Bibliographic record
Abstract
Understanding the stochastic behavior of credit spreads and devising models for derivatives with credit spreads as underliers grows increasingly important every day. Mean reversion in spreads is clearly evident in the data, as is time-varying volatility. In this paper, Tahani models credit spreads using a mean-reverting GARCH framework. Adopting Heston and Nandi9s GARCH specification allows closed-form option valuation formulas to be obtained by inversion of the characteristic function. Moreover, Tahani9s model contains the Longstaff-Schwartz spread model as a special case. The model is then taken to the data, by fitting it to the credit spreads between Moody9s Aaa and Baa bond yields and U.S. Treasuries. The GARCH specification fits that data well, and the GARCH option model calibrated to the bond yields exhibits the same kinds of unusual behavior (e.g., option prices below intrinsic values, in some cases) as Longstaff and Schwartz found with their more restricted model. TOPICS:Options, statistical methods
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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 | 0.004 | 0.018 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".