Modelling exchange rates: smooth transitions, neural networks, and linear models
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.
|Date of creation:||Nov 2000|
|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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- Rech, Gianluigi & Teräsvirta, Timo & Tschernig, Rolf, 1999.
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SSE/EFI Working Paper Series in Economics and Finance
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- Eitrheim, Øyvind & Teräsvirta, Timo, 1995. "Testing the Adequacy of Smooth Transition Autoregressive Models," SSE/EFI Working Paper Series in Economics and Finance 56, Stockholm School of Economics.
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- Richard A. Meese & Andrew K. Rose, 1991. "An Empirical Assessment of Non-Linearities in Models of Exchange Rate Determination," Review of Economic Studies, Oxford University Press, vol. 58(3), pages 603-619.
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