Detecting changes in cross-sectional dependence in multivariate time series

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.

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Document type	Article de périodique (Journal article) – Article de recherche
Access type	Accès interdit
Publication date	2014
Language	Anglais
Journal information	"Journal of Multivariate Analysis" - Vol. 132, p. 111-128 (2014)
Peer reviewed	yes
Publisher	Academic Press (Maryland Heights)
issn	0047-259X
e-issn	1095-7243
Publication status	Publié
Affiliation	UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles
Keywords	Change-point detection ; Empirical copula ; Multiplier central limit theorem ; Partial-sum process ; Ranks ; Strong mixing
Links	http://hdl.handle.net/2078.1/151300[Handle] https://doi.org/10.1016/j.jmva.2014.07.012[DOI]

See also
	[boreal:139264]	Detecting changes in cross-sectional dependence in multivariate time series