Testing and Dating Structural Changes in Copula-based Dependence Measures

TL;DR

This paper develops a bootstrap-based CUSUM test for detecting and dating changes in dependence structures of multivariate time series, with applications to financial data during crises.

Contribution

It introduces a new testing and dating methodology for copula-based dependence changes using bootstrap critical values and pivot confidence intervals.

Findings

Test effectively detects dependence breaks in financial data.

Bootstrap approach accurately estimates critical values.

Method distinguishes multiple break points in complex dependence structures.

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.