Study on the application of basis trade with option in Chinese maize contract farming using the BAW model
Bibliographic record
Abstract
Contract farming is one of the key indicators of agricultural modernization in a country. However, due to the lack of fair pricing and risk management mechanisms, the default rate in China’s contract farming has remained high. This paper explores the feasibility of using basis trade with option as a solution for China’s maize contract farming. Basis trade with option, based on futures prices, integrates the difference between spot and futures prices (basis) and the rights of options, incorporating pricing and risk management into spot trade. This paper employs an analytical pricing method and hedging formula suitable for American futures options in the Chinese market using the Barone-Adesi and Whaley (BAW) approach. The accuracy of the pricing model and the effectiveness of the hedging strategy, as well as their feasibility for contract farming, are comprehensively verified through Monte Carlo simulation. The results show that this solution can provide fair pricing for both parties of the contract, offer a minimum price guarantee and favorable price fluctuation benefits for the contract seller, and allow traders as contract buyers to transfer risk through hedging. This fundamentally reduces the possibility of contract default.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".