Interdependencies between Leverage and Capital Ratios in the Central and Eastern European Banks
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 Central and Eastern Europe (CEE) and particularly in the Czech Republic. In the empirical study, we applied a data sample of 198 major banks operating in seven countries across the CEE region over the period 2007-2014. The data of the Czech banking sector confirms stronger capital ratios and an overall solid leverage level with only few historical observations being lower than the regulatory guidelines. By analyzing the components of ratios, we conclude that the Czech banks during the last seven years are focusing more on the optimization of risk weighted assets and structuring portfolios with lower risks. We propose an empirical model that allows to test how the leverage ratios and its variables respond to the changes in the cycle. Our analysis across financial institutions in the CEE region shows that the leverage in normal times is strongly related to capital ratio. The statistic evidences on the risk profile and strategy as measured by risk proxy in the model are pointing out on incentives of the banks to manage actively their balance sheet and reduce the riskiness of their portfolios in adverse economic conditions.
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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 | 0.002 | 0.009 |
| 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".