Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models
The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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| Language: | English |
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Inter-American Development Bank
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| Subjects: | Economy, Exchange Rate, Interest Rate, Rating, N76 - Latin America • Caribbean, O13 - Agriculture • Natural Resources • Energy • Environment • Other Primary Products, C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes, C53 - Forecasting and Prediction Methods • Simulation Methods, Q47 - Energy Forecasting, macroeconomics;fundamental theories;R software;statistics;prediction, |
| Online Access: | http://dx.doi.org/10.18235/0004491 https://publications.iadb.org/en/realdollar-exchange-rate-prediction-combining-machine-learning-and-fundamental-models |
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