Factor-adjusted multiple testing of correlations

Lilun Du, Wei Lan, Ronghua Luo and Pingshou Zhong

Computational Statistics & Data Analysis, 2018, vol. 128, issue C, 34-47

Abstract: Both global and multiple testing procedures have previously been proposed to untangle the correlation structures among high-dimensional data. In this article, we extend the results of both tests to learn the correlations of the factor-adjusted residuals in an approximate factor model, which can be used to simultaneously detect the highly matched pairs of stocks in finance. The factor-adjusted residuals are not observed and estimated using the method of principal components. We theoretically investigate the effects of estimating the factor-adjusted residuals on the subsequent global and multiple testing procedures. Furthermore, we demonstrate that the correlation structure of the factor-adjusted residuals can be recovered if appropriate thresholds are used in the proposed multiple testing procedure. Extensive simulation studies and a real data analysis are presented in which the proposed method is applied to select stock pairs in China’s stock market.

Keywords: Factor-adjusted correlation learning; False discovery rate; Pairs trading; Model selection consistency (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:128:y:2018:i:c:p:34-47

DOI: 10.1016/j.csda.2018.06.001

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