TY - JOUR
T1 - A Stein-type shrinkage estimator of the covariance matrix for portfolio selections
AU - Sun, Ruili
AU - Ma, Tiefeng
AU - Liu, Shuangzhe
N1 - Funding Information:
Acknowledgements We would like to thank the Editor, Associate Editor and Referees very much for their constructive comments, which significantly helped us improve the manuscript. The first two authors’ research was supported by the Fundamental Research Funds for Central Universities (Nos. JBK1607121, JBK120509, JBK140507). This study was also supported by the National Natural Science Foundation of China (Nos. 11471264, 11401148, 11571282, 51437003).
Publisher Copyright:
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - The covariance matrix plays a crucial role in portfolio optimization problems as the risk and correlation measure of asset returns. An improved estimation of the covariance matrix can enhance the performance of the portfolio. In this paper, based on the Cholesky decomposition of the covariance matrix, a Stein-type shrinkage strategy for portfolio weights is constructed under the mean-variance framework. Furthermore, according to the agent’s maximum expected utility value, a portfolio selection strategy is proposed. Finally, simulation experiments and an empirical study are used to test the feasibility of the proposed strategy. The numerical results show our portfolio strategy performs satisfactorily.
AB - The covariance matrix plays a crucial role in portfolio optimization problems as the risk and correlation measure of asset returns. An improved estimation of the covariance matrix can enhance the performance of the portfolio. In this paper, based on the Cholesky decomposition of the covariance matrix, a Stein-type shrinkage strategy for portfolio weights is constructed under the mean-variance framework. Furthermore, according to the agent’s maximum expected utility value, a portfolio selection strategy is proposed. Finally, simulation experiments and an empirical study are used to test the feasibility of the proposed strategy. The numerical results show our portfolio strategy performs satisfactorily.
KW - Cholesky decomposition
KW - Covariance matrix
KW - Portfolio optimization
KW - Stein shrinkage
KW - Utility function
UR - http://www.scopus.com/inward/record.url?scp=85047424789&partnerID=8YFLogxK
U2 - 10.1007/s00184-018-0663-2
DO - 10.1007/s00184-018-0663-2
M3 - Article
AN - SCOPUS:85047424789
SN - 0026-1335
VL - 81
SP - 931
EP - 952
JO - Metrika
JF - Metrika
IS - 8
ER -