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Real Time Detection of Structural Breaks in GARCH Models

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Author Info
Zhongfang He
John M Maheu

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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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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
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Handle: RePEc:tor:tecipa:tecipa-336

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

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Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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  1. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138. [Downloadable!] (restricted)
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    Other versions:
  5. Roberto Casarin & Carmine Trecroci, 2006. "Business Cycle and Stock Market Volatility: A Particle Filter Approach," Working Papers ubs0603, University of Brescia, Department of Economics. [Downloadable!]
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  8. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June. [Downloadable!] (restricted)
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  11. Nicolas Chopin, 2007. "Dynamic Detection of Change Points in Long Time Series," Annals of the Institute of Statistical Mathematics, Springer, vol. 59(2), pages 349-366, June. [Downloadable!] (restricted)
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  15. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 493-530. [Downloadable!] (restricted)
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  17. Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, EconWPA. [Downloadable!]
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  19. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September. [Downloadable!] (restricted)
  20. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Blackwell Publishing, vol. 74(3), pages 763-789, 07. [Downloadable!] (restricted)
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