Δεν διατίθεται στα ελληνικά.
Peter Sarlin
- 11 October 2018
- WORKING PAPER SERIES - No. 2182Details
- 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.
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G33 : Financial Economics→Corporate Finance and Governance→Bankruptcy, Liquidation
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
- 23 February 2017
- WORKING PAPER SERIES - No. 2025Details
- Abstract
- Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable t