Predicting Distress in European Banks
By F. Betz, Silviu Opricǎ, Tuomas A. Peltonen, Peter Sarlin. Social Science Research Network, 2014
307 citations recorded by the source corpus.
The paper develops an early-warning model for predicting vulnerabilities leading to distress in European banks using both bank and country-level data. As outright bank failures have been rare in Europe, the paper introduces a novel dataset that complements bankruptcies and defaults with state interventions and mergers in distress. The signals of the early-warning model are calibrated not only according to the policymaker’s preferences between type I and II errors, but also to take into account the potential systemic relevance of each individual financial institution. The key findings of the paper are that complementing bank-specific vulnerabilities with indicators for macro-financial imbalances and banking sector vulnerabilities improves model performance and yields useful out-of-sample predictions of bank distress during the current financial crisis.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/5ff64d9713b8716d96ba577d21f2c0237ea6088a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Barbara Casu
- Samuel Rönnqvist
- Frederik Mergaerts
- L. Chiaramonte
- Miguel Boucinha
- C. Detken
- Benjamin Klaus
- Lucia Alessi
In Finance
- Samuel Rönnqvist
- Barbara Casu
- Peter Sarlin
- Frederik Mergaerts
- L. Chiaramonte
- Miguel Boucinha
- C. Detken
- Benjamin Klaus
Citations in the corpus
- Detect & Describe: Deep Learning of Bank Stress in the News (2015, methodology): “[2], the events include government interventions and state aid, as well as direct failures and distressed mergers.”
- Bank distress in the news: Describing events through deep learning (2016, methodology): “[5] who obtain Ur of 19-42% and Peltonen et al.”
- Deep learning bank distress from news and numerical financial data (2017, background): “9 in accordance with the indications of previous studies like (Betz et al. 2014) and (Constantin et al.”
- Business models and bank performance: a long-term perspective (2016, background): “Recent studies have also demonstrated that a higher reliance on customer deposits reduces bank distress (Altunbas et al., 2011; Betz et al., 2013) and inspires market confidence (Beltratti and Stulz, 2012).”
- Operationalising the Countercyclical Capital Buffer: Indicator Selection, Threshold Identification and Calibration Options (2014, methodology): “More recently the AUROC has been used as an evaluation criterion in the economics literature, for instance Berge and Jordà (2011), Jordà and Taylor (2011), Candelon et al. (2012), Jordà (2012), Drehmann and Juselius (2014), Betz et al. (2013) and Behn et al. (2013a).”
- Capital and liquidity ratios and financial distress. Evidence from the European banking industry (2017, methodology): “See, among others, Kumar et al., 2003; Fuertes and Kalotychou, 2007; Davis and Karim, 2008; Poghosyan and Čihak, 2011; LoDuca and Peltonen, 2013; Sarlin and Peltonen, 2013; and Betz et al., 2014.”