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Consistency of the Model Order Change-Point Estimator for GARCH Models

Irungu, Irene W., Mwita, Peter N., Waititu, Antony G. (2018) Consistency of the Model Order Change-Point Estimator for GARCH Models. Journal of Mathematical Finance, 8 (2). pp. 266-282. ISSN 2162-2434. (doi:10.4236/jmf.2018.82018) (KAR id:97236)


GARCH models have been commonly used to capture volatility dynamics in financial time series. A key assumption utilized is that the series is stationary as this allows for model identifiability. This however violates the volatility clustering property exhibited by financial returns series. Existing methods attribute this phenomenon to parameter change. However, the assumption of fixed model order is too restrictive for long time series. This paper proposes a change-point estimator based on Manhattan distance. The estimator is applicable to GARCH model order change-point detection. Procedures are based on the sample autocorrelation function of squared series. The asymptotic consistency of the estimator is proven theoretically.

Item Type: Article
DOI/Identification number: 10.4236/jmf.2018.82018
Uncontrolled keywords: Autocorrelation Function, Change-Point, Consistency, GARCH, Manhattan Distance, Model Order
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Irene Irungu
Date Deposited: 30 Sep 2022 13:56 UTC
Last Modified: 03 Oct 2022 10:29 UTC
Resource URI: (The current URI for this page, for reference purposes)

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