Change point detection with multivariate observations based on characteristic functions
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Hlávka, Zdeněk
Meintanis, Simos G.
Hušková, Marie
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Springer
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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
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Hlávka, Z. et al. 2017. Change point detection with multivariate observations based on characteristic functions. (In Ferger, D., Manteiga, W.G., Schmidt, T. & Wang, J.-L., eds. From statistics to mathematical finance: Festschrift in honour of Winfried Stute: 273-290. [https://doi.org/10.1007/978-3-319-50986-0_14]