Data-Driven Distributionally Robust CVaR Portfolio Optimization Under A Regime-Switching Ambiguity Set

IF 4.8 3区管理学 Q1 MANAGEMENT

M&som-Manufacturing & Service Operations Management Pub Date : 2023-09-01 DOI:10.1287/msom.2023.1229

Chi Seng Pun, Tianyu Wang, Zhenzhen Yan

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引用次数: 0

Abstract

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

查看原文本刊更多论文

状态交换模糊集下数据驱动的分布鲁棒CVaR投资组合优化

问题定义:随机环境的非平稳性是不确定环境下决策的关键问题。本文以一个典型的时变金融市场决策问题——投资组合选择问题为例，说明了非平稳性的挑战和解决框架。方法/结果:本文通过一个状态切换模糊集来模拟非平稳性。特别是，我们将随机环境的时变特征融入到传统的Wasserstein模糊集中来构建我们的状态切换模糊集。这个建模框架具有很强的金融解释，因为金融市场会受到不同经济周期的影响。我们证明了所提出的分布优化框架在计算上是可处理的。我们进一步提供了一个基于协变量估计和隐马尔可夫模型的通用数据驱动的投资组合分配框架。我们证明了该方法在样本容量大于一个定量界限时，可以高概率地包含潜在的分布，并由此进一步分析了所得到的投资组合的质量。广泛的实证研究表明，所提出的投资组合在时间和数据集上始终优于等加权投资组合(1/N策略)和其他基准。特别是，我们表明，由于我们提出的模型的时变特征，通过将财富重新分配到适当的资产类别，我们提出的投资组合对2008年金融危机中的政权变化表现出了迅速的反应。管理意义:建议的框架有助于决策者对冲时变的不确定性。具体而言，将所提出的框架应用于投资组合选择问题有助于投资者及时响应金融市场的制度变化，并相应地调整其投资组合配置。基金资助:本研究得到了海王星东方航线奖学金[NOL21RP04]、新加坡教育部学术研究基金第二级[MOE-T2EP20220-0013]和新加坡教育部学术研究基金第一级[Grant RG17/21]的支持。补充材料:电子伴侣可在https://doi.org/10.1287/msom.2023.1229上获得

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来源期刊

M&som-Manufacturing & Service Operations Management 管理科学-运筹学与管理科学

CiteScore

9.30

自引率

12.70%

发文量

184

审稿时长

12 months

期刊介绍： M&SOM is the INFORMS journal for operations management. The purpose of the journal is to publish high-impact manuscripts that report relevant research on important problems in operations management (OM). The field of OM is the study of the innovative or traditional processes for the design, procurement, production, delivery, and recovery of goods and services. OM research entails the control, planning, design, and improvement of these processes. This research can be prescriptive, descriptive, or predictive; however, the intent of the research is ultimately to develop some form of enduring knowledge that can lead to more efficient or effective processes for the creation and delivery of goods and services. M&SOM encourages a variety of methodological approaches to OM research; papers may be theoretical or empirical, analytical or computational, and may be based on a range of established research disciplines. M&SOM encourages contributions in OM across the full spectrum of decision making: strategic, tactical, and operational. Furthermore, the journal supports research that examines pertinent issues at the interfaces between OM and other functional areas.