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Portfolio Selection Problem with Minimax Type Risk Function

Author

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  • K.L. Teo
  • X.Q. Yang

Abstract

The investor's preference in risk estimation of portfolio selection problems is important as it influences investment strategies. In this paper a minimax risk criterion is considered. Specifically, the investor aims to restrict the standard deviation for each of the available stocks. The corresponding portfolio optimization problem is formulated as a linear program. Hence it can be implemented easily. A capital asset pricing model between the market portfolio and each individual return for this model is established using nonsmooth optimization methods. Some numerical examples are given to illustrate our approach for the risk estimation. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • K.L. Teo & X.Q. Yang, 2001. "Portfolio Selection Problem with Minimax Type Risk Function," Annals of Operations Research, Springer, vol. 101(1), pages 333-349, January.
    • Handle: RePEc:spr:annopr:v:101:y:2001:i:1:p:333-349:10.1023/a:1010909632198
    DOI: 10.1023/A:1010909632198
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    Citations

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    Cited by:

    1. Fusheng Wang, 2013. "A hybrid algorithm for linearly constrained minimax problems," Annals of Operations Research, Springer, vol. 206(1), pages 501-525, July.
    2. Polak, George G. & Rogers, David F. & Sweeney, Dennis J., 2010. "Risk management strategies via minimax portfolio optimization," European Journal of Operational Research, Elsevier, vol. 207(1), pages 409-419, November.
    3. Esther Mohr & Robert Dochow, 2017. "Risk management strategies for finding universal portfolios," Annals of Operations Research, Springer, vol. 256(1), pages 129-147, September.
    4. Shuanglin Li & Kok Lay Teo, 2019. "Post-disaster multi-period road network repair: work scheduling and relief logistics optimization," Annals of Operations Research, Springer, vol. 283(1), pages 1345-1385, December.
    5. Yunchol Jong, 2012. "Optimization Method for Interval Portfolio Selection Based on Satisfaction Index of Interval inequality Relation," Papers 1207.1932, arXiv.org.
    6. Bhuvnesh Sharma & M. Ramkumar & Nachiappan Subramanian & Bharat Malhotra, 2019. "Dynamic temporary blood facility location-allocation during and post-disaster periods," Annals of Operations Research, Springer, vol. 283(1), pages 705-736, December.
    7. He, Guang & Huang, Nan-jing, 2014. "A new particle swarm optimization algorithm with an application," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 521-528.
    8. 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.
    9. Shrey Jain & Siddhartha P. Chakrabarty, 2020. "Does Marginal VaR Lead to Improved Performance of Managed Portfolios: A Study of S&P BSE 100 and S&P BSE 200," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(2), pages 291-323, June.

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