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, Volume 26, Issue 2, pp 331–352 | Cite as

Dating multiple change points in the correlation matrix

  • Pedro GaleanoEmail author
  • Dominik Wied
Original Paper

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 

Mathematics Subject Classification

62M10 62G10 91B84 

Notes

Acknowledgements

Financial support by Ministerio de Economía y Competitividad Grant ECO2015-66593-P and Deutsche Forschungsgemeinschaft (SFB 823, project A1) is gratefully acknowledged. We also would like to thank three anonymous referees for very helpful comments.

Supplementary material

11749_2016_513_MOESM1_ESM.pdf (129 kb)
Supplementary material 1 (pdf 129 KB)

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Copyright information

© Sociedad de Estadística e Investigación Operativa 2016

Authors and Affiliations

  1. 1.Department of Statistics, UC3M-BS Institute of Financial Big DataUniversidad Carlos III de MadridGetafe, MadridSpain
  2. 2.Institute for Econometrics and StatisticsUniversity of CologneKölnGermany
  3. 3.TU DortmundFakultät StatistikDortmundGermany

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