Novel evidence from APEC countries on stock market integration and volatility spillover: A Diebold and Yilmaz approach
Bibliographic record
Abstract
The interconnection of stock markets offers valuable insights into the broader dynamics of global financial markets. This study uses the Diebold and Yilmaz index model to analyze and measure volatility spillovers and interconnectedness among APEC stock markets. The objective is to identify major transmitters of volatility spillovers and assess the magnitude of different crisis cycles. The results show that the US is the major contributor (69.54%) to volatility spillovers in APEC stock markets, followed by Canada (52.92%) and Mexico (37.09%). These three economies are part of the highly integrated regional bloc, say, North American Free Trade Agreement (NAFTA). New Zealand has the highest net inflow of spillovers, while spillovers account for 32.86% of the error variance across APEC equity markets. Moreover, notable spikes in volatility spillovers have been observed as a result of various events, including the Chinese stock bubble, the Global Financial Crisis (2007–2008), European debt crises, the Chinese stock market crash, the cryptocurrency crash, the COVID-19 pandemic, and the Russia-Ukraine conflict. The study’s findings imply that policymakers should enhance economic integration and cooperation within APEC countries to manage volatility spillovers effectively. The research highlights market interactions for a large sample, aiding in identifying investment opportunities and risk management strategies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".