RIDING THE WAVE: OIL PRICE FLUCTUATIONS AND STOCK MARKETS’ RESPONSES IN OIL EXPORTING VS IMPORTING ECONOMIES
Bibliographic record
Abstract
This study identified the dynamics of return spillover between crude oil return and stock markets’ return of crude oil exporting and importing economies. For this objective, the study employs an extensive sample of major oil-exporting countries (United Arab Emirates, Saudi Arabia, Iraq, Canada, and Russia) and oil-importing countries (United States, China, India, South Korea, and Japan). The analysis covered a dataset of 1035 observations from 2019 to 2022 by using the spillover index methodologies of Diebold & Yilmaz (2012) and the Spillover Asymmetric Measures (SAM) model of Barunik et al. (2016). The key findings revealed notable spillover effects of stock returns between the stock return and the change in the price of crude oil. An increase in return spillover was noted among the exporting and importing countries at the time of increasing crude oil prices compared to aggregated spillover and periods of decreasing oil pricing. The magnitude of return spillover was high in oil-importing economies as compared to oilexporting economies. During periods of price decline and price increase for crude oil. Overall, the study's findings posit valuable insights into the complex dynamics of spillover effects between crude oil prices and stock markets in exporting and importing 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".