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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Benign Neglect of Covenant Violations: Blissful Banking or Ignorant Monitoring?
Stefano Colonnello, Michael Koetter, Moritz Stieglitz
Abstract
Theoretically, bank‘s loan monitoring activity hinges critically on its capitalisation. To proxy for monitoring intensity, we use changes in borrowers‘ investment following loan covenant violations, when creditors can intervene in the governance of the firm. Exploiting granular bank-firm relationships observed in the syndicated loan market, we document substantial heterogeneity in monitoring across banks and through time. Better capitalised banks are more lenient monitors that intervene less with covenant violators. Importantly, this hands-off approach is associated with improved borrowers‘ performance. Beyond enhancing financial resilience, regulation that requires banks to hold more capital may thus also mitigate the tightening of credit terms when firms experience shocks.
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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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Avoiding the Fall into the Loop: Isolating the Transmission of Bank-to-Sovereign Distress in the Euro Area and its Drivers
Hannes Böhm, Stefan Eichler
Abstract
We isolate the direct bank-to-sovereign distress channel within the eurozone’s sovereign-bank-loop by exploiting the global, non-eurozone related variation in stock prices. We instrument banking sector stock returns in the eurozone with exposure-weighted stock market returns from non-eurozone countries and take further precautions to remove any eurozone crisis-related variation. We find that the transmission of instrumented bank distress, while economically relevant, is significantly smaller than the corresponding coefficient in the unadjusted OLS framework, confirming concerns on reverse causality and omitted variables in previous studies. Furthermore, we show that the spillover of bank distress is significantly stronger for countries with poorer macroeconomic performances, weaker financial sectors and financial regulation and during times of elevated political uncertainty.
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Effectiveness and (In)Efficiencies of Compensation Regulation: Evidence from the EU Banker Bonus Cap
Stefano Colonnello, Michael Koetter, Konstantin Wagner
Abstract
We investigate the (unintended) effects of bank executive compensation regulation. Capping the share of variable compensation spurred average turnover rates driven by CEOs at poorly performing banks. Other than that, banks‘ responses to raise fixed compensation sufficed to retain the vast majority of non-CEO executives and those at well performing banks. We fail to find evidence that banks with executives that are more affected by the bonus cap became less risky. In fact, numerous results indicate an increase of risk, even in its systemic dimension according to selected measures. The return component of bank performance appears to be unaffected by the bonus cap. Risk hikes are consistent with an insurance effect associated with raised the increase in fixed compensation of executives. The ability of the policy to enhance financial stability is therefore doubtful.
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19.04.2018 • 7/2018
Joint Economic Forecast Spring 2018: Germany’s Economic Experts Raise Forecast Slightly
Berlin, 19 April – Germany’s leading economic experts raised their forecasts for 2018 and 2019 slightly in their Spring Joint Economic Forecast released on Thursday in Berlin. They now expect economic growth of 2.2 percent for this year and 2.0 percent for 2019, versus 2.0 percent and 1.8 percent respectively in their autumn forecast. “The German economy is still booming, but the air is getting thinner as unused capacities are shrinking“, notes Timo Wollmershaeuser, ifo Head of Economic Forecasting. Commenting on the new German government’s economic policy, he adds: “It is precisely when the government’s coffers are full that fiscal policy should reflect the implications of its actions for overall economic stability and the sustainability of public finances. The extension of statutory pension benefits outlined in the coalition agreement runs counter to the idea of sustainability.”
Oliver Holtemöller
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Should Banks Diversify or Focus? Know Thyself: The Role of Abilities
Bill Francis, Iftekhar Hasan, A. Melih Küllü, Mingming Zhou
Economic Systems,
No. 1,
2018
Abstract
The paper investigates whether diversification/focus across assets, industries and borrowers affects bank performance when banks’ abilities (screening and monitoring) are considered. The initial results show that diversification (focus) at the asset, industry and borrower levels is expected to decrease (increase) returns. However, once banks’ screening and monitoring abilities are controlled for, the effect of diversification/focus either gets weaker or disappears. Further, in some cases, these abilities enhance banks’ long-run performance, but in others they prove to be costly, at least, in the short run. Thus, the level of monitoring and screening abilities should be taken into consideration in understanding, planning and implementing diversification/focus strategies.
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Sovereign Stress, Banking Stress, and the Monetary Transmission Mechanism in the Euro Area
Oliver Holtemöller, Jan-Christopher Scherer
IWH Discussion Papers,
No. 3,
2018
Abstract
In this paper, we investigate to what extent sovereign stress and banking stress have contributed to the increase in the level and in the heterogeneity of nonfinancial firms’ refinancing costs in the Euro area during the European debt crisis and how they did affect the monetary transmission mechanism. We identify the increasing effect of government bond yield spreads (sovereign stress) and the share of non-performing loans (banking stress) on firms’ financing costs using an instrumental-variable approach. Moreover, we estimate both sources of stress to have significantly impaired the monetary transmission mechanism during the European debt crisis.
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