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Approximating exact expected utility via portfolio efficient frontiers

Author

Listed:
  • Alessandra Carleo

    (University of Roma Tre)

  • Francesco Cesarone

    (University of Roma Tre)

  • Andrea Gheno

    (University of Roma Tre)

  • Jacopo Maria Ricci

    (University of Roma Tre)

Abstract

Expected utility theory is nowadays accepted as the standard for rational choice among risky assets. However, as Harry Markowitz recently pointed out, the problem of how the maximum expected utility along the risk–return portfolio efficient frontiers approximates the exact maximum expected utility is still open. This paper shows that some popular risk–return models are actually able to approximate expected utility maximization with respect to classical and new distance measures. It also analyzes the ability of the whole risk–return efficient frontiers to approximate the exact one. Our empirical analysis is based on recent publicly available real-world data sets.

Suggested Citation

  • Alessandra Carleo & Francesco Cesarone & Andrea Gheno & Jacopo Maria Ricci, 2017. "Approximating exact expected utility via portfolio efficient frontiers," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 115-143, November.
    • Handle: RePEc:spr:decfin:v:40:y:2017:i:1:d:10.1007_s10203-017-0201-0
    DOI: 10.1007/s10203-017-0201-0
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    References listed on IDEAS

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    Cited by:

    1. Cesarone, Francesco & Lampariello, Lorenzo & Sagratella, Simone, 2019. "A risk-gain dominance maximization approach to enhanced index tracking," Finance Research Letters, Elsevier, vol. 29(C), pages 231-238.
    2. 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.
    3. Francesco Cesarone & Manuel L. Martino & Fabio Tardella, 2023. "Mean-Variance-VaR portfolios: MIQP formulation and performance analysis," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(3), pages 1043-1069, September.
    4. Cesarone, Francesco & Mango, Fabiomassimo & Mottura, Carlo Domenico & Ricci, Jacopo Maria & Tardella, Fabio, 2020. "On the stability of portfolio selection models," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 210-234.
    5. Benati, S. & Conde, E., 2022. "A relative robust approach on expected returns with bounded CVaR for portfolio selection," European Journal of Operational Research, Elsevier, vol. 296(1), pages 332-352.
    6. Francesco Cesarone & Manuel Luis Martino & Alessandra Carleo, 2022. "Does ESG Impact Really Enhance Portfolio Profitability?," Sustainability, MDPI, vol. 14(4), pages 1-28, February.
    7. Gianni Filograsso & Giacomo Tollo, 2023. "Adaptive evolutionary algorithms for portfolio selection problems," Computational Management Science, Springer, vol. 20(1), pages 1-38, December.

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    More about this item

    Keywords

    Portfolio optimization; Expected utility; Multiobjective optimization; Asset management;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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