Research on Spillover Effect and Information Transmission within Oil Futures Markets at Home and Abroad
Bibliographic record
Abstract
This study measures the returns and volatility spillover effects in China's palm,soybean and rapeseed oil futures markets and American soybean oil future market and Canadian canola future market by using VAR( 1)-GARCH( 1,1)-BEKK models. Furthermore,co-integration test and error correction model are applied to analyze the information transmission within oil futures markets. The results reveal that there is an unidirectional returns spillover effect from American soybean oil future market and Canadian canola future market to the three oil futures markets of China; as for China's three oil futures markets,returns spillover effects from soybean oil to palm oil and from palm oil to rapeseed oil have been found; there are bidirectional volatility spillover effects among all the markets except for the effect from China's soybean,palm oil markets to rapeseed oil futures market,which is one-way; All markets share a stable co-integration relationship and have the same information transmission efficiency. In conclusion, the main finding of this study is that Chicago soybean oil futures market and Canadian canola futures market function as world's soybean oil and rapeseed oil pricing center respectively while Dalian futures market is the pricing center of China's domestic oil product.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 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".