Dating Multiple Change Points in the Correlation Matrix

Abstract

We propose a nonparametric procedure for detecting and dating multiple change points in the correlation matrix of a sequence of random variables. The procedure is based on a test for changes in correlation matrices at an unknown point in time recently proposed by Wied (2014). Although the procedure requires constant expectations and variances, only mild assumptions on the serial dependence structure are assumed. We show the validity of the procedure including the convergence rate of the change point estimators. Moreover, we illustrate its performance in finite samples by means of a simulation study and the analysis of a real data example with financial returns. These examples show that the proposed algorithm has large power in finite samples.

Keywords: Binary segmentation algorithm; Correlation matrix; CUSUM statistics; Financial returns; Multiple change point detection; Nonparametric estimation

JEL Classification: C12, C13, C14, C32, C58