Global Market Shocks and Tail Risk Spillovers: Evidence from a Copula-Based Contagion Framework
Bibliographic record
Abstract
This study investigates the dynamics of financial contagion using a flexible mixture copula framework, specifically a combination of the Survival Clayton and Survival Gumbel copulas, to estimate the lower tail dependence coefficient, interpreted as a measure of extreme downside co-movement or contagion. The model captures nonlinear and asymmetric dependencies between the global stock market and nine national markets: Australia, China, Hungary, India, New Zealand, Spain, Thailand, the United Kingdom, and the United States. The analysis spans the period from 2018 to 2024 and focuses on three major global crises: the China–U.S. trade war, the COVID-19 pandemic, and the Russia–Ukraine conflict. The results reveal substantial heterogeneity in contagion intensity across countries and crises. The COVID-19 pandemic generated the highest and most synchronized levels of contagion, with tail dependence exceeding 0.8 in the United States and above 0.6 in several developed and emerging markets. The China–U.S. trade war resulted in moderate contagion, particularly in countries with close trade links to the U.S. and China. The Russia–Ukraine conflict produced elevated contagion in European and energy-sensitive markets such as the UK and Spain. Conversely, China and New Zealand exhibited relatively lower levels of contagion across all periods
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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 | 0.002 | 0.014 |
| 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 | 0.003 |
| 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".