Template-Type: ReDIF-Paper 1.0
Author-Name: Emilio Colombo
Author-Email: emilio.colombo@unicatt.it
Author-Name: Matteo Pelagatti
Author-Email: matteo.pelagatti@unimib.it
Title: Statistical Learning and Exchange Rate Forecasting
Abstract: his study uses the most innovative tools recently proposed in the statistical learning literature to assess the ability of standard exchange rate models to predict the exchange rate in the short and long run.
Our results show that statistical learning methods display impressive performances, consistently outperforming the random walk in forecasting the exchange rate at different forecasting horizons, with the exception of the very short term (a period of 1-2 months). We use these tools to compare the predictive ability of different exchange rate models and model specifications. We find that sticky price versions of the monetary model with the error correction specification exhibit the best performance. We also explore the functioning of statistical learning models by developing measures of variable importance and by analyzing the kind of relationship that links each variable with the outcome. This allows us to improve our understanding of the relationship between the exchange rate and economic fundamentals, which appears complex and characterized by strong non-linearities.
Classification-JEL: F37, C53
Creation-Date: 2019
File-URL: http://dipartimenti.unicatt.it/diseis-wp_1901.pdf
File-Format: Application/PDF
Number: dis1901
Handle: RePEc:dis:wpaper:dis1901