An Early Warning System for Predicting Systemic Banking Crises in the Eurozone: A Logit Regression Approach
An Early Warning System for Predicting Systemic Banking Crises in the Eurozone: A Logit Regression Approach (2018), Chryssanthi Filippopoulou and Spyros Spyrou, BeFin Lab AUEB, WPS 3-18
40 Pages Posted: 7 Aug 2018 Last revised: 13 Nov 2018
Date Written: July 18, 2018
Eurosystem macroprudential policies require shared action between national authorities and the European Central Bank (ECB). This has created the need for a common basis for macroprudential analysis, and as a result the Macroprudential Database (MPDB) was created by the ECB and the European Systemic Risk Board (ESRB), in 2015, in order to support the central bank’s functions and ESRB's needs. This paper evaluates a binary multivariate logit regression Early Warning Model (EWM) for systemic banking crises using indicators drown mainly from the recently constructed MPDB for 10 main Eurozone countries. The results suggest that the majority of MPDB risk indicators are important in forecasting systemic banking crisis from 2 up to 4 years ahead; the most important leading indicators proved to be a high unemployment rate and a high proportion of government debt to GDP, low levels of investment, high systemic country risk, adverse liquidity and funding conditions in the banking system, a highly concentrated banking sector, restricted economic freedom at country level, as well as extreme and persistent market euphoria. Our results further suggest that, the establishment of the Macroprudential Database in 2015 was an important development towards even more efficient central bank supervision and may assist significantly the prevention and management of systemic risk in the Euro system.
Keywords: European Central Bank; Macroprudential Database; Early Warning Systems; Multivariate binary Logistic Regression; Systemic Risk
JEL Classification: E3, E5, E6
Suggested Citation: Suggested Citation