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.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Language: | English |
| Published: |
Inter-American Development Bank
|
| 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 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
dig-bid-node-32718 |
|---|---|
| record_format |
koha |
| spelling |
dig-bid-node-327182022-09-29T22:02:30ZReal/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models 2022-09-29T00:09:00+0000 http://dx.doi.org/10.18235/0004491 https://publications.iadb.org/en/realdollar-exchange-rate-prediction-combining-machine-learning-and-fundamental-models Inter-American Development Bank 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 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. Inter-American Development Bank Gustavo Pompeu José Luiz Rossi IDB Publications Brazil Southern Cone en |
| institution |
BID |
| collection |
DSpace |
| country |
Estados Unidos |
| countrycode |
US |
| component |
Bibliográfico |
| access |
En linea |
| databasecode |
dig-bid |
| tag |
biblioteca |
| region |
America del Norte |
| libraryname |
Biblioteca Felipe Herrera del BID |
| language |
English |
| topic |
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 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 |
| spellingShingle |
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 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 Inter-American Development Bank Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models |
| description |
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. |
| author2 |
Gustavo Pompeu |
| author_facet |
Gustavo Pompeu Inter-American Development Bank |
| topic_facet |
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 |
| author |
Inter-American Development Bank |
| author_sort |
Inter-American Development Bank |
| title |
Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models |
| title_short |
Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models |
| title_full |
Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models |
| title_fullStr |
Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models |
| title_full_unstemmed |
Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models |
| title_sort |
real/dollar exchange rate prediction combining machine learning and fundamental models |
| publisher |
Inter-American Development Bank |
| url |
http://dx.doi.org/10.18235/0004491 https://publications.iadb.org/en/realdollar-exchange-rate-prediction-combining-machine-learning-and-fundamental-models |
| work_keys_str_mv |
AT interamericandevelopmentbank realdollarexchangeratepredictioncombiningmachinelearningandfundamentalmodels |
| _version_ |
1806237877434056704 |