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Structural change and estimated persistence in the GARCH(1,1)-model

  • Prof. Dr. Walter Krämer


    (Faculty of Statistics, Dortmund University of Technology)

  • Baudouin Tameze Azamo


    (Faculty of Statistics, Dortmund University of Technology)

It has long been known that the estimated persistence parameter in the GARCH(1,1) - model is biased upwards when the parameters of the model are not constant throughout the sample. The present paper explains the mechanics of this behavior for a particular class of estimates of the model parameters and for a particular type of structural change. It shows for any given sample size that the estimated persistence must tend to one in probability if the structural change is ignored and large enough.

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Paper provided by Business and Social Statistics Department, Technische Universität Dortmund in its series Working Papers with number 5.

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Length: 16 pages
Date of creation:
Date of revision: May 2006
Publication status: Published in Economics Letters, October 2007, pages 17-23
Handle: RePEc:dor:wpaper:5
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  1. Christian Francq & Michel Roussignol & Jean-Michel Zakoïan, 1998. "Conditional Heteroskedasticity Driven by Hidden Markov Chains," Working Papers 98-45, Centre de Recherche en Economie et Statistique.
  2. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(4), pages 493-530.
  3. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
  4. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
  5. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-34, April.
  6. Cao, C Q & Tsay, R S, 1992. "Nonlinear Time-Series Analysis of Stock Volatilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S165-85, Suppl. De.
  7. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  8. Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, EconWPA.
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