Climate policies, energy shocks and spillovers between green and brown stock price indices
Bibliographic record
Abstract
This paper examines the effects of climate policies and energy shocks on mean and volatility spillovers between green and brown stock price indices in five countries (Canada, India, Japan, the UK and the US). More specifically, bivariate GARCH-BEKK models including dummy variables controlling for these shocks are estimated using weekly series with start dates ranging from 13 March 2009 to 24 August 2012 (depending on data availability for the green index) and an end date of 29 December 2023. Significant dynamic linkages between green and brown indices are found when climate policy and oil shocks are considered jointly. Some common patterns emerge, such as shifts in spillover dynamics between green and brown assets, but also country-specific effects of the climate policy shocks which reflect differences in regulatory frameworks and policies. By contrast, energy shocks tend to have a more uniform impact. Further, the interaction between climate policy and energy shocks weakens cross-market linkages, enhancing portfolio diversification opportunities for green investors. The conditional correlation analysis confirms this finding, suggesting that green stocks can be used as an effective hedge. These results highlight the benefits of incorporating green assets into diversified portfolios, particularly in financial centers where, in recent years, they have offered higher returns and lower volatility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".