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A neural network approach to long-run exchange rate prediction

✍ Scribed by William Verkooijen

Publisher
Springer US
Year
1996
Tongue
English
Weight
924 KB
Volume
9
Category
Article
ISSN
1572-9974

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✦ Synopsis

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 outof-sample prediction performance of the linear versions. Empirical results are reported in the case of the US dollar-Deutsche Mark exchange rate.

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