IDEAS home Printed from
   My bibliography  Save this article

Estimating the Optimal Stochastic Dominance Efficient Set with a Mean-Semivariance Algorithm


  • Bey, Roger P.


The theoretical desirability of stochastic dominance (SD) as a decision rule is well established [1, 3, 4, 7, and 11]. However, implementation of SD as a decision rule has been hindered seriously by the lack of an optimal search algorithm [8]. An optimal search algorithm is desirable since it takes the distribution of returns for a group of assets and determines the optimal proportion of each asset which should be combined to provide efficient combinations. For example, for a given expected value (variance) the mean-variance (EV) algorithm builds the portfolio with the smallest (largest) variance (expected value). The EV algorithm determines which assets should be combined and the proportion of the total investment that should be invested in each asset. An analogous algorithm does not exist for SD.

Suggested Citation

  • Bey, Roger P., 1979. "Estimating the Optimal Stochastic Dominance Efficient Set with a Mean-Semivariance Algorithm," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 14(5), pages 1059-1070, December.
  • Handle: RePEc:cup:jfinqa:v:14:y:1979:i:05:p:1059-1070_00

    Download full text from publisher

    File URL:
    File Function: link to article abstract page
    Download Restriction: no


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Shalit, Haim & Yitzhaki, Shlomo, 1985. "Evaluating the Mean-Gini Approach to Portfolio Selection," Working Papers 232632, Hebrew University of Jerusalem, Center for Agricultural Economic Research.
    2. Kaplanski, Guy & Kroll, Yoram, 2002. "VaR Risk Measures versus Traditional Risk Measures: an Analysis and Survey," MPRA Paper 80070, University Library of Munich, Germany.
    3. Stevenson, Simon, 2001. "Emerging markets, downside risk and the asset allocation decision," Emerging Markets Review, Elsevier, vol. 2(1), pages 50-66, March.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:jfinqa:v:14:y:1979:i:05:p:1059-1070_00. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Keith Waters). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.