TY - JOUR
T1 - Vast portfolio selection with gross-exposure constraints
AU - Fan, Jianqing
AU - Zhang, Jingjin
AU - Yu, Ke
N1 - Funding Information:
Jianqing Fan is Frederick L. Moore Professor, Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08540, and Honorary Professor, Department of Statistics, Shanghai University of Economics and Finance, Shanghai, China (E-mail: jqfan@princeton. edu). Jingjin Zhang, McKinsey & Company, Shanghai, China (E-mail: [email protected]). Ke Yu, JPMorgan Chase & Co., Singapore (E-mail: [email protected]). This project was supported by the NSF grant DMS-070433 and National Institute of General Medical Sciences of NIH through grant number R01-GM072611. The authors are truly grateful to the editor, Xuming He, the associate editor, and three referees for the insightful comments and constructive suggestions, which helped improve significantly the article.
PY - 2012
Y1 - 2012
N2 - This article introduces the large portfolio selection using gross-exposure constraints. It shows that with gross-exposure constraints, the empirically selected optimal portfolios based on estimated covariance matrices have similar performance to the theoretical optimal ones and there is no error accumulation effect from estimation of vast covariance matrices. This gives theoretical justification to the empirical results by Jagannathan andMa. It also shows that the no-short-sale portfolio can be improved by allowing some short positions. The applications to portfolio selection, tracking, and improvements are also addressed. The utility of our new approach is illustrated by simulation and empirical studies on the 100 Fama-French industrial portfolios and the 600 stocks randomly selected from Russell 3000.
AB - This article introduces the large portfolio selection using gross-exposure constraints. It shows that with gross-exposure constraints, the empirically selected optimal portfolios based on estimated covariance matrices have similar performance to the theoretical optimal ones and there is no error accumulation effect from estimation of vast covariance matrices. This gives theoretical justification to the empirical results by Jagannathan andMa. It also shows that the no-short-sale portfolio can be improved by allowing some short positions. The applications to portfolio selection, tracking, and improvements are also addressed. The utility of our new approach is illustrated by simulation and empirical studies on the 100 Fama-French industrial portfolios and the 600 stocks randomly selected from Russell 3000.
KW - Mean-variance efficiency
KW - Portfolio improvement
KW - Portfolio optimization
KW - Risk assessment
KW - Risk optimization
KW - Short-sale constraint
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U2 - 10.1080/01621459.2012.682825
DO - 10.1080/01621459.2012.682825
M3 - Article
C2 - 23293404
AN - SCOPUS:84864394076
SN - 0162-1459
VL - 107
SP - 592
EP - 606
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 498
ER -