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Real Time Detection of Structural Breaks in GARCH Models

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  • Zhongfang He
  • John M. Maheu

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.

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Bibliographic Info

Paper provided by Bank of Canada in its series Working Papers with number 09-31.

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Length: 34 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:bca:bocawp:09-31

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Keywords: Econometric and statistical methods; Financial markets;

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References

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Citations

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Cited by:
  1. Chan, Joshua C.C. & Koop, Gary, 2014. "Modelling breaks and clusters in the steady states of macroeconomic variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 186-193.
  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. Raggi, Davide & Bordignon, Silvano, 2012. "Long memory and nonlinearities in realized volatility: A Markov switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3730-3742.
  4. He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
  5. De Wachter, Stefan & Tzavalis, Elias, 2012. "Detection of structural breaks in linear dynamic panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3020-3034.
  6. 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.
  7. He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.
  8. 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.
  9. 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.
  10. 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 University Rotterdam.
  11. 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.

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