Application of MS-GARCH Class Models in the Analysis of EUR/PLN Exchange Rate Risk in the Context of Poland’s Monetary Integration and the ERM II Mechanism

Patryk Kołbyko

doi:10.56583/br.2953

Published : 2025-12-03

Vol. 21 No. 1 (2025)

Application of MS-GARCH Class Models in the Analysis of EUR/PLN Exchange Rate Risk in the Context of Poland’s Monetary Integration and the ERM II Mechanism

Patryk Kołbyko

https://orcid.org/0000-0001-6550-8452

DOI: https://doi.org/10.56583/br.2953

Abstract

The aim of this article is to identify a hypothetical central EUR/PLN exchange rate within the ERM II framework and to assess the risk of exceeding the permissible fluctuation band for the 2025–2028 horizon. The study uses daily exchange rate data from 2020–2024 and applies nonlinear models of the MS-GARCH (Markov-Switching GARCH) class in both homogeneous and heterogeneous variants, estimated using the quasi-maximum likelihood method. The research procedure included the selection of optimal P and Q parameters according to the AIC information criterion, the identification of periods characterized by heightened volatility regimes, and the forecasting of exchange rate volatility using the MS-GARCH model combined with Monte Carlo simulations for logarithmic returns. The results indicate an optimal ERM II central rate of PLN 4.53, with the heterogeneous MS-GARCH model revealing 21 days of elevated risk of exceeding the upper threshold in November and December 2027. The study contributes an empirical, atheoretical approach to determining the ERM II central rate, filling a gap in the analysis of emerging economies outside the euro area.

Keywords:

euro area, ERM II, monetary integration process, EUR/PLN exchange rate risk

Details

References

Statistics

Authors

Download files

PDF

Citation rules

Kołbyko, P. (2025). Application of MS-GARCH Class Models in the Analysis of EUR/PLN Exchange Rate Risk in the Context of Poland’s Monetary Integration and the ERM II Mechanism. Regional Barometer. Analyses & Prognoses, 21(1), 7–23. https://doi.org/10.56583/br.2953

Altmetric indicators

Cited by / Share

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Anghel, L.C., F. Pinzaru, M. Dinu, and L.-M. Treapat. 2014. “Fixing the Central Parity and the Evolution of the Currency within the Exchange Rate Mechanism II in the Countries that Joined the Euro Zone.” Management Dynamics in the Knowledge Economy 2 (4): 21–40.

Baxter, M., and A.C. Stockman. 1989. “Business Cycles and the Exchange-Rate Regime: Some International Evidence.” Journal of Monetary Economics 23 (3): 377–400. doi: 10.1016/0304-3932(89)90039-1.

Belfrage, C.-J., J. Hansson, and A. Vredin. 2023. How Should We View the Development of the Krona? Economic Commentary, https://www.riksbank.se/en-gb/press-and-published/notices-and-press-releases/notices/2023/how-should-we-view-the-development-of-the-krona/.

Bęza-Bojanowska, J., and R. MacDonald. 2009. “The Behavioural Zloty/Euro Equilibrium Exchange Rate.” National Bank of Poland Working Paper (55): 5–35.

Bijak-Kaszuba, M. 2012. “Ustanawianie unii gospodarczej i walutowej w procesie integracji europejskiej.” Acta Universitatis Lodziensis. Folia Oeconomica 264: 7–34.

Bollerslev, T. 1986. “Generalized Autoregressive Conditional Heteroskedasticity.” Journal of Econometrics 31 (3): 307–327. doi: 10.1016/0304-4076(86)90063-1.

Brkić, M. 2022. “Preserving Economic and Financial Stability in an Emerging Market Country during the Pandemic Crisis: Croatia’s Experience.” Public Sector Economics 46 (3): 321–354. doi: 10.3326/pse.46.3.1.

Chakrabarti, A., and J.K. Ghosh. 2011. “AIC, BIC and Recent Advances in Model Selection.” In Philosophy of Statistics, edited by P.S. Bandyopadhyay and M.R. Forster, 583–605. Amsterdam: North-Holland.

de Mendonca, H.F., L. Vereda, and L.M.M. de Araujo. 2025. “Fundamentals Models Versus Random Walk: Evidence From an Emerging Economy.” Journal of Forecasting 44 (6): 1884–1906. doi: 10.1002/for.3279.

