Bibliographic record
Abstract
The aim of this paper is to propose a numerical method to price the Chicago Board of Trade Treasury-bond futures. This contract is one of the most traded in the world, largely because of its ability to hedge long term interest rate risk. The di¢ culty to price it arises from its multiple inter-dependent embedded delivery options, which can be exercised at various times and dates during the delivery month. We consider a continuous time model with a continuous underlying factor (the interest rate), moving according to a Markov di¤usion process consistent with the no-arbitrage principle. We propose a model that can handle all the delivery rules embedded in the CBOT T-bond futures, interpreted here as an American-style interest rate derivative. Our pricing procedure is a backward numerical algorithm combining Dynamic Programming (DP), approximation by nite elements, and xed point evaluation. Numerical illustrations are provided under the Vacisek and CIR models. JEL Classi cation: C61; C63; G12; G13.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".