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Biodiversity loss poses a significant threat to the global economy and affects ecosystem services on which most large companies rely heavily. The severe financial implications of such a reduced species diversity have attracted the attention of companies and stakeholders, with numerous calls to increase corporate transparency. Using textual analysis, this study thus investigates the current state of voluntary biodiversity reporting of 359 European blue-chip companies and assesses the extent to which it aligns with the upcoming disclosure framework of the Task Force on Nature-related Financial Disclosures (TNFD). The descriptive results suggest a substantial gap between current reporting practices and the proposed TNFD framework, with disclosures largely lacking quantification, details and clear targets. In addition, the disclosures appear to be relatively unstandardized. Companies in sectors or regions exposed to higher nature-related risks as well as larger companies are more likely to report on aspects of biodiversity. This study contributes to the emerging literature on nature-related risks and provides detailed insights on the extent of the reporting gap in light of the upcoming standards.
Unconventional green
(2023)
We analyze the effects of the PEPP (Pandemic Emergency Purchase Programme), the temporary quantitative easing implemented by the ECB immediately after the burst of the Covid-19 pandemic. We show that the differences in aim, size and flexibility with respect to the traditional Corporate Sector Purchase Programme (CSPP) were able to significantly involve, in addition to the directly targeted bonds, also the green bond segment. Via a standard difference- in-differences model we estimate that the yield on green bonds declined by more than 20 basis points after the PEPP. In order to take into account also the differences attributable to the eligibility to the programme, we employ a triple difference estimator. Bonds that at the same time were green and eligible benefitted of an additional premium of 39 basis points.
ChatGPT, der Prototyp eines Chatbot, von dem amerikanischen Unternehmen OpenAI entwickelt, ist im Augenblick in aller Munde. Gefragt wird auch: Stellt diese Software eine Herausforderung für den Bildungsbereich dar, werden künftig damit Haus- und Abschlussarbeiten erstellt? Prof. Uwe Walz, Professor für VWL, insbesondere Industrieökonomie an der Goethe-Universität, hat den Chatbot bereits im laufenden Wintersemester mit Studierenden analysiert.
The European low-carbon transition began in the last few decades and is accelerating to achieve net-zero emissions by 2050. This paper examines how climate-related transition indicators of a large European corporate firm relate to its CDS-implied credit risk across various time horizons. Findings show that firms with higher GHG emissions have higher CDS spreads at all tenors, including the 30-year horizon, particularly after the 2015 Paris Agreement, and in prominent industries such as Electricity, Gas, and Mining. Results suggest that the European CDS market is currently pricing, to some extent, albeit small, the exposure to transition risk for a firm across different time horizons. However, it fails to account for a company’s efforts to manage transition risks and its exposure to the EU Emissions Trading Scheme. CDS market participants seem to find challenging to risk-differentiate ETS-participating firms from other firms.
Auszubildende sollen durch die Berufsausbildung u.a. die Kompetenz erlangen, berufliche Probleme zu lösen. Abschlussprüfungen dienen der Kompetenzerfassung, schriftlich-kaufmännische Prüfungsaufgaben bilden allerdings noch unzureichend Problemsituationen ab, deren Lösung Problemlösekompetenz erfordert. An der Erstellung von Prüfungsaufgaben sind auch Lehrkräfte kaufmännisch-beruflicher Schulen beteiligt. In der Arbeit wird untersucht, wie sie in der ersten und zweiten Phase der Lehrer*innenbildung auf das Erstellen problemhaltiger Aufgaben zu summativ-diagnostischen Zwecken vorbereitet werden. Hierfür werden Dokumentenanalysen zu beiden Phasen der Lehrer*innenbildung durchgeführt. Die Ergebnisse werden mittels einer Fragebogenstudie mit Studiengangsleiter*innen sowie Interviews mit Fachleiter*innen der Studienseminare gesichert. Um die Wahrnehmung angehender Lehrkräfte zu erfahren, werden Interviews mit Masterstudierenden der Wirtschaftspädagogik sowie Lehrkräfte im Vorbereitungsdienst (LiV) an kaufmännisch-beruflichen Schulen durchgeführt.
