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Parameter changes in GARCH model

Author

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  • Kosei Fukuda

Abstract

A new method for detecting the parameter changes in generalized autoregressive heteroskedasticity GARCH (1,1) model is proposed. In the proposed method, time series observations are divided into several segments and a GARCH (1,1) model is fitted to each segment. The goodness-of-fit of the global model composed of these local GARCH (1,1) models is evaluated using the corresponding information criterion (IC). The division that minimizes IC defines the best model. Furthermore, since the simultaneous estimation of all possible models requires huge computational time, a new time-saving algorithm is proposed. Simulation results and empirical results both indicate that the proposed method is useful in analysing financial data.

Suggested Citation

  • Kosei Fukuda, 2010. "Parameter changes in GARCH model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(7), pages 1123-1135.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1123-1135
    DOI: 10.1080/02664760902914524
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    Citations

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    Cited by:

    1. Borzykh, Dmitriy & Yazykov, Artem, 2019. "The new KS method for a structural break detection in GARCH(1,1) models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 54, pages 90-104.
    2. Borzykh, Dmitriy & Khasykov, Mikhail, 2018. "The refinement procedure of ICSS algorithm for structural breaks detection in GARCH-models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 51, pages 126-139.
    3. Badagian Baharian, Ana Laura & Kaiser Remiro, Regina & Peña, Daniel, 2013. "The change-point problem and segmentation of processes with conditional heteroskedasticity," DES - Working Papers. Statistics and Econometrics. WS ws131718, Universidad Carlos III de Madrid. Departamento de Estadística.

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