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Record W1875159043 · doi:10.26509/wp-201437

The Effect of Safe Assets on Financial Fragility in a Bank-Run Model

2014· report· en· W1875159043 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorking paper · 2014
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsBank of CanadaGovernment of Canada
Fundersnot available
KeywordsFinancial fragilityBank runMarket liquidityBusinessFragilityFinancial intermediaryAsset (computer security)FinanceInvestment (military)Monetary economicsEconomicsFinancial crisis

Abstract

fetched live from OpenAlex

Risk-averse investors induce competitive intermediaries to hold safe assets, thereby lowering the probability of a run and reducing financial fragility. We revisit Goldstein and Pauzner (2005), who obtain a unique equilibrium in the banking model of Diamond and Dybvig (1983) by introducing risky investment and noisy private signals. We show that, in the optimal demand-deposit contract subject to sequential service, banks hold safe assets to insure investors against investment risk. Consequently, fewer investors withdraw prematurely, which reduces the probability of a bank run. Safe asset holdings increase investor welfare and may increase the bank’s provision of liquidity.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.252
Teacher spread0.226 · 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