Detecting changes in cross-sectional dependence in multivariate time series

TL;DR

This paper introduces a new statistical test for detecting changes in the dependence structure of multivariate time series, improving sensitivity over existing methods by using subsample ranks and a specialized resampling scheme.

Contribution

A novel test based on a variant of the sequential empirical copula process that enhances detection of dependence changes in multivariate time series.

Findings

Test shows improved power in simulations

Resampling scheme effectively accounts for serial dependence

Case studies demonstrate practical applicability

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.