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Volatility regime-switching in European exchange rates prior to monetary unification


  • Wilfling, Bernd


Several theoretical models suggest that the mere announcement of entering a currency union in the future triggers instantaneous changes in exchange-rate volatility. First, this paper develops a Markov-switching framework by which, in fact, volatility regime-switching in foreign exchange rates can be detected for all currencies in the run-up to the European Monetary Union (EMU). Second, the paper attributes the currency-specific volatility regime-switches to decisive economic, institutional and political factors prior to EMU. All in all, the empirical results suggest that for future EMU accession countries volatility regime-switching models provide a useful tool for a broad range of financial applications (e.g. for the pricing of currency options or for the construction of EMU probability calculators).

Suggested Citation

  • Wilfling, Bernd, 2009. "Volatility regime-switching in European exchange rates prior to monetary unification," Journal of International Money and Finance, Elsevier, vol. 28(2), pages 240-270, March.
  • Handle: RePEc:eee:jimfin:v:28:y:2009:i:2:p:240-270

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    References listed on IDEAS

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    Cited by:

    1. Roberto Casarin & Marco Tronzano & Domenico Sartore, 2013. "Bayesian Markov Switching Stochastic Correlation Models," Working Papers 2013:11, Department of Economics, University of Venice "Ca' Foscari".
    2. Michael Frömmel, 2010. "Volatility Regimes in Central and Eastern European Countries’ Exchange Rates," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 60(1), pages 2-21, February.
    3. Shi, Yanlin & Ho, Kin-Yip, 2015. "Modeling high-frequency volatility with three-state FIGARCH models," Economic Modelling, Elsevier, vol. 51(C), pages 473-483.
    4. Bohl, Martin T. & Reher, Gerrit & Wilfling, Bernd, 2016. "Short selling constraints and stock returns volatility: Empirical evidence from the German stock market," Economic Modelling, Elsevier, vol. 58(C), pages 159-166.
    5. Meulemann, Max & Uebele, Martin & Wilfling, Bernd, 2014. "The restoration of the gold standard after the US Civil War: A volatility analysis," Journal of Financial Stability, Elsevier, vol. 12(C), pages 37-46.
    6. José Dias & Sofia Ramos, 2014. "The aftermath of the subprime crisis: a clustering analysis of world banking sector," Review of Quantitative Finance and Accounting, Springer, vol. 42(2), pages 293-308, February.
    7. Dias, José G. & Ramos, Sofia B., 2013. "A core–periphery framework in stock markets of the euro zone," Economic Modelling, Elsevier, vol. 35(C), pages 320-329.
    8. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
    9. Jeff Fleming & Chris Kirby, 2013. "Component-Driven Regime-Switching Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 11(2), pages 263-301, March.
    10. 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.
    11. Syllignakis, Manolis N. & Kouretas, Georgios P., 2011. "Markov-switching regimes and the monetary model of exchange rate determination: Evidence from the Central and Eastern European markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(5), pages 707-723.
    12. repec:spr:annopr:v:262:y:2018:i:1:d:10.1007_s10479-016-2297-y is not listed on IDEAS
    13. Grossmann, Axel & Orlov, Alexei G., 2012. "Exchange rate misalignments in frequency domain," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 185-199.
    14. BenSaïda, Ahmed, 2015. "The frequency of regime switching in financial market volatility," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 63-79.


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