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Portfolio optimization under partial uncertainty and incomplete information: a probability multimeasure-based approach

Author

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  • D. La Torre

    (University of Milan
    Khalifa University)

  • F. Mendivil

    (Acadia University)

Abstract

Markowitz’s work has had a major impact on academic research and the financial industry as a whole. The main idea of his model is risk aversion of average investors and their desire to maximise the expected return with the least risk. In this paper we extend the classical Markowitz’s model by introducing a portfolio optmization model in which the underlying space of events is described in terms of a probability multimeasure. The notion of probability multimeasure allows to formalize the concept of imprecise probability measure and incomplete information.

Suggested Citation

  • D. La Torre & F. Mendivil, 2018. "Portfolio optimization under partial uncertainty and incomplete information: a probability multimeasure-based approach," Annals of Operations Research, Springer, vol. 267(1), pages 267-279, August.
    • Handle: RePEc:spr:annopr:v:267:y:2018:i:1:d:10.1007_s10479-016-2298-x
    DOI: 10.1007/s10479-016-2298-x
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    References listed on IDEAS

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    7. Ralph E. Steuer & Yue Qi & Markus Hirschberger, 2008. "Portfolio Selection in the Presence of Multiple Criteria," Springer Optimization and Its Applications, in: Constantin Zopounidis & Michael Doumpos & Panos M. Pardalos (ed.), Handbook of Financial Engineering, pages 3-24, Springer.
    8. Markus Hirschberger & Ralph E. Steuer & Sebastian Utz & Maximilian Wimmer & Yue Qi, 2013. "Computing the Nondominated Surface in Tri-Criterion Portfolio Selection," Operations Research, INFORMS, vol. 61(1), pages 169-183, February.
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    Cited by:

    1. Junna Bi & Jun Cai & Yan Zeng, 2021. "Equilibrium reinsurance-investment strategies with partial information and common shock dependence," Annals of Operations Research, Springer, vol. 307(1), pages 1-24, December.
    2. Murcia, Nathanaëlle N.S. & Ferreira, Fernando A.F. & Ferreira, João J.M., 2022. "Enhancing strategic management using a “quantified VRIO”: Adding value with the MCDA approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
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    4. D. La Torre & F. Mendivil, 2022. "Stochastic efficiency and inefficiency in portfolio optimization with incomplete information: a set-valued probability approach," Annals of Operations Research, Springer, vol. 311(2), pages 1085-1098, April.

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