Regulatory Implications of the Supervision and Management of Liquidity Risk: An Analysis of Recent Developments in Spanish Financial Institutions
Bibliographic record
Abstract
The aim of this paper is to analyze the evolution of bank liquidity regulations, considering the global regulatory framework applicable to financial institutions, from the beginning of the banking and liquidity crisis in 2007–2008 to the present. The new liquidity requirements under Basel III regulations are defined. An analysis is made of the recent evolution of credit institutions in Spain from different banking prisms to determine how the new banking regulation and supervision, following the start of supervisory powers by the European Central Bank at the end of 2014, has affected them. The methodology applied has been firstly the literature review, followed by a compilation and analysis of the financial and statistical evidence available on the main Spanish financial institutions, from the European Central Bank and the Bank of Spain, as well as information published by other agencies and the financial institutions themselves. It concludes with a reflection and analysis of the outlook for the sector once the most recent impacts, derived from COVID-19, and the supply crisis with the rise in global inflation and the increase in interest rates have been overcome. It can be stated that credit institutions in Spain have significantly improved their liquidity position over the last 15 years.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 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".