文件夹
投资组合优化
模棱两可
计算机科学
计量经济学
CVAR公司
项目组合管理
数学优化
树篱
现代投资组合理论
资产配置
集合(抽象数据类型)
经济
财务
数学
预期短缺
生物
管理
程序设计语言
生态学
项目管理
作者
Chi Seng Pun,Tianyu Wang,Zhenzhen Yan
标识
DOI:10.1287/msom.2023.1229
摘要
Problem definition: Nonstationarity of the random environment is a critical yet challenging concern in decision-making under uncertainty. We illustrate the challenge from the nonstationarity and the solution framework using the portfolio selection problem, a typical decision problem in a time-varying financial market. Methodology/Results: This paper models the nonstationarity by a regime-switching ambiguity set. In particular, we incorporate the time-varying feature of the stochastic environment into the traditional Wasserstein ambiguity set to build our regime-switching ambiguity set. This modeling framework has strong financial interpretations because the financial market is exposed to different economic cycles. We show that the proposed distributional optimization framework is computationally tractable. We further provide a general data-driven portfolio allocation framework based on a covariate-based estimation and a hidden Markov model. We prove that the approach can include the underlying distribution with a high probability when the sample size is larger than a quantitative bound, from which we further analyze the quality of the obtained portfolio. Extensive empirical studies are conducted to show that the proposed portfolio consistently outperforms the equally weighted portfolio (the 1/N strategy) and other benchmarks across both time and data sets. In particular, we show that the proposed portfolio exhibited a prompt response to the regime change in the 2008 financial crisis by reallocating the wealth into appropriate asset classes on account of the time-varying feature of our proposed model. Managerial implications: The proposed framework helps decision-makers hedge against time-varying uncertainties. Specifically, applying the proposed framework to portfolio selection problems helps investors respond promptly to the regime change in financial markets and adjust their portfolio allocation accordingly. Funding: This work was supported by the Neptune Orient Lines Fellowship [NOL21RP04], Singapore Ministry of Education Academic Research Fund Tier 2 [MOE-T2EP20220-0013], and Singapore Ministry of Education Academic Research Fund Tier 1 [Grant RG17/21]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1229
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