A Framework for Early-Warning Modeling with an Application to Banks
43 Pages Posted: 12 Oct 2018
Date Written: October 11, 2018
This paper proposes a framework for deriving early-warning models with optimal out-of-sample forecasting properties and applies it to predicting distress in European banks. The main contributions of the paper are threefold. First, the paper introduces a conceptual framework to guide the process of building early-warning models, which highlights and structures the numerous complex choices that the modeler needs to make. Second, the paper proposes a flexible modeling solution to the conceptual framework that supports model selection in real-time. Specifically, our proposed solution is to combine the loss function approach to evaluate early-warning models with regularized logistic regression and cross-validation to find a model specification with optimal real-time out-of-sample forecasting properties. Third, the paper illustrates how the modeling framework can be used in analysis supporting both micro- and macro-prudential policy by applying it to a large dataset of EU banks and showing some examples of early-warning model visualizations.
Keywords: early-warning models, financial crises, bank distress, regularization, micro- and macro-prudential analysis
JEL Classification: G01, G17, G21, G33, C52, C54
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