Impact of External Shocks on Global Major Stock Market Interdependence: Insights from Vine-Copula Modeling
Bibliographic record
Abstract
Abstract This article investigates the dynamic changes in the interdependence structure and strength among ten financially significant stock markets across Asia, Europe, and the USA, in the context of recent global public health events and regional conflicts. Employing the Vine-Copula model, our analysis reveals that major events exert varying impacts on the interdependencies across different regions. The COVID-19 pandemic shifted European markets from a symmetric dependence structure to an asymmetric structure that is more sensitive to negative news. Conversely, the impact on Asian markets is the opposite, and the interdependence between China’s stock market and other major markets shows a decreasing trend. The Russia–Ukraine conflict has had minimal impact on stock markets, excluding Russia. Moreover, stock markets exhibit stronger co-movements during market downturns. Our research provides new insights into how global events impact stock market interdependencies and underscores the importance of region-specific strategies in managing financial risks and maintaining market stability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".