YAN Xuechen, LI Lu, WANG Yashi, . Worst-Case Distortion Risk Measure with Application to Robust Portfolio Selection[J]. Chinese Journal of Applied Probability and Statistics, 2024, 40(1): 122-138.
Citation:
YAN Xuechen, LI Lu, WANG Yashi, . Worst-Case Distortion Risk Measure with Application to Robust Portfolio Selection[J]. Chinese Journal of Applied Probability and Statistics, 2024, 40(1): 122-138.
YAN Xuechen, LI Lu, WANG Yashi, . Worst-Case Distortion Risk Measure with Application to Robust Portfolio Selection[J]. Chinese Journal of Applied Probability and Statistics, 2024, 40(1): 122-138.
Citation:
YAN Xuechen, LI Lu, WANG Yashi, . Worst-Case Distortion Risk Measure with Application to Robust Portfolio Selection[J]. Chinese Journal of Applied Probability and Statistics, 2024, 40(1): 122-138.
Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, 230026, China
Postdoctoral Programme of Bank of Communications, Shanghai, 200120, China
School of Information Management for Law, China University of Political Science and Law, Beijing, 100027, China
Funds: The project was supported by Qian Duansheng Distinguished Scholar Support Program of China University of Political Science and Law (Grant No. DSJCXZ180403)
Portfolio selection depends heavily on the underlying distribution of loss. When the distribution information of loss can only be observed through a limited sample of data, robustness of the portfolio selection model is of crucial importance. Assuming that the underlying distribution of loss has a known mean and variance and lies within a ball centred on the reference distribution with the Wasserstein distance as the radius, this paper proposes a robust portfolio strategy model based on the distortion risk measure and translates it into a simpler equivalent form. Furthermore, simulation and empirical study are used to demonstrate the validity of the model.
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