Real Time Detection of Structural Breaks in GARCH
John M. Maheu
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.
particle filter, GARCH model, change point, sequential Monte Carlo
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