Banking Supervision and Risk Management in Times of Crisis: Evidence from Greece’s Systemic Banks (2015–2024)
Bibliographic record
Abstract
This study examines the role of supervisory frameworks in shaping the risk management behavior of Greece’s four systemic banks during the period of 2015–2024. It explores how regulatory reforms under Capital Requirements Regulation II, Basel III, and European Central Bank oversight influenced capital adequacy, asset quality, and liquidity metrics. Employing a quantitative methodology, this study analyzes secondary data from Pillar III disclosures, annual financial reports, and supervisory statements. Key risk indicators (capital adequacy ratio, non-performing exposure ratio, liquidity coverage ratio, and risk-weighted assets) are evaluated in conjunction with regulatory interventions, such as International Financial Reporting Standards 9 transitional relief, the Hercules Asset Protection Scheme, and European Central Bank liquidity measures. The findings reveal that enhanced supervision contributed to improved resilience and regulatory compliance. International Financial Reporting Standards 9 transitional arrangements were pivotal in maintaining capital thresholds during stress periods. Supervisory flexibility and extraordinary European Central Bank support measures helped banks absorb shocks and improve risk governance. Differences across banks highlight the impact of institutional strategy on regulatory performance. This study offers a rare longitudinal assessment of supervisory influence on bank risk behavior in a high-volatility Eurozone context. Covering an entire decade (2015–2024), it uniquely links institutional strategies with evolving regulatory frameworks, including crisis-specific interventions such as International Financial Reporting Standards 9 relief and asset protection schemes. The results provide insights for policymakers and regulators on how targeted supervisory interventions and transitional mechanisms can enhance banking sector resilience during protracted crises.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".