Dynamic nexus of clean energy metals, energy commodities and traditional assets: Multidimensional techniques and portfolio analysis
Bibliographic record
Abstract
Given the rising demand for clean energy, we investigate the dynamic linkages between clean energy metals (lithium, nickel), fossil fuels (oil, gas), precious metals (gold, silver) and major equity markets. We employ the extended joint connectedness approach to study spillovers via daily data from January 2017 to September 2024. A comparative analysis of risk transmission during the pandemic-driven crisis and ongoing geopolitical tensions reveals that connectedness increases during stress episodes. We document that silver, Canadian and Indian stocks are persistent receivers of volatility, whereas nickel, gold, and gas are persistent transmitters. Severe shocks cause lithium and French stocks to shift from receiver to transmitter, whereas the inverse holds for the U.S., China and oil. We report that during periods of crisis the minimum connectedness portfolio outperforms the minimum correlation portfolio and minimum variance portfolio. The optimal hedge ratio results provide important portfolio rebalancing insights.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".