Volatility regimes of selected central European stock returns: a Markov switching GARCH approach
Abstract
This paper investigates the weekly stock market data of the Hungarian stock index BUX, the Czech stock index PX and the Polish stock index WIG20 spanning from January 7, 2001 to April 18, 2021. The period of more than 20 years enabled to analyse the behaviour of returns and their volatility during both the calm as well as various crises/turmoil periods. Besides the traditional GARCH-type models (GARCH and GJR-GARCH) the two-regime Markov Switching GARCHtype models (MS-GARCH and MS-GJR-GARCH) were estimated in order to examine the volatility switches of the Central European transition stock markets. The t-distribution of error terms was used to capture the dynamics of analysed returns more precisely. The results proved high volatility persistence of individual markets which substantially differed across the both regimes. Furthermore, the GJR-GARCH and MS-GJR-GARCH models clearly confirmed the presence of the leverage effect. Consideration of the MS-GARCH-type models enabled to capture various volatility switches during the analysed period attributable mainly to the global financial crisis 2008–2009, to European debt crisis in 2011 and to the Covid-19 pandemic in 2020. Interesting results were received for the Czech market with the strong leverage effect indicating completely different specification of volatility regimes by the MS-GJR-GARCH model.
First published online 4 April 2022
Keyword : stock returns, volatility, GARCH, GJR-GARCH, Markov-switching (MS), regime, MS-GARCH, MS-GJR-GARCH
How to Cite
Chocholatá, M. (2022). Volatility regimes of selected central European stock returns: a Markov switching GARCH approach. Journal of Business Economics and Management, 23(4), 876–894. https://doi.org/10.3846/jbem.2022.16648
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH Models. Studies in Nonlinear Dynamics & Econometrics, 9(4), 1–55. https://doi.org/10.2202/1558-3708.1145
Moore, T., & Wang, P. (2007). Volatility in stock returns for new EU member states: Markov regime switching model. International Review of Financial Analysis, 16(3), 282–292. https://doi.org/10.1016/j.irfa.2007.03.006
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. https://doi.org/10.2307/2938260
Pyo, D.-J. (2021). The COVID-19 and stock return volatility: Evidence from South Korea. East Asian Economic Review, 25(2), 205–230. https://doi.org/10.11644/KIEP.EAER.2021.25.2.396
Raihan, T. (2017). Performance of Markov-switching GARCH model forecasting inflation uncertainty (MPRA Paper No. 82343). Retrieved February 8, 2021, from https://mpra.ub.uni-muenchen.de/82343/1/MPRA_paper_82343.pdf
Rotta, P. N., & Pereira, P. L. V. (2016). Analysis of contagion from the dynamic conditional correlation model with Markov Regime switching. Applied Economics, 48(25), 2367–2382. https://doi.org/10.1080/00036846.2015.1119794
Sajjad, R., Coakley, J., & Nankervis, J. C. (2008). Markov-switching GARCH modelling of value-at-risk. Studies in Nonlinear Dynamics & Econometrics, 12(3), 1–29. https://doi.org/10.2202/1558-3708.1522
Sema, G., Konte, M. A., & Diongue, A. K. (2021). Forecasting value-at-risk using Markov regime-switching asymmetric GARCH model with stable distribution in the context of the COVID-19 pandemic. African Journal of Applied Statistics, 8(1), 1049–1071.
