Exchange rates forecasting: Can jump models combined with macroeconomic fundamentals help?

Abstract

Connection between macroeconomic variables and foreign exchange (FX) rates evaluated in the context of out-of-sample forecasting is a well-known problem in economics. We propose a method that utilizes stochastic models based on jump processes (namely the normal inverse Gaussian and Meixner models), combines them with macroeconomic fundamentals, and using a moving (rolling or recursive) regularized estimation procedure produces forecasts of FX rates. These are compared to benchmark models, namely the direct forecast and the Gauss model forecast. Empirical out-of-sample experiments are performed on EUR/USD and USD/DKK currencies.