Time Varying Impact of Oil Prices on Stock Returns: Evidence from Developing Markets
Bibliographic record
Abstract
In this study, we provide new evidence on the relationship between crude oil, exchange rate and stock returns before and after the official announcement of COVID-19 as a pandemic by WHO. Data for the present study consists of the major stock indices of ten emerging markets (Brazil, China, India, Indonesia, Mexico, Russia, Saudi Arabia, South Africa, Taiwan and Thailand), their exchange rates, And prices of Brent crude oil. We employ panel vector autoregression and provide evidence based on panel granger causality, impulse response function and forecast error variance decomposition. Panel granger causality reveals that after the declaration of COVID-19 as pandemic, interdependence between oil price changes and stock returns has increased. We find positive (negative) impact of oil market (exchange rate) shocks on stock returns. Analysis of impulse response suggests that during pandemic shocks to crude oil, exchange rate and stock market have larger and longer own and cross-market impact. Thus, there is a need for sharing timely and adequate information to minimize uncertainties in financial and commodity markets. This would benefit investors by lessening the transmission of shocks during the times of crisis.
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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 | 0.005 |
| 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 | 0.001 |
| 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".