Please use this identifier to cite or link to this item:
Title: A large CVaR-based portfolio selection model with weight constraints
Authors: Xu, Q
Zhou, Y
Jiang, C
Yu, K
Niu, X
Keywords: Finance;CVaR-based portfolio;Risk assessment;Weight constraints;Quantile regression
Issue Date: 2016
Citation: Economic Modelling, 2016, 59 pp. 436 - 447
Abstract: Although the traditional CVaR-based portfolio methods are successfully used in practice, the size of a portfolio with thousands of assets makes optimizing them difficult, if not impossible to solve. In this article we introduce a large CVaR-based portfolio selection method by imposing weight constraints on the standard CVaR-based portfolio selection model, which effectively avoids extreme positions often emerging in traditional methods. We propose to solve the large CVaR-based portfolio model with weight constraints using penalized quantile regression techniques, which overcomes the difficulties of large scale optimization in traditional methods. We illustrate the method via empirical analysis of optimal portfolios on Shanghai and Shenzhen 300 (HS300) index and Shanghai Stock Exchange Composite (SSEC) index of China. The empirical results show that our method is efficient to solve a large portfolio selection and performs well in dispersing tail risk of a portfolio by only using a small amount of financial assets.
ISSN: 0264-9993
Appears in Collections:Dept of Mathematics Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf4.24 MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.