Dating multiple change points in the correlation matrix Articles uri icon

published in

publication date

  • June 2017

start page

  • 331

end page

  • 332

issue

  • 2

volume

  • 26

international standard serial number (ISSN)

  • 1133-0686

electronic international standard serial number (EISSN)

  • 1863-8260

abstract

  • A nonparametric procedure for detecting and dating multiple change points in the correlation matrix of sequences of random variables is proposed. The procedure 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.

keywords

  • binary segmentation algorithm; correlation matrix; cusum statistics; financial returns; multiple change point detection; nonparametric estimation; multivariate time-series; binary segmentation; cumulative sums; break detection; models; parameters; variance