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Record W3122872099

Bank capital and systemic stability

2014· preprint· en· W3122872099 on OpenAlexaff
Deniz Anginer, Asli Demirgüç‐Kunt

Bibliographic record

VenueRePEc: Research Papers in Economics · 2014
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSystemic riskAsset qualityCapital requirementFragilityVolatility (finance)Financial fragilityMarket liquidityCapital adequacy ratioMonetary economicsBusinessFinancial systemCapital (architecture)EconomicsDiversification (marketing strategy)Asset (computer security)Financial crisisFinanceMacroeconomicsMarket economy
DOInot available

Abstract

fetched live from OpenAlex

This paper distinguishes among various types of capital and examines their effect on system-wide fragility. The analysis finds that higher quality forms of capital reduce the systemic risk contribution of banks, whereas lower quality forms can have a destabilizing impact, particularly during crisis periods. The impact of capital on systemic risk is less pronounced for smaller banks, for banks located in countries with more generous safety nets, and in countries with institutions that allow for better public and private monitoring of financial institutions. The results show that regulatory capital is effective in reducing systemic risk and that regulatory risk weights are correlated with higher future asset volatility, but this relationship is significantly weaker for larger banks. The paper also finds that increased regulatory risk-weights not correlated with future asset volatility increase systemic fragility. Overall, the results are consistent with the theoretical literature that emphasizes capital as a potential buffer in absorbing liquidity, information, and economic shocks reducing contagious defaults.

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)0.002
metaresearch head score (Gemma)0.009
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: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.035
GPT teacher head0.274
Teacher spread0.240 · 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
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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