Do ESG Ratings Reduce the Asymmetry Behavior in Volatility?
Bibliographic record
Abstract
It is well noted in the literature that volatility responds differently to positive and negative shocks. In this paper, we explore the impact of ESG ratings on such asymmetric behavior of volatility. For this analysis, we use the return data, ESG ratings, and solvency ratios of the constituent stocks of S&P Europe 350 for the period January 2016–December 2021. We apply autoregressive moving average models for the conditional means and GARCH and stochastic volatility models for the conditional variances to estimate the asymmetry coefficients. Afterwards, these coefficients are regressed via Arellano–Bond and lagged first difference methods to estimate the impact of ESG ratings. Our findings confirm that stocks of riskier firms are more likely to suffer from asymmetry behavior of volatility. We also confirm that firm leverage is linked to this asymmetry behavior. We found evidence that the impact of ESG ratings was negative before COVID-19, but positive afterwards. For some sectors, higher ESG ratings are linked to higher asymmetry. Finally, we found that during COVID-19, the asymmetry behavior became more pronounced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".