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High Frequency Monitoring of Credit Creation: A New Tool for Central Banks in Emerging Market Economies

Author

Listed:
  • Giraldo, Carlos

    (Latin American Reserve Fund)

  • Giraldo, Iader

    (Latin American Reserve Fund)

  • Gomez-Gonzalez, Jose E.

    (City University of New York – Lehman College)

  • Uribe, Jorge M.

    (Universitat Oberta de Catalunya)

Abstract

This study utilizes weekly datasets on loan growth in Colombia to develop a daily indicator of credit expansion using a two-step machine learning approach. Initially, employing Random Forests (RF), missing data in the raw credit indicator is filled using high frequency indicators like spreads, interest rates, and stock market returns. Subsequently, Quantile Random Forest identifies periods of excessive credit creation, particularly focusing on growth quantiles above 95%, indicative of potential financial instability. Unlike previous studies, this research combines machine learning with mixed frequency analysis to create a versatile early warning instrument for identifying instances of excessive credit growth in emerging market economies. This methodology, with its ability to handle nonlinear relationships and accommodate diverse scenarios, offers significant value to central bankers and macroprudential authorities in safeguarding financial stability.

Suggested Citation

  • Giraldo, Carlos & Giraldo, Iader & Gomez-Gonzalez, Jose E. & Uribe, Jorge M., 2024. "High Frequency Monitoring of Credit Creation: A New Tool for Central Banks in Emerging Market Economies," Documentos de trabajo 21077, FLAR.
    • Handle: RePEc:col:000566:021077
    Note: Link to the paper: https://flar.com/wp-content/uploads/2024/03/Paper-High-Frequency-Monitoring-of-Credit-Creation-A-New-Tool-for-Central-Banks-in-Emerging-Market-Economies.pdf
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    References listed on IDEAS

    as
    1. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Credit growth; Machine learning methodology; Excessive credit creation; Financial stability;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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