Forecasting High-Risk Composite CAMELS Ratings

19 Pages Posted: 22 Oct 2019

See all articles by Lewis Gaul

Lewis Gaul

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Jonathan Jones

Office of the Comptroller of the Currency

Pinar Uysal

Board of Governors of the Federal Reserve System

Date Written: 2019-07-23

Abstract

We investigate whether statistical learning models can contribute to supervisors' off-site monitoring of banks' overall condition. We use five statistical learning and two forecast combination models to forecast high-risk composite CAMELS ratings over time (1984-2015), where a high-risk composite CAMELS rating is defined as a CAMELS rating of 3, 4, or 5. Our results indicate that the standard logit model, which is already widely used to forecast CAMELS ratings, comes close enough to be an adequate model for predicting high-risk ratings. We also find that the overall accuracy of the individual forecasts could be modestly improved upon by using forecast combination methods.

Keywords: Bank supervision and regulation, early warning models, CAMELS ratings, machine learning

JEL Classification: G21, G28, C53

Suggested Citation

Gaul, Lewis and Jones, Jonathan and Uysal, Pinar, Forecasting High-Risk Composite CAMELS Ratings (2019-07-23). FRB International Finance Discussion Paper No. 1252. Available at SSRN: https://ssrn.com/abstract=3473063 or http://dx.doi.org/10.17016/IFDP.2019.1252

Lewis Gaul (Contact Author)

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th Street SW
Washington, DC 20219
United States

Jonathan Jones

Office of the Comptroller of the Currency ( email )

400 7th Street SW
Washington, DC 20219
United States

Pinar Uysal

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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