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

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Author Info
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)

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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.

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Publisher Info
Paper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 432.

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Length: 27 pages
Date of creation: Nov 2000
Date of revision:
Publication status: Published in IEEE Transactions on Neural Networks - Special Issue: Neural Network in Financial Engineering - v. 12, p.755-764
Handle: RePEc:rio:texdis:432

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  1. Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 10 Feb 2000.
  2. Meese, Richard A & Rose, Andrew K, 1991. "An Empirical Assessment of Non-linearities in Models of Exchange Rate Determination," Review of Economic Studies, Blackwell Publishing, vol. 58(3), pages 603-19, May. [Downloadable!] (restricted)
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  3. 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. [Downloadable!] (restricted)
  4. 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 636-61, October. [Downloadable!] (restricted)
  5. 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. [Downloadable!] (restricted)
  6. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September. [Downloadable!] (restricted)
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  7. Gencay, Ramazan, 1999. "Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules," Journal of International Economics, Elsevier, vol. 47(1), pages 91-107, February. [Downloadable!] (restricted)
  8. G. Rech & T. Teräsvirta & R. Tschernig, . "A Simple variable selection technique for nonlinear models," Sonderforschungsbereich 373 1999-26, Humboldt Universitaet Berlin.
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  9. Chung-Ming Kuan & Halbert White, 1994. "Artificial neural networks: an econometric perspective," Econometric Reviews, Taylor and Francis Journals, vol. 13(1), pages 1-91. [Downloadable!] (restricted)
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  10. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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