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Distributed Optimisation of a Portfolio's Omega

Author

Listed:
  • Manfred Gilli

    (Department of Econometrics, University of Geneva and Swiss Finance Institute)

  • Enrico Schumann

    (Department of Econometrics, University of Geneva)

Abstract

We investigate portfolio selection with alternative objective functions in a distributed computing environment. In particular, we optimise a portfolio's 'Omega' which is the ratio of two partial moments of the returns distributions. Since finding optimal portfolios under such performance measures and realistic constraints leads to non-convex problems, we suggest to solve the problem with a heuristic method called Threshold Accepting (TA). TA is a very flexible technique as it requires no simplifications of the problem and allows for a straightforward implementation of all kinds of constraints. Applying this algorithm to actual data, we find that TA is well-adapted to optimisation problems of this type. Furthermore, we show that the computations can easily be distributed which leads to considerable speedups.

Suggested Citation

  • Manfred Gilli & Enrico Schumann, 2008. "Distributed Optimisation of a Portfolio's Omega," Swiss Finance Institute Research Paper Series 08-17, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp0817
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    Citations

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    Cited by:

    1. Caporin, Massimiliano & Costola, Michele & Jannin, Gregory & Maillet, Bertrand, 2018. "“On the (Ab)use of Omega?”," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 11-33.
    2. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    3. Abdallah Ben Saida & Jean-luc Prigent, 2018. "On the robustness of portfolio allocation under copula misspecification," Annals of Operations Research, Springer, vol. 262(2), pages 631-652, March.
    4. Guastaroba, G. & Mansini, R. & Ogryczak, W. & Speranza, M.G., 2016. "Linear programming models based on Omega ratio for the Enhanced Index Tracking Problem," European Journal of Operational Research, Elsevier, vol. 251(3), pages 938-956.
    5. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    6. Zhu, Min, 2013. "Return distribution predictability and its implications for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 209-223.

    More about this item

    Keywords

    Optimization heuristics; Threshold Accepting; Portfolio Optimization; Distributed Computing;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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