Bibliographic record
Abstract
The paper examines the pricing performance of a convertible bond valuation model developed within the Duffie and Singleton (1999) reduced-form credit risk valuation framework. A recent sample of monthly U.S. convertible bond prices observed during the period from January 2001 to September 2002 is used. To our knowledge this is the only recent study to have used recent U.S. data and as such it enhances greatly our understanding of the particular market. We find that the model produces prices that are consistently lower than observed market prices when the embedded conversion option is in-the-money and higher than observed market prices when the conversion option is out-of-the-money. While at least part of the in-the-money bias can be attributed to the firm's optimal call policy assumed by the theoretical model, the out-of-the-money bias is more difficult to explain. Evidence from our sample suggests that the deep out-of-the-money bias is not related to the theoretical model's performance. Instead the bias is the result of the fact that convertible bonds with low conversion value seem to be generally underpriced to the extent that their prices often imply negative embedded option values.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".