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.
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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 | 0.018 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".