Durch die Vorstudien gelingt es, Optimierungsbedarfe in der Ausbildung von Lehrkräften kaufmännisch-beruflicher Schulen festzuhalten. Davon ausgehend wird ein Trainingskonzept begründet ausgewählt. Die Evaluation dessen erfolgt mittels einer quasi-experimentellen Studie mit Masterstudierende und LiV. Zur qualitativen Evaluation werden Interviews mit Teilnehmenden der Interventionsgruppe durchgeführt. Die Ergebnisse zeigen, dass die Teilnehmenden das Training als Intervention überwiegend positiv wahrnehmen und dieser, zumindest mit Blick auf das Erstellen von problemhaltigen Aufgaben, zu einem Lernzuwachs führt. Durch die bedarfsorientierte Intervention und dessen Evaluationsergebnisse wird ein Konzept vorgeschlagen, welches eine Lösung zur Deckung bestehender Optimierungsbedarfe bietet. Die Ergebnisse der Arbeit haben das Potential, langfristig einen Beitrag zur Verbesserung der Lehrer*innenbildung zu leisten und somit u.a. Assessmentaufgaben valider zu gestalten.
I have assessed changes in the monetary policy stance in the euro area since its inception by applying a Bayesian time-varying parameter framework in conjunction with the Hamiltonian Monte Carlo algorithm. I find that the estimated policy response has varied considerably over time. Most of the results suggest that the response weakened after the onset of the financial crisis and while quantitative measures were still in place, although there are also indications that the weakening of the response to the expected inflation gap may have been less pronounced. I also find that the policy response has become more forceful over the course of the recent sharp rise in inflation. Furthermore, it is essential to model the stochastic volatility relating to deviations from the policy rule as it materially influences the results.
Testing frequency and severity risk under various information regimes and implications in insurance
(2023)
We build on Peter et al. (2017) who examined the benefit of testing frequency risk under various information regimes. We first consider testing only severity risk, and whether the principle of indemnity, i.e. the usual contract term that excludes claims payments above the resulting insured loss, affects the insurance contracts offered and purchased. Under information regimes which are less restrictive (in terms of obtaining and using customer information), it is possible for the insurer to offer different contracts for tested and untested individuals. In the absence of the principle of indemnity, individuals will test their severity risk and a separating equilibrium ensues. With the principle of indemnity, given an actuarially fair pooled contract, individuals will not test for severity under less restrictive information regimes; a pooling equilibrium thus ensues. Under more restrictive information regimes, the insurer offers separating contracts. Individuals will test for severity and purchase appropriate contracts. We also consider testing for both frequency and severity risk. The results here are more varied. The highest gain in efficiency from testing results from one of the more restrictive information regimes. Generally under all information regimes, there is a greater gain in efficiency without the principle of indemnity than with the principle of indemnity.
In the euro area, monetary policy is conducted by a single central bank for 20 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a neural network to assess whether the European Central Bank (ECB) conducted monetary policy between 2002 and 2022 according to the weighted average of the inflation rates within the European Monetary Union (EMU) or reacted more strongly to the inflation rate developments of certain EMU countries.
The New Keynesian model first generates data which is used to train and evaluate several machine learning algorithms. They authors find that a neural network performs best out-of-sample. They use this algorithm to generally classify historical EMU data, and to determine the exact weight on the inflation rate of EMU members in each quarter of the past two decades. Their findings suggest disproportional emphasis of the ECB on the inflation rates of EMU members that exhibited high inflation rate volatility for the vast majority of the time frame considered (80%), with a median inflation weight of 67% on these countries. They show that these results stem from a tendency of the ECB to react more strongly to countries whose inflation rates exhibit greater deviations from their long-term trend.