Journal
JOURNAL OF MULTIVARIATE ANALYSIS
Volume 132, Issue -, Pages 111-128Publisher
ELSEVIER INC
DOI: 10.1016/j.jmva.2014.07.012
Keywords
Change-point detection; Empirical copula; Multiplier central limit theorem; Partial-sum process; Ranks; Strong mixing
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Funding
- German Research Foundation (DFG) [SFB 823]
- Belgian government (Belgian Science Policy) [P7/06]
- contract Projet d'Actions de Recherche Concertees of the Communaute francaise de Belgique [12/17-045]
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Classical and more recent tests for detecting distributional changes in multivariate time series often lack power against alternatives that involve changes in the cross-sectional dependence structure. To be able to detect such changes better, a test is introduced based on a recently studied variant of the sequential empirical copula process. In contrast to earlier attempts, ranks are computed with respect to relevant subsamples, with beneficial consequences for the sensitivity of the test. For the computation of p-values we propose a multiplier resampling scheme that takes the serial dependence into account. The large-sample theory for the test statistic and the resampling scheme is developed. The finite-sample performance of the procedure is assessed by Monte Carlo simulations. Two case studies involving time series of financial returns are presented as well. (C) 2014 Elsevier Inc. All rights reserved.
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