Does US Infectious Disease Equity Market Volatility Index Predict G7 Stock Returns? Evidence Beyond Symmetry
Bibliographic record
Abstract
During the COVID-19 pandemic, Baker et al. (2020) [The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies, 10, 742–758.] proposed the infectious disease equity market volatility (ID-EMV) index, which tracks US equity market volatility caused by infectious diseases. We extended the literature by using this newly developed ID-EMV index to examine its asymmetric effect on the share market returns of the G7 countries, which include the United Kingdom, Italy, Japan, Germany, France, Canada, and the United States of America. Moreover, we used novel techniques like the quantile-on-quantile regression test, quantile cointegration test, and quantile unit root test. The quantile cointegration test indicates that the infectious disease EMV index is cointegrated with G7 stock returns. Moreover, the quantile-on-quantile regression technique reveals that the infectious disease index positively affects stock returns during bullish states of the stock markets. In contrast, it negatively affects stock returns during bearish states of the stock market returns. The negative effect of the bearish states implies that investors may discourage investments during the downturns of the economy, whereas they need to boost their investments during economic booms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".