Real Time Detection of Structural Breaks in GARCH Models

Zhongfang He and John Maheu

Working Papers from University of Toronto, Department of Economics

Abstract: 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.

Keywords: particle filter; GARCH model; change point; sequential Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 G10 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2008-09-19
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Real time detection of structural breaks in GARCH models (2010)
Working Paper: Real Time Detection of Structural Breaks in GARCH Models (2009)
Working Paper: Real Time Detection of Structural Breaks in GARCH Models (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-336

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