European Banking in Transformational Times: Regulation, Crises, and Challenges
Michael Koetter, Huyen Nguyen
IWH Studies,
Nr. 7,
2023
Abstract
This paper assesses the progress made towards the creation of the European Banking Union (EBU) and the evolution of the banking industry in the European Union since the financial crisis of 2007. We review major regulatory changes pertaining to the three pillars of the EBU and the effects of new legislation on both banks and the real economy. Whereas farreaching reforms pertaining to the EBU pillars of supervision and resolution regimes have been implemented, the absence of a European Deposit Scheme remains a crucial deficiency. We discuss how European banks coped with recent challenges, such as the Covid-19 pandemic, a high inflation environment, and digitalization needs, followed by an outlook on selected major challenges lying ahead of this incomplete EBU, notably the transition towards a green economy.
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The Cleansing Effect of Banking Crises
Reint E. Gropp, Steven Ongena, Jörg Rocholl, Vahid Saadi
Economic Inquiry,
Nr. 3,
2022
Abstract
We assess the cleansing effects of the 2008–2009 financial crisis. U.S. regions with higher levels of supervisory forbearance on distressed banks see less restructuring in the real sector: fewer establishments, firms, and jobs are lost when more distressed banks remain in business. In these regions, the banking sector has been less healthy for several years after the crisis. Regions with less forbearance experience higher productivity growth after the crisis with more firm entries, job creation, and employment, wages, patents, and output growth. Forbearance is greater for state-chartered banks and in regions with weaker banking competition and more independent banks.
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Banking Globalization, Local Lending, and Labor Market Effects: Micro-level Evidence from Brazil
Felix Noth, Matias Ossandon Busch
Journal of Financial Stability,
October
2021
Abstract
Recent financial crises have prompted the interest in understanding how banking globalization interacts with domestic institutions in shaping foreign shocks’ transmission. This paper uses regional banking data from Brazil to show that a foreign funding shock to banks negatively affects lending by their regional branches. This effect increases in the presence of frictions in internal capital markets, which affect branches’ capacity to access funding from other regions via intra-bank linkages. These results also matter on an aggregate level, as municipality-level credit and job flows drop in exposed regions. Policies aiming to reduce the fragmented structure of regional banking markets could moderate the propagation of foreign shocks.
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The Cleansing Effect of Banking Crises
Reint E. Gropp, Steven Ongena, Jörg Rocholl, Vahid Saadi
Abstract
We assess the cleansing effects of the recent banking crisis. In U.S. regions with higher levels of supervisory forbearance on distressed banks during the crisis, there is less restructuring in the real sector and the banking sector remains less healthy for several years after the crisis. Regions with less supervisory forbearance experience higher productivity growth after the crisis with more firm entries, job creation, and employment, wages, patents, and output growth. Supervisory forbearance is greater for state-chartered banks and in regions with weaker banking competition and more independent banks, while recapitalisation of distressed banks through TARP does not facilitate cleansing.
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The Cleansing Effect of Banking Crises
Reint E. Gropp, Steven Ongena, Jörg Rocholl, Vahid Saadi
Abstract
We assess the cleansing effects of the recent banking crisis. In U.S. regions with higher levels of supervisory forbearance on distressed banks during the crisis, there is less restructuring in the real sector and the banking sector remains less healthy for several years after the crisis. Regions with less supervisory forbearance experience higher productivity growth after the crisis with more firm entries, job creation, and employment, wages, patents, and output growth. Supervisory forbearance is greater for state-chartered banks and in regions with weaker banking competition and more independent banks, while recapitalization of distressed banks through TARP does not facilitate cleansing.
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Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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