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A Data-Driven Optimization Heuristic for Downside Risk Minimization

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Author Info

  • M. Gilli

    ()
    (Dept. of Econometrics University of Geneva)

  • E. Kellezi

    (Mirabaud and Cie, Geneva)

  • H. Hysi

    (Dept. of Econometrics University of Geneva)

Abstract

In practical portfolio choice models risk is often defined as VaR, expected shortfall, maximum loss, Omega function, etc. and is computed from simulated future scenarios of the portfolio value. It is well known that the minimization of these functions can not, in general, be performed with standard methods. We present a multi-purpose data-driven optimization heuristic capable to deal efficiently with a variety of risk functions and practical constraints on the positions in the portfolio. The efficiency and robustness of the heuristic is illustrated by solving a collection of real world portfolio optimization problems using different risk functions such as VaR, expected shortfall, maximum loss and Omega function with the same algorithm

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 355.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:355

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Keywords: Portfolio optimization; Heuristic optimization; Threshold accepting; Downside risk;

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Cited by:
  1. Zhu, Min, 2013. "Return distribution predictability and its implications for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 209-223.
  2. Thiemo Krink & Sandra Paterlini, 2008. "Differential Evolution for Multiobjective Portfolio Optimization," Center for Economic Research (RECent) 021, University of Modena and Reggio E., Dept. of Economics.
  3. Manfred Gilli & Enrico Schumann, 2009. "Robust regression with optimisation heuristics," Working Papers 011, COMISEF.
  4. Thapar, Rishi & Minsky, Bernard & Obradovic, M & Tang, Qi, 2009. "Applying a global optimisation algorithm to Fund of Hedge Funds portfolio optimisation," MPRA Paper 17099, University Library of Munich, Germany.
  5. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
  6. Konstantinos Anagnostopoulos & Georgios Mamanis, 2011. "Multiobjective evolutionary algorithms for complex portfolio optimization problems," Computational Management Science, Springer, vol. 8(3), pages 259-279, August.
  7. repec:hal:journl:halshs-00375765 is not listed on IDEAS
  8. Jin Zhang & Dietmar Maringer, 2010. "Asset Pair-Copula Selection with Downside Risk Minimization," Working Papers 037, COMISEF.
  9. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.
  10. Manfred Gilli & Enrico Schumann, 2009. "Optimal enough?," Working Papers 010, COMISEF.
  11. Thiemo Krink & Sandra Paterlini, 2011. "Multiobjective optimization using differential evolution for real-world portfolio optimization," Computational Management Science, Springer, vol. 8(1), pages 157-179, April.
  12. Iwona Konarzewska, 2008. "On measuring the sensitivity of the optimal portfolio allocation," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 2, pages 55-73.
  13. Cyril Caillault & Dominique Guegan, 2009. "Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00375765, HAL.
  14. Yuichi Takano & Jun-ya Gotoh, 2010. "α-Conservative approximation for probabilistically constrained convex programs," Computational Optimization and Applications, Springer, vol. 46(1), pages 113-133, May.
  15. J. Baixauli-Soler & Eva Alfaro-Cid & Matilde Fernandez-Blanco, 2011. "Mean-VaR Portfolio Selection Under Real Constraints," Computational Economics, Society for Computational Economics, vol. 37(2), pages 113-131, February.

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