  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.
