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Record W3088893438 · doi:10.3390/jrfm13100228

The Impact of BASEL Accords on the Management of Vietnamese Commercial Banks

2020· article· en· W3088893438 on OpenAlexvenueno aff
Hai Long Pham, Kevin Daly

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsRisk-adjusted return on capitalBasel IIRisk-weighted assetCapital adequacy ratioCapital requirementOperational riskBasel IBasel IIIVietnameseEconomic capitalRisk managementActuarial scienceBusinessEconomicsAccountingFinanceFinancial capitalCapital formationMicroeconomics

Abstract

fetched live from OpenAlex

This paper is an attempt to empirically examine the impact of Basel Accord regulatory guidelines on the risk-based capital adequacy regulation and bank risk management of Vietnamese commercial banks. Our research aims to assess how Vietnamese commercial banks manage their capital ratio and bank risk under the latest Basel Accord capital adequacy ratio requirements. Building on previous studies, this research uses a simultaneous equation modeling (SiEM) with three-stage least squares regression (3SLS) to analyze the endogenous relationship between risk-based capital adequacy standards and bank risk management. A year dummy variable (dy2013) is included in the model to take account of changes in the regulation of the Vietnamese banking system. Furthermore, we add a value-at-risk variable developed by as an independent variable into equations of the empirical models. The results reveal a significant impact of Basel capital adequacy regulatory pressure on the risk-based capital adequacy standards and bank risk management of Vietnamese commercial banks. Moreover, banks under the latest Basel capital adequacy regulations are induced to reduce risks and increase banks’ financial performance.

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 score0.840
Threshold uncertainty score0.338

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 studies0.0000.000
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 head0.019
GPT teacher head0.235
Teacher spread0.216 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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