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Forecasting Nominal Exchange Rate using Deep Neural Networks

Author

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  • Jonathan Garita-Garita

    (Department of Economic Research, Central Bank of Costa Rica)

  • César Ulate-Sancho

    (Department of Economic Research, Central Bank of Costa Rica)

Abstract

This paper offers a daily-frequency analysis and short-term forecasting of Costa Rica’s foreign currency market using deep neural network algorithms. These algo-rithms efficiently integrates multiple high-frequency data to capture trends, seasonal patterns, and daily movements in the exchange rate from 2017 to March 2025. The results indicate that these models excels in predicting the observed exchange rate up to five days in advance, outperforming traditional time series forecasting methods in terms of accuracy. *** Resumen: Este artículo realiza un análisis de alta frecuencia del mercado de divisas de Costa Rica utilizando algoritmos de redes neuronales profundas. Se emplean datos diarios de acceso público de MONEX desde 2017 hasta marzo de 2025 para identificar quiebres de tendencia, patrones estacionales y la importancia relativa de las variables explicativas que determinan los movimientos diarios del tipo de cambio en MONEX. El modelo calibrado muestra una alta precisión para comprender la información histórica y realizar proyecciones del tipo de cambio a cinco días. Los resultados sugieren que los movimientos observados del tipo de cambio en 2024 están alineados con su tendencia y que existen factores estacionales significativos que influyen en el tipo de cambio a lo largo del año.

Suggested Citation

  • Jonathan Garita-Garita & César Ulate-Sancho, 2025. "Forecasting Nominal Exchange Rate using Deep Neural Networks," Documentos de Trabajo 2505, Banco Central de Costa Rica.
    • Handle: RePEc:apk:doctra:2505
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    File URL: https://repositorioinvestigaciones.bccr.fi.cr/handle/20.500.12506/504
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    References listed on IDEAS

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    1. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
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    Keywords

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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • O24 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Trade Policy; Factor Movement; Foreign Exchange Policy

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