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

Author

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  • Prof. Dr. Walter Krämer

    (Faculty of Statistics, Dortmund University of Technology)

  • Baudouin Tameze Azamo

    (Faculty of Statistics, Dortmund University of Technology)

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.

Suggested Citation

  • Prof. Dr. Walter Krämer & Baudouin Tameze Azamo, "undated". "Structural change and estimated persistence in the GARCH(1,1)-model," Working Papers 5, Business and Social Statistics Department, Technische Universität Dortmund, revised May 2006.
  • Handle: RePEc:dor:wpaper:5
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    1. Christian Francq & Michel Roussignol & Jean‐Michel Zakoian, 2001. "Conditional Heteroskedasticity Driven by Hidden Markov Chains," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(2), pages 197-220, March.
    2. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
    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. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 493-530.
    5. 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.
    6. Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, University Library of Munich, Germany.
    7. 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.
    8. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    9. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
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    Cited by:

    1. Charles, Amélie & Darné, Olivier, 2014. "Volatility persistence in crude oil markets," Energy Policy, Elsevier, vol. 65(C), pages 729-742.
    2. Fang, WenShwo & Miller, Stephen M., 2009. "Modeling the volatility of real GDP growth: The case of Japan revisited," Japan and the World Economy, Elsevier, vol. 21(3), pages 312-324, August.
    3. Giorgio Canarella & WenShwo Fang & Stephen M. Miller & Stephen K. Pollard, 2008. "Is the Great Moderation Ending? UK and US Evidence," Working Papers 0801, University of Nevada, Las Vegas , Department of Economics.
    4. WenShwo Fang & Stephen M. Miller & ChunShen Lee, 2008. "Cross‐Country Evidence On Output Growth Volatility: Nonstationary Variance And Garch Models," Scottish Journal of Political Economy, Scottish Economic Society, vol. 55(4), pages 509-541, September.
    5. WenShwo Fang & Stephen M. Miller & ChunShen Lee, 2008. "The Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis," Working papers 2008-48, University of Connecticut, Department of Economics.
    6. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2015. "Shifts in volatility driven by large stock market shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 130-147.
    7. Dejan ŽIVKOV & Jovan NJEGIĆ & Ivan MILENKOVIĆ, 2018. "Interrelationship between DAX Index and Four Largest Eastern European Stock Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 88-103, September.
    8. 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.
    9. Krämer, Walter & Messow, Philip, 2012. "Structural Change and Spurious Persistence in Stochastic Volatility," Ruhr Economic Papers 310, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    10. Anthony Msafiri Nyangarika & Alexey Yurievich Mikhaylov & Bao-jun Tang, 2018. "Correlation of Oil Prices and Gross Domestic Product in Oil Producing Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 42-48.
    11. Alexey Yurievich Mikhaylov, 2018. "Volatility Spillover Effect between Stock and Exchange Rate in Oil Exporting Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 321-326.
    12. Krämer, Walter, 2008. "Long memory with Markov-Switching GARCH," Economics Letters, Elsevier, vol. 99(2), pages 390-392, May.
    13. Ahmad Zubaidi Baharumshah & Nor Aishah Hamzah & Shamsul Rijal Muhammad Sabri, 2011. "Inflation uncertainty and economic growth: evidence from the LAD ARCH model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(1), pages 195-206.
    14. Dejan Živkov & Marina Gajic-Glamoclija & Jasmina Duraskovic & Mirela Momcilovic, 2022. "Assessing Permanent and Transitory Volatility Spillover Effect from Oil to Stocks in Baltic and Visegrad Countries," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 70(6), pages 523-542, June.
    15. Han, Heejoon & Park, Joon Y., 2014. "GARCH with omitted persistent covariate," Economics Letters, Elsevier, vol. 124(2), pages 248-254.
    16. Chourdakis, Kyriakos & Dendramis, Yiannis & Tzavalis, Elias, 2014. "Are regime-shift sources of risk priced in the market?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 151-170.
    17. 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.
    18. Messow, Philip & Krämer, Walter, 2013. "Spurious persistence in stochastic volatility," Economics Letters, Elsevier, vol. 121(2), pages 221-223.

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    Keywords

    long memory; GARCH; structural change;
    All these keywords.

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