Les bandes de Bollinger comme technique de réduction de la variance des prix d’options sur obligations obtenus par la simulation de Monte-Carlo
Bibliographic record
Abstract
Dans cet article, nous proposons une nouvelle technique de réduction de la variance d’une simulation : les bandes de Bollinger. Nous montrons comment le recours aux bandes de Bollinger, une procédure utilisée en analyse technique, peut accroître considérablement la performance d’une simulation de Monte-Carlo en termes de réduction de la variance des simulations. La technique des bandes de Bollinger sert à filtrer les variations extrêmes qui s’observent dans une simulation. Nous appliquons cette technique à la simulation des prix de 38 options sur obligations (OBK) transigées à la Bourse de Montréal, ce en tirant profit du modèle de Fong et Vasicek pour simuler les prix des options. L’erreur quadratique moyenne des simulations se voit réduite considérablement à la suite de l’utilisation des bandes de Bollinger. Le modèle de Fong et Vasicek renforcé par les bandes de Bollinger se compare également très favorablement à celui de Black, Derman et Toy, soit le modèle encore le plus utilisé dans la pratique financière pour évaluer les options sur taux d’intérêt et sur obligations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".