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Real Time Detection of Structural Breaks in GARCH Models

Zhongfang He () and John Maheu ()

Staff Working Papers from Bank of Canada

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 results in strong evidence of breaks; while more flexible return distributions such as t-innovations or a GARCH-jump mixture model still favors breaks but indicates much more uncertainty regarding the time and impact of them.

Keywords: Econometric and statistical methods; Financial markets (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C53 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2009
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Journal Article: Real time detection of structural breaks in GARCH models (2010) Downloads
Working Paper: Real Time Detection of Structural Breaks in GARCH Models (2009) Downloads
Working Paper: Real Time Detection of Structural Breaks in GARCH Models (2008) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:09-31

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