Systematic Risk, Debt Maturity, and the Term Structure of Credit Spreads
Bibliographic record
Abstract
We build a dynamic capital structure model to study the link between firms' systematic risk exposures and their time-varying debt maturity choices, as well as its implications for the term structure of credit spreads. Compared to short-term debt, long-term debt helps reduce rollover risks, but its illiquidity raises the costs of financing. With both default risk and liquidity costs changing over the business cycle, our calibrated model implies that debt maturity is pro-cyclical, firms with high systematic risk favor longer debt maturity, and that these firms will have more stable maturity structures over the cycle. Moreover, pro-cyclical maturity variation can significantly amplify the impact of aggregate shocks on the term structure of credit spreads, especially for firms with high beta, high leverage, or a lumpy maturity structure. We provide empirical evidence for the model predictions on both debt maturity and credit spreads.
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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 | 0.010 | 0.002 |
| 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".