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

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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.

Details

Original languageEnglish
Pages (from-to)675-685
JournalEuropean Journal of Operational Research
Volume264
Issue number2
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • Expected utility, Mean-risk model, Portfolio optimization, Second-order stochastic dominance, Stochastic programming

ID: 14523004