Modelling exchange rates: smooth transitions, neural networks, and linear models
AbstractThe 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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Bibliographic InfoPaper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 432.
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
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-05-02 (All new papers)
- NEP-CMP-2001-05-02 (Computational Economics)
- NEP-ETS-2001-05-02 (Econometric Time Series)
- NEP-FMK-2001-05-02 (Financial Markets)
- NEP-IFN-2001-05-02 (International Finance)
- NEP-NET-2001-05-02 (Network Economics)
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