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

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

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-tes...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 11; no. 18; p. 4018
Main Authors: Arrouch, Mohamed Salah Eddine, Elharfaoui, Echarif, Ngatchou-Wandji, Joseph
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-09-2023
Subjects:
Analysis
Autoregressive models
change-points
CHARN models
Commodity futures
conditional least-squares
Financial analysis
Hypotheses
Information management
Mathematical functions
Mathematics
mixing
Parameter estimation
Quality control
Random variables
Statistics
tests
Time series
Volatility
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11184018