Bibliographic record
Abstract
NB REWRITE THIS] Abstract: It is shown that credit basket derivatives such as CDOs which depend on a large number M of firms (M ≥ 100 is typical in some contexts) can be modeled in a parsimonious and computationally efficient manner within the affine Markov chain (AMC) framework for multifirm credit migration introduced in a companion paper [Hurd and Kuznetsov (2006)]. The proposed method has a number of merits. First, since our AMC models extend the intensity based doubly stochastic framework for multifirm default to a credit migration setting, they can be flexibly fit to observed market bond data for the individual constituent firms, and they can in principle explain the dynamics of this data. Second, the method handles some of the variations of CDOs such as nonhomogeneous hazard rates and unequal notational amounts that industry practitioners need to use. Thirdly, the approximation schemes we use can be verified in special cases, and prove to perform to basis point accuracy with typical parameter choices. Finally, in our model, prices and sensitivities for such derivatives are reduced to low dimensional integrals which can often be computed on a desktop computer in fractions of seconds. In this paper we develop an illustrative version of the modeling framework and present a number of sample CDO computations which illustrate the power of the method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".