Forecasting Commodity Currencies: The Role of Fundamentals with Short-Lived Predictive Content

Abstract

Research demonstrates that commodity price changes exhibit a short-lived, yet robust contemporaneous effect on commodity currencies, which is mainly detectable in daily (high)-frequency data. We show that using MIxed DAta Sampling (MIDAS) models in a Bayesian setting to suitably exploit such short-lived effects, leads to out-of-sample exchange rate forecast improvements at monthly horizon both on point and density forecasting. Further, the usual low-frequency predictors, such as money supplies and interest rates differentials, typically receive little support from the data at this horizon, whereas daily commodity prices are highly likely. We also introduce the random walk Metropolis-Hastings technique as a new tool to estimate MIDAS regressions.

Keywords: Exchange rate point and density forecasting, Commodity prices, MIDAS model, Bayesian model averaging, Metropolis-Hastings algorithm

JEL Classification: C53, C55, F37