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Regime switching GARCH models

Author

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  • Luc, BAUWENS

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Center for Operations Research and Econometrics (CORE))

  • Arie, PREMINGER

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Center for Operations Research and Econometrics (CORE))

  • Jeroen, ROMBOUTS
Abstract
We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for stationarity and existence of moments. Because of path dependence, maximum likehood estimation is infeasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We apply this model using the NASDAQ daily returns series.

Suggested Citation

  • Luc, BAUWENS & Arie, PREMINGER & Jeroen, ROMBOUTS, 2006. "Regime switching GARCH models," Discussion Papers (ECON - Département des Sciences Economiques) 2006006, Université catholique de Louvain, Département des Sciences Economiques.
    • Handle: RePEc:ctl:louvec:2006006
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    File URL: http://sites.uclouvain.be/econ/DP/IRES/2006-6.pdf
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    References listed on IDEAS

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    1. Vlaar, Peter J G & Palm, Franz C, 1993. "The Message in Weekly Exchange Rates in the European Monetary System: Mean Reversion, Conditional Heteroscedasticity, and Jumps," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 351-360, July.
    2. 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.
    3. Kai-Li Wang & Christopher Fawson & Christopher B. Barrett & James B. McDonald, 2001. "A flexible parametric GARCH model with an application to exchange rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(4), pages 521-536.
    4. Markku Lanne & Pentti Saikkonen, 2003. "Modeling the U.S. Short-Term Interest Rate by Mixture Autoregressive Processes," Journal of Financial Econometrics, Oxford University Press, vol. 1(1), pages 96-125.
    5. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September.
    6. Chris Brooks, 2005. "Autoregressive Conditional Kurtosis," Journal of Financial Econometrics, Oxford University Press, vol. 3(3), pages 399-421.
    7. Bauwens, L. & Bos, C.S. & van Dijk, H.K., 1999. "Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk," Econometric Institute Research Papers TI 99-082/4, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
    9. L. Bauwens & J.V.K. Rombouts, 2007. "Bayesian inference for the mixed conditional heteroskedasticity model," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 408-425, July.
    10. 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.
    11. Thomas Mikosch & Cătălin Stărică, 2004. "Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 378-390, February.
    12. repec:bla:jfinan:v:44:y:1989:i:5:p:1115-53 is not listed on IDEAS
    13. Bollen, Nicolas P. B. & Gray, Stephen F. & Whaley, Robert E., 2000. "Regime switching in foreign exchange rates: Evidence from currency option prices," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 239-276.
    14. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    15. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-229, March.
    16. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    17. Markus Haas, 2004. "Mixed Normal Conditional Heteroskedasticity," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 211-250.
    18. Luc Bauwens & Charles S. Bos & Herman K. van Dijk & Rutger D. van Oest, 2002. "Adaptive Polar Sampling," Computing in Economics and Finance 2002 307, Society for Computational Economics.
    19. 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.
    20. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    21. 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. Lanne, Markku & Luoto, Jani, 2008. "Robustness of the risk-return relationship in the U.S. stock market," Finance Research Letters, Elsevier, vol. 5(2), pages 118-127, June.
    2. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    3. He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
    4. Bauwens, L. & Hafner, C.M. & Rombouts, J.V.K., 2007. "Multivariate mixed normal conditional heteroskedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3551-3566, April.
    5. Yuan, Chunming, 2011. "The exchange rate and macroeconomic determinants: Time-varying transitional dynamics," The North American Journal of Economics and Finance, Elsevier, vol. 22(2), pages 197-220, August.
    6. Bauwens Luc & Storti Giuseppe, 2009. "A Component GARCH Model with Time Varying Weights," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-33, May.
    7. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 326-360, Summer.
    8. Vo, Minh T., 2009. "Regime-switching stochastic volatility: Evidence from the crude oil market," Energy Economics, Elsevier, vol. 31(5), pages 779-788, September.
    9. Nyberg, Henri, 2010. "QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles," MPRA Paper 23724, University Library of Munich, Germany.
    10. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
    11. Philippe Charlot & Vêlayoudom Marimoutou, 2008. "Hierarchical hidden Markov structure for dynamic correlations: the hierarchical RSDC model," Working Papers halshs-00285866, HAL.
    12. Xiufeng Yan, 2021. "Autoregressive conditional duration modelling of high frequency data," Papers 2111.02300, arXiv.org.
    13. Yuan, Chunming, 2011. "Forecasting exchange rates: The multi-state Markov-switching model with smoothing," International Review of Economics & Finance, Elsevier, vol. 20(2), pages 342-362, April.
    14. Robert J. Elliott & John W. Lau & Hong Miao & Tak Kuen Siu, 2012. "Viterbi-Based Estimation for Markov Switching GARCH Model," Applied Mathematical Finance, Taylor & Francis Journals, vol. 19(3), pages 219-231, August.
    15. Michail Karoglou, 2010. "Breaking down the non-normality of stock returns," The European Journal of Finance, Taylor & Francis Journals, vol. 16(1), pages 79-95.
    16. Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Time-varying mixture GARCH models and asymmetric volatility," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 602-623.
    17. Michail Karoglou & Panicos Demetriades & Siong Law, 2011. "One date, one break?," Empirical Economics, Springer, vol. 41(1), pages 7-24, August.
    18. Aloui, Chaker & Jammazi, Rania, 2009. "The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach," Energy Economics, Elsevier, vol. 31(5), pages 789-799, September.
    19. Xiufeng Yan, 2021. "Multiplicative Component GARCH Model of Intraday Volatility," Papers 2111.02376, arXiv.org.
    20. Nagaraj Naik & Biju R. Mohan, 2021. "Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market," Mathematics, MDPI, vol. 9(14), pages 1-18, July.

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    More about this item

    Keywords

    GARCH; regime switching; Bayesian inference;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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