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Sequential change-point detection in time series models with conditional heteroscedasticity

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  • Lee, Youngmi
  • Kim, Sungdon
  • Oh, Haejune

Abstract

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.

Suggested Citation

  • Lee, Youngmi & Kim, Sungdon & Oh, Haejune, 2024. "Sequential change-point detection in time series models with conditional heteroscedasticity," Economics Letters, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:ecolet:v:236:y:2024:i:c:s0165176524000806
    DOI: 10.1016/j.econlet.2024.111597
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    References listed on IDEAS

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    1. Haejune Oh & Sangyeol Lee, 2018. "On score vector- and residual-based CUSUM tests in ARMA–GARCH models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 385-406, August.
    2. Berkes, István & Gombay, Edit & Horváth, Lajos & Kokoszka, Piotr, 2004. "SEQUENTIAL CHANGE-POINT DETECTION IN GARCH(p,q) MODELS," Econometric Theory, Cambridge University Press, vol. 20(6), pages 1140-1167, December.
    3. Chu, Chia-Shang James & Stinchcombe, Maxwell & White, Halbert, 1996. "Monitoring Structural Change," Econometrica, Econometric Society, vol. 64(5), pages 1045-1065, September.
    4. Okyoung Na & Youngmi Lee & Sangyeol Lee, 2011. "Monitoring parameter change in time series models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(2), pages 171-199, June.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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