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Real Time Detection of Structural Breaks in GARCH Models

Author

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  • Zhongfang He

    (University of Toronto (Canada))

  • John M. Maheu

    (University of Toronto (Canada); Rimini Centre for Economic Analysis, Rimini, Italy)

Abstract

A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is proposed. Particle filtering techniques allow for fast and efficient updates of posterior quantities and forecasts in real-time. The method conveniently deals with the path dependence problem that arises in these type of models. The performance of the method is shown to work well using simulated data. Applied to daily NASDAQ returns, the evidence favors a partial structural break specification in which only the intercept of the conditional variance equation has breaks compared to the full structural break specification in which all parameters are subject to change. The empirical application underscores the importance of model assumptions when investigating breaks. A model with normal return innovations result in strong evidence of breaks; while more flexible return distributions such as t-innovations or a GARCH-jump mixture model still favor breaks but indicate much more uncertainty regarding the time and impact of them.

Suggested Citation

  • Zhongfang He & John M. Maheu, 2009. "Real Time Detection of Structural Breaks in GARCH Models," Working Paper series 11_09, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:11_09
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    References listed on IDEAS

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    More about this item

    Keywords

    particle filter; GARCH model; change point; sequential Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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