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Structural Change and long memory in the GARCH(1,1)-model

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  • Azamo, Baudouin Tameze
  • Krämer, Walter

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. It gives sufficient conditions for the estimated persistence to tend to one when the mean of the process changes, both for a given sample size (as the size of the structural change increases), and as sample size increases, extending previous results that were concerned with changes in the volatility parameters. --

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Bibliographic Info

Paper provided by Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen in its series Technical Reports with number 2006,33.

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Date of creation: 2006
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Handle: RePEc:zbw:sfb475:200633

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Keywords: structural change; long memory; GARCH;

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  1. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
  2. 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.
  3. Engle, Robert F & Smith, Aaron, 1998. "Stochastic Permanent Breaks," University of California at San Diego, Economics Working Paper Series qt99v0s0zx, Department of Economics, UC San Diego.
  4. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  5. Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, EconWPA.
  6. 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.
  7. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
  8. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
  9. 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.
  10. Dittmann, Ingolf & Granger, Clive W.J., 2000. "Properties of Nonlinear Transformations of Fractionally Integrated Processes," University of California at San Diego, Economics Working Paper Series qt0kk9x0mc, Department of Economics, UC San Diego.
  11. 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.
  12. Klaassen, F.J.G.M., 1998. "Improving Garch Volatility Forecasts," Discussion Paper 1998-52, Tilburg University, Center for Economic Research.
  13. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
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