Bibliographic record
Abstract
Abstract This study examines the volatility risk for firms that are rated high on environmental, social, and governance (ESG) dimensions in emerging markets and developed markets outside the United States and Canada. Employing the Morgan Stanley Capital International (MSCI) ESG Leader indices, this study investigates the impact of good news and bad news on the volatility risk for the highest ESG‐rated firms through multivariate DCC‐EGARCH modeling. This study finds that the impact of a negative news shock of size 2 standard deviations is approximately 45% higher than that of a positive news shock of the same size for the case of the developed markets under interest. With respect to emerging markets, this study reports that the impact of a negative news shock of size 2 standard deviations is 41% higher than that of a positive news shock of the same size. The results provide empirical evidence in support of the hypothesis that the volatility impact of news for high ESG‐rated firms in developed markets and emerging markets is larger for bad news compared to good news, and the results are robust across time. The empirical findings underline the importance of reporting‐related disclosures of ESG initiatives, and provide seminal evidence of a slow response to news by high ESG‐rated firms in emerging markets.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".