Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 21 August 2023 / Approved: 21 August 2023 / Online: 21 August 2023 (15:20:45 CEST)

A peer-reviewed article of this Preprint also exists.

Arrouch, M.S.E.; Elharfaoui, E.; Ngatchou-Wandji, J. Change-Point Detection in the Volatility of Conditional Heteroscedastic Autoregressive Nonlinear Models. Mathematics 2023, 11, 4018. Arrouch, M.S.E.; Elharfaoui, E.; Ngatchou-Wandji, J. Change-Point Detection in the Volatility of Conditional Heteroscedastic Autoregressive Nonlinear Models. Mathematics 2023, 11, 4018.

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 also applied to two sets of real data.

Keywords

change-points; CHARN models; conditional least-squares; mixing; tests

Subject

Computer Science and Mathematics, Probability and Statistics

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