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14.02.2023 • 4/2023
Study on Europe's top bankers: Risky business despite bonus cap
Ten years ago, the EU Parliament decided to cap the flexible remuneration of bank managers. But the cap on bonuses misses its target: Managers of systemically important European banks take high risks without changes, shows a study by the Halle Institute for Economic Research (IWH).
Michael Koetter
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The Appropriateness of the Macroeconomic Imbalance Procedure for Central and Eastern European Countries
Geraldine Dany-Knedlik, Martina Kämpfe, Tobias Knedlik
Empirica,
No. 1,
2021
Abstract
The European Commission’s Scoreboard of Macroeconomic Imbalances is a rare case of a publicly released early warning system. It was published first time in 2012 by the European Commission as a reaction to public debt crises in Europe. So far, the Macroeconomic Imbalance Procedure takes a one-size-fits-all approach with regard to the identification of thresholds. The experience of Central and Eastern European Countries during the global financial crisis and in the resulting public debt crises has been largely different from that of other European countries. This paper looks at the appropriateness of scoreboard of the Macroeconomic Imbalances Procedure of the European Commission for this group of catching-up countries. It is shown that while some of the indicators of the scoreboard are helpful to predict crises in the region, thresholds are in most cases set too narrow since it largely disregarded the specifics of catching-up economies, in particular higher and more volatile growth rates of various macroeconomic variables.
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Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Macroeconomic Dynamics,
No. 1,
2021
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Private Equity and Portfolio Companies: Lessons From the Global Financial Crisis
Shai B. Bernstein, Josh Lerner, Filippo Mezzanotti
Journal of Applied Corporate Finance,
No. 3,
2020
Abstract
Critics of private equity have warned that the high leverage often used in PE-backed companies could contribute to the fragility of the financial system during economic crises. The proliferation of poorly structured transactions during booms could increase the vulnerability of the economy to downturns. The alternative hypothesis is that PE, with its operating capabilities, expertise in financial restructuring, and massive capital raised but not invested ("dry powder"), could increase the resilience of PE-backed companies. In their study of PE-backed buyouts in the U.K. - which requires and thereby makes accessible more information about private companies than, say, in the U.S. - the authors report finding that, during the 2008 global financial crisis, PE-backed companies decreased their overall investments significantly less than comparable, non-PE firms. Moreover, such PE-backed firms also experienced greater equity and debt inflows, higher asset growth, and increased market share. These effects were especially notable among smaller, riskier PE-backed firms with less access to capital, and also for those firms backed by PE firms with more dry powder at the crisis onset. In a survey of the partners and staff of some 750 PE firms, the authors also present compelling evidence that PEs firms play active financial and operating roles in preserving or restoring the profitability and value of their portfolio companies.
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06.07.2020 • 13/2020
IWH issues warning of a new banking crisis
The coronavirus recession could mean the end for dozens of banks across Germany – even if Germany survives the economic crisis relatively unscathed. An analysis by the Halle Institute for Economic Research (IWH) shows that many savings banks and cooperative banks are particularly at risk. Loans worth hundreds of billions of euros are on the balance sheets of the financial institutions concerned. IWH President Gropp warns of a potentially high additional burden for the already weakened real economy.
Reint E. Gropp
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05.06.2020 • 8/2020
IWH Bankruptcy Update: Increase in large firm bankruptcies
With overall corporate bankruptcies remaining constant, ever more employees are subject to employer bankruptcy in Germany. This is the latest insight from the IWH Bankruptcy Update provided monthly by the Halle Institute for Economic Research (IWH).
Steffen Müller
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12.03.2020 • 4/2020
Global economy under the spell of the coronavirus epidemic
The epidemic is obstructing the economic recovery in Germany. Foreign demand is falling, private households forgo domestic consumption if it comes with infection risk, and investments are postponed. Assuming that the spread of the disease can be contained in short time, GDP growth in 2020 is expected to be 0.6% according to IWH spring economic forecast. Growth in East Germany is expected to be 0.9% and thus higher than in West Germany. If the number of new infections cannot be decreased in short time, we expect a recession in Germany.
Oliver Holtemöller
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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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