A self-normalization break test for correlation matrix

Ji Eun Choi, Dong Wan Shin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We construct a new test for correlation matrix break based on the self-normalization method. The self-normalization test has practical advantage over the existing test: easy and stable implementation; not having the singularity issue and the bandwidth selection issue of the existing test; remedying size distortion problem of the existing test under (near) singularity, serial dependence, conditional heteroscedasticity or unconditional heteroscedasticity. This advantage is demonstrated experimentally by a Monte-Carlo simulation and theoretically by showing no need for estimation of complicated covariance matrix of the sample correlations. We establish the asymptotic null distribution and consistency of the self-normalization test. We apply the correlation matrix break tests to the stock log returns of the companies of 10 largest weight of the NASDAQ 100 index and to five volatility indexes for options on individual equities.

Original languageEnglish
Pages (from-to)2333-2353
Number of pages21
JournalStatistical Papers
Issue number5
StatePublished - Oct 2021

Bibliographical note

Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.


  • CUSUM test
  • Conditional heteroscedasticity
  • Correlation matrix break
  • Self-normalization
  • Serial dependence
  • Unconditional heteroscedasticity


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