We show that emergency liquidity provision by the Federal Reserve transmitted to non-U.S. banking markets. Based on manually collected holding company structures, we identify banks in Germany with access to U.S. facilities. Using detailed interest rate data reported to the German central bank, we compare lending and borrowing rates of banks with and without such access. U.S. liquidity shocks cause a significant decrease in the short-term funding costs of the average German bank with access. This reduction is mitigated for banks with more vulnerable balance sheets prior to the inception of emergency liquidity. We also find a significant pass-through in terms of lower corporate credit rates charged for banks with the lowest pre-crisis leverage, US-dollar funding needs, and liquidity buffers. Spillover effects from U.S. emergency liquidity provision are generally confined to short-term rates.

The global financial crisis in 2007-2009 exposed the absence of adequate regulation within the banking sector which had built up an excessive amount of on- and off-balance sheet leverage and had neglected basic principles of liquidity risk management (Acharya and Richardson, 2009, Adrian and Shin, 2010, Goodhart, 2008). As a response, the Basel Committee on Banking Supervision presented a revised and augmented regulatory framework. Since the financial crisis had demonstrated that microprudential regulation is not sufficient in safeguarding financial stability, the regulators put an emphasizes on macroprudential policies to improve the resilience of the financial sector. Key reforms in this respect are the tightening of capital requirements and the introduction of uniform liquidity requirements (BCBS, 2010).

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.

We explore the sources of the decline in household nonmortgage debt following the collapse of the housing market in 2006. First, we use data from the Federal Reserve Board's Senior Loan Officer Opinion Survey to document that, post-2006, banks tightened consumer lending standards more in counties that experienced a more pronounced house price decline (the pre-2006 "boom" counties). We then use the idea that renters did not experience an adverse wealth or collateral shock when the housing market collapsed to identify a general consumer credit supply shock. Our evidence suggests that a tightening of the supply of non-mortgage credit that was independent of the direct effects of lower housing collateral values played an important role in households' non-mortgage debt reduction. Renters decreased their non-mortgage debt more in boom counties than in non-boom counties, but homeowners did not. We argue that this wedge between renters and homeowners can only have arisen from a general tightening of banks' consumer lending stance. Using an IV approach, we trace this effect back to a reduction in bank capital of banks in boom counties.

This paper investigates how declines in the deposit facility rate set by the ECB affect euro area banks’ incentives to hold reserves at the central bank. We find that, in the face of lower deposit rates, banks with a more interest-sensitive business model are more likely to reduce reserve holdings and allocate freed-up liquidity to loans. The result is driven by well-capitalized banks in the non-GIIPS countries of the euro area. This reveals that conventional monetary policy instruments have limited effects in restoring monetary policy transmission during times of crisis.

Kurzfristig war die Finanzkrise, ursprünglich ausgelöst durch exzessive Vergabe von Hypotheken an weniger kreditwürdige Haushalte, verbunden mit der weitverbreiteten Verbriefung dieser Hypotheken, mit schweren realwirtschaftlichen Konsequenzen verbunden. Die Volkswirtschaften aller Industrieländer schrumpften stark, die Arbeitslosigkeit stieg kräftig an. Firmen waren nicht in der Lage, neue Investitionen zu finanzieren, da es für Banken in vielen Ländern nicht möglich war, Kredite zu vergeben. Gleichzeitig führten die Rettungsaktionen der Regierungen zu einer starken Erhöhung der Schuldenstände und zu einer Nullzinspolitik, verbunden mit Anleihekäufen, der wichtigsten Zentralbanken.

Wo stehen wir heute, über zehn Jahre nach der Pleite von Lehman, die symbolisch noch immer eng mit der Krise verbunden ist? Und gibt es langfristige Auswirkungen auf die Wirtschaft – Auswirkungen, die wir noch heute spüren und möglicherweise noch viele Jahre spüren werden?

Die globale Bankenkrise der Jahre 2008/2009 hatte weltweit signifikant negative Auswirkungen auf die Realwirtschaft, und in vielen Ländern fiel die folgende wirtschaftliche Erholung deutlich langsamer aus als in vorherigen Rezessionen. In den Monaten nach der Insolvenz der amerikanischen Investmentbank Lehman Brothers reduzierten Banken ihre Kreditvergabe an Unternehmen, was zu einem Anstieg der Arbeitslosigkeit, einem Rückgang an Investitionen und einer Verringerung der Produktivität führte. Während diese kurzfristigen Effekte in der bisherigen Forschung gut dokumentiert sind, sind die langfristigen Auswirkungen von Bankenkrisen bisher weit weniger gut verstanden. Zwei aktuelle Studien unter IWH-Beteiligung zeigen, dass Bankenkrisen generell negative langfristige Effekte auf das Wachstum von Firmen haben, dass die Rettung von schwachen Banken während der Krise mit Produktivitätsverlusten in späteren Jahren einhergeht, und dass diese negativen langfristigen Effekte durch die Existenz inflexibler Löhne verstärkt werden.

We employ a unique identification strategy linking survey data on household consumption expenditure to bank-level data to estimate the effects of bank funding stress on consumer credit and consumption expenditures. We show that households whose banks were more exposed to funding shocks report lower levels of nonmortgage liabilities. This, however, only translates into lower levels of consumption for low income households. Hence, adverse credit supply shocks are associated with significant heterogeneous effects.

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.

Understanding how banks react to unexpected events has become a very important economic and social question, especially since the financial crisis (Ivashina and Scharfstein, 2010; Puri et al., 2011). Whereas previous financial crises had largely stayed in the realm of finance, or very limited areas of the economy, the financial crisis of 2007-2008 demonstrated that unexpected financial shocks can have severe implications for the real economy in general, impacting the lives of a large cross-section of the population, for example through general reductions in employment (Chodorow- Reich, 2014; Popov and Rocholl, 2017). This new realization has led to an extensive literature on how banks react to unexpected events, especially if and how they transfer such shocks to firms and households. As a result, understanding exactly how shocks are transferred not only between banks (Popov and Udell, 2012; Schnabl, 2012), but also between banks and firms has become a crucial aspect of financial research (Peek and Rosengren, 2000; Gan, 2007; Ongena et al., 2015; Acharya et al., 2018; Gropp et al., 2018; Huber, 2018). It has returned into focus the idea that a functioning connection between banks and firms constitutes a crucial part of a well-functioning economy. This thesis aims to contribute to the understanding of how this bank-firm relationship functions and what pitfalls it might entail.