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Threshold Accepting Approach to Improve Bound-based Approximations for Portfolio Optimization

In: Computational Methods in Financial Engineering

Author

Listed:
  • Daniel Kuhn

    (Imperial College London)

  • Panos Parpas

    (Imperial College London)

  • Berç Rustem

    (Imperial College London)

Abstract

A discretization scheme for a portfolio selection problem is discussed. The model is a benchmark relative, mean-variance optimization problem in continuous time. In order to make the model computationally tractable, it is discretized in time and space. This approximation scheme is designed in such a way that the optimal values of the approximate problems yield bounds on the optimal value of the original problem. The convergence of the bounds is discussed as the granularity of the discretization is increased. A threshold accepting algorithm that attempts to find the most accurate discretization among all discretizations of a given complexity is also proposed. Promising results of a numerical case study are provided.

Suggested Citation

  • Daniel Kuhn & Panos Parpas & Berç Rustem, 2008. "Threshold Accepting Approach to Improve Bound-based Approximations for Portfolio Optimization," Springer Books, in: Erricos J. Kontoghiorghes & Berç Rustem & Peter Winker (ed.), Computational Methods in Financial Engineering, pages 3-26, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-77958-2_1
    DOI: 10.1007/978-3-540-77958-2_1
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    Cited by:

    1. D. Kuhn, 2009. "Convergent Bounds for Stochastic Programs with Expected Value Constraints," Journal of Optimization Theory and Applications, Springer, vol. 141(3), pages 597-618, June.

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