Learning from trees: A mixed approach to building early warning systems for systemic banking crises
Download PDF: Working Paper 40
This paper develops an early warning model of systemic banking crises that combines regression tree technology with a statistical algorithm to improve its accuracy and overcome some drawbacks of more standard models.
Author: Carmine Gabriele (ESM)
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.
Disclaimer: This Working Paper should not be reported as representing the views of the ESM. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the ESM or ESM policy. No responsibility or liability is accepted by the ESM in relation to the accuracy or completeness of the information, including any data sets, presented in this Working Paper.
Keywords: Early warning system, banking crises, regression tree, ensemble methods.
JEL codes: C40, G01, G21, E44, F37