Comovements in Asia-Pacific Equity Markets: Developing Patterns in APEC
Bibliographic record
Abstract
This paper examines the short and long-term price linkages among Asia-Pacific Economic Cooperation (APEC) equity markets over the period 1995 to 2000. Seven developed markets (Australia, Canada, Hong Kong, Japan, New Zealand, Singapore and the United States) and eleven emerging markets (China, Chile, Indonesia, Korea, Malaysia, Mexico, Peru, the Philippines, Russia, Taiwan and Thailand) are included in the analysis. Multivariate cointegration procedures, Granger-causality tests, level VAR and generalised variance decomposition analyses based on error-correction and vector autoregressive models are conducted to examine long and short-run relationships among these markets. The results indicate that there is a stationary long-run relationship and significant short-run causal linkages among the APEC equity markets. The results also indicate that the degree of comovement and codependencies among APEC's domestic and sub-regional markets varies considerably. In general, Australasian, Northern Asian and South American markets are relatively more influenced by domestic market conditions, North American markets relatively more by regional factors and Southern Asian markets more strongly influenced by markets outside either their own or geographical close domestic markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".