Co-sponsors: Bank of America Bank of Montreal BZW Deutsche Morgan Grenfell KMV Corporation Swiss Bank Corporation Union Bank of Switzerland
Bibliographic record
Abstract
This document reviews: . the conceptual framework of our methodologies for estimating credit risk; . the description of the obligors' credit quality characteristics, their statistical description and associated statistical models; . the description of credit exposure types across "market-driven" instruments and the more traditional corporate finance credit products; and . the data set that we update periodically and provide to the market for free. In the interest of establishing a benchmark in a field with as little standardization and precise data as credit risk measurement, we have invited five leading banks, Bank of America, BZW, Deutsche Morgan Grenfell, Swiss Bank Corporation, and Union Bank of Switzerland, and a leading credit risk analytics firm, KMV Corporation, to be co-sponsors of CreditMetrics. All these firms have spent a significant amount of time working on their own credit risk management issues, and we are pleased to have received their input and support in the development of CreditMetrics. With their sponsorship we hope to send one clear and consistent message to the marketplace in an area with little clarity to date. We have also had many fruitful dialogues with professionals from Central Banks, regulators, competitors, and academics. We are grateful for their insights, help, and encouragement. Of course, all remaining errors and omissions are solely our responsibility. How is this related to RiskMetrics^TM? We developed CreditMetrics to be as good a methodology for capturing counterparty default risk as the available data quality would allow. Although we never mandated during this development that CreditMetrics must resemble RiskMetrics, the outcome has yielded philosophically similar models. One major difference in the models was driven by the diffe...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.366 | 0.223 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".