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Comparing SSD-Efficient Portfolios with a Skewed Reference Distribution

Author

Listed:
  • Francesco Cesarone

    (Department of Business Studies, Roma Tre University, 00145 Roma, Italy
    These authors contributed equally to this work.)

  • Raffaello Cesetti

    (Department of Business Studies, Roma Tre University, 00145 Roma, Italy
    These authors contributed equally to this work.)

  • Giuseppe Orlando

    (Department of Economics and Finance, University of Bari “Aldo Moro”, 70124 Bari, Italy
    Department of Economics HSE University, 190121 Saint Petersburg, Russia
    Department of Mathematics, University of Jaén, 23071 Jaén, Spain
    These authors contributed equally to this work.)

  • Manuel Luis Martino

    (Department of Business Studies, Roma Tre University, 00145 Roma, Italy
    These authors contributed equally to this work.)

  • Jacopo Maria Ricci

    (Department of Economics, University of Bergamo, 24127 Bergamo, Italy
    These authors contributed equally to this work.)

Abstract

Portfolio selection models based on second-order stochastic dominance (SSD) have the advantage of providing portfolios that reflect the behavior of risk-averse investors without the need to specify the utility function. Several scholars apply SSD conditions with respect to a reference distribution, typically that of the market index, to find its dominant SSD portfolio. However, since the reference distribution could strongly influence asset allocation, in this article, we compare two SSD-based portfolio selection strategies with a reshaping of the reference distribution in terms of its skewness and, consequently, its variance. Through an extensive empirical analysis based on multiasset investment universes, we empirically show that the SSD portfolios dominating the new skewed benchmark index generally perform better.

Suggested Citation

  • Francesco Cesarone & Raffaello Cesetti & Giuseppe Orlando & Manuel Luis Martino & Jacopo Maria Ricci, 2022. "Comparing SSD-Efficient Portfolios with a Skewed Reference Distribution," Mathematics, MDPI, vol. 11(1), pages 1-20, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:50-:d:1012571
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    Cited by:

    1. Francesco Cesarone & Justo Puerto, 2024. "New approximate stochastic dominance approaches for Enhanced Indexation models," Papers 2401.12669, arXiv.org.
    2. Francesco Cesarone & Manuel Luis Martino & Federica Ricca & Andrea Scozzari, 2023. "Managing ESG Ratings Disagreement in Sustainable Portfolio Selection," Papers 2312.10739, arXiv.org.
    3. Francesco Cesarone & Rosella Giacometti & Manuel Luis Martino & Fabio Tardella, 2023. "A return-diversification approach to portfolio selection," Papers 2312.09707, arXiv.org.

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