Sparse and robust mean-variance portfolio optimization problems

被引:29
作者
Dai, Zhifeng [1 ,2 ]
Wang, Fei [1 ]
机构
[1] Changsha Univ Sci & Technol, Coll Math & Stat, Changsha, Hunan, Peoples R China
[2] Changsha Univ Sci & Technol, Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha, Hunan, Peoples R China
关键词
Portfolio optimization; Mean-variance portfolio; Regularization; Robust optimization; VALUE-AT-RISK; SELECTION; CONSTRAINTS;
D O I
10.1016/j.physa.2019.04.151
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Mean-variance portfolios have been criticized because of unsatisfying out-of-sample performance and the presence of extreme and unstable asset weights. The bad performance is caused by estimation errors in inputs parameters, that is the covariance matrix and the expected return vector, especially the expected return vector. This topic has attracted wide attention. In this paper, we aim to find better portfolio optimization model to reduce the undesired impact of parameter uncertainty and estimation errors of mean-variance portfolio model. Firstly, we introduce a sparse mean-variance portfolio model, and give some insight about sparsity. Secondly, we propose two sparse and robust portfolio models by using objective function regularization and robust optimization. Finally, three empirical studies are proposed with real market data. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:1371 / 1378
页数:8
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