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Evaluating volatility dynamics and the forecasting ability of Markov switching models

Author

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  • George S. Parikakis

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

  • Anna Merika

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

Abstract

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

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    Cited by:

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    2. Oscar V. De la Torre-Torres & Evaristo Galeana-Figueroa & José Álvarez-García, 2020. "Markov-Switching Stochastic Processes in an Active Trading Algorithm in the Main Latin-American Stock Markets," Mathematics, MDPI, vol. 8(6), pages 1-23, June.
    3. T. G. Saji, 2019. "Can BRICS Form a Currency Union? An Analysis under Markov Regime-Switching Framework," Global Business Review, International Management Institute, vol. 20(1), pages 151-165, February.
    4. Diteboho Xaba & Ntebogang Dinah Moroke & Ishmael Rapoo, 2019. "Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model," Journal of Economics and Behavioral Studies, AMH International, vol. 11(3), pages 10-22.
    5. Oscar V. De la Torre-Torres & Dora Aguilasocho-Montoya & María de la Cruz del Río-Rama, 2020. "A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures," Mathematics, MDPI, vol. 8(6), pages 1-19, June.

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