Financial Stability
Financial Systems: The Anatomy of the Market Economy How the financial system is constructed, how it works, how to keep it fit and what good a bit of chocolate can do. Dossier In…
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EVA-KULT
EVA-KULT Establishing Evidence-based Evaluation Methods for Subsidy Programmes in Germany The project aims at expanding the Centre for Evidence-based Policy Advice at the Halle…
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The Political Economy of the European Banking Union
The Political Economy of the European Banking Union Junior Professorship Lena Tonzer, PhD: The Political Economy of the European Banking Union: Causes for National Differences in…
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Projects
Our Projects 07.2022 ‐ 12.2026 Evaluation of the InvKG and the federal STARK programme On behalf of the Federal Ministry of Economics and Climate Protection, the IWH and the RWI…
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Department Profiles
Research Profiles of the IWH Departments All doctoral students are allocated to one of the four research departments (Financial Markets – Laws, Regulations and Factor Markets –…
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A Note of Caution on Quantifying Banks' Recapitalization Effects
Felix Noth, Kirsten Schmidt, Lena Tonzer
Journal of Money, Credit and Banking,
No. 4,
2022
Abstract
Unconventional monetary policy measures like asset purchase programs aim to reduce certain securities' yield and alter financial institutions' investment behavior. These measures increase the institutions' market value of securities and add to their equity positions. We show that the extent of this recapitalization effect crucially depends on the securities' accounting and valuation methods, country-level regulation, and maturity structure. We argue that future research needs to consider these factors when quantifying banks' recapitalization effects and consequent changes in banks' lending decisions to the real sector.
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A Note of Caution on Quantifying Banks' Recapitalization Effects
Felix Noth, Kirsten Schmidt, Lena Tonzer
Abstract
Unconventional monetary policy measures like asset purchase programs aim to reduce certain securities' yield and alter financial institutions' investment behavior. These measures increase the institutions' market value of securities and add to their equity positions. We show that the extent of this recapitalization effect crucially depends on the securities' accounting and valuation methods, country-level regulation, and maturity structure. We argue that future research needs to consider these factors when quantifying banks' recapitalization effects and consequent changes in banks' lending decisions to the real sector.
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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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Inference in Structural Vector Autoregressions when the Identifying Assumptions are not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations
Christiane Baumeister, James D. Hamilton
Journal of Monetary Economics,
2018
Abstract
Point estimates and error bands for SVARs that are set identified are only justified if the researcher is persuaded that some parameter values are a priori more plausible than others. When such prior information exists, traditional approaches can be generalized to allow for doubts about the identifying assumptions. We use information about both structural coefficients and impacts of shocks and propose a new asymmetric t-distribution for incorporating information about signs in a nondogmatic way. We apply these methods to a three-variable macroeconomic model and conclude that monetary policy shocks are not the major driver of output, inflation, or interest rates.
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