A machine learning approach to rank the determinants of banking crises over time and across countries

Jane Casabianca, E., Catalano, M., Forni, L., Giarda, E., & Passeri, S. (2022). A machine learning approach to rank the determinants of banking crises over time and across countries. Journal of International Money and Finance e102739. 10.1016/j.jimonfin.2022.102739. (In Press)

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Abstract

We use a machine learning approach, namely AdaBoost, to rank the determinants of banking crises over time and across countries. We cover a total of 100 countries, advanced and emerging, over the years from 1970 to 2017. The paper first shows that AdaBoost has a better predictive performance than the logit model, both in-sample and out-of-sample; then, it employs AdaBoost to classify the major macroeconomic factors leading to banking crises. The baseline analysis reveals that the US 10yr Treasury interest rate and world growth play a key role in anticipating a crisis, and that these two variables explain a growing share of the results over time, for both country groups. Other variables, which have been highlighted as important in the literature on crises - such as inflation, current account, public and external debt and credit - are relevant in the lead up to banking crises, but their role has been decreasing over time compared to the aforementioned variables. We present also extensions of the model, which confirm and add to the main results of the baseline model.

Item Type: Article
Uncontrolled Keywords: banking crises; predictive models; machine learning
Research Programs: Advancing Systems Analysis (ASA)
Advancing Systems Analysis (ASA) > Exploratory Modeling of Human-natural Systems (EM)
Depositing User: Luke Kirwan
Date Deposited: 13 Sep 2022 14:02
Last Modified: 13 Sep 2022 14:02
URI: https://pure.iiasa.ac.at/18218

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