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Second-order stochastic dominance constrained portfolio optimization: Theory and computational tests

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  • Kallio, Markku
  • Dehghan Hardoroudi, Nasim

Abstract

Due to the definition of second-order stochastic dominance (SSD) in terms of utility theory, portfolio optimization with SSD constraints is of major practical interest. We contribute to the field in two ways: first, we present a self-contained theory with some new results and new proofs of known results; second, we perform a set of tests for computational efficiency. We provide new and simple arguments for the formulation of SSD constraints in a mathematical programming framework. For many individuals, an SSD constraint may seem too severe wherefore various relaxations (ASSD), have been proposed. We introduce yet another relaxation, directional SSD, where a candidate portfolio is admissible if a step from the benchmark in the direction of the candidate yields a dominating portfolio. Optimal step size depends on individual preferences reflected by the objective function. We compare computational efficiency of seven approaches for SD constrained portfolio problems, including SSD and ASSD constrained cases.

Suggested Citation

  • Kallio, Markku & Dehghan Hardoroudi, Nasim, 2018. "Second-order stochastic dominance constrained portfolio optimization: Theory and computational tests," European Journal of Operational Research, Elsevier, vol. 264(2), pages 675-685.
    • Handle: RePEc:eee:ejores:v:264:y:2018:i:2:p:675-685
    DOI: 10.1016/j.ejor.2017.06.067
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    Cited by:

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    2. Sree Vinutha Venkataraman & S. V. D. Nageswara Rao, 2023. "Stochastic dominance algorithms with application to mutual fund performance evaluation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 681-698, January.
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