BREAK DETECTION IN THE COVARIANCE STRUCTURE OF MULTIVARIATE TIME SERIES MODELS

• 成果类型: Article
• 署名作者: Aue, Alexander; Hormann, Siegfried; Horvath, Lajos; Reimherr, Matthew
• 署名单位: University of California System; University of California Davis; Utah System of Higher Education; University of Utah; University of Chicago
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/09-AOS707
• 发表日期: 2009
• 页码: 4046-4087
• 关键词: weak dependence; garch processes; ARCH; heteroskedasticity; stationarity; squares; sums

摘要

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.