The Asymmetric Effects of Covid-19 and the Russia-Ukraine War on Developed Countries' Stock Markets: Evidence from the GJR-GARCH Model
Bibliographic record
Abstract
This study examines the impact of the COVID-19 pandemic and Russia's invasion of Ukraine on the stock markets of France, Canada, the United States and Germany using the GJR-GARCH model.The sample period was February 26, 2020, to December 30, 2023, divided into two sub-periods: the COVID-19 period and the Russia-Ukraine war period.The results of the study show that there was persistent volatility in these markets.In addition, the results of the model applied indicate that the asymmetric term was significant in all the markets analyzed, confirming that bad news, such as the pandemic and the war, had a stronger impact on the conditional variance of returns compared to good news.It was also found that the US and Canadian markets were more affected by the COVID-19 pandemic, while the French and German markets were more affected by Russia's invasion of Ukraine.The results have important implications for international investors in terms of portfolio management and minimizing investment risks in the face of events such as the pandemic and war.
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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 | 0.003 | 0.009 |
| 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 | 0.002 | 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".