The Financial Derivatives Market and the Pandemic: BioNTech and Moderna Volatility
Bibliographic record
Abstract
Global society’s comfort and well-established certainties have been unpredictably and foundationally undermined by the emergence of the COVID-19 virus. The announcement of the pandemic by the WHO has halted global economic activities, and the financial markets have recorded drastic losses. In this context of uncertainty and economic downturn, many traditional companies have been negatively impacted, but the biotechnology sector, which has already been growing for some years, registered high growth rates and earnings. In particular, this study focused on the two most significant biotech companies, BioNTech and Moderna, the two start-ups that first commercialized COVID-19 vaccines. The GARCH (1,1) model examines the relation of two stock prices and the volatility of derivatives markets before and after the outbreak of the pandemic. The variables used in the analysis are the U.S. technologic market index, the market volatility, and Brent future prices. The results suggest a different reaction of market volatility and Brent future prices on the return of both companies. Additionally, during the COVID-19 period, a contagion effect between both companies and the technological market was observed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".