TY - JOUR
T1 - High-dimensional test for alpha in linear factor pricing models with sparse alternatives
AU - Feng, Long
AU - Lan, Wei
AU - Liu, Binghui
AU - Ma, Yanyuan
N1 - Funding Information:
The authors gratefully acknowledge NSFC grants 11571068, 11501092, 71532001, 11931014, 71991472; the Special Fund for Key Laboratories of Jilin Province, China grant 20190201285JC; the Joint Lab of Data Science and Business Intelligenceat Southwestern University of Finance and Economics. The work is partially supported by grants from National Science Foundation and National Institute of Health. We are very grateful to the Co-Editor Prof. Torben G. Andersen for the insightful comments, helpful suggestions and detailed editorial job, which have a great effect on improving the quality of this paper.
Funding Information:
The authors gratefully acknowledge NSFC grants 11571068 , 11501092 , 71532001 , 11931014 , 71991472 ; the Special Fund for Key Laboratories of Jilin Province, China grant 20190201285JC ; the Joint Lab of Data Science and Business Intelligence at Southwestern University of Finance and Economics. The work is partially supported by grants from National Science Foundation and National Institute of Health . We are very grateful to the Co-Editor Prof. Torben G. Andersen for the insightful comments, helpful suggestions and detailed editorial job, which have a great effect on improving the quality of this paper.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/7
Y1 - 2022/7
N2 - We consider the problem of testing for the presence of alpha in Linear Factor Pricing Models. We propose a novel test of the max-of-squares type, which is designed to deal with the high dimensionality of the securities and the sparse alternatives. We rigorously show that the proposed test has attractive theoretical properties and demonstrate its superior performance via Monte Carlo experiments. These results are established when the number of securities is larger than the time dimension of the return series, and the alternative hypothesis is sparse, i.e. the alpha vector is sparse. As a general alternative, we suggest to combine the max-of-squares type test and a sum-of-squares type test, to benefit from the power advantages of both tests. We apply the two proposed tests to the monthly returns on securities in the Chinese and the U.S. stock markets, respectively under the Fama–French three-factor model, and confirm the usefulness of the proposed tests in detecting the presence of alpha.
AB - We consider the problem of testing for the presence of alpha in Linear Factor Pricing Models. We propose a novel test of the max-of-squares type, which is designed to deal with the high dimensionality of the securities and the sparse alternatives. We rigorously show that the proposed test has attractive theoretical properties and demonstrate its superior performance via Monte Carlo experiments. These results are established when the number of securities is larger than the time dimension of the return series, and the alternative hypothesis is sparse, i.e. the alpha vector is sparse. As a general alternative, we suggest to combine the max-of-squares type test and a sum-of-squares type test, to benefit from the power advantages of both tests. We apply the two proposed tests to the monthly returns on securities in the Chinese and the U.S. stock markets, respectively under the Fama–French three-factor model, and confirm the usefulness of the proposed tests in detecting the presence of alpha.
UR - http://www.scopus.com/inward/record.url?scp=85113308027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85113308027&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2021.07.011
DO - 10.1016/j.jeconom.2021.07.011
M3 - Article
AN - SCOPUS:85113308027
VL - 229
SP - 152
EP - 175
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 1
ER -