Change Point Detection with Multivariate Observations Based on Characteristic Functions

Zdeněk Hlávka (), Marie Hušková () and Simos G. Meintanis ()
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Zdeněk Hlávka: Charles University, Faculty of Mathematics and Physics, Department of Statistics
Marie Hušková: Charles University, Faculty of Mathematics and Physics, Department of Statistics
Simos G. Meintanis: National and Kapodistrian University of Athens, Department of Economics

Chapter Chapter 14 in From Statistics to Mathematical Finance, 2017, pp 273-290 from Springer

Abstract: Abstract 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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-50986-0_14

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DOI: 10.1007/978-3-319-50986-0_14

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