15 October 2013

ECB: Predicting distress in European banks

As outright bank failures have been rare in Europe, this paper introduces a novel dataset that complements bankruptcies and defaults with state interventions and mergers in distress.

This paper presents research conducted within the Macro-prudential Research Network (MaRs). The paper is released in order to make the research of MaRs generally available, in preliminary form, to encourage comments and suggestions prior to final publication.

The global financial crisis has had a significant impact on the health of the European banking system and on the soundness of individual banks. Beyond the direct bailout costs and output losses, the interplay of fiscally strained sovereigns and weak banking systems that characterise the ongoing sovereign debt crisis show the importance of the euro area banking sector for the stability of the entire European Monetary Union. The motivation for an early-warning model for European banks is thus clear.

To derive an early-warning model for European banks, this paper introduces a novel dataset of bank distress events. As bank defaults are rare in Europe, the data set complements bankruptcies, liquidations and defaults by also taking into account state interventions, and mergers in distress. State interventions comprise capital injections and asset reliefs (asset protection and guarantees). A distressed merger occurs if (i) a parent receives state aid within 12 months after the merger or (ii) if a merged entity has a coverage ratio (capital equity and loan reserves minus non-performing loans to total assets) smaller than 0 within 12 months before the merger.

The outbreak of a financial crisis is known to be difficult to predict (e.g. Rose and Spiegel, 2011). Recently, the early-warning literature has therefore focused on detecting underlying vulnerabilities, and finding common patterns preceding financial crises (e.g. Reinhart and Rogofi, 2008; 2009). Thus, this paper focuses on predicting vulnerable states, where one or multiple triggers could lead to a bank distress event. The early-warning model applies a micro-macro perspective to measure bank vulnerability. Beyond bank-specific and banking-sector vulnerability indicators, the paper uses measures of macroeconomic and financial imbalances from the EU Alert Mechanism Report related to the EU Macroeconomic Imbalance Procedure (MIP).

The models are estimated to derive probabilities of banks being in vulnerable states, but a policy maker needs to know when to act. Following Sarlin (2013), the signals of the model are evaluated taking into account the policymaker's preferences between type I and type II errors, the uneven frequency of tranquil and distress events, and the systemic relevance of each bank.

This paper presents the first application of the evaluation framework to a bank-level model and represents a bank's systemic relevance with its size. Thus, the early-warning model can also be calibrated to focus on predicting systemic banking failures.

The paper finds that complementing bank-specific vulnerabilities with indicators of macro-financial imbalances and banking sector vulnerabilities improves model performance. The results also confirm the usefulness of the vulnerability indicators introduced recently as part of the EU MIP as well as findings in earlier literature. Moreover, the paper shows that an early-warning exercise using only publicly available data yields useful out-of-sample predictions of bank distress during the global financial crisis.

Full working paper

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