Policy Uncertainty and the Volatility of the S&P 500: Before and After the Launch of the S&P 500 ESG
Bibliographic record
Abstract
This paper examines the asymmetric effect of economic policy uncertainty, geopolitical risk, and climate policy uncertainty on the volatility of the S&P 500 stock index, before and after the launch of the S&P 500 ESG Index, by using a Non-linear Autoregressive Distributed Lag (NARDL) model, for the period January 2010 to august 2022.We provide evidence on the asymmetric impact of climate policy uncertainty on the volatility of the S&P 500 both in the short-run and in the long-run, and this asymmetry is more frequent after the launch of the S&P 500 ESG Index. Moreover, in the long-run, a decrease in the economic policy uncertainty after the launch of the S&P 500 ESG has greater effect on volatility of the S&P 500, than the short-run. We also find that positive and negative shocks to geopolitical risk before and after the launch of the S&P500 ESG index do not affect the volatility of the S&P 500 stock market index in the short -run and long.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".