Valuing Credit Default Swaps Ii: Modeling Default Correlations
Bibliographic record
Abstract
This paper extends the analysis in Valuing Credit Default Swaps I: No Counter-party Default Risk to provide a methodology for valuing credit default swaps that takes account of counterparty default risk and allows the payoff to be contingent on defaults by multiple reference entities. It develops a model of default correlations between different corporate or sovereign entities. The model is applied to the valuation of vanilla credit default swaps when the seller may default and to the valuation of basket credit default swaps. 2 In Hull and White (2000) we explained how a vanilla credit default swap (CDS) can be valued when there is no counterparty default risk. This is a two stage procedure. The first stage is to calculate the risk-neutral probability of default at future times from the yields on bonds issued by the reference entity (or by corporations considered to have the same risk of default as the reference entity). The second stage is to calculate the present value of both the expected future payoff and expected future payments on the credit default
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".