An Overview of Micro- and Macroprudential Policy Tools in the EU in the Times of the COVID-19 Pandemic Economic Shock
Bibliographic record
Abstract
The fi nancial crisis from 2008 and the following Eurozone crisis from 2012 created an incentive to establish a system of fi nancial supervision at the European Union (“EU”) level, due to the fact that the policy tool commonly used turned out to be ineffective. With regards to banking supervision, the package of legislations: “CRR/CRD” and “BRRD” has been adopted as a response to fi nancial system shortcomings, in order to make it more resilient and harmonised. One of the challenges was to take control of the so-called: “too big to fail” fi nancial institutions, therefore next to macroprudential supervision, microprudential policy pools were introduced. This constituted the phenomena of the shift from regulationbased supervision to risk-based supervision with the aim of reducing the systemic risk in each and every EU Member State and, in turn, prevent possible future crises. In this paper, those methods will be gathered, presented, and discussed in the light of the current COVID-19 pandemic crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".