Do Vaccines’ Announcements Cure Stock Market Volatility? Evidence From the Gulf Cooperation Council (GCC) Markets
Bibliographic record
Abstract
COVID-19 has been impacting stock markets worldwide. Yet, a scant amount of research has been done on the stock markets of the Gulf Cooperation Council (GCC) markets. In this work, we aim to investigate whether and to what extent local and international events linked to the COVID-19 outbreak have impacted stock market volatility of the GCC countries. We model stocks’ returns of these countries between January and December 2020, decomposing the errors’ heteroskedasticity to account for main international and local events related to COVID-19. These events have been included as structural breaks and measured using dichotomous variables. Both local and international events were found to be associated with significant variations in volatility; however, local events seem to have impacted volatility to a lesser extent compared to international events. The announcement of the status of pandemic by the WHO had the greatest impact on volatility across the GCC markets, even greater than the impact associated to the drop in oil prices. The announcement of local approval of vaccine led to a reduction in volatility in UAE (ADX), Qatar, Saudi Arabia and Bahrain.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".