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Deciphering robust portfolios

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  • Kim, Woo Chang
  • Kim, Jang Ho
  • Fabozzi, Frank J.

Abstract

Robust portfolio optimization has been developed to resolve the high sensitivity to inputs of the Markowitz mean–variance model. Although much effort has been put into forming robust portfolios, there have not been many attempts to analyze the characteristics of portfolios formed from robust optimization. We investigate the behavior of robust portfolios by analytically describing how robustness leads to higher dependency on factor movements. Focusing on the robust formulation with an ellipsoidal uncertainty set for expected returns, we show that as the robustness of a portfolio increases, its optimal weights approach the portfolio with variance that is maximally explained by factors.

Suggested Citation

  • Kim, Woo Chang & Kim, Jang Ho & Fabozzi, Frank J., 2014. "Deciphering robust portfolios," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 1-8.
    • Handle: RePEc:eee:jbfina:v:45:y:2014:i:c:p:1-8
    DOI: 10.1016/j.jbankfin.2014.04.021
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    Citations

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    Cited by:

    1. Giemza Dawid, 2021. "Ranking of optimal stock portfolios determined on the basis of expected utility maximization criterion," Journal of Economics and Management, Sciendo, vol. 43(1), pages 154-178, January.
    2. Panos Xidonas & Ralph Steuer & Christis Hassapis, 2020. "Robust portfolio optimization: a categorized bibliographic review," Annals of Operations Research, Springer, vol. 292(1), pages 533-552, September.
    3. Jang Ho Kim & Yongjae Lee & Woo Chang Kim & Frank J. Fabozzi, 2022. "Goal-based investing based on multi-stage robust portfolio optimization," Annals of Operations Research, Springer, vol. 313(2), pages 1141-1158, June.
    4. Jang Ho Kim & Woo Chang Kim & Frank J. Fabozzi, 2018. "Recent advancements in robust optimization for investment management," Annals of Operations Research, Springer, vol. 266(1), pages 183-198, July.
    5. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2022. "Robust portfolio selection problems: a comprehensive review," Operational Research, Springer, vol. 22(4), pages 3203-3264, September.
    6. Heonbae Jeon & Soonbong Lee & Hongseon Kim & Seung Bum Soh & Seongmoon Kim, 2023. "Portfolio Evaluation with the Vector Distance Based on Portfolio Composition," Mathematics, MDPI, vol. 11(1), pages 1-19, January.
    7. Sandra Caçador & Joana Matos Dias & Pedro Godinho, 2020. "Global minimum variance portfolios under uncertainty: a robust optimization approach," Journal of Global Optimization, Springer, vol. 76(2), pages 267-293, February.
    8. Jang Ho Kim & Woo Chang Kim & Frank J. Fabozzi, 2017. "Penalizing variances for higher dependency on factors," Quantitative Finance, Taylor & Francis Journals, vol. 17(4), pages 479-489, April.
    9. Lauren Stagnol, 2016. "The Risk Parity Principle applied on a Corporate Bond Index using Duration Times Spread," Working Papers hal-04141582, HAL.
    10. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2021. "Robust Portfolio Selection Problems: A Comprehensive Review," Papers 2103.13806, arXiv.org, revised Jan 2022.

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    More about this item

    Keywords

    Robust portfolio optimization; Mean–variance model; Fundamental factors;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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