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Unveiling change-point detection with ARMA-GARCH models

Year: 2024
Type: article
Abstract: This paper delves into the asymptotic theory of a Cumulative Sum (CUSUM)-type test designed for detecting change points in the variance of AutoRegressive Moving-Average models, incorporating Generalized Autoregressive Conditional Heteroscedasticity innovations. We demonstrate that, under the null hypothesis (no change), the CUSUM test statistic converges to the supremum of a standard Brownian bridge. Using a Monte Carlo simulation, we highlight the strong performance of our proposed test when compared to the methods by Song and Kang. Furthermore, we provide a real-world data analysis to showcase the practical application of our test.
Language: English
Subfield: Finance
Open Access status: diamond
OpenAlex ID: W4402958205
FWCI: 0.365
Cites:
Cited by: 1
Related to: 10