Detecting changes in cross-sectional dependence in multivariate time series

Abstract

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.

32 pages, 6 tables

References in corpus (4)

• Asymptotics of empirical copula processes under non-restrictive smoothness assumptions
• A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing
• Testing for Changes in Kendall's Tau
• A fluctuation test for constant Spearman's rho with nuisance-free limit distribution

Cited by in corpus (6)

• A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing
• Testing for Changes in Kendall's Tau
• A fluctuation test for constant Spearman's rho with nuisance-free limit distribution
• Dependent multiplier bootstraps for non-degenerate $U$-statistics under mixing conditions with applications
• Robust change point tests by bounded transformations
• Testing the constancy of Spearman's rho in multivariate time series