High frequency monitoring of credit creation: A new tool for central banks in emerging market economies

Carlos Giraldo (), Iader Giraldo, Jose Gomez-Gonzalez and Jorge Uribe

The Quarterly Review of Economics and Finance, 2024, vol. 97, issue C

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.

Keywords: Credit growth; Machine learning methodology; Excessive credit creation; Financial stability (search for similar items in EconPapers)
JEL-codes: C45 E44 G21 (search for similar items in EconPapers)
Date: 2024
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Working Paper: High Frequency Monitoring of Credit Creation: A New Tool for Central Banks in Emerging Market Economies (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:97:y:2024:i:c:s1062976924000991

DOI: 10.1016/j.qref.2024.101893

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