This paper analyzes the impact of reduced tax incentives for equity financing on banks' regulatory capital ratios under the Basel III regime. We are particularly interested in a recent interest rate cut in the Italian corporate equity allowance, which reduces the relative tax advantage of equity financing. The results show that banks respond to this increased tax disparity by significantly reducing their regulatory capital while at the same time reducing their risk-taking. The decline in capital is more pronounced for small banks and outweighs the initial capital gains from the introduction of this tax instrument. Our results challenge the use of equity allowances, in that financial stability gains persist only as long as costly tax subsidies remain intact and diminish as the size of the subsidy is reduced.

We investigate whether lenders employ sustainability pricing provisions to manage borrowers’ environmental risk. Using unexpected negative environmental incidents of borrowers as exogenous shocks that reveal information on environmental risk, we find that lenders manage borrowers’ environmental risk by conventional tools such as imposing higher interest rates, utilizing financial and net worth covenants, showing reluctance to refinance, and demanding increased collateral. In contrast, the inclusion of sustainability pricing provisions in loan agreements for high environmental risk borrowers is reduced by 11 percentage points. Our study suggests that sustainability pricing provisions may not primarily serve as risk management tools but rather as instruments to attract demand from institutional investors and facilitate secondary market transactions.

Finanzmarktkrisen verursachen in der Regel hohe Kosten. Banken müssen stabilisiert werden, um einen Zusammenbruch des Bankensystems zu verhindern, was immense Kosten für den Staat bedeuten kann. Ebenso kommt es im Zuge von Finanzmarktkrisen zu einem starken Rückgang der wirtschaftlichen Aktivität, der im Vergleich zu gewöhnlichen Rezessionen länger anhält. Die Finanzmarktkrise hat dies ein weiteres Mal verdeutlicht und eine Phase der signifikanten Verschärfung der Regulierung und Aufsicht von Banken eingeleitet. Die Legislative hat das »Window of Opportunity« gut genutzt, und sowohl auf nationaler als auch auf europäischer Ebene wurden neue gesetzliche Grundlagen für eine stärkere Regulierung des Bankensystems erfolgreich eingeführt. Ein erster Erfolg des neuen regulatorischen Umfelds zeigte sich während der Corona-Pandemie, in der das Bankensystem stabil blieb. Dies wird auch durch die aktuell steigenden Eigenkapitalquoten und vergleichsweise niedrigen Ausfallraten im Kreditportfolio der Banken deutlich. Hervorzuheben ist außerdem, dass nicht nur auf nationaler Ebene Anstrengungen unternommen wurden, das regulatorische Umfeld für Banken zu verbessern, sondern dass es auch auf Ebene der Europäischen Union (EU) gelungen ist, mit dem »Single Rulebook« einen einheitlichen regulatorischen Rahmen zu schaffen. Dies wirkt Verschiebungen von Risiken innerhalb der EU entgegen. Trotz dieser Erfolge und positiven Entwicklungen darf nicht übersehen werden, dass sich durch staatliches Eingreifen und die Einführung neuer Regulierungsvorschriften nicht nur der betroffene Sektor, also die Banken, anpassen. Es kann auch zu Auswirkungen auf verschiedenste Akteure kommen, die direkt oder indirekt mit dem Bankensystem interagieren. Zudem kann es im Anpassungsprozess zu dynamischen Effekten kommen. Im Beitrag gehen wir auf zwei ausgewählte Aspekte ein, welche in diesem Zusammenhang von der Legislative zu beachten sind. 

Der Beitrag ist Teil des Artikels “Die Zukunft des europäischen Finanzsystems – zwischen Risiken und mangelnder Wettbewerbsfähigkeit?“, erschienen in: ifo Schnelldienst, 2024, 77, Nr. 07, 03-36.

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.

We investigate whether a firm’s exposure to climate change, as proxied by disclosures during quarterly earnings conference calls, provides forward-looking information to investors regarding the long-term association of stock prices with current earnings and the book values of equity. Following a key regulatory mandate around the formation of the cap-and-trade program to reduce emissions related to climate change, firms’ climate change exposure decreases the association between current earnings and stock prices while increasing the relevance of book values of equity (i.e., historical earnings). However, these relationships flip when the sentiment around climate change exposure is negative, suggesting that the risks related to climate change exposure provide forward-looking information to investors when they evaluate the ability of current earnings to predict firm values. Such a relationship is stronger for new economy firms and is sensitive to conservative accounting. We also observe that the inclusion of climate change disclosure to our models improves the joint ability of earnings and book values to predict stock prices.