Applying a global optimisation algorithm to Fund of Hedge Funds portfolio optimisation
AbstractPortfolio 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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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 17099.
Date of creation: 19 Aug 2009
Date of revision:
portfolio optimisation; optimization; fund of hedge funds; global search optimisation; direct search; pgsl; hedge fund portfolio;
Find related papers by JEL classification:
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
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- M. Gilli & E. Kellezi & H. Hysi, 2006.
"A Data-Driven Optimization Heuristic for Downside Risk Minimization,"
Computing in Economics and Finance 2006
355, Society for Computational Economics.
- 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.
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