Study on the application of basis trade with option in Chinese maize contract farming using the BAW model
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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