The Spillover Effects of Market Sentiments on Global Stock Market Volatility: A Multi-Country GJR-GARCH-MIDAS Approach
Bibliographic record
Abstract
In behavioral economics, it has widely been documented that there might be a close relationship between overall market sentiment and economic performance, such as GDP per capita. In this paper, we investigate the effects of market sentiment on stock market volatility, which has widely been recognized as an important factor for economic sustainability. In particular, we aim to identify the existence of spillover effects of market sentiments on global stock market volatility. As a first attempt, we chose ten countries from major economic regions over the world (including America, Asia, Europe, and Oceania), and analyzed their interdependence and interconnectedness using a GJR-GARCH-MIDAS model. The results highlight that an individual country’s stock market volatility is significantly influenced not only by its own market sentiment (proxied by the consumer confidence index) but also by the overall market sentiments of other countries across the world. The results also highlight significant country-by-country heterogeneity in the time lags of the global spillover effects, which indicates substantial heterogeneity in the behavioral dynamics of individual countries.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".