Robustness to Dependency in Portfolio Optimization Using Overlapping Marginals

• 成果类型: Article
• 署名作者: Doan, Xuan Vinh; Li, Xiaobo; Natarajan, Karthik
• 署名单位: University of Warwick; University of Warwick; University of Minnesota System; University of Minnesota Twin Cities; Singapore University of Technology & Design
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2015.1424
• 发表日期: 2015
• 页码: 1468-1488
• 关键词: value-at-risk; worst-case value; minimum fill-in; bounds; distributions; uncertainty; hypergraphs; selection; graphs; MODEL

摘要

In this paper, we develop a distributionally robust portfolio optimization model where the robustness is across different dependency structures among the random losses. For a Frechet class of discrete distributions with overlapping marginals, we show that the distributionally robust portfolio optimization problem is efficiently solvable with linear programming. To guarantee the existence of a joint multivariate distribution consistent with the overlapping marginal information, we make use of a graph theoretic property known as the running intersection property. Building on this property, we develop a tight linear programming formulation to find the optimal portfolio that minimizes the worst-case conditional value-at-risk measure. Lastly, we use a data-driven approach with financial return data to identify the Frechet class of distributions satisfying the running intersection property and then optimize the portfolio over this class of distributions. Numerical results in two different data sets show that the distributionally robust portfolio optimization model improves on the sample-based approach.