An Empirical Implementation of a Non‐parametric Estimation Approach for a Two‐Factor Term Structure Model
Bibliographic record
Abstract
Abstract Knight, Li, and Yuan (1999) have developed a non‐parametric procedure for estimating diffusion functions in a general multivariate diffusion process. We apply this estimation procedure to a two‐factor term structure model. Furthermore, under this two‐factor term structure model, we propose and perform a numerical procedure, the Monte Carlo simulation procedure, to value interest rate derivative securities. The results are compared with those calculated under an alternative parametric model and show significant differences. Résumé Knight, Li, et Yuan (1999) ont développé une procédure non‐paramétrique pour estimer des functions de diffusion d'un processus de diffusion multivarié général. Nous appliquons cette procédure d'estimation à un modèle de structure à terme à deux facteurs. De plus, pour ce modèle, nous proposons et utilisons une procédure numérique, la procédure de simulation de Monte Carlo, pour évaluer des produits dérivés de taux d'intérět. Les résultats sont comparés avec ceux calculés au moyen d'un modèle paramétrique alternatif, et des différences significatives sont obtenues.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".