On the Pricing of Cross Currency Futures Options for Canadian Grains and Livestock
Bibliographic record
Abstract
This paper explores the problem of pricing an option on the cash commodity in Canadian dollars when the commodity is priced relative to a U.S. futures market. A general option pricing model is developed that separates out the value of a quantos risk and basis risk. The paper uses daily data for cattle, corn and soybeans in Ontario, and the model is employed to price the option on the cash commodity with basis risk and the option on a quantos, without basis risk. The relationship between the pricing model and over‐the‐counter options and market revenue insurance is also discussed. L'article examine le problème que pose l'établissement du prix d'une option sur le marché au comptant canadien des denrées de base quand le prix du produit en question doit être établi en fonction d'un marchéà terme américain. Les auteurs proposent un modèle général dans lequel on sépare la valeur du risque par quantos de celle du risque de base pour établir le prix des options. Ils utilisent pour cela le cours quotidien des bovins, du maïs et du soja en Ontario. Le modèle permet de calculer le prix d'une option sur le marché au comptant des denrées de base avec le risque de base et le prix de l'option sur un quantos, sans risque de base. Les auteurs examinent aussi les liens entre le modèle et les options au comptoir ainsi que les assurance‐revenu du marché.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".