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Data-driven robust mean-CVaR portfolio selection under distribution ambiguity

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  • Zhilin Kang
  • Xun Li
  • Zhongfei Li
  • Shushang Zhu

Abstract

In this paper, we present a computationally tractable optimization method for a robust mean-CVaR portfolio selection model under the condition of distribution ambiguity. We develop an extension that allows the model to capture a zero net adjustment via a linear constraint in the mean return, which can be cast as a tractable conic programme. Also, we adopt a nonparametric bootstrap approach to calibrate the levels of ambiguity and show that the portfolio strategies are relatively immune to variations in input values. Finally, we show that the resulting robust portfolio is very well diversified and superior to its non-robust counterpart in terms of portfolio stability, expected returns and turnover. The results of numerical experiments with simulated and real market data shed light on the established behaviour of our distributionally robust optimization model.

Suggested Citation

  • Zhilin Kang & Xun Li & Zhongfei Li & Shushang Zhu, 2019. "Data-driven robust mean-CVaR portfolio selection under distribution ambiguity," Quantitative Finance, Taylor & Francis Journals, vol. 19(1), pages 105-121, January.
    • Handle: RePEc:taf:quantf:v:19:y:2019:i:1:p:105-121
    DOI: 10.1080/14697688.2018.1466057
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    Citations

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    Cited by:

    1. 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.
    2. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2022. "Robust portfolio selection problems: a comprehensive review," Operational Research, Springer, vol. 22(4), pages 3203-3264, September.
    3. Wu, Xu & Zhang, Linlin & Li, Jia & Yan, Ruzhen, 2021. "Fractal statistical measure and portfolio model optimization under power-law distribution," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    4. Xin Hai & Kihun Nam, 2023. "Robust Wasserstein Optimization and its Application in Mean-CVaR," Papers 2306.15524, arXiv.org.
    5. Yang, Tingting & Huang, Xiaoxia, 2022. "Two new mean–variance enhanced index tracking models based on uncertainty theory," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    6. 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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