Silva, C. A. G. (2021). The influence of the COVID-19 pandemic on the volatility of stock market index (Ibovespa): Application of the Markov switching autoregressive model. Brazilian Journal of Business, 3(3), 2445–2458. https://doi.org/10.34140/bjbv3n3-030
Spulbar, C., Trivedi, J., & Birau, R. (2020). Investigating abnormal volatility transmission patterns between emerging and developed stock markets: A case study. Journal of Business Economics and Management, 21(6), 1561–1592. https://doi.org/10.3846/jbem.2020.13507
Stooq. (2021). Indices Europe. Retrieved April 23, 2021, from https://stooq.com/t/?i=525
Zhang, N., Wang, A., Naveed-Ul- Haq, & Nosheen, S. (2021). The impact of COVID-19 shocks on the volatility of stock markets in technologically advanced countries. Economic Research-Ekonomska Istraživanja. https://doi.org/10.1080/1331677X.2021.1936112
Živkov, D., Kuzman, B., & Subić, J. (2020). What Bayesian quantiles can tell about volatility transmission between the major agricultural futures? Agricultural Economics – Czech, 66(5), 215–225. https://doi.org/10.17221/127/2019-AGRICECON
Aktan, B., Korsakienė, R., & Smaliukienė, R. (2010). Time-varying volatility modelling of Baltic stock markets. Journal of Business Economics and Management, 11(3), 511–532. https://doi.org/10.3846/jbem.2010.25
Ardia, D., Bluteau, K., Boudt, K., & Catania, L. (2018). Forecasting risk with Markov-switching GARCH models: A large-scale performance study. International Journal of Forecasting, 34(4), 733–747. https://doi.org/10.1016/j.ijforecast.2018.05.004
Ardia, D., Bluteau, K., Boudt, K., Catania, L., & Trottier, D.-A. (2019). Markov-switching GARCH models in R: The MSGARCH package. Journal of Statistical Software, 91(4), 1–38. https://doi.org/10.18637/jss.v091.i04
Ardia, D., Bluteau, K., Boudt, K., Catania, L., Ghalanos, A., Peterson, B., & Trottier, D.-A. (2020). MSGARCH: Markov-switching GARCH models in R. R package version 2.42. https://CRAN.R-project.org/package=MSGARCH
Bialkowski, J. (2004). Modelling returns on stock indices for western and central European stock exchanges – A Markov switching approach. South-Eastern Europe Journal of Economics, 2(2), 81–100. Retrieved December 8, 2020, from http://www.asecu.gr/Seeje/issue03/bialkowski.pdf
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
Brooks, C. (2008). Introductory econometrics for finance (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511841644
Cai, J. (1994). A Markov model of switching-regime ARCH. Journal of Business & Economic Statistics, 12(3), 309–316. https://doi.org/10.2307/1392087
Czech, K., Wielechowski, M., Kotyza, P., Benešová, I., & Laputková, A. (2020). Shaking Stability: COVID-19 impact on the Visegrad Group countries’ financial markets. Sustainability, 12(15), 6282. https://doi.org/10.3390/su12156282
Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
FrÖmmel, M. (2010). Volatility regimes in Central and Eastern European countries’ exchange rates. Czech Journal of Economics and Finance, 60(1), 2–21. Retrieved January 10, 2021, from https://journal.fsv.cuni.cz/storage/1177_1177_str_2_21_-_froemmel.pdf
Ghalanos, A. (2020). rugarch: Univariate GARCH models. R package version 1.4-4. https://CRAN.R-project.org/package=rugarch
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
Gray, S. (1996). Modeling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics, 42(1), 27–62. https://doi.org/10.1016/0304-405X(96)00875-6
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357–384. https://doi.org/10.2307/1912559
Hamilton, J. D., & Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 64(1–2), 307–333. https://doi.org/10.1016/0304-4076(94)90067-1
Haas, M., Mittnik, S., & Paolella, M. S. (2004). A new approach to Markov-switching GARCH models. Journal of Financial Econometrics, 2(4), 493–530. https://doi.org/10.1093/jjfinec/nbh020
