Structural change and estimated persistence in the GARCH(1,1)-model
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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- Christian Francq & Michel Roussignol & Jean-Michel Zakoïan, 1998.
"Conditional Heteroskedasticity Driven by Hidden Markov Chains,"
98-45, Centre de Recherche en Economie et Statistique.
- Christian Francq & Michel Roussignol & Jean-Michel Zakoian, 1998. "Conditional heteroskedasticity driven by hidden Markov chains," SFB 373 Discussion Papers 1998,86, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
- 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.
- Michael J. Dueker, 1995. "Markov switching in GARCH processes and mean reverting stock market volatility," Working Papers 1994-015, Federal Reserve Bank of St. Louis.
- 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.
- 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 165-185, Suppl. De.
- Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, EconWPA.
- 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 225-234, April.
- Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
- Tom Doan, "undated". "RATS programs to estimate Hamilton-Susmel Markov Switching ARCH model," Statistical Software Components RTZ00083, Boston College Department of Economics.