Dating multiple change points in the correlation matrix
Pedro Galeano () and
Additional contact information
Pedro Galeano: Universidad Carlos III de Madrid
TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2017, vol. 26, issue 2, 331-352
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; 62M10; 62G10; 91B84 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s11749-016-0513-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:testjl:v:26:y:2017:i:2:d:10.1007_s11749-016-0513-3
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/11749/PS2
Access Statistics for this article
TEST: An Official Journal of the Spanish Society of Statistics and Operations Research is currently edited by Alfonso Gordaliza and Ana F. Militino
More articles in TEST: An Official Journal of the Spanish Society of Statistics and Operations Research from Springer, Sociedad de Estadística e Investigación Operativa
Bibliographic data for series maintained by Sonal Shukla ().