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This paper addresses the need for transparent sustainability disclosure in the European Auto Asset-Backed Securities (ABS) market, a crucial element in achieving the EU's climate goals. It proposes the use of existing vehicle identifiers, the Type Approval Number (TAN) and the Type-Variant-Version Code (TVV), to integrate loan-level data with sustainability-related vehicle information from ancillary sources. While acknowledging certain challenges, the combined use of TAN and TVV is the optimal solution to allow all stakeholders to comprehensively assess the environmental characteristics of securitised exposure pools in terms of data protection, matching accuracy, and cost-effectiveness.
In this study, we unpack the ESG ratings of four prominent agencies in Europe and find that (i) each single E, S, G pillar explains the overall ESG score differently,(ii) there is a low co-movement between the three E, S, G pillars and (iii) there are specific ESG Key Performance Indicators (KPIs) that are driving these ratings more than others. We argue that such discrepancies might mislead firms about their actual ESG status, potentially leading to cherry-picking areas for improvement, thus raising questions about the accuracy and effectiveness of ESG evaluations in both explaining sustainability and driving capital toward sustainable companies.
This paper analyzes the current implementation status of sustainability and taxonomy-aligned disclosure under the Sustainable Finance Disclosure Regulation (SFDR) as well as the development of the SFDR categorization of funds offered via banks in Germany. Examining data provided by WM Group, which consists of more than 10,000 investment funds and 2,000 index funds between September 2022 and March 2023, we have observed a significant proportion of Article 9 (dark green) funds transitioning to Article 8 (light green) funds, particularly among index funds. As a consequence of this process, the profile of the SFDR classes has sharpened, which reflects an increased share of sustainable investments in the group of Article 9 funds. When differentiating between environmental and social investments, the share of environmental investments increased, but the share of social investments decreased in the group of Article 9 funds at the beginning of 2023. The share of taxonomy-aligned investments is very low, but slightly increasing for Article 9 funds. However, by March 2023 only around 1,000 funds have reported their sustainability proportions and this picture might change due to legal changes which require all funds in the scope of the SFDR to report these proportions in their annual reports being published after 1 January 2023.
We develop a quantity-driven general equilibrium model that integrates the term structure of interest rates with the repurchase agreements (repo) market to shed light on the com-bined effects of quantitative easing (QE) on the bond and money markets. We characterize in closed form the endogenous dynamic interaction between bond prices and repo rates, and show (i) that repo specialness dampens the impact of any given quantity of asset pur-chases due to QE on the slope of the term structure and (ii) that bond scarcity resulting from QE increases repo specialness, thus strengthening the local supply channel of QE.
This literature survey explores the potential avenues for the design of a green auto asset-backed security by focusing on the European auto securitization market. In this context, we examine the entire value chain of the securitization process to understand the incentives and interests involved at various stages of the transaction. We review recent regulatory developments, feasibility concerns, and potential designs of a sustainable securitization framework. Our study suggests that a Green Auto ABS should be based on both a green use of proceeds and a green collateral-based methodology.
The SVB case is a wake-up call for Europe’s regulators as it demonstrates the destructive power of a bank-run: it undermines the role of loss absorbing capital, elbowing governments to bailout affected banks. Many types of bank management weaknesses, like excessive duration risk, may raise concerns of bank losses – but to serve as a run-trigger, there needs to be a large enough group of bank depositors that fails to be fully covered by a deposit insurance scheme. Latent run-risk is the root cause of inefficient liquidations, and we argue that a run on SVB assets could have been avoided altogether by a more thoughtful deposit insurance scheme, sharply distinguishing between loss absorbing capital (equity plus bail-in debt) and other liabilities which are deemed not to be bail-inable, namely demand deposits. These evidence-based insights have direct implications for Europe’s banking regulation, suggesting a minimum and a maximum for a banks’ loss absorption capacity.
To ensure the credibility of market discipline induced by bail-in, neither retail investors nor peer banks should appear prominently among the investor base of banks’ loss absorbing capital. Empirical evidence on bank-level data provided by the German Federal Financial Supervisory Authority raises a few red flags. Our list of policy recommendations encompasses disclosure policy, data sharing among supervisors, information transparency on holdings of bail-inable debt for all stakeholders, threshold values, and a well-defined upper limit for any bail-in activity. This document was provided by the Economic Governance Support Unit at the request of the ECON Committee.
This policy note summarizes our assessment of financial sanctions against Russia. We see an increase in sanctions severity starting from (1) the widely discussed SWIFT exclusions, followed by (2) blocking of correspondent banking relationships with Russian banks, including the Central Bank, alongside secondary sanctions, and (3) a full blacklisting of the ‘real’ export-import flows underlying the financial transactions. We assess option (1) as being less impactful than often believed yet sending a strong signal of EU unity; option (2) as an effective way to isolate the Russian banking system, particularly if secondary sanctions are in place, to avoid workarounds. Option (3) represents possibly the most effective way to apply economic and financial pressure, interrupting trade relationships.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the imple- mented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.