Using Supra-Covered Bonds to Enhance Liquidity in the Euro Area: Assessment of Advantages for the Banking Sector
Bibliographic record
Abstract
The discussion on the necessity of a larger volume of very highly quality liquid assets (VHQLA) in the euro area has been very extensive. The debate on expanding the pool of comparable euro area assets focuses on “safe assets”, often on various combinations of government bonds, most of which would not entail a strong increase in euro VHQLA. This paper explores a different option, complementary to the existing ones, based on the creation of a safe European asset backed by fully private assets. The paper proposes the issuance of supra-covered bonds by a central European institution. The latter are bonds issued by the central issuer and backed by covered bonds, which banks would have created using their mortgages as their cover pool. The aim is to increase substantially the outstanding amount of euro VHQLA. Such an asset would also be very beneficial during crisis periods, such as the current COVID19 crisis, by allowing banks to transform mortgages into very high quality liquid assets that can be used for funding and as a collateral in operations with the Eurosystem, thus enhancing the possible credit to sustain small and medium-sized enterprises (SMEs). This paper assesses the main effects of such a proposal on banks under different possible scenarios.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".