COVID-19 Vaccinations and the Volatility of Energy Companies in International Markets
Bibliographic record
Abstract
The COVID-19 pandemic has elevated both the risk and volatility of energy companies. Can mass vaccinations restore stability within this sector? To answer this question, we investigate stock market data from fifty-eight countries from January 2020 to April 2021. We document that vaccination programs assist in decreasing the volatility of energy stocks around the world. The drop in volatility is statistically and economically significant and robust to many considerations. The observed phenomenon survives a broad battery of control variables; it is also independent of the employed regression model or the volatility measurement approach. Moreover, the effect is not driven by the dynamics of the pandemic itself or the associated government interventions. Finally, we find the influence of vaccinations on energy stock volatility to be more pronounced in developed markets rather than in emerging ones. Our findings bear clear practical implications: policy makers around the world should consider the essential role of vaccinations in the energy sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".