Non-Linear Interactions and Exchange Rate Prediction: Empirical Evidence Using Support Vector Regression

Abstract

This paper analysed the prediction of the spot exchange rate of 10 currency pairs using support vector regression (SVR) based on a fundamentalist model composed of 13 explanatory variables. Different structures of non-linear dependence introduced by nine different Kernel functions were tested and the predictions were compared to the Random Walk benchmark. We checked the explanatory power gain of SVR models over the Random Walk by applying White’s Reality Check Test. The results showed that the majority of SVR models achieved better out-of-sample performance than the Random Walk, but in overall they failed to achieve statistical significance of predictive superiority. Furthermore, we observed that non-mainstream Kernel functions performed better than the ones commonly used in the machine-learning literature, a finding that can provide new insights regarding machine-learning methods applications and the predictability of exchange rates using non-linear interactions between the predictors.

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