The Performance of U.S. Futures Market in Hedging International Crude Oil
Bibliographic record
Abstract
We study the effectiveness of U.S. futures markets in hedging price risks in the international crude oil spot market. Three international markets, Australia, Canada and Mexico are selected. We devote our attention to three hedging strategies: hedge a spot position that does not recognize native currency exchange rate fluctuations with a single commodity futures position; hedge a spot position that does recognize native currency exchange rate fluctuations with a single commodity futures position; hedge a spot position that does recognize native currency exchange rate fluctuations with both commodity and currency futures positions. Three base hedge ratio estimation models are developed based on these three hedging strategies. We compare the effectiveness of these hedging strategies over each of the three hedging horizons, one week, four weeks and twelve weeks for each country. Empirical hedge ratio estimation models are selected to deal with seasonality, auto correlation and heteroscedasticity. Hedge effectiveness is properly estimated by comparing hedged and unhedged outcome variances of the auto regression-corrected and/or heteroscedasticity-corrected transformed data.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".