Effects of growing-season weather on the dynamic price relationships between biofuel feedstocks
Bibliographic record
Abstract
Our study is the first to examine the effects of growing-season weather conditions on both the mean and variance of futures returns in the multipurpose agricultural commodity markets of U.S. soybean oil , Canadian canola, and Malaysian/Indonesian palm oil, based on their significance as substitutes in the global food and energy sectors. We use the vegetation health index (VHI) from major feedstock-growing regions in North America, Brazil, Malaysia, and Indonesia as an indicator of the anticipations of the future crop supply. We assess the impact of current VHI on price dynamics in these markets, we employ the EGARCH-X-DCC framework, which captures the effect of VHI-related news on both returns and short-term volatility in the food and biofuel markets. We also extend our analysis to explore how a crop's VHI, as a slow-moving determinant, influences volatility not only in its own market but also in substitute markets. For this, we use the GARCH-MIDAS-DCC framework, in which one year's worth of VHI acts as the slow-moving component in the MIDAS filter, allowing us to isolate the impact of slowly changing growing conditions on daily volatilities through the long-run component and the dynamic correlations between commodity returns. We find that information about current and longer-term growing-season weather conditions affects both the primary crop market and its substitutes. Furthermore, the broader set of crop condition information increases variability in the long-run correlation between commodity returns.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".