A nonparametric procedure for detecting and dating multiple change points
in the correlation matrix of sequences of random variables is proposed. The proce-
dure is based on a recently proposed test for changes in correlation matrices at an
unknown point in time. Although the procedure requires constant expectations and
variances, only mild assumptions on the serial dependence structure are assumed. The
convergence rate of the change point estimators is derived and the asymptotic validity
of the procedure is proved. Moreover, the performance of the proposed algorithm in
finite samples is illustrated by means of a simulation study and the analysis of a real
data example with financial returns. These examples show that the algorithm has large
power in finite samples.

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• Dating multiple change points in the correlation matrix