Learning from Trees: A Mixed Approach to Building Early Warning Systems for Systemic Banking Crises

39 Pages Posted: 15 Nov 2019

Date Written: October 30, 2019

Abstract

Banking crises can be extremely costly. The early detection of vulnerabilities can help prevent or mitigate those costs. We develop an early warning model of systemic banking crises that combines regression tree technology with a statistical algorithm (CRAGGING) to improve its accuracy and overcome the drawbacks of previously used models. Our model has a large set of desirable features. It provides endogenously-determined critical thresholds for a set of useful indicators, presented in the intuitive form of a decision tree structure. Our framework takes into account the conditional relations between various indicators when setting early warning thresholds. This facilitates the production of accurate early warning signals as compared to the signals from a logit model and from a standard regression tree. Our model also suggests that high credit aggregates, both in terms of volume and as compared to a long-term trend, as well as low market risk perception, are amongst the most important indicators for predicting the build-up of vulnerabilities in the banking sector.

Keywords: early warning system, banking crises, regression tree, ensemble methods

JEL Classification: C40, G01, G21, E44, F37

Suggested Citation

Gabriele, Carmine, Learning from Trees: A Mixed Approach to Building Early Warning Systems for Systemic Banking Crises (October 30, 2019). European Stability Mechanism Working Paper No. 40. Available at SSRN: https://ssrn.com/abstract=3486928 or http://dx.doi.org/10.2139/ssrn.3486928

Carmine Gabriele (Contact Author)

European Stability Mechanism ( email )

6a Circuit de la Foire Internationale
L-1347
Luxembourg

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