Stock market volatility and oil shocks: A study of G7 economies
Bibliographic record
Abstract
Oil shocks have caused economic recessions over the years, affecting various markets, especially the stock market. The objective of this study is to analyze how global oil price index variable and shocks related to supply, economic activity, demand, and inventory affect the volatility and dynamics of G7 countries' stock market indices in the context of the 2014 oil shock. Using monthly data from January 2003 to September 2023, a combined methodology of Vector AutoRegressive (VAR) and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models was applied to capture mean and conditional volatility dynamics, complemented with exponential GARCH (EGARCH) models to detect asymmetries. The results indicate that oil shocks have a significant impact on stock index volatility, with Canada, Japan and the UK showing high sensitivity, especially during and after the 2014 oil shock. Negative shocks affect volatility more than positive ones. Therefore, economic policies to mitigate extreme volatility and reduce economic uncertainty are necessary. Moreover, for oil-dependent economies, such as Canada, their vulnerability to oil price fluctuations needs to be reduced. This study provides a comprehensive understanding of the influence of oil shocks on the volatility and dynamics of G7 stock markets, offering valuable implications for policymaking and future research. • Oil price index affected volatility of stock market indices during 2014 oil shock. • Negative shocks affect volatility more than positive ones. • Economic policies to reduce risk and economic uncertainty are necessary in oil shocks.
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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 | 0.001 |
| 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".