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Record W3124643711 · doi:10.1287/mnsc.2017.2869

Imperfect Renegotiations in Interbank Financial Networks

2017· article· en· W3124643711 on OpenAlexaff
Alexander David, Alfred Lehar

Bibliographic record

VenueManagement Science · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInsolvencyInterbank lending marketFinancial contagionMarket liquiditySystemic riskEconomicsClearingBusinessBank runFinancial systemFinanceFinancial marketFinancial crisis

Abstract

fetched live from OpenAlex

Interbank financial networks enable banks to share the risks in their assets but potentially also increase systemic spillovers of insolvency from one bank to others in the network. We model a renegotiation game to explicitly examine the forgiveness of commitments of insolvent banks by solvent banks to limit the systemic transmission of financial distress. The assets of the insolvent bank can be appropriated by the forgiving bank in the two-bank network, but not the three-bank network, where they may be appropriated by the third bank, leading to a renegotiation breakdown. We also show how banks can ex ante optimally construct networks from interbank loans and derivatives to minimize the costs of such inefficient financial distress. This paper was accepted by Itay Goldstein, finance.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.243
Teacher spread0.223 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2017
Admission routes1
Has abstractyes

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