Quantifying Liquidity and Default Risks of Corporate Bonds over the Business Cycle
Bibliographic record
Abstract
We develop a structural credit risk model to examine how the interactions of liquidity and default risk affect corporate bond pricing. By explicitly modeling debt rollover and by endogenizing the holding costs via collateralized financing, our model generates rich links between liquidity risk and default risk. The introduction of macroeconomic risks helps the model capture realistic time variation in default risk premia and the default-liquidity spiral over the business cycle. Across different credit ratings, our calibrated model can simultaneously match the average default probabilities, credit spreads, and bond liquidity measures including Bond-CDS spreads and bid-ask spreads in the data. Through a structural decomposition, we show that the interactions between liquidity and default risk account for 25 40% of the observed credit spreads and up to 55% of the credit spread changes over the business cycle. As an application, we use this framework to quantitatively evaluate the effects of liquidity-provision policies for the corporate bond market.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".