ESG Metrics: Exploring their Role in Predicting Systemic Risks in the European Financial System
Bibliographic record
Abstract
The study aims to explore the relationship between European financial banks' ESG pillars and their contribution to systemic risk, with a focus on the Eurozone banking industry. Utilizing the €ΔCoVaR metric to capture systemic risk, we analyzed a sample of 35 publicly listed banks across 12 European Union countries for the period of 2019 to 2023. The methodology consists of three steps. The first step is estimating VaR for each bank using the Basic Historical Simulation method. The VaR results will allow for the computation of CoVaR, ΔCoVaR, and €ΔCoVar. The last step is to analyze the relationship between ESG and €∆CoVaR. Our primary hypothesis (H0) posited that banks with higher ESG would contribute less to systemic risk; however, our findings indicate a positive correlation, diverging from pre-pandemic studies which generally reported a negative link. Our secondary hypothesis (H1) examined the distinct impacts of individual ESG pillars on systemic risk, revealing that while the environmental and social pillars have positive impact on systemic risk, the governance pillar shows a comparatively weaker association with systemic risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".