Hedging von Mengenrisiken in der Landwirtschaft – Wie teuer dürfen „ineffektive“ Wetterderivate sein?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the mid-nineties, agricultural economists discuss the suitability of “weather derivatives” as hedging instruments for volumetric risks in agriculture. Contrary to traditional insurance contracts, the payoffs of such derivatives are linked to weather indices (e.g. accumulated rainfall or temperature over a certain period) that are measured objectively at a defined meteorological station. While weather derivatives thus circumvent the problem of moral hazard and adverse selection, weather derivative markets for the agricultural sector are still in their infancy all-over the world. Some economists attribute this to theoretical valuation problems and the lack of a pricing method which is accepted by all market participants. Others think that the low hedging effectiveness of (standardized and non-customized) weather contracts cripple the market. Motivated by the question of how weather derivatives should be priced to agricultural firms, this paper describes a risk programming model which can be used to determine farmers’ willingness-to-pay (demand function) for weather derivatives. The model considers both the derivative’s farm-specific risk reduction capacity and the individual farmer’s risk acceptance. Applying it to the exemplary case of a Brandenburg farm reveals that even a highly standardized contract which is based on the accumulated rainfall at the capital’s meteorological station in Berlin-Tempelhof generates a relevant willingness-to-pay. We find that a potential underwriter could even add a loading on the actuarially fair price that exceeds the loading level of traditional insurances. Since transaction costs are low compared to insurance contracts, this indicates that there may be a significant trading potential.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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