Navigating APEC Countries: TVP-VAR Insights into Developed and Emerging Stock Markets
Bibliographic record
Abstract
The interdependence of stock markets provides important discernment into the behavior of the larger international financial markets. This study investigates magnitude and directional volatility spillover patterns among developed and emerging countries within the APEC bloc, utilizing the TVP-VAR model. The findings indicate that Russia (15.06%), Vietnam11.64%), and Thailand (11.57%) are identified as major transmitters, and Malaysia (-28.95%), Philippines (-9.28%), China (-9.53%) are major receptor of the volatility spillovers in APEC emerging countries. In APEC-developed countries, the United States (56.85%) and Canada (42.6%) are major transmitters, and Japan (-34.02%) and Australia (-53.54%) are identified as a major receptor of the spillover. Moreover, COVID-19 was the most significant crisis, with the highest volatility spillover identified in the APEC bloc's developed and emerging economies. The discoveries have substantial ramifications, offering valuable insights into optimal investment strategies by identifying patterns, magnitudes, and directions of economic volatility shocks.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".