Estimating structural exchange rate models by artificial neural networks

A feedforward artificial neural network specification is investigated to determine whether it improves the prediction performance of structural and random walk exchange rate models and a new test for univariate nonlinear cointegration is derived.

Abstract

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.