Portfolios dominating indices: Optimization with second-order stochastic dominance constraints vs. minimum and mean variance portfolios

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Keçeci, Neslihan Fidan ; Kuzmenko, Viktor ; Uryasev, Stan (2016)
  • Publisher: Basel: MDPI
  • Journal: (issn: 1911-8074)
  • Related identifiers: doi: 10.3390/jrfm9040011
  • Subject: DAX index | portfolio selection | HD61 | portfolio optimization | S&P 100 Index | CVaR | conditional value-at-risk | Dow Jones Index | partial moment | HG1-9999 | stochastic dominance | S&amp | stochastic order | Risk in industry. Risk management | P 100 Index | Finance
    • ddc: ddc:330
    arxiv: Computer Science::Computational Engineering, Finance, and Science | Statistics::Other Statistics | Mathematics::Optimization and Control

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.
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