Modelling volatility spillovers between prices of petroleum and stock sector indices: A multivariate GARCH comparison
Bibliographic record
Abstract
This study compares four multivariate GARCH approaches in modelling bilateral return and volatility spillovers between petroleum prices and self-constructed stock sector indices of net petroleum exporters (Canada and Saudi Arabia) and net petroleum importers (the United States and China). The estimates are subsequently used to quantify optimal portfolio weights and hedge ratios and to evaluate the effectiveness of the resulting hedging strategies. The outputs point to the presence of heterogeneous volatility interdependencies, which are more evident for Canada and the United States. The optimal weight of petroleum is greater in portfolios comprising stock sector indices of Saudi Arabia and China, which also provide lower hedging costs. Time-varying conditional correlations, portfolio weights, and hedge ratios exhibit considerable variations, particularly during turbulent periods. Finally, the hedging strategies generated from the VAR-DCC-GARCH specification result in the greatest reduction, although not substantial, of risks for portfolios involving stock sector indices of all countries.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".