Changes in Crude Oil Price under the Russia-Ukraine Conflict and Dynamics of Stock Market in G7 Countries
Bibliographic record
Abstract
On 24 February 2022, the conflict between Russia and Ukraine, due to the war, caused the stock market with greater sensitivities to the change of crude oil prices. This paper studies the effect of crude oil on G7 countries’ stock markets before and after the Russian-Ukrainian conflict. Using a VAR model, impulse response plots are constructed to measure the extent of crude oil price impacts on stock prices in seven countries, and an ARMA-GRCH-X model is applied to measure the correlation between the prices of crude oil and stocks in seven countries. This study shows that variations in crude oil prices have the greatest influence on the stock markets of France, German, and Italy, with the UK following. The impact on the stock markets of Japan, Canada, and the US is relatively small. The results of the ARMA-GARCH-X model examination demonstrate that the prices of crude oil have a remarkable positive correlation with the stock markets of France, Germany, and Italy. This study shows that during the Russia-Ukraine conflict, stock markets were more influenced by the prices of crude oil and stock investors are advised to invest with caution during war.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".