Bank Risk Capital and Its Effectiveness in Selected Euro Area Banking Sectors
Bibliographic record
Abstract
Risk capital or capital at risk (CaR) refers to the amount of capital set aside and maintained by banks to cover different types of risk. For banks, it is used as a buffer against claims or expenses in the event that ordinary capital is not enough to cover them. Thereby, risk capital can also be recognized as risk-bearing capital or surplus funds. Risk capital may generate very high costs, but on the other hand it protects against insolvency. That’s why a bank needs to find the ‘Gold mean’—the optimal value of risk capital that will not lower its efficiency, but still ensure financial security. The main objective of the study is identification of interdependencies between bank risk capital and effectiveness of the aggregated Eurozone banking sector and selected national banking sectors of the euro area. The paper tries to answer the research question whether the risk capital supports or lowers banks’ operational effectiveness. The adopted research hypothesis stated that there is a positive correlation between profitability and size of bank risk capital. To verify the hypothesis regression models were used. The results indicate that the size and structure of bank capital impact on the credit institutions’ effectiveness in the analyzed banking sectors, however with different intensity. Thereby, the article fulfils a research gap in the field of research studies that take into account how capital at risk and specific capital adequacy regulations may impact on a bank’s efficiency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".