Sequential Change-Point Detection in Time Series Models with Conditional Heteroscedasticity

In this study, we investigate a sequential procedure for the early detection of parameter changes in conditionally heteroscedastic time series models. We introduce the detectors based on the cumulative sum of score vectors and residuals for this procedure. The asymptotic properties of the monitoring procedures are established under the null and alternative hypotheses. Simulation results are provided for illustration

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Year of publication:	2023
Authors:	Lee, Youngmi ; Kim, Sungdon ; Oh, Haejune
Publisher:	[S.l.] : SSRN
Subject:	Zeitreihenanalyse \| Time series analysis \| ARCH-Modell \| ARCH model \| Heteroskedastizität \| Heteroscedasticity \| Schätztheorie \| Estimation theory

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Extent:	1 Online-Ressource (5 p)
Type of publication:	Book / Working Paper
Language:	English
Other identifiers:	10.2139/ssrn.4366558 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014260758