Portfolio selection under distributional uncertainty: A relative robust CVaR approach

被引：88

作者：

Huang, Dashan ^{[2
]}

Zhu, Shushang ^{[3
]}

Fabozzi, Frank J. ^{[4
]}

Fukushima, Masao ^{[1
]}

机构：

[1] Kyoto Univ, Dept Appl Math & Phys, Grad Sch Informat, Kyoto 6068501, Japan

[2] Washington Univ, John M Olin Sch Business, St Louis, MO 63130 USA

[3] Fudan Univ, Dept Management Sci, Sch Management, Shanghai 200433, Peoples R China

[4] Yale Univ, Sch Management, New Haven, CT 06520 USA

来源：

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2010年 / 203卷 / 01期

基金：

日本学术振兴会; 美国国家科学基金会;

关键词：

Conditional value-at-risk; Worst-case conditional value-at-risk; Relative robust conditional value-at-risk; Portfolio selection problem; Linear programming; VALUE-AT-RISK; ASSET ALLOCATION; OPTIMIZATION; PROGRAMS;

D O I：

10.1016/j.ejor.2009.07.010

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

Robust optimization, one of the most popular topics in the field of optimization and control since the late 1990s, deals with an optimization problem involving uncertain parameters. In this paper, we consider the relative robust conditional Value-at-risk portfolio selection problem where the underlying probability distribution of portfolio return is only known to belong to a certain set. Our approach not only takes into account the worst-case scenarios of the uncertain distribution, but also pays attention to the best possible decision with respect to each realization of the distribution. We also illustrate how to construct a robust portfolio with multiple experts (priors) by solving a sequence of linear programs or a second-order cone program. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：185 / 194

页数：10

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