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Real Time Detection of Structural Breaks in GARCH Models

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  • Zhongfang He
  • John M Maheu

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.

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Bibliographic Info

Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-336.

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Length: 38 pages
Date of creation: 19 Sep 2008
Date of revision:
Handle: RePEc:tor:tecipa:tecipa-336

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

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References

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  1. John M. Maheu & Stephen Gordon, 2004. "Learning, Forecasting and Structural Breaks," Cahiers de recherche, CIRPEE 0422, CIRPEE.
  2. Zhongfang He & John M. Maheu, 2009. "Real Time Detection of Structural Breaks in GARCH Models," Working Papers, Bank of Canada 09-31, Bank of Canada.
  3. Pesaran, M Hashem & Pettenuzzo, Davide & Timmermann, Allan G, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," CEPR Discussion Papers, C.E.P.R. Discussion Papers 4636, C.E.P.R. Discussion Papers.
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Citations

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Cited by:
  1. 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.
  2. Chan, Joshua C.C. & Koop, Gary, 2011. "Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2011-22, Scottish Institute for Research in Economics (SIRE).
  3. Luc Bauwens & Arnaud Dufays & Jeroen V.K. Rombouts, 2011. "Marginal Likelihood for Markov-Switching and Change-Point GARCH Models," Cahiers de recherche, CIRPEE 1138, CIRPEE.
  4. Zhongfang He & John M Maheu, 2008. "Real Time Detection of Structural Breaks in GARCH Models," Working Papers, University of Toronto, Department of Economics tecipa-336, University of Toronto, Department of Economics.
  5. Joshua C.C. Chan & Gary Koop, 2013. "Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables," ANU Working Papers in Economics and Econometrics, Australian National University, College of Business and Economics, School of Economics 2013-603, Australian National University, College of Business and Economics, School of Economics.
  6. Stefan de Wachter & Elias Tzavalis, 2004. "Detection of Structural Breaks in Linear Dynamic Panel Data Models," Working Papers, Queen Mary, University of London, School of Economics and Finance 505, Queen Mary, University of London, School of Economics and Finance.
  7. 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.
  8. He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.
  9. 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.
  10. Sigauke, C. & Chikobvu, D., 2011. "Prediction of daily peak electricity demand in South Africa using volatility forecasting models," Energy Economics, Elsevier, Elsevier, vol. 33(5), pages 882-888, September.
  11. 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, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasm 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 University Rotterdam.
  12. Ross, Gordon J., 2013. "Modelling financial volatility in the presence of abrupt changes," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 392(2), pages 350-360.

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