IDEAS home Printed from https://ideas.repec.org/p/apk/doctra/2505.html
   My bibliography  Save this paper

Forecasting Nominal Exchange Rate using Deep Neural Networks

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://repositorioinvestigaciones.bccr.fi.cr/handle/20.500.12506/504
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    2. Shoshan, Vered & Hazan, Tamir & Plonsky, Ori, 2023. "BEAST-Net: Learning novel behavioral insights using a neural network adaptation of a behavioral model," OSF Preprints kaeny, Center for Open Science.
    3. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    4. Stéphane Helleringer & Insa Diouf & Cheikh Tidiane Ndiaye & Laetitia Douillot & Valerie Delaunay & Rene Vidal & Laurence Fleury & Chong You, 2019. "Improving age measurement in low- and middle-income countries through computer vision: A test in Senegal," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(9), pages 219-260.
    5. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    6. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    7. Hurmeranta, Risto & Lyytikäinen, Teemu, 2025. "Nominal Loss Aversion in the Housing Market and Household Mobility," Working Papers 178, VATT Institute for Economic Research.
    8. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    9. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    10. Thiemo Fetzer & Stephan Kyburz, 2024. "Cohesive Institutions and Political Violence," The Review of Economics and Statistics, MIT Press, vol. 106(1), pages 133-150, January.
    11. Dang, Hai-Anh & Carleto, Gero & Gourlay, Sydney & Abanokova, Kseniya, 2023. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," 2023 Annual Meeting, July 23-25, Washington D.C. 335648, Agricultural and Applied Economics Association.
    12. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
    13. Seongyoun Shin & Jaewoong Won, 2025. "Investigating the Role of Urban Factors in COVID-19 Transmission During the Pre- and Post-Omicron Periods: A Case Study of South Korea," Sustainability, MDPI, vol. 17(5), pages 1-17, February.
    14. Sascha O. Becker & Thiemo Fetzer, 2018. "Has Eastern European Migration Impacted UK-born Workers?," CAGE Online Working Paper Series 376, Competitive Advantage in the Global Economy (CAGE).
    15. Bailliu, Jeannine & Han, Xinfen & Kruger, Mark & Liu, Yu-Hsien & Thanabalasingam, Sri, 2019. "Can media and text analytics provide insights into labour market conditions in China?," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1118-1130.
    16. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    17. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    18. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2024. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Post-Print hal-05056934, HAL.
    19. Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
    20. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:apk:doctra:2505. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Departamento de Investigación Económica (email available below). General contact details of provider: https://edirc.repec.org/data/bccrrcr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.