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 breakpoints 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 10 large financial firms during the last financial crisis from 2002 to mid-2013.

本文涉及在多元时间序列的依存结构中测试和确定结构中断的日期。我们考虑针对基于常数copula的依赖度量（例如，Spearman的秩相关和分位数依赖）进行累积总和（CUSUM）类型测试。渐近零分布以闭合形式未知，临界值通过iid引导程序估算。我们在模拟研究中，在不同的依赖度量设置（例如偏态分布和肥尾分布）下分析大小和功率属性。为了确定断点的日期并确定两个估计的中断位置是否属于同一中断事件，我们提出了一个枢轴置信区间程序。最后，我们将测试应用于2002年至2013年中的上一次金融危机期间的10家大型金融公司的历史数据。