The fluctuation linkages and price volatility risk on agricultural commodity market: Evidence from Vietnamese coffee
Bibliographic record
Abstract
This paper uses the DCC-GARCH and Value at Risk (VaR) model to analyze the fluctuation, linkage, and price volatility risk among coffee price series in the period of 2004 - 2020. In terms of the fluctuation, the study points out, the volatility of Vietnamese coffee price and the price of Robusta coffee in two markets were affected by two ARCH terms and GARCH terms at 1 percent level. Meanwhile, the coffee price of Brazil and Colombia is only impacted by the ARCH term. The linkage between Brazil and Colombia is the biggest. The average coefficient linkage among Vietnam with two main competitors is relatively small. In terms of price volatility risk, the price volatility risk of Vietnamese coffee is the smallest and the biggest risk is belonging to Brazilian coffee price. The results obtained would be a valuable reference for stakeholders, policymakers, coffee processing and exporting enterprises, and coffee farmers to clearly understand the fluctuation and linkage among coffee export price series, and thereby have appropriate and effective solutions and strategies in price volatility risk management to sustainable development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".