A Neural Network Approach to Long-Run Exchange Rate Prediction.

In the economics literature on exchange rate determination, no theory has yet been found that performs well in out-of-sample prediction experiments. Until today, the simple random walk model has never been significantly outperformed. We have identified a set of fundamental long-run exchange rate models from literature that are well-known among economists. This paper investigates whether a neural network representation of these structural exchange rate models improves the out-of-sample prediction performance of the linear versions. Empirical results are reported in the case of the U.S. dollar-Deutsche Mark exchange rate. Citation Copyright 1996 by Kluwer Academic Publishers.