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Real Time Detection of Structural Breaks in GARCH Models

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  • Zhongfang He

    ()
    (University of Toronto (Canada))

  • John M. Maheu

    ()
    (University of Toronto (Canada); Rimini Centre for Economic Analysis, Rimini, Italy)

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 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.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 11_09.

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Date of creation: Jan 2009
Date of revision: Jan 2009
Handle: RePEc:rim:rimwps:11_09

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Keywords: particle filter; GARCH model; change point; sequential Monte Carlo;

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References

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Citations

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Cited by:
  1. Stefan de Wachter & Elias Tzavalis, 2004. "Detection of Structural Breaks in Linear Dynamic Panel Data Models," Working Papers 505, Queen Mary, University of London, School of Economics and Finance.
  2. Luc Bauwens & Arnaud Dufays & Jeroen Rombouts, 2011. "Marginal Likelihood for Markov-Switching and Change-Point Garch Models," CIRANO Working Papers 2011s-72, CIRANO.
  3. Gary Koop & Joshua Chan, 2011. "Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables," Working Papers 1111, University of Strathclyde Business School, Department of Economics.
  4. Ross, Gordon J., 2013. "Modelling financial volatility in the presence of abrupt changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(2), pages 350-360.
  5. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
  6. He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.
  7. Zhongfang He & John M. Maheu, 2009. "Real Time Detection of Structural Breaks in GARCH Models," Working Papers 09-31, Bank of Canada.
  8. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
  9. Almeida e Santos Nogueira, R.J. & Basturk, N. & Kaymak, U. & Costa Sousa, J.M., 2013. "Estimation of flexible fuzzy GARCH models for conditional density estimation," ERIM Report Series Research in Management ERS-2013-013-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
  10. Sigauke, C. & Chikobvu, D., 2011. "Prediction of daily peak electricity demand in South Africa using volatility forecasting models," Energy Economics, Elsevier, vol. 33(5), pages 882-888, September.
  11. Mario Bonino & Matteo Camelia & Paolo Pigato, 2014. "A multivariate model for financial indexes and an algorithm for detection of jumps in the volatility," Papers 1404.7632, arXiv.org.

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