Portfolios Dominating Indices: Optimization with Second-Order Stochastic Dominance Constraints vs. Minimum and Mean Variance Portfolios

Neslihan Fidan Keçeci, Viktor Kuzmenko and Stan Uryasev
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Neslihan Fidan Keçeci: Istanbul University, School of Business, Avcılar 34850, Istanbul, Turkey
Viktor Kuzmenko: Glushkov Institute of Cybernetics, Kyiv 03115, Ukraine
Stan Uryasev: Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA

JRFM, 2016, vol. 9, issue 4, 1-14

Abstract: The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The paper is focused on practical applications of the portfolio optimization and uses the Portfolio Safeguard (PSG) package, which has precoded modules for optimization with SSD constraints, mean-variance and minimum variance portfolio optimization. We have done in-sample and out-of-sample simulations for portfolios of stocks from the Dow Jones, S&P 100 and DAX indices. The considered portfolios’ SSD dominate the Dow Jones, S&P 100 and DAX indices. Simulation demonstrated a superior performance of portfolios with SD constraints, versus mean-variance and minimum variance portfolios.

Keywords: stochastic dominance; stochastic order; portfolio optimization; portfolio selection; Dow Jones Index; S&P 100 Index; DAX index; partial moment; conditional value-at-risk; CVaR (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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