Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/208216
Authors: 
Lang, Jan Hannes
Peltonen, Tuomo
Sarlin, Peter
Year of Publication: 
2018
Series/Report no.: 
ECB Working Paper 2182
Abstract: 
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 microand macro-prudential policy by applying it to a large dataset of EU banks and showing some examples of early-warning model visualizations.
Subjects: 
Early-warning models
Financial crises
Bank distress
Regularization
Micro- and macro-prudential analysis
JEL: 
G01
G17
G21
G33
C52
C54
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-3287-5
Document Type: 
Working Paper

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