The Levered Equity Risk Premium and Credit Spreads: A Unified Framework
Why this work is in the frame
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Bibliographic record
Abstract
We embed a structural model of credit risk inside a dynamic continuous-time consumption-based asset pricing model, which allows us to price equity and corporate debt in a unified framework. Our key economic assumptions are that the first and second moments of earnings and consumption growth depend on the state of the economy, which switches randomly, creating intertemporal risk, which agents prefer to resolve sooner rather than later, because they have Epstein-Zin-Weil preferences. Agents optimally choose dynamic capital structure and default times. For a dynamic cross-section of firms, our model endogenously generates a realistic average term structure and time series of actual default probabilities and credit spreads, together with a reasonable levered equity risk premium, which varies with macroeconomic conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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