We consider break-detection procedures for vector observations, both under independence as well as under an underlying structural time series scenario. The new methods involve L2-type criteria based on empirical characteristic functions. Asymptotic as well as Monte-Carlo results are presented. The new methods are also applied to time-series data from the financial sector.
CITATION STYLE
Hlávka, Z., Hušková, M., & Meintanis, S. G. (2017). Change point detection with multivariate observations based on characteristic functions. In From Statistics to Mathematical Finance: Festschrift in Honour of Winfried Stute (pp. 273–290). Springer International Publishing. https://doi.org/10.1007/978-3-319-50986-0_14
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