Stock Market Volatility and the COVID-19 Pandemic in Emerging and Developed Countries: An Application of the Asymmetric Exponential GARCH Model
Bibliographic record
Abstract
The objective of this research is to analyze the influence of COVID-19 on the return and volatility of stock market indices of emerging and developed countries (Brazil, Canada, United States, France, India, and Mexico) using an asymmetric exponential GARCH model. The daily returns of the market indices from January 2019 to December 2020 were considered. The results reveal negative average daily returns for all stock market indices during the first period of the COVID-19 pandemic (January 2020 to June 2020). Although the second half of the pandemic period (2020) reflects a recovery of all indices with altered strengths, volatility remains higher than in normal periods, signaling a bearish trend in the market. The variable COVID-19 has been shown to have a positive impact on the volatility of stock returns for all indices, i.e., indicating increased volatility in the analyzed stock markets. In addition, it is also found that the COVID variable has a negative impact on average returns only in the stock market of Brazil and France.
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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".