Affine Modeling of Credit Risk, Pricing of Credit Events, and Contagion
Bibliographic record
Abstract
We propose a discrete-time affine pricing model that simultaneously allows for (i) the presence of systemic entities by departing from the no-jump condition on the factors’ conditional distribution, (ii) contagion effects, and (iii) the pricing of credit events. Our affine framework delivers explicit pricing formulas for default-sensitive securities such as bonds and credit default swaps (CDSs). We estimate a euro-area multicountry version of the model and address economic questions related to the pricing of sovereign credit risk. We find that both frailty (common factors) and contagion phenomena are important to account for the joint dynamics of credit spreads. Our results also provide evidence of credit-event pricing, which is at the source of substantial credit risk premiums, even for short maturities. Finally, we extract measures of depreciation-at-default from CDSs denominated in different currencies. This paper was accepted by Kay Giesecke, finance.
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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 | 0.001 | 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 | 0.000 | 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".