Bibliographic record
Abstract
This paper studies the implications of ambiguity for the credit spreads. We consider two ways of incorporating ambiguity into the Duffie and Lando [2001] model of credit spreads under incomplete information. We begin by examining the credit spreads in a setting where news about the asset value are of an uncertain quality. In this setting, ambiguity-averse investors act as if they take the worst-case assessment of quality. Thus, they react more strongly to bad news than to good news. As a result, changes in the information environ-ment can lead to a widening of credit spreads and implied default intensities, even if firm fundamentals do not change. If the underlying asset value dynamics are also ambiguous, then the ambiguity-averse investors in the secondary debt market act as if the drift rate of the underlying asset value is lower than it actually is. We calibrate the two competing models to match the observed increase in credit spreads after August 9, 2007. Comparing the implications of the two models for default spreads, we find that ambiguity about the underlying asset value dynamics is better able to match the patterns observed in the data. ∗I would like to thank Lars P. Hansen, Lubos Pastor, three anonymous faculty reviewers and participants in the Finance Brownbag, University of Chicago Booth School of Business, for helpful comments on the previous drafts of this paper. All the remaining errors are mine. 1 1
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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 | 0.008 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".