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Efficient Skewness/Semivariance Portfolios

Author

Listed:
  • Rui Pedro Brito

    () (Faculty of Economics, University of Coimbra, Portugal)

  • Hélder Sebastião

    () (Faculty of Economics, University of Coimbra and GEMF, Portugal)

  • Pedro Godinho

    () (Faculty of Economics, University of Coimbra and GEMF, Portugal)

Abstract

This paper proposes a new way to measure and deal with risk within the portfolio selection problem using a skewness/semivariance biobjective optimization framework. The solutions of this biobjective optimization problem allow the investor to analyse the efficient trade-off between skewness and semivariance. Due to the endogeneity of the cosemivariance matrix, the biobjective problem is solved using a derivative-free algorithm based on direct multisearch. For four datasets, collected from the Fama/French benchmark collection, the direct multisearch was able to determine the in-sample Pareto frontier. The out-of-sample performance of the skewness/semivariance model was assessed by choosing three portfolios (the portfolio that maximizes a skewness per semivariance ratio, the portfolio that maximizes the Sharpe ratio and the portfolio that maximizes the Sortino ratio) at each in-sample Pareto frontier and measuring their performance in terms of skewness per semivariance ratio, Sharpe ratio, Sortino ratio and turnover. The results show that the efficient skewness/semivariance portfolios are consistently competitive, and often superior, comparatively to the benchmark portfolios considered. Both in-sample and the out-of-sample performance analysis were conducted using three different benchmark returns for the semivariance computations.

Suggested Citation

  • Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2015. "Efficient Skewness/Semivariance Portfolios," GEMF Working Papers 2015-05, GEMF, Faculty of Economics, University of Coimbra.
  • Handle: RePEc:gmf:wpaper:2015-05.
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    References listed on IDEAS

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    Cited by:

    1. R. P. Brito & H. Sebastião & P. Godinho, 2017. "Portfolio choice with high frequency data: CRRA preferences and the liquidity effect," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(2), pages 65-86, August.
    2. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(1), pages 1-34, March.

    More about this item

    Keywords

    portfolio selection; semivariance; skewness; multiobjective optimization; derivative-free optimization; direct multisearch.;

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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