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Minimax portfolio optimization: empirical numerical study

Author

Listed:
  • X Cai

    (The Chinese University of Hong Kong)

  • K L Teo

    (The Hong Kong Polytechnic University)

  • X Q Yang

    (The Hong Kong Polytechnic University)

  • X Y Zhou

    (The Chinese University of Hong Kong)

Abstract

In this paper, we carry out the empirical numerical study of the l ∞ portfolio selection model where the objective is to minimize the maximum individual risk. We compare the numerical performance of this model with that of the Markowitz's quadratic programming model by using real data from the Stock Exchange of Hong Kong. Our computational results show that the l ∞ model has a similar performance to the Markowitz's model and that the l ∞ model is not sensitive to the data. For the situation with only two assets, we establish that the expected return of the minimum variance model is less than that of the minimum l ∞ model when both variance and the return rate of one asset is less than the corresponding values of another asset.

Suggested Citation

  • X Cai & K L Teo & X Q Yang & X Y Zhou, 2004. "Minimax portfolio optimization: empirical numerical study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 65-72, January.
    • Handle: RePEc:pal:jorsoc:v:55:y:2004:i:1:d:10.1057_palgrave.jors.2601648
    DOI: 10.1057/palgrave.jors.2601648
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    References listed on IDEAS

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    2. Ya Ping Fang & Kaiwen Meng & Xiao Qi Yang, 2012. "Piecewise Linear Multicriteria Programs: The Continuous Case and Its Discontinuous Generalization," Operations Research, INFORMS, vol. 60(2), pages 398-409, April.

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