Change Point Detection with Multivariate Observations Based on Characteristic Functions

  • Zdeněk HlávkaEmail author
  • Marie Hušková
  • Simos G. Meintanis


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.



The research of Simos Meintanis was partially supported by grant number 11699 of the Special Account for Research Grants (E\(\Lambda \)KE) of the National and Kapodistrian University of Athens. The research of Marie Hušková and Zdeněk Hlávka was partially supported by grant GAČR 15-09663S and AP research network grant Nr. P7/06 of the Belgian government (Belgian Science Policy).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zdeněk Hlávka
    • 1
    Email author
  • Marie Hušková
    • 1
  • Simos G. Meintanis
    • 2
    • 3
  1. 1.Faculty of Mathematics and Physics, Department of StatisticsCharles UniversityPragueCzech Republic
  2. 2.Department of EconomicsNational and Kapodistrian University of AthensAthensGreece
  3. 3.Unit for Business Mathematics and InformaticsNorth-West UniversityPotchefstroomSouth Africa

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