An Analysis of the True Notional Bond System Applied to the CBOT T-Bond Futures
Bibliographic record
Abstract
The main purpose of this paper is to apply the True Notional Bond System (TNBS) proposed by Oviedo (2006) for the theoretical pricing of the Chicago Board of Trade Treasury-bond futures, one of the most traded derivatives in the world. This system is proposed as an alternative to the current conversion factor system (CFS), whose poor performance is well known. In this paper, we price the CBOT Tbond futures as well as all its embedded delivery options and compare the corresponding results under the CFS in a stochastic interest rate framework. Our pricing procedure is an adaptation of the Dynamic Programming (DP) algorithm described in Ben-Abdallah et al. (2006), giving the value of the futures contract under the TNBS as a function of time and current short-term interest rate. Numerical illustrations, provided under the Vacisek and CIR models, show that the TNBS reduces dramatically the value of all the delivery options embedded in the CBOT T-bond futures. JEL Classi cation: C61; C63; G12; G13.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".