Pandemics and Stock Price Volatility: A Sectoral Analysis
Bibliographic record
Abstract
In this paper, we assess the impacts of the five most recent pandemics on the volatility of stock prices across forty-nine sectors of the economy in the United States. These five most recent pandemics are the 1957–1958 Asian flu, the 1977 Russian flu, SARS-CoV-1, swine flu and COVID-19. Applying the GJR-GARCH model, we find that pandemics other than COVID-19 have heterogeneous impacts on the volatility of stock returns. The results of our analysis indicate that COVID-19 has increased the volatility of stock returns in all sectors. Similarly, stocks in more than seventy percent of sectors in our study declined during the ongoing pandemic, perhaps reflecting the severity of the pandemic. In addition, our results on sectors such as healthcare and natural gas diverge from other literature. The mixed results on SARS-CoV-1 are partially explained by the fact it emerged at a time when stock valuations were particularly pessimistic. In the case of Russian flu, it was relatively short-lived and limited in spread relative to other pandemics in our study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".