Data-driven robust mean-CVaR portfolio selection under distribution ambiguity |
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Authors: | Zhilin Kang Xun Li Shushang Zhu |
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Institution: | 1. School of Mathematical Sciences, Huaqiao University , Fujian, 362021 P.R. China.;2. School of Mathematics, Sun Yat-sen University , Guangzhou, 510275 P.R. China.;3. Department of Applied Mathematics, The Hong Kong Polytechnic University , .;4. Sun Yat-sen Business School, Sun Yat-sen University , Guangzhou, 510275 P.R. China. |
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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. |
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Keywords: | Portfolio selection Distributionally robust optimization Zero net adjustment Bootstrap Conic programmes |
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