English, Article, Journal or magazine article edition: Real Time Detection of Structural Breaks in GARCH Models Zhongfang He; John M. Maheu

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Bookmark: http://trove.nla.gov.au/version/1297
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Real Time Detection of Structural Breaks in GARCH Models
Author
  • Zhongfang He
  • John M. Maheu
Physical Description
  • preprint
Notes
  • 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
  • RePEc:rim:rimwps:11-09
Language
  • English
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