Using derivatives to manage risk for western Canadian agriculture
Bibliographic record
Abstract
Canadian agriculture faces considerable price risk. This thesis is composed of three essays investigating the effectiveness of using derivatives to manage risk in the Western Canadian grain industry. The objective of the first essay is to examine the use of grain price futures and options for reducing net indemnities paid by crop insurers on yield loss insurance. Data includes premiums and indemnities paid for canola in Manitoba and canola futures prices. Results show canola futures and options hedges were not effective in reducing net indemnities paid, while a spread position incorporating soybean oil futures showed some effectiveness. This analysis is applicable to crop insurers considering additional risk management methods. The objective of the second essay is to examine the use of futures contracts to manage price risk for Canadian wheat producers, using futures contracts based in the United States. Wheat in Canada is unique in that it has only recently traded on an ‘open market’ in Western Canada since 2012. Mean square error is used to examine hedging effectiveness. Downside risk measures are also considered. Data includes Manitoba and North Dakota hard red spring wheat prices and Minneapolis futures prices. Results show hedging to be effective for Manitoba farms, although less effective than in North Dakota. The objective of the third essay is to examine managing price risk across an entire grain farm in Western Canada. Hedging with futures can be a useful for reducing price risk, although basis risk, including foreign exchange risk, may impact hedging effectiveness for Canadian producers for most contracts. Many farms also grow multiple crops and benefit from diversification. Mean square error is used to determine hedge effectiveness, with data including Manitoba prices for hard red spring wheat, canola, corn and soybeans and their respective futures prices. Results show diversification reduces variability and downside risk over individual crop returns, and hedging is found to be more effective than simple diversification.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".