-02, Swiss Finance Institute.
  3. Stefano Ciliberti & Imre Kondor & Marc Mezard, 2006. "On the Feasibility of Portfolio Optimization under Expected Shortfall," Papers physics/0606015, arXiv.org.
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