Emerging Market Indexes During the Pandemic Period
Bibliographic record
Abstract
The thesis empirically examines and analyzes an unusual episode in the behavior of emerging indexes. Specifically, it investigates the sensitivity of high-frequency five-minute interval index price movements to COVID-19-related news announcements and macroeconomic news announcements during the pandemic. The author hypothesized that COVID-19 infection cases, deaths, vaccination counts, major vaccine development announcements, and government response measures related to COVID significantly impact the emerging equity markets’ returns and volatility, namely Argentine, Brazilian, Chilean, and Mexican equity indexes. They also hypothesized an asymmetric effect of macroeconomic news before and during the pandemic. Findings reveal that pandemic cases, vaccination, and death-related news announcements exhibit a statistically significant effect on intraday volatility but not so much on returns. At the same time, government response measures have a more pronounced and significant effect on return and volatility. Additionally, vaccine research & development and approval news increase intraday volatility. Findings also suggest that very few macroeconomic news indicators exhibit statistically significant asymmetric interaction before and during the pandemic, and fewer US macroeconomic news indicators are significant during the pandemic than before. The results support previous findings that US macroeconomic news announcements significantly impact Canadian and Mexican equity indexes, suggesting a linkage between them with US financial markets.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".