Break detection in the covariance structure of multivariate time series models

Alexander Aue, Siegfried Hörmann, Lajos Horváth, Matthew Reimherr

Research output: Contribution to journalArticlepeer-review

210 Scopus citations


In this paper, we introduce an asymptotic test procedure to assess the stability of volatilities and cross-volatilites of linear and nonlinear multivariate time series models. The test is very flexible as it can be applied, for example, to many of the multivariate GARCH models established in the literature, and also works well in the case of high dimensionality of the underlying data. Since it is nonparametric, the procedure avoids the difficulties associated with parametric model selection, model fitting and parameter estimation. We provide the theoretical foundation for the test and demonstrate its applicability via a simulation study and an analysis of financial data. Extensions to multiple changes and the case of infinite fourth moments are also discussed.

Original languageEnglish (US)
Pages (from-to)4046-4087
Number of pages42
JournalAnnals of Statistics
Issue number6 B
StatePublished - Dec 2009

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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