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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The Corona Recession and Bank Stress in Germany
Reint E. Gropp, Michael Koetter, William McShane
IWH Online,
No. 4,
2020
Abstract
We conduct stress tests for a large sample of German banks across different recoveries from the Corona recession. We find that, depending on how quickly the economy recovers, between 6% to 28% of banks could become distressed from defaulting corporate borrowers alone. Many of these banks are likely to require regulatory intervention or may even fail. Even in our most optimistic scenario, bank capital ratios decline by nearly 24%. The sum of total loans held by distressed banks could plausibly range from 127 to 624 billion Euros and it may take years before the full extent of this stress is observable. Hence, the current recession could result in an acute contraction in lending to the real economy, thereby worsening the current recession , decelerating the recovery, or perhaps even causing a “double dip” recession. Additionally, we show that the corporate portfolio of savings and cooperative banks is more than five times as exposed to small firms as that of commercial banks and Landesbanken. The preliminary evidence indicates small firms are particularly exposed to the current crisis, which implies that cooperative and savings banks are at especially high risk of becoming distressed. Given that the financial difficulties may seriously impair the recovery from the Covid-19 crisis, the pressure to bail out large parts of the banking system will be strong. Recent research suggests that the long run benefits of largely resisting these pressures may be high and could result in a more efficient economy.
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Transmitting Fiscal Covid-19 Counterstrikes Effectively: Mind the Banks!
Reint E. Gropp, Michael Koetter, William McShane
IWH Online,
No. 2,
2020
Abstract
The German government launched an unprecedented range of support programmes to mitigate the economic fallout from the Covid-19 pandemic for employees, self-employed, and firms. Fiscal transfers and guarantees amount to approximately €1.2 billion by now and are supplemented by similarly impressive measures taken at the European level. We argue in this note that the pandemic poses, however, also important challenges to financial stability in general and bank resilience in particular. A stable banking system is, in turn, crucial to ensure that support measures are transmitted to the real economy and that credit markets function seamlessly. Our analysis shows that banks are exposed rather differently to deteriorated business outlooks due to marked differences in their lending specialisation to different economic sectors. Moreover, a number of the banks that were hit hardest by bleak growth prospects of their borrowers were already relatively thinly capitalised at the outset of the pandemic. This coincidence can impair the ability and willingness of selected banks to continue lending to their mostly small and medium sized entrepreneurial customers. Therefore, ensuring financial stability is an important pre-requisite to also ensure the effectiveness of fiscal support measures. We estimate that contracting business prospects during the first quarter of 2020 could lead to an additional volume of non-performing loans (NPL) among the 40 most stressed banks ‒ mostly small, regional relationship lenders ‒ on the order of around €200 million. Given an initial stock of NPL of €650 million, this estimate thus suggests a potential level of NPL at year-end of €1.45 billion for this fairly small group of banks already. We further show that 17 regional banking markets are particularly exposed to an undesirable coincidence of starkly deteriorating borrower prospects and weakly capitalised local banks. Since these regions are home to around 6.8% of total employment in Germany, we argue that ensuring financial stability in the form of healthy bank balance sheets should be an important element of the policy strategy to contain the adverse real economic effects of the pandemic.
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Banks’ Equity Performance and the Term Structure of Interest Rates
Elyas Elyasiani, Iftekhar Hasan, Elena Kalotychou, Panos K. Pouliasis, Sotiris Staikouras
Financial Markets, Institutions and Instruments,
No. 2,
2020
Abstract
Using an extensive global sample, this paper investigates the impact of the term structure of interest rates on bank equity returns. Decomposing the yield curve to its three constituents (level, slope and curvature), the paper evaluates the time-varying sensitivity of the bank’s equity returns to these constituents by using a diagonal dynamic conditional correlation multivariate GARCH framework. Evidence reveals that the empirical proxies for the three factors explain the variations in equity returns above and beyond the market-wide effect. More specifically, shocks to the long-term (level) and short-term (slope) factors have a statistically significant impact on equity returns, while those on the medium-term (curvature) factor are less clear-cut. Bank size plays an important role in the sense that exposures are higher for SIFIs and large banks compared to medium and small banks. Moreover, banks exhibit greater sensitivities to all risk factors during the crisis and postcrisis periods compared to the pre-crisis period; though these sensitivities do not differ for market-oriented and bank-oriented financial systems.
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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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26.06.2019 • 14/2019
Study: How financial crises lower life satisfaction and how to prevent this
Financial crises not only result in severe disruptions to the economic system, they also affect people’s life satisfaction. A new study by Martin Luther University Halle-Wittenberg (MLU) and the Halle Institute for Economic Research (IWH) shows that weaker members of society are more affected by increased uncertainty during crisis times, even if they may not be speculating on the stock market themselves. This could potentially also lower their propensity to consume, thereby intensifying the impact of a financial crisis. The study was recently published in “The B.E. Journal of Economic Analysis & Policy”.
Lena Tonzer
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Delay Determinants of European Banking Union Implementation
Michael Koetter, Thomas Krause, Lena Tonzer
European Journal of Political Economy,
2019
Abstract
Most countries in the European Union (EU) delay the transposition of European Commission (EC) directives, which aim at reforming banking supervision, resolution, and deposit insurance. We compile a systematic overview of these delays to investigate if they result from strategic considerations of governments conditional on the state of their financial, regulatory, and political systems. Transposition delays pertaining to the three Banking Union directives differ considerably across the 28 EU members. Bivariate regression analyses suggest that existing national bank regulation and supervision drive delays the most. Political factors are less relevant. These results are qualitatively insensitive to alternative estimation methods and lag structures. Multivariate analyses highlight that well-stocked deposit insurance schemes speed-up the implementation of capital requirements, banking systems with many banks are slower in implementing new bank rescue and resolution rules, and countries with a more intensive sovereign-bank nexus delay the harmonization of EU deposit insurance more.
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Drivers of Systemic Risk: Do National and European Perspectives Differ?
Claudia M. Buch, Thomas Krause, Lena Tonzer
Journal of International Money and Finance,
March
2019
Abstract
With the establishment of the Banking Union, the European Central Bank has been granted the power to impose stricter regulations than the national regulator if systemic risks are not adequately addressed at the national level. We ask whether there is a cross-border externality in the sense that a bank’s systemic risk differs when applying a national versus a European perspective. On average, banks’ contribution to systemic risk is similar at the two regional levels, and so is the ranking of banks. Generally, larger banks and banks with a lower share of loans are more systemically important. The effects of these variables are qualitatively but not quantitatively similar at the national versus the European level.
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How Do Banks React to Catastrophic Events? Evidence from Hurricane Katrina
Claudia Lambert, Felix Noth, Ulrich Schüwer
Review of Finance,
No. 1,
2019
Abstract
This paper explores how banks react to an exogenous shock caused by Hurricane Katrina in 2005, and how the structure of the banking system affects economic development following the shock. Independent banks based in the disaster areas increase their risk-based capital ratios after the hurricane, while those that are part of a bank holding company on average do not. The effect on independent banks mainly comes from the subgroup of highly capitalized banks. These independent and highly capitalized banks increase their holdings in government securities and reduce their total loan exposures to non-financial firms, while also increasing new lending to these firms. With regard to local economic development, affected counties with a relatively large share of independent banks and relatively high average bank capital ratios show higher economic growth than other affected counties following the catastrophic event.
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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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