Hedging crop yield with exchange-traded weather derivatives
Why this work is in the frame
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Bibliographic record
Abstract
Purpose – The application of weather derivatives in hedging crop yield risk is gaining more interest. However, the further development of weather derivatives – particularly exchange-traded – in the agricultural sector has been impeded by concerns over their hedging performance. The purpose of this paper is to develop a new framework to derive the optimal hedging strategy and evaluate hedging effectiveness. Design/methodology/approach – This framework incorporates a stochastic temperature model, a crop yield model, a risk-neutral pricing method and a profit optimization procedure. Based on a large number of simulated scenarios, the authors study crop yield hedge for a future year. The authors allow the hedger to choose from different types of exchange-traded weather derivatives, and examine the impact of various factors on the optimal hedging strategy. Findings – The analysis shows that hedging objective, pricing method and geographical location of the hedged exposure all play important roles in choosing the best hedging strategy and assessing hedging effectiveness. Originality/value – This framework is forward-looking, because it focusses on the crop yield hedge for a future year rather than on the historical hedging effectiveness often studied in literature. It utilizes the most up-to-date information related to temperature and crop yield, and hence produces a hedging strategy which is more relevant to the year under consideration.
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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 | 0.001 |
| 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 it