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Evaluating volatility dynamics and the forecasting ability of Markov switching models


  • George S. Parikakis

    (EFG Eurobank Ergasias S.A, Credit Division, Athens, Greece)

  • Anna Merika

    (Deree College, The American College of Greece, Aghia Paraskevi, Greece)


This paper uses Markov switching models to capture volatility dynamics in exchange rates and to evaluate their forecasting ability. We identify that increased volatilities in four euro-based exchange rates are due to underlying structural changes. Also, we find that currencies are closely related to each other, especially in high-volatility periods, where cross-correlations increase significantly. Using Markov switching Monte Carlo approach we provide evidence in favour of Markov switching models, rejecting random walk hypothesis. Testing in-sample and out-of-sample Markov trading rules based on Dueker and Neely ( Journal of Banking and Finance , 2007) we find that using econometric methodology is able to forecast accurately exchange rate movements. When applied to the Euro|US dollar and the euro|British pound daily returns data, the model provides exceptional out-of-sample returns. However, when applied to the euro|Brazilian real and the euro|Mexican peso, the model loses power. Higher volatility exercised in the Latin American currencies seems to be a critical factor for this failure. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • George S. Parikakis & Anna Merika, 2009. "Evaluating volatility dynamics and the forecasting ability of Markov switching models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(8), pages 736-744.
  • Handle: RePEc:jof:jforec:v:28:y:2009:i:8:p:736-744 DOI: 10.1002/for.1135

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    References listed on IDEAS

    1. Clarida, Richard H. & Sarno, Lucio & Taylor, Mark P. & Valente, Giorgio, 2003. "The out-of-sample success of term structure models as exchange rate predictors: a step beyond," Journal of International Economics, Elsevier, vol. 60(1), pages 61-83, May.
    2. Dueker, Michael & Neely, Christopher J., 2007. "Can Markov switching models predict excess foreign exchange returns?," Journal of Banking & Finance, Elsevier, vol. 31(2), pages 279-296, February.
    3. Engel, Charles & Hamilton, James D, 1990. "Long Swings in the Dollar: Are They in the Data and Do Markets Know It?," American Economic Review, American Economic Association, vol. 80(4), pages 689-713, September.
    4. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    5. Cheung, Yin-Wong, 1993. "Long Memory in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 93-101, January.
    6. Christopher J. Neely & Lucio Sarno, 2002. "How well do monetary fundamentals forecast exchange rates?," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 51-74.
    7. Chinn, Menzie D. & Meese, Richard A., 1995. "Banking on currency forecasts: How predictable is change in money?," Journal of International Economics, Elsevier, vol. 38(1-2), pages 161-178, February.
    8. Dewachter, Hans, 2001. "Can Markov switching models replicate chartist profits in the foreign exchange market?," Journal of International Money and Finance, Elsevier, vol. 20(1), pages 25-41, February.
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