High-dimensional test for alpha in linear factor pricing models with sparse alternativesLong Feng, Wei Lan, Binghui Liu and Yanyuan Ma Journal of Econometrics, 2022, vol. 229, issue 1, 152-175 Abstract: 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. Keywords: High dimensionality; Linear factor pricing model; Securities in stock markets; Sparse alternatives; Tests for alpha (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:229:y:2022:i:1:p:152-175 DOI: 10.1016/j.jeconom.2021.07.011 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
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