Statistical Learning and Exchange Rate Forecasting

Emilio Colombo and Matteo Pelagatti

No dis1901, DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo from Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS)

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.

JEL-codes: C53 F37 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-big, nep-for and nep-ore
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Related works:
Journal Article: Statistical learning and exchange rate forecasting (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:dis:wpaper:dis1901

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