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Factor-adjusted multiple testing of correlations

Author

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  • Du, Lilun
  • Lan, Wei
  • Luo, Ronghua
  • Zhong, Pingshou

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.

Suggested Citation

  • Du, Lilun & Lan, Wei & Luo, Ronghua & Zhong, Pingshou, 2018. "Factor-adjusted multiple testing of correlations," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 34-47.
    • Handle: RePEc:eee:csdana:v:128:y:2018:i:c:p:34-47
    DOI: 10.1016/j.csda.2018.06.001
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    References listed on IDEAS

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