Quantifying Liquidity and Default Risks of Corporate Bonds over the Business Cycle
Why this work is in the frame
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Bibliographic record
Abstract
We develop a structural credit model to examine how interactions between default and liquidity affect corporate bond pricing. The model features debt rollover and bond-price-dependent holding costs. Over the business cycle and in the cross-section, the model matches average default rates and credit spreads in the data, and captures variations in bid-ask and bond-CDS spreads. A structural decomposition reveals that default-liquidity interactions can account for 10%–24% of the level of credit spreads and 16%–46% of the changes in spreads over the business cycle. Further, liquidity-related corporate bond financing costs amount to 6% of the total issuance amount from 1996 to 2015. Received July 12, 2015; editorial decision April 15, 2017 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press website next to the link to the final published paper online.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it