Real time detection of structural breaks in GARCH models
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This paper proposes a sequential Monte Carlo method for estimating
GARCH models subject to an unknown number of structural breaks.
We use particle filtering techniques that 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.
Our 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 adding jumps to the model
still favor breaks but indicate much more uncertainty regarding the time and
impact of them.
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