Bibliographic record
Abstract
Valuing portfolio credit derivatives, such as CDO tranches based on a broad portfolio of credits, like the CDX.NA.IG index portfolio with 125 investment grade names, remains a very difficult problem. Only approximate and imperfect solutions are available so far. The industry standard Gaussian copula model has several known shortcomings, including the fact that it is a static model which does not offer a clear way to tie together valuation for CDO tranches with different maturities, and it doesn9t generate wide enough spreads to match market prices for the supersenior tranches. Efforts to make the default hazard rate dynamic by modeling it as a diffusion run into the second difficulty when the models are calibrated to market prices. This article introduces a dynamic process for the cumulative hazard rate for a credit portfolio, that allows discrete jumps with jump size increasing in the number of jumps. This approach ties together the market quotes for CDO tranches of different maturities into a unified valuation framework. It also can generate large enough default intensity once several defaults have occurred to produce default risk on the senior and supersenior tranches of the magnitude that the market seems to be incorporating into their prices. TOPICS:Credit default swaps, factor-based models, CLOs, CDOs, and other structured credit
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".