An Early Warning System for banking crises: From regression-based analysis to machine learning techniques

Elizabeth Casabianca (), Michele Catalano, Lorenzo Forni, Elena Giarda () and Simone Passeri

No 235, "Marco Fanno" Working Papers from Dipartimento di Scienze Economiche "Marco Fanno"

Abstract: Ten years after the outbreak of the 2007-2008 crisis, renewed attention is directed to money and credit fluctuations, financial crises and policy responses. By using an integrated dataset that includes 100 countries (advanced and emerging) spanning from 1970 to 2017, we propose an Early Warning System (EWS) to predict the build-up of systemic banking crises. The paper aims at (i) identifying the macroeconomic drivers of banking crises, (ii) going beyond the use of traditional discrete choice models by applying supervised machine learning (ML) and (iii) assessing the degree of countries’ exposure to systemic risks by means of predicted probabilities. Our results show that ML algorithms can have a better predictive performance than the logit models. All models deliver increasing predicted probabilities in the last years of the sample for the advanced countries, warning against the possible build-up of pre-crisis macroeconomic imbalances.

Keywords: banking crises; EWS; machine learning; decision trees; AdaBoost (search for similar items in EconPapers)
JEL-codes: C25 C40 E44 G01 G21 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2019-08
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp, nep-dcm, nep-mac and nep-rmg
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://economia.unipd.it/sites/decon.unipd.it/files/20190235.pdf (application/pdf)

Related works:
Journal Article: A machine learning approach to rank the determinants of banking crises over time and across countries (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pad:wpaper:0235

Access Statistics for this paper

More papers in "Marco Fanno" Working Papers from Dipartimento di Scienze Economiche "Marco Fanno" Contact information at EDIRC.
Bibliographic data for series maintained by Raffaele Dei Campielisi ().