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Solving non-linear portfolio optimization problems with interval analysis

Author

Listed:
  • Xiaoning Xu

    (University of Science & Technology Beijing, Beijing, P.R. China)

  • Feng He

    (University of Science & Technology Beijing, Beijing, P.R. China)

  • Rong Chen

    (Tsinghua University, Beijing, P.R. China)

  • Qingzhi Zhang

    (Tsinghua University, Beijing, P.R. China)

Abstract

Estimation errors or uncertainities in expected return and risk measures create difficulties for portfolio optimization. The literature deals with the uncertainty using stochastic, fuzzy or probability programming. This paper proposes a new approach to treating uncertainty. By assuming that the expected return and risk vary within a bounded interval, this paper uses interval analysis to extend the classical mean-variance portfolio optimization problem to the cases with bounded uncertainty. To solve the interval quadratic programming problem, the paper adopts order relations to transform the uncertain programme into a deterministic programme, and includes the investors’ risk preference into the model. Numerical analysis illustrates the advantage of this new approach against conventional methods.

Suggested Citation

  • Xiaoning Xu & Feng He & Rong Chen & Qingzhi Zhang, 2015. "Solving non-linear portfolio optimization problems with interval analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(6), pages 885-893, June.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:6:p:885-893
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    Citations

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    Cited by:

    1. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    2. Jianjian Wang & Feng He & Xin Shi, 2019. "Numerical solution of a general interval quadratic programming model for portfolio selection," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.

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