Comparative analysis of precious metals as hedges for clean energy stocks
Bibliographic record
Abstract
• We analyze volatility spillovers between clean energy stocks and precious metals. • We apply DCC-GARCH and Diebold-Yilmaz methods to 2014–2024 market data. • Gold offers strong, time-varying hedge benefits across clean energy sectors. • Correlations with interest rates show gold's robust North American hedge role. • Global events shape hedge effectiveness and market connectedness over time. This study investigates the dynamic interactions between clean-energy subsectors and precious metals. Using a comprehensive dataset from 2014 to 2024, we apply the Diebold and Yilmaz (2012) volatility spillover framework alongside the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. The analysis reveals pronounced volatility transmissions within and between clean-energy sectors and precious metals. Precious metals, especially gold, serve as cost-effective hedges against clean-energy equities, with hedging effectiveness varying over time and across sectors. Correlation results show that gold maintains stable positive associations with US and Canadian interest rates, reinforcing its hedging potential during periods of economic uncertainty in North American markets. Conversely, weak or negative correlations with Japanese and Chinese rates suggest regional differences in hedging effectiveness. The study also highlights how global events—such as the COVID-19 pandemic and the Russia–Ukraine conflict, significantly influenced market connectedness and hedge performance, especially during periods of heightened uncertainty. These findings provide insight into the role of precious metals in clean-energy portfolio risk management, offering practical implications for investors seeking diversification strategies under volatile market conditions. By accounting for sector-specific behavior and time-varying correlations, the study enhances our understanding of hedging strategies in the clean-energy–commodity nexus.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".