Stock Market Returns and Crude Oil Price Volatility: A Comparative Study Between Oil-Exporting and Oil-Importing Countries
Bibliographic record
Abstract
This study employs a modern GARCH framework to conduct a comparative analysis of the volatility transmission between crude oil prices and a comprehensive set of financial assets, including sectoral equities, precious metals, and cryptocurrencies, across oil-exporting and oil-importing countries. Our central finding reveals a stark pre-pandemic dichotomy: before COVID-19, oil price volatility exhibited a significant positive correlation with nearly all sectoral stock returns in oil-exporting countries (the United States and Canada), reflecting a systemic, demand-driven linkage. In contrast, this relationship was largely insignificant in oil-importing countries (the United Kingdom, France, and Japan), with the exception of the energy sector. The COVID-19 crisis temporarily erased this fundamental distinction, as sectoral stock markets in both country groups moved in significant positive correlation with oil, driven by the synchronized global demand shock. This transition underscores that the oil–equity relationship is structurally determined by a country’s net oil trade position, a dynamic that can be overridden during systemic global crises. These findings offer crucial insights for international portfolio diversification and risk management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".