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A self-normalization test for correlation change

Title
A self-normalization test for correlation change
Authors
Choi, Ji-EunShin, Dong Wan
Ewha Authors
신동완
SCOPUS Author ID
신동완scopus
Issue Date
2020
Journal Title
ECONOMICS LETTERS
ISSN
0165-1765JCR Link

1873-7374JCR Link
Citation
ECONOMICS LETTERS vol. 193
Keywords
Conditional heteroscedasticityCorrelation breakCUSUM testSelf-normalizationSerial dependence
Publisher
ELSEVIER SCIENCE SA
Indexed
SSCI; SCOPUS WOS scopus
Document Type
Article
Abstract
We propose a new CUSUM-type test for a correlation break based on the self-normalization method. The self-normalization test is implemented much simpler than the existing tests based on the longrun variance which need to specify bandwidths and to evaluate complicated consistent estimators for the long-run variances. The limiting null distribution and consistency of the proposed test under an alternative are established. A Monte Carlo simulation demonstrates that the self-normalization test has reasonable size and comparable power, but the existing tests have severe size distortions for serially correlated and/or conditionally heteroscedastic samples. An analysis of returns and realized volatilities of some US, Europe and Japan stock prices by the proposed test indicates absence of correlation break during the period of global financial crisis while those by the existing tests indicate presence of it. (C) 2019 Elsevier B.V. All rights reserved.
DOI
10.1016/j.econlet.2019.02.007
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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