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A self-normalization break test for correlation matrix

Title
A self-normalization break test for correlation matrix
Authors
Choi J.-E.Shin D.W.
Ewha Authors
신동완
SCOPUS Author ID
신동완scopus
Issue Date
2021
Journal Title
Statistical Papers
ISSN
0932-5026JCR Link
Citation
Statistical Papers vol. 62, no. 5, pp. 2333 - 2353
Keywords
Conditional heteroscedasticityCorrelation matrix breakCUSUM testSelf-normalizationSerial dependenceUnconditional heteroscedasticity
Publisher
Springer Science and Business Media Deutschland GmbH
Indexed
SCIE; SCOPUS scopus
Document Type
Article
Abstract
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. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
DOI
10.1007/s00362-020-01188-y
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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