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Record W1591701027 · doi:10.34989/swp-2010-29

Understanding Systemic Risk: The Trade-Offs between Capital, Short-Term Funding and Liquid Asset Holdings

2021· preprint· en· W1591701027 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

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsBank of Canada
Fundersnot available
KeywordsSystemic riskTerm (time)Asset (computer security)BusinessEconomicsFinanceFinancial systemMonetary economicsMacroeconomicsComputer scienceFinancial crisis

Abstract

fetched live from OpenAlex

We offer a multi-period systemic risk assessment framework with which to assess recent liquidity and capital regulatory requirement proposals in a holistic way. Following Morris and Shin (2009), we introduce funding liquidity risk as an endogenous outcome of the interaction between market liquidity risk, solvency risk, and the funding structure of banks. To assess the overall impact of different mix of capital and liquidity, we simulate the framework under a severe but plausible macro scenario for different balance-sheet structures. Of particular interest, we find that (1) capital has a decreasing marginal effect on systemic risk, (2) increasing capital alone is much less effective in reducing liquidity risk than solvency risk, (3) high liquid asset holdings reduce the marginal effect of increasing short term liability on systemic risk, and (4) changing liquid asset holdings has little effect on systemic risk when short term liability is sufficiently low.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.070
GPT teacher head0.252
Teacher spread0.182 · 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