Volatility Spillover Analysis Post Implementation of AEC 2015 Agreement: Empirical Study on ASEAN-5 Stock Market
Bibliographic record
Abstract
Efforts to improve financial integration that continue to be implemented after the implementation of the Asean Economic Community 2015 agreement, can encourage increased integration of capital markets in countries within the region. This study was conducted to investigate the spillover of volatility between stock markets that accompanied the ongoing efforts of financial integration carried out by ASEAN member countries. Investigation of volatility spillover is done by applying Exponential GARCH method on time series daily data of stock return of ASEAN-5 countries period September 2016 - December 20, 2017. If previous studies found significant spillover of volatility from Singapore, Malaysia, Thailand and Philippines, the results of this study show that only Singapore's stock exchanges consistently have a significant impact on the Indonesian stock market. The turmoil in the Singapore stock market will be consistently transmitted to the Indonesian stock market. However, efforts to improve the financial integration carried out by ASEAN member countries have not consistently caused the turmoil in Malaysia, Thailand and the Philippines stock exchange to be transmitted to the Indonesian stock market.
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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 | 0.003 |
| 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 | 0.001 |
| 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".