Duan, Fang and Wied, Dominik (2018). A residual-based multivariate constant correlation test. Metrika, 81 (6). S. 653 - 688. HEIDELBERG: SPRINGER HEIDELBERG. ISSN 1435-926X

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Abstract

We propose a new multivariate constant correlation test based on residuals. This test takes into account the whole correlation matrix instead of the considering merely marginal correlations between bivariate data series. In financial markets, it is unrealistic to assume that the marginal variances are constant. This motivates us to develop a constant correlation test which allows for non-constant marginal variances in multivariate time series. However, when the assumption of constant marginal variances is relaxed, it can be shown that the residual effect leads to nonstandard limit distributions of the test statistics based on residual terms. The critical values of the test statistics are not directly available and we use a bootstrap approximation to obtain the corresponding critical values for the test. We also derive the limit distribution of the test statistics based on residuals under the null hypothesis. Monte Carlo simulations show that the test has appealing size and power properties in finite samples. We also apply our test to the stock returns in Euro Stoxx 50 and integrate the test into a binary segmentation algorithm to detect multiple break points.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Duan, FangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wied, DominikUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-178564
DOI: 10.1007/s00184-018-0675-y
Journal or Publication Title: Metrika
Volume: 81
Number: 6
Page Range: S. 653 - 688
Date: 2018
Publisher: SPRINGER HEIDELBERG
Place of Publication: HEIDELBERG
ISSN: 1435-926X
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
TIME-SERIES; MOMENTS ESTIMATORS; GENERALIZED-METHOD; BREAK DETECTION; VARIANCE; MATRIX; VALUESMultiple languages
Statistics & ProbabilityMultiple languages
Refereed: Yes
URI: http://kups.ub.uni-koeln.de/id/eprint/17856

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