The impact of Ukraine-Russia war on stock market volatility in G7: An empirical analysis using EGARCH model.
Bibliographic record
Abstract
The current study investigates how the recent war between Ukraine and Russia impacted the volatility of G7 economies of the stock markets in major industrialized countries like United States (US), the United Kingdom (UK), Canada, Japan, France, Germany, and Italy. The paper applies EGRACH model to detect the influence of the war on stock markets volatility. EGRACH estimations revealed that there is a direct impact of the information content of the war on the volatility of the majority of the countries under study. More specifically, four countries are negatively influenced by the war, Canada, France, Germany and UK. While three countries are not affected by the news which are Japan, USA and Italy. Granger causality reveals that there is a unidirectional relationship between war news and stock indices of three economies which are Germany, France and Italy. However, other indices did not show any unidirectional relationship (Japan, USA, UK and Canada). To find out if there is a long-term association between indices and the information content of the war, co-integration test was employed. The results showed the long-term association between the two variables.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".