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Record W3111070374 · doi:10.2866/58387

Liquidity in resolution: Comparing frameworks for liquidity provision across jurisdictions

2020· article· en· W3111070374 on OpenAlexaboutno aff
Sebastian Grund, Nele Nomm, Florian Walch

Bibliographic record

VenueRePEc: Research Papers in Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsMarket liquidityEuropean unionCollateralBusinessLiquidity riskLiquidity crisisLender of last resortTaxpayerFinancial systemFinanceInternational economicsEconomicsEconomic policyMonetary economicsMonetary policyCentral bankMacroeconomics

Abstract

fetched live from OpenAlex

As a response to the global financial crisis that started in 2008, many countries established dedicated resolution regimes that seek to limit the use of taxpayer money while maintaining the functions of failing banks that are critical for financial stability. This paper extends the existing research by zooming in on the specific topic of liquidity provision to banks in resolution. It examines the provision of liquidity in the United States, the United Kingdom, Japan, Canada and the banking union of the European Union (thereafter: the "banking union"). The paper observes the differences and commonalities of policy choices across jurisdictions with regard to both the relationship between private prefunding and temporary public liquidity provision and the roles of the public budget and the central bank. The comparison also reveals that the role of fiscal authorities is strong and that guarantees from a public budget are a common feature. The framework for the provision of liquidity in the banking union is not yet complete as the construction of a public sector backstop of sufficient size and speed is comparatively more complex in the banking union than in other jurisdictions. Therefore, the idea of establishing a European-level guarantee framework - which would allow access to Eurosystem liquidity for banks coming out of resolution with limited collateral - is being further investigated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0020.007
Scholarly communication0.0110.009
Open science0.0030.008
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.068
GPT teacher head0.329
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicBanking stability, regulation, efficiencyFrench-language works237,207