Interdependencies between Leverage and Capital Ratios in the Banking Sector of the Czech Republic
Bibliographic record
Abstract
In this paper we discuss the implications of the Basel III requirements on the leverage ratio for the banking sector in the Czech Republic. We identify the potential binding constraints from regulatory limits and analyze the interactions among leverage and capital ratios over the country’s economic cycle (during the period 2007-2014). The historical data confirm stronger capital ratios of the banks and an overall solid leverage level with only 5% of the total historical observations being lower than the regulatory recommendations. By analyzing the components of ratios, we conclude that the banks are focusing more on the optimization of risk weighted assets. Strong co-movement patterns between leverage and assets point to the active management of leverage as a means of expanding and contracting the size of balance sheets and maximizing the utility of the capital. The analysis of correlation patterns among the variables indicates that the total assets (and exposure) in contrast to Tier 1 capital are the main contributors to the cyclical movements in the leverage. The leverage and the total assets also demonstrate a weak correlation with GDP, but a strong co-movement with loans to the private sector.
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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) | 0.001 | 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".