Data-Driven Robust Mean-CVaR Portfolio Selection Under Distribution Ambiguity

Zhilin Kang, Xun Li, Zhongfei Li, Shushang Zhu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)105-121
Number of pages17
JournalQuantitative Finance
Issue number1
Publication statusPublished - 2 Jan 2019


  • Bootstrap
  • C14
  • C52
  • C61
  • C63
  • Conic programmes
  • Distributionally robust optimization
  • G11
  • Portfolio selection
  • Zero net adjustment

ASJC Scopus subject areas

  • Finance
  • Economics, Econometrics and Finance(all)


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