Testing and dating structural changes in copula-based dependence measures

Abstract

This paper is concerned with testing and dating structural breaks in the dependence structure of multivariate time series. We consider a cumulative sum (CUSUM) type test for constant copula-based dependence measures, such as Spearman's rank correlation and quantile dependencies. The asymptotic null distribution is not known in closed form and critical values are estimated by an i.i.d. bootstrap procedure. We analyze size and power properties in a simulation study under different dependence measure settings, such as skewed and fat-tailed distributions. To date break points and to decide whether two estimated break locations belong to the same break event, we propose a pivot confidence interval procedure. Finally, we apply the test to the historical data of ten large financial firms during the last financial crisis from 2002 to mid-2013.

Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method

“…Bücher et al [10] proposed a change point detection method based on empirical copula process. Stark and Otto [11] proposed to test structural changes in multivariate time series in copula-base dependence measures, such as Spearman's ρ and quantile dependencies.…”