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Record W1570263271

THE BASEL COMMITTEE PROPOSAL ON RISK-WEIGHTS AND EXTERNAL RATINGS: WHAT DO WE LEARN FROM BOND SPREADS?

2003· preprint· en· W1570263271 on OpenAlexaboutno aff
Andrea R Esti, Andrea Sironi

Bibliographic record

VenueRePEc: Research Papers in Economics · 2003
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsnot available
Fundersnot available
KeywordsIssuerCredit ratingBondMaturity (psychological)Credit riskBasel IIIBusinessRisk-weighted assetBond credit ratingBasel IILoanEurobondActuarial scienceCollateralFinancial systemEconomicsCapital requirementFinanceCredit reference
DOInot available

Abstract

fetched live from OpenAlex

The Basel Committee for Banking Supervision proposed a system of risk weights (the so called “standardised approach”) to measure the riskiness of banks’ loan portfolios. Its ability to adequately reflect risk is empirically investigated in this paper, through an analysis of the economic capital allocations implied in corporate bond spreads. This is based on a unique dataset of issuance spreads, ratings and other relevant bond variables (such as maturity, face value, time of issuance and currency of denomination) including 3,307 eurobonds issued by Canadian, European, Japanese and U.S. companies during 1991-2001. Three main results emerge. First, the spread/rating relationship is strongly significant with spreads increasing when ratings worsen. Second, the estimated spreads per rating class indicate a much steeper risk/rating relationship than the one proposed by the Basel Committee. Finally, no significant difference appears to exist in the spread/rating relation of banks and non- financial firms issuers. Following this empirical evidence, we propose some relevant changes in the standardised approach risk-weights.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.003
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 head0.027
GPT teacher head0.272
Teacher spread0.244 · 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.

Study designOther design
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

Citations6
Published2003
Admission routes1
Has abstractyes

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