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Change-Point Detection in the Volatility of Conditional Heteroscedastic Autoregressive Nonlinear Models

Author

Listed:
  • Mohamed Salah Eddine Arrouch

    (Department of Mathematics, Faculty of Sciences, Chouaïb Doukkali University, El Jadida 24000, Morocco)

  • Echarif Elharfaoui

    (Department of Mathematics, Faculty of Sciences, Chouaïb Doukkali University, El Jadida 24000, Morocco)

  • Joseph Ngatchou-Wandji

    (EHESP French School of Public Health, Université de Rennes, 35043 Rennes, France
    Institut Élie Cartan de Lorraine, Université de Lorraine, 54052 Vandoeuvre-Lès-Nancy, France)

Abstract

This paper studies single change-point detection in the volatility of a class of parametric conditional heteroscedastic autoregressive nonlinear (CHARN) models. The conditional least-squares (CLS) estimators of the parameters are defined and are proved to be consistent. A Kolmogorov–Smirnov type-test for change-point detection is constructed and its null distribution is provided. An estimator of the change-point location is defined. Its consistency and its limiting distribution are studied in detail. A simulation experiment is carried out to assess the performance of the results, which are compared to recent results and applied to two sets of real data.

Suggested Citation

  • Mohamed Salah Eddine Arrouch & Echarif Elharfaoui & Joseph Ngatchou-Wandji, 2023. "Change-Point Detection in the Volatility of Conditional Heteroscedastic Autoregressive Nonlinear Models," Mathematics, MDPI, vol. 11(18), pages 1-31, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:4018-:d:1245015
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    References listed on IDEAS

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