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Modelling exchange rates: smooth transitions, neural networks, and linear models

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  • Marcelo Cunha Medeiros

    () (Department of Economics PUC-Rio)

  • Álvaro Veiga

    (Department of Electrical Engineering PUC-Rio)

  • Carlos Eduardo Pedreira

    (Department of Electrical Engineering PUC-Rio)

Abstract

The goal of this paper is to test for and model nonlinearities in several monthly exchange rates time series. We apply two different nonlinear alternatives, namely: the artificial neural network time series model estimated with Bayesian regularization and a flexible smooth transition specifica-tion, called the neuro-coefficient smooth transition autoregression. The linearity test rejects the null hypothesis of linearity in ten out of fourteen series. We compare, using different measures, the fore-casting performance of the nonlinear specifications with the linear autoregression and the random walk models.

Suggested Citation

  • Marcelo Cunha Medeiros & Álvaro Veiga & Carlos Eduardo Pedreira, 2000. "Modelling exchange rates: smooth transitions, neural networks, and linear models," Textos para discussão 432, Department of Economics PUC-Rio (Brazil).
  • Handle: RePEc:rio:texdis:432
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    References listed on IDEAS

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    1. Chang, P H Kevin & Osler, Carol L, 1999. "Methodical Madness: Technical Analysis and the Irrationality of Exchange-Rate Forecasts," Economic Journal, Royal Economic Society, vol. 109(458), pages 636-661, October.
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    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," SSE/EFI Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 29 Apr 2004.
    9. Sarantis, Nicholas, 1999. "Modeling non-linearities in real effective exchange rates," Journal of International Money and Finance, Elsevier, vol. 18(1), pages 27-45, January.
    10. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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    Cited by:

    1. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    2. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Jan 2021.
    3. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 245-264, October.
    4. Leila Ali & Marie Lebreton, 2013. "The Fall of Bretton Woods: Which Geography Matters?," Economics Bulletin, AccessEcon, vol. 33(2), pages 1396-1419.
    5. Houda Ben Hadj Boubaker, 2011. "The Forecasting Performance of Seasonal and Nonlinear Models," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 1(1), pages 26-39, March.

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