Fengler, M.R., and J. Polivka. 2025. “Structural Volatility Impulse Response Analysis.” Journal of Financial Econometrics 23 (2). doi: 10.1093/jjfinec/nbae036.

Fidrmuc, J., and R. Horváth. 2008. “Volatility of Exchange Rates in Selected New EU Members: Evidence from Daily Data.” Economic Systems 32 (1): 103–118. doi: 10.1016/j.ecosys.2007.09.004.

Filardo, A., G. Gelos, and T. McGregor. 2022. “Exchange-Rate Swings and Foreign Currency Intervention.” IMF Working Papers 2022 (158). doi: 10.5089/9798400215322.001.

Frömmel, M. 2006. “Volatility Regimes in Central and Eastern European Countries’ Exchange Rates.” Hannover Economic Papers (Discussion Paper No. 333).

Gottschalk, S. 2023. “From Black Wednesday to Brexit: Macroeconomic Shocks and Correlations of Equity Returns in France, Germany, Italy, Spain, and the United Kingdom.” International Journal of Finance & Economics 28 (3): 2843–2873. doi: 10.1002/ijfe.2567.

Hansen, P.R., and A. Lunde. 2005. “A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?” Journal of Applied Econometrics 20 (7): 873–889. doi: 10.1002/jae.800.

Hartman, J., and J. Sedlak. 2013. Forecasting Conditional Correlation for Exchange Rates using Multivariate GARCH models with Historical Value-at-Risk Application, Department of Economics and Department of Statistics, Uppsala University, Uppsala.

Kilic, R. 2025. “Linear and Nonlinear Econometric Models against Machine Learning Models: Realized Volatility Prediction.” Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (2025–061). doi: 10.17016/FEDS.2025.061.

Kołbyko, P. 2024. “Application of a Single-Equation SARIMA Model for Short-Term Conditional Forecast (Projection) of CPI Price Dynamics in Poland.” Research Papers in Economics and Finance 8 (1): 128–157.

Lütkepohl, H., and T. Woźniak. 2020. “Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity.” Journal of Economic Dynamics and Control 113: 103862. doi: 10.1016/j.jedc.2020.103862.

Makore, I., and C.N. Chikutuma. 2025. “Exchange Rate Volatility and Its Impact on International Trade: Evidence from Zimbabwe.” Journal of Risk and Financial Management 18 (7): 376. doi: 10.3390/jrfm18070376.

Marisetty, N. 2024. “Evaluating the Efficacy of GARCH Models in Forecasting Volatility Dynamics Across Major Global Financial Indices: A Decade-Long Analysis.” Journal of Economics, Management and Trade 30 (9): 16–33. doi: 10.9734/jemt/2024/v30i91238.

Meese, R.A., and K. Rogoff. 1983. “Empirical Exchange Rate Models of the Seventies: Do They Fit out of Sample?” Journal of International Economics 14 (1): 3–24. doi: 10.1016/0022-1996(83)90017-X.

Melecky, M., and L. Komarek. 2007. “The Behavioral Equilibrium Exchange Rate of the Czech Koruna.” Transition Studies Review 14 (1): 105–121. doi: 10.1007/s11300-007-0136-1.

Ockiya Atto Kennedy, Orumie Ukamaka Cynthia, and Emmanuel Oyinebifun. 2023. “Multivariate Garch Models Comparison in Terms of the Symmetric and Asymmetric Models.” African Journal of Mathematics and Statistics Studies 6 (2): 24–39. doi: 10.52589/AJMSS-XUADTPPA.

Oreiro, J.L. 2023. “Thirlwall’s Law and New-Developmentalism: What Are the Limits for Long-Run Growth?” Investigacion Economica 82 (326): 98-126. doi: 10.22201/fe.01851667p.2023.326.86496.

Pastucha, F. 2024. “The Real Equilibrium Exchange Rate of the Czech Koruna — the BEER Approach.” Statistika: Statistics and Economy Journal 104 (3): 278–291. doi: 10.54694/stat.2023.50.

Purfield, C., and C.B. Rosenberg. 2010. “Adjustment Under a Currency Peg: Estonia, Latvia and Lithuania During the Global Financial Crisis 2008-09.” IMF Working Papers 2010 (213). doi: 10.5089/9781455205448.001.

Silvennoinen, A., and T. Terasvirta. 2008. “Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model.” CREATES Research Paper (2008-5). doi: 10.2139/ssrn.1148137.