Estimating structural exchange rate models by artificial neural networks

No theory of structural exchange rate determination has yet been found that performs well in prediction experiments. Only very seldom has the simple random walk model been significantly outperformed. Referring to three, sometimes highly nonlinear, monetary and nonmonetary structural exchange rate mo...

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Bibliographic Details
Published in:Applied financial economics 1998-10, Vol.8 (5), p.541-551
Main Authors: Plasmans, Joseph, Verkooijen, William, Daniels, Hennie
Format: Article
Language:English
Subjects:
Comparative analysis
Data analysis
Economic models
Exchange rate determination
Exchange rates
Foreign exchange rates
International finance
Monetary models
Neural networks
Studies
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Summary:No theory of structural exchange rate determination has yet been found that performs well in prediction experiments. Only very seldom has the simple random walk model been significantly outperformed. Referring to three, sometimes highly nonlinear, monetary and nonmonetary structural exchange rate models, a feedforward artificial neural network specification is investigated to determine whether it improves the prediction performance of structural and random walk exchange rate models. A new test for univariate nonlinear cointegration is also derived. Important nonlinearities are not detected for monthly data of US dollar rates in Deutsche marks, Dutch guilders, British pounds and Japanese yens.
ISSN:0960-3107
1466-4305
DOI:10.1080/096031098332844