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Change-point detection in GARCH models: asymptotic and bootstrap tests


  • KOKOSZKA, Piotr
  • TEYSSIÈRE, Gilles


Two classes of tests designed to detect changes in volatility are proposed. Procedures based on squared model residuals and on the likelihood ratio are considered. The tests are applicable to parametric nonlinear models like GARCH. Both asymptotic and bootstrap tests are investigated by means of a simulation study and applied to returns data. The tests based onthe likelihood ratio are shown to be generally preferable. A wavelet based estimator of long memory is applied to returns data to shed light on the interplay of change points and long memory.

Suggested Citation

  • KOKOSZKA, Piotr & TEYSSIÈRE, Gilles, 2002. "Change-point detection in GARCH models: asymptotic and bootstrap tests," CORE Discussion Papers 2002065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:2002065

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    References listed on IDEAS

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    Cited by:

    1. Kirman, Alan & Teyssiere, Gilles, 2005. "Testing for bubbles and change-points," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 765-799, April.
    2. Berkes, Istvan & Horváth, Lajos & Kokoszka, Piotr, 2004. "Testing for parameter constancy in GARCH(p,q) models," Statistics & Probability Letters, Elsevier, vol. 70(4), pages 263-273, December.
    3. TEYSSIERE, Gilles, 2003. "Interaction models for common long-range dependence in asset price volatilities," CORE Discussion Papers 2003026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    More about this item


    GARCH model; change-point; likelihood ratio; parametric bootstrap; squared residuals; size-power curves; wavelets;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes


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