Rare Disasters, Credit, and Option Market Puzzles
Bibliographic record
Abstract
We embed systematic default, procyclical recovery rates, and external habit persistence into a model with a slight possibility of a macroeconomic disaster of reasonable magnitude. We derive analytical solutions for defaultable bond prices and show that a single set of structural parameters calibrated to the real economy can simultaneously explain several key empirical regularities in equity, credit, and options markets. Our model captures the empirical level and volatility of credit spreads, generates a flexible credit risk term structure, and provides a good fit to a century of observed spreads. The model also matches high-yield and collaterized debt obligation tranche spreads, equity market moments, and index option skewness. Finally, our model implies a time-varying relationship between bond and option prices that depends on the state of the economy and that explains the conflicting empirical evidence found in the literature. This paper was accepted by Jerome Detemple, 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 | 0.000 | 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 | 0.001 |
| 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".