Stable Portfolio Selection Strategy for Mean-Variance-CVaR Model under High-Dimensional Scenarios
Joint Authors
Zhao, Xia
Shi, Yu
Jiang, Fengwei
Zhu, Yipin
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This paper aims to study stable portfolios with mean-variance-CVaR criteria for high-dimensional data.
Combining different estimators of covariance matrix, computational methods of CVaR, and regularization methods, we construct five progressive optimization problems with short selling allowed.
The impacts of different methods on out-of-sample performance of portfolios are compared.
Results show that the optimization model with well-conditioned and sparse covariance estimator, quantile regression computational method for CVaR, and reweighted L1 norm performs best, which serves for stabilizing the out-of-sample performance of the solution and also encourages a sparse portfolio.
American Psychological Association (APA)
Shi, Yu& Zhao, Xia& Jiang, Fengwei& Zhu, Yipin. 2020. Stable Portfolio Selection Strategy for Mean-Variance-CVaR Model under High-Dimensional Scenarios. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1194025
Modern Language Association (MLA)
Shi, Yu…[et al.]. Stable Portfolio Selection Strategy for Mean-Variance-CVaR Model under High-Dimensional Scenarios. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1194025
American Medical Association (AMA)
Shi, Yu& Zhao, Xia& Jiang, Fengwei& Zhu, Yipin. Stable Portfolio Selection Strategy for Mean-Variance-CVaR Model under High-Dimensional Scenarios. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1194025
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194025