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Change Point Detection with Multivariate Observations Based on Characteristic Functions

In: From Statistics to Mathematical Finance

Author

Listed:
  • 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
    North-West University, Unit for Business Mathematics and Informatics)

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.

Suggested Citation

  • Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2017. "Change Point Detection with Multivariate Observations Based on Characteristic Functions," Springer Books, in: Dietmar Ferger & Wenceslao González Manteiga & Thorsten Schmidt & Jane-Ling Wang (ed.), From Statistics to Mathematical Finance, chapter 0, pages 273-290, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-50986-0_14
    DOI: 10.1007/978-3-319-50986-0_14
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