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

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  • Bey, Roger P.

Abstract

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
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    Cited by:

    1. Kaplanski, Guy & Kroll, Yoram, 2002. "VaR Risk Measures versus Traditional Risk Measures: an Analysis and Survey," MPRA Paper 80070, University Library of Munich, Germany.
    2. Stevenson, Simon, 2001. "Emerging markets, downside risk and the asset allocation decision," Emerging Markets Review, Elsevier, vol. 2(1), pages 50-66, March.
    3. 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.

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