Hedging Adverse Bioclimatic Conditions Employing a Short Condor Position
Bibliographic record
Abstract
Abstract Weather derivatives are a relatively new form of financial security, providing firms with the ability to hedge the impact of weather related risks to their activities. Participants in the energy industry have employed standardized temperature contracts trading on organized exchanges since 1999, and the availability and use of non-standardized contracts designed for specialized weather related risks is growing dramatically. The primary goal of this paper is to consider the potential design and use of a weather contract to hedge the risks faced in viticulture as measured by bioclimatic indices. Specifically we examine the Winkler and Huglin bioclimatic indices over a 43 year period for the Niagara region of Ontario, Canada's largest wine producing region, and identify a mixed jump diffusion stochastic process for cumulative growing season index values. We then employ Monte Carlo simulation to derive a range of benchmark prices for a “short condor” contract employing the Huglin index as the underlying variable. The results show that valuable hedging opportunities can be provided by such contracts. (JEL Classification: G13, G32, Q14, Q51, Q54)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".