Price Volatility Spillovers Among Major Wheat Markets in the World
Bibliographic record
Abstract
This research determined price volatility spillovers among major wheat markets in the world using time series data (1966-2018) of six major wheat producing countries in the world. The data were sourced from FAO and UNCTAD databanks and were analyzed using descriptive statistics, multiple regression, unit root test and GARCH models. The findings showed that there is low and high persistence in the wheat prices of Canada and USA; and, Australia and India, respectively. Thus, it was established that the prices in the former markets were characterized by short memory; the effect of shock is temporary as the prices return to the attractor level within a short period. However, bad news on the prices of the latter markets has pronounced effect and takes a longer period for the price series to normalize. On the other hand, French and Chinese market price series exhibited an explosive pattern; the price series have infinite memory and the effect of innovation is permanent as price series will not normalize. Therefore, it can be concluded that the future trade of wheat is useful in the market given the persistence behavior of the prices as their price trends are tailored towards a rational expectation rather than a naïve expectation. However, for the market prices that are explosive, the market participants should focus on rational market expectation as a trade barometer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".