Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

Abstract

In this research, we work with data of futures contracts on foreign exchange rates for British pound (BP), Canadian dollar (CD), and Japanese yen (JY) that are traded at the Chicago Mercantile Exchange (CME) against US dollars. We model relationships between exchange rates in these currencies using linear models, feed forward artificial neural networks (ANN), and three versions of recurrent neural networks (RNN1, RNN2 and RNN3) for predicting exchange rates in these currencies against the US dollar. Our results on forecast evaluations based on AGS test the tests of forecast equivalence between any two competing models among the entire models employed for each of the series show that ANN and the three versions of RNN models offer superior forecasts for predicting BP, CD and JY exchange rates although the forecast evaluations based on MGN test are in sharp contrast. On the other hand forecast based on SIGN test shows that ANN and all the versions of RNN models offer superior forecasts for BP and CD in exception of JY exchange rates. The results for forecast evaluation for all the models for each of the series based on summary measures of forecast evaluations show that RNN3 model appears to offer the most accurate predictions of BP and RNN1 for JP exchange rates. However, none of the RNN models appear to be statistically superior to the benchmark (i.e., linear model) for predicting CD exchange rates.