Volatility Spillovers of Sharia Index during the Covid-19 Pandemic in ASEAN
Bibliographic record
Abstract
This study aims to the rise in global economic integration is due to an expansion in volatility spillovers. Therefore, it is extraordinarily necessary to analyze the volatility spillovers for growing and developed international locations through the use of portfolio funding and danger management. This lookup investigates the Volatility Spillovers of Sharia Index on 6 ASEAN international locations all through the Covid-19 Pandemic the usage of the EGARCH model. Data have been received from 5 international locations with enormous volatility spillovers, particularly Indonesia, Malaysia, Singapore, Thailand, and Vietnam, to decide the reciprocal relationship of the inventory index in ASEAN as properly as the route of volatility movements. The result confirmed that this lookup is necessary for ASEAN traders besides for the Philippines. Furthermore, this lookup has sturdy sensible significance due to the fact the correct prediction of the volatility spillovers in worldwide fairness markets is quintessential for decreasing portfolio risk.
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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 | 0.001 |
| 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".