Bibliographic record
Abstract
Abstract This paper provides a general approach to deriving no‐arbitrage Gaussian term structure models with a three‐fold contribution. First, we present a general relationship between the short rate, forward rates, and futures rates, and apply it to derive no‐arbitrage Gaussian term structure models. We show two examples, the extended‐Vasicek model of Hull and White (1990) and the two‐factor model of Hull and White (1994b), in order to demonstrate the derivation procedure. Although many results presented in this article are not new to the literature, our methodology is simple, straightforward, and provides intuitive explanations. Second, our analysis fills a gap in the understanding of the relationship between the short rate process and the forward rate process, and thus provides a linkage between Heath, Jarrow, and Morton's (1990, 1992) model and Hull and White's (1990) model. Therefore, this paper contributes pedagogical value to the term structure model literature. Third, as our models use discrete time setting, it is relatively easy to implement them with numerical procedures such as the lattice approach or Monte Carlo simulations so as to price interest rate derivatives. We give numerical examples to show how to elaborate on the numerical procedure of Hull and White (1994a) using our discrete time model. Résumé Cet article fournit une approche non‐arbitrage générale applicable à des modèles gaussiens de structure par terme des taux d'intérêt. Notre contribution est triple. Tout d'abord, nous présentons une relation générale entre les taux court, le taux à terme forward et le taux à terme futures, et l'appliquons pour obtenir des modèles gaussiens de structure par terme. Le procédé de dérivation est illustré par deux exemples: le modèle Vasicek amélioré de Hull et White (1990) et le modèle à deux facteurs de Hull et White (1994b). Un bon nombre de résultats présentés sont standards dans la littérature et notre méthodologie fournit des explications intuitives. En second lieu, notre analyse comble une lacune dans la compréhension du rapport entre le processus court de taux et le processus de taux à terme, et fournit ainsi un lien entre les modèles de Heath, Jarrow, et Morton (1990, 1992) et celui de Hull et White (1990). Par conséquent, cet article apporte des valeurs pédagogiques à la littérature sur la structure par terme des taux d'intérêt. Troisièmement, étant donné que nos modèles sont mis en oeuvre en temps discret, afin d‘évaluer des dérivés sur les taux d'intérêt, il est relativement facile de mettre ces modèles en application avec des procédures numériques telles que l'approche par “grilles” ou les simulations Monte‐Carlo. Nous donnons des exemples numériques pour démontrer comment adapter le procédé numérique de Hull et White (1994a) en utilisant notre modèle à temps discret.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".