High-dimensional CoVaR risk spillover network from oil market to global stock markets—Lessons from the Kyoto Protocol
Bibliographic record
Abstract
This paper explores the impact of the Kyoto Protocol by investigating the correlation and risk spillover between the crude oil market and the stock markets of 28 countries during its two commitment periods. Besides time-varying Copula-CoVaR models, the Adaptive Lasso-VAR model with oracle properties is employed in generalized variance decomposition, and a risk connectedness network is constructed to explore risk spillovers between the stock markets of various countries when the crude oil market is at risk. The results reveal positive correlations between the crude oil market and stock markets, which become weaker in the second commitment period than in the first. The crude oil market has both upside and downside spillover effects to most stock markets during both commitment periods, and the upside risk spillover effect is stronger than the downside effect. Overall, most non-signatories of the Kyoto Protocol are net receivers of risk spillovers when the crude oil market is at risk, while most signatories are net exporters of risk spillovers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".