Dating multiple change points in the correlation matrix
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Date
2014-05-12
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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.
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Keywords
binary segmentation algorithm, nonparametric estimation, multiple change point detection, financial returns, CUSUM statistics, correlation matrix