Haas, M., & Paolella, M. S. (2012). Mixture and regime-switching GARCH models. In L. Bauwens, C. Hafner, & S. Laurentet (Eds.), Handbook of volatility models and their applications (pp. 71–102). John Wiley & Sons. https://doi.org/10.1002/9781118272039.ch3
Klaassen, F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH. Empirical Economics, 27(2), 363–394. https://doi.org/10.1007/s001810100100
Kouretas, G. P., & Syllignakis, M. N. (2012). Switching volatility in emerging stock markets and financial liberalization: Evidence from the new EU member countries. Central European Journal of Economic Modelling and Econometrics, 4(2), 65–93. https://doi.org/10.24425/cejeme.2012.119277
Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business & Economic Statistics, 8(2), 225–234. https://doi.org/10.2307/1391985
Linne, T. (2002). A Markov switching model of stock returns: An application to the emerging markets in Central and Eastern Europe. In W. W. Charemza & K. Strzała (Eds.), East European Transition and EU Enlargement (pp. 371–384). Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57497-9_23
Liu, H. Y., Manzoor, A., Wang, C. Y., Zhang, L., & Manzoor, Z. (2020). The COVID-19 outbreak and affected countries stock markets response. International Journal of Environmental Research and Public Health, 17(8), 2800. https://doi.org/10.3390/ijerph17082800
Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH Models. Studies in Nonlinear Dynamics & Econometrics, 9(4), 1–55. https://doi.org/10.2202/1558-3708.1145
Moore, T., & Wang, P. (2007). Volatility in stock returns for new EU member states: Markov regime switching model. International Review of Financial Analysis, 16(3), 282–292. https://doi.org/10.1016/j.irfa.2007.03.006
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. https://doi.org/10.2307/2938260
Pyo, D.-J. (2021). The COVID-19 and stock return volatility: Evidence from South Korea. East Asian Economic Review, 25(2), 205–230. https://doi.org/10.11644/KIEP.EAER.2021.25.2.396
Raihan, T. (2017). Performance of Markov-switching GARCH model forecasting inflation uncertainty (MPRA Paper No. 82343). Retrieved February 8, 2021, from https://mpra.ub.uni-muenchen.de/82343/1/MPRA_paper_82343.pdf
Rotta, P. N., & Pereira, P. L. V. (2016). Analysis of contagion from the dynamic conditional correlation model with Markov Regime switching. Applied Economics, 48(25), 2367–2382. https://doi.org/10.1080/00036846.2015.1119794
Sajjad, R., Coakley, J., & Nankervis, J. C. (2008). Markov-switching GARCH modelling of value-at-risk. Studies in Nonlinear Dynamics & Econometrics, 12(3), 1–29. https://doi.org/10.2202/1558-3708.1522
Sema, G., Konte, M. A., & Diongue, A. K. (2021). Forecasting value-at-risk using Markov regime-switching asymmetric GARCH model with stable distribution in the context of the COVID-19 pandemic. African Journal of Applied Statistics, 8(1), 1049–1071.
Silva, C. A. G. (2021). The influence of the COVID-19 pandemic on the volatility of stock market index (Ibovespa): Application of the Markov switching autoregressive model. Brazilian Journal of Business, 3(3), 2445–2458. https://doi.org/10.34140/bjbv3n3-030
Spulbar, C., Trivedi, J., & Birau, R. (2020). Investigating abnormal volatility transmission patterns between emerging and developed stock markets: A case study. Journal of Business Economics and Management, 21(6), 1561–1592. https://doi.org/10.3846/jbem.2020.13507
Stooq. (2021). Indices Europe. Retrieved April 23, 2021, from https://stooq.com/t/?i=525
Zhang, N., Wang, A., Naveed-Ul- Haq, & Nosheen, S. (2021). The impact of COVID-19 shocks on the volatility of stock markets in technologically advanced countries. Economic Research-Ekonomska Istraživanja. https://doi.org/10.1080/1331677X.2021.1936112
Živkov, D., Kuzman, B., & Subić, J. (2020). What Bayesian quantiles can tell about volatility transmission between the major agricultural futures? Agricultural Economics – Czech, 66(5), 215–225. https://doi.org/10.17221/127/2019-AGRICECON