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

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  • Kramer, Walter
  • Azamo, Baudouin Tameze

Abstract

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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Bibliographic Info

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 97 (2007)
Issue (Month): 1 (October)
Pages: 17-23

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Handle: RePEc:eee:ecolet:v:97:y:2007:i:1:p:17-23

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Web page: http://www.elsevier.com/locate/ecolet

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References

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  1. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  2. 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.
  3. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
  4. 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.
  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.
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Cited by:
  1. Walter Kraemer, 2008. "Long Memory with Markov-Switching GARCH," CESifo Working Paper Series 2225, CESifo Group Munich.
  2. Walter Krämer & Philip Mess, 2012. "Structural Change and Spurious Persistence in Stochastic Volatility," Ruhr Economic Papers 0310, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  3. Amélie Charles & Olivier Darné, 2014. "Volatility persistence in crude oil markets," Post-Print hal-00940312, HAL.
  4. WenShwo Fang & Stephen M. Miller & ChunShen Lee, 2009. "The Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis," Working Papers 0903, University of Nevada, Las Vegas , Department of Economics.
  5. Krämer, Walter & Tameze, Baudouin & Christou, Konstantinos, 2012. "On the origin of high persistence in GARCH-models," Economics Letters, Elsevier, vol. 114(1), pages 72-75.
  6. Messow, Philip & Krämer, Walter, 2013. "Spurious persistence in stochastic volatility," Economics Letters, Elsevier, vol. 121(2), pages 221-223.
  7. WenShwo Fang & Stephen M. Miller, 2008. "Modeling the Volatility of Real GDP Growth: The Case of Japan Revisited," Working papers 2008-47, University of Connecticut, Department of Economics.
  8. Aslanidis, Nektarios & Dungey, Mardi & Savva, Christos S., 2008. "Progress Towards to Equity Market Integration in Eastern Europe," Working Papers 2072/13265, Universitat Rovira i Virgili, Department of Economics.
  9. Giorgio Canarella & WenShwo Fang & Stephen M. Miller & Stephen K. Pollard, 2008. "Is the Great Moderation Ending? UK and US Evidence," Working papers 2008-24, University of Connecticut, Department of Economics.

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