Statistical learning and exchange rate forecasting

Emilio Colombo, Matteo Pelagatti

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

1 Citazioni (Scopus)


This study uses innovative tools recently proposed in the statistical learning literature to assess the capability of standard exchange rate models to predict the exchange rate in the short and long runs. Our results show that statistical learning methods deliver remarkably good performance, outperforming the random walk in forecasting the exchange rate at different forecasting horizons, with the exception of the very short term (a period of one to two months). These results were robust across countries, time, and models. We then used these tools to compare the predictive capabilities of different exchange rate models and model specifications, and found that sticky price versions of the monetary model with an error correction specification delivered the best performance. We also explain the operation of the statistical learning models by developing measures of variable importance and analyzing the kind of relationship that links each variable with the outcome. This gives us a better understanding of the relationship between the exchange rate and economic fundamentals, which appears complex and characterized by strong non-linearities.
Lingua originaleEnglish
pagine (da-a)1260-1289
Numero di pagine30
RivistaInternational Journal of Forecasting
Stato di pubblicazionePubblicato - 2020


  • Exchange rate
  • Forecasting
  • Machine learning
  • Nonlinear models
  • Statistical learning


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