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Volatility Switching between Two Regimes

Authors: Josip Visković, Josip Arnerić, Ante Rozga


Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modeling time varying volatility are GARCH type models. When financial returns exhibit sudden jumps that are due to structural breaks, standard GARCH models show high volatility persistence, i.e. integrated behavior of the conditional variance. In such situations models in which the parameters are allowed to change over time are more appropriate. This paper compares different GARCH models in terms of their ability to describe structural changes in returns caused by financial crisis at stock markets of six selected central and east European countries. The empirical analysis demonstrates that Markov regime switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility when sudden switching occurs in response to financial crisis.

Keywords: Financial Crisis, Transition Probabilities, central and east European countries, Markov switching GARCH model

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[1] F. R. Engle, "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation”, Econometrica, Vol. 50, pp. 987-1108., 1892.
[2] T. Bollerslev, "Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, Vol 31. pp. 307-327, 1986).
[3] F. R. Engle "The Use of ARCH/GARCH Models in Applied Econometrics”, Journal of Economic Perspectives, Vol. 15, No. 4, pp. 157-168., 2001.
[4] C. G. Lamouoreux and W. D. Lastrapes (1990) "Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects”, Journal of Finance, Vol 45. No. 1., pp. 221-229, 1990
[5] M. Haas, S. Mittnik and S. M. Paolella (2004), "A New Approach to Markov-Switching GARCH Models”, Journal of Financial Econometrics, Vol. 2, No. 4, pp. 493-530.
[6] J. D. Hamilton, "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, Vol 57, pp. 357-384, 1989.
[7] A. M. Carnero, "Persistence and Kurtosis in GARCH and Stohastic Volatility Models”, Journal of Financial Econometrics, Oxford University Press, Vol. 2, No. 2, pp. 319-342., 2004.
[8] J. D. Hamilton and R. Susmel "ARCH and Changes in Regime”, Journal of Econometrics, Vol. 64, pp. 307-333, 1994.
[9] J. Marcucci, "Forecasting Stock Market Volatility with Regime-Switching GARCH Models”, Studies in Nonlinear Dynamics and Econometrics, Vol 9, No. 4, pp. 1-42, 2005.
[10] C-J. Kim and C. R. Nelson, State-Space Models with Regime Switching (Classical and Gibbs-Sampling Approaches with Applications), Massachusetts Institute of Technology Press, Cambridge, 1999.
[11] S. F. Gray, "Modeling the conditional distribution of interest rates as a regime-switching process, Journal of Financial Economics, Vol. 42, pp. 27-62, 1996.
[12] F. Klaassen, "Improving GARCH Volatility Forecasts with Regime-Switching GARCH”, Empirical Economics, Vol. 27, No. 2, pp. 363-394, 2002.
[13] L. Bauwnes Luc, A. Preminger and J. Romboust, "Regime Switching GARCH Models”, CORE Discussion Paper, No. 11, pp. 1-23. 2006.