Detecting changes in cross-sectional dependence in multivariate time series

JOURNAL OF MULTIVARIATE ANALYSIS

卷:132

Article

Buecher, Axel¹  Kojadinovic, Ivan²  Rohmer, Tom²  Segers, Johan³ 

[1] Heidelberg Univ, Inst Angew Math, D-69120 Heidelberg, Germany

[2] Univ Pau & Pays Adour, UMR CNRS 5142, Lab Math & Applicat, F-64013 Pau, France

[3] Catholic Univ Louvain, Inst Stat Biostat & Sci Actuarielles, B-1348 Louvain, Belgium

关键词: Change-point detection;    Empirical copula;    Multiplier central limit theorem;    Partial-sum process;    Ranks;    Strong mixing;   

DOI  :  10.1016/j.jmva.2014.07.012

来源: Elsevier

【 摘 要 】

Classical and more recent tests for detecting distributional changes in multivariate time series often lack power against alternatives that involve changes in the cross-sectional dependence structure. To be able to detect such changes better, a test is introduced based on a recently studied variant of the sequential empirical copula process. In contrast to earlier attempts, ranks are computed with respect to relevant subsamples, with beneficial consequences for the sensitivity of the test. For the computation of p-values we propose a multiplier resampling scheme that takes the serial dependence into account. The large-sample theory for the test statistic and the resampling scheme is developed. The finite-sample performance of the procedure is assessed by Monte Carlo simulations. Two case studies involving time series of financial returns are presented as well. (C) 2014 Elsevier Inc. All rights reserved.

【 授权许可】

Free