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

Marcelo Medeiros (), Álvaro Veiga and Carlos Pedreira ()
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Álvaro Veiga: Department of Electrical Engineering PUC-Rio

No 432, Textos para discussão from Department of Economics PUC-Rio (Brazil)

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.

Pages: 27 pages
Date: 2000-11
New Economics Papers: this item is included in nep-cmp, nep-ets, nep-fmk, nep-ifn and nep-net
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Citations: View citations in EconPapers (5)

Published in IEEE Transactions on Neural Networks - Special Issue: Neural Network in Financial Engineering - v. 12, p.755-764

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Persistent link: https://EconPapers.repec.org/RePEc:rio:texdis:432

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