Oil price uncertainty and the risk‐return relation in stock markets: Evidence from oil‐importing and oil‐exporting countries
Bibliographic record
Abstract
Abstract This article examines the role of oil price uncertainty measured by the crude oil volatility index (OVX) in the risk‐return relation of stock markets from oil‐importing and exporting countries with the extended GARCH‐M models. It is found that oil price uncertainties have significant impacts on the stock risk‐return relationship in oil importers and exporters. Specifically, there is a positive risk‐return relation during the decreasing period of oil price uncertainty. This positive correlation will be undermined and become negative during the rising period of oil price uncertainty in most countries studied. What's more, change in oil price uncertainty negatively affects the stock risk‐return relation in general, and it has a more significant asymmetric effect in oil exporters than that in oil importers for the whole sample. In addition, we examine whether the impact of oil price uncertainty is sensitive to the global financial crisis in 2008. Our empirical results reveal that, on average, the stock risk‐return relation is more susceptible to OVX changes after the crisis period than that during the crisis period, suggesting impacts of OVX changes are undermined due to the extremely unstable global economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".