Valuing Credit Default Swaps Ii: Modeling Default Correlations
Why this work is in the frame
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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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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it