Canadian Ice Wine Production: A Case for the Use of Weather Derivatives *
Bibliographic record
Abstract
Weather derivatives are a relatively new form of financial security that can provide firms with the ability to hedge against the impact of weather related risks to their activities. Participants in the energy industry have employed standardized weather contracts trading on organized exchanges since 1999 and the interest in non-standardized contracts for specialized weather related risks is growing at an increasing rate. The purpose of this paper is to examine the potential use of weather derivatives to hedge against temperature related risks in Canadian ice wine production. Specifically we examine historical data for the Niagara region of the province of Ontario, Canada, the largest icewine producing region of the world, to determine an appropriate underlying variable for the design of an option contact that could be employed by icewine producers. Employing monte carlo simulation we derive a range of benchmark option values based upon varying assumptions regarding the stochastic process for an underlying temperature variable. The results show that such option contracts can provide valuable hedging opportunities for producers, given the historical seasonal temperature variations in the region. (JEL Classification: G13, G32, Q14, Q51, Q54) I.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".