Navigating global financial turbulence: The evergrande collapse and its contagion effect
Bibliographic record
Abstract
This study investigates the contagion effects of the Evergrande collapse across international financial markets, with emphasis on tail-risk dynamics. Unlike prior work focusing on average spillovers or event windows, we employ a Quantile Vector Autoregression (QVAR) framework to capture state-dependent connectedness under bearish, median, and bullish market conditions, as well as calm versus turbulent volatility regimes. Using daily data for nine major stock indices (2015-2024), we find that the Evergrande crisis significantly amplified global spillovers, but with heterogeneous magnitudes across quantiles. At the 95% volatility quantile, returns spillovers in the median quantile from Shanghai to the EU increased, during the Evergrande crisis, by approximately 3.5% in the Net Pairwise Connectedness (NPC) case. In contrast, with very few exceptions, Canadian spillovers remained negligible, confirming its resilience and diversification potential. These results show that extreme market states reveal contagion patterns invisible in average-state analyses, underscoring the systemic role of Hong Kong as a transmission hub and the conditional global influence of Shanghai. The findings provide actionable insights for policymakers on monitoring tail-risk channels and for investors seeking hedging strategies in insulated 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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".