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Record W3022568459 · doi:10.22495/rgcv10i1p6

Bank regulation and risk in Europe and Central Asia since the global financial crisis

2020· article· en· W3022568459 on OpenAlexaff
Deniz Anginer, Asli Demirgüç‐Kunt, Davide Salvatore Mare

Bibliographic record

VenueRisk Governance and Control Financial Markets & Institutions · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsSimon Fraser University
FundersWorld Bank Group
KeywordsCapital requirementCapital adequacy ratioFinancial crisisFinancial systemEconomic capitalBusinessCapital (architecture)LaggingBasel IIIFinancial capitalLeverage (statistics)EconomicsMacroeconomicsMarket economyGeography

Abstract

fetched live from OpenAlex

This paper examines changes in bank capital and capital regulations since the global financial crisis, in the Europe and Central Asia region. It shows that banks in Europe and Central Asia are better capitalized, as measured by regulatory capital ratios, than they were prior to the crisis. However, the increase in simple equity ratios for the same banks has been smaller over the past 10 years. The increases in regulatory capital ratios have coincided with a reduction in the stringency of the definition of Tier 1 capital and reduction in risk-weights. We further analyze the relationship between bank capital and bank risk using individual bank data. We show that bank risk in Europe and Central Asia is more sensitive to changes in simple leverage ratios than changes in regulatory capital ratios, consistent with the notion that equity ratios only include high-quality capital and do not rely on internal risk models to compute risk-weights. Although there has been some effort to increase capital and liquidity requirements for institutions deemed systemically important, the region has been lagging in addressing the resolution of these institutions. In line with Demirguc-Kunt, Detragiache, and Merrouche (2013), our findings show the importance of the definition of bank capital to assure bank financial stability in Europe and Central Asia.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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