Volume 2,Issue 3
Multiscale Spillovers between Clean Energy and Related Metal Markets
The global energy transition has strengthened the linkages between clean energy and metal markets, yet the direction and structure of these linkages may vary across investment horizons and market conditions. This study examines multiscale connectedness between metal futures and global clean energy equity markets using daily data from January 2017 to October 2025. Wavelet coherence, MODWT, and the TVP-VAR-DY framework are employed to identify time–frequency dependence and directional spillovers. The results show clear horizon dependence. The total connectedness index rises from 36.65% at D1 to 58.34% at D4 and 67.19% at D6, indicating stronger medium- and longer-term linkages. Market roles also vary across scales: copper dominates short-term transmission, nickel becomes the main transmitter at the medium-term scale, while tin and copper dominate at the longer-term scale. Cobalt remains a net receiver, while the roles of wind and solar energy markets vary across horizons. The COVID-19 pandemic and the Russia–Ukraine conflict are associated with distinct changes in the identity of key transmitters and receivers. These findings show that cross-market connectedness depends jointly on investment horizon and external-shock conditions, with implications for portfolio allocation, raw-material risk management, and market monitoring.
[1] Zhao X, Mohammed KS, Wang Y, et al., 2024, Effect of Geopolitical Risk and Economic Uncertainty Indices on Renewable Energy. Geoscience Frontiers, 15(3): 101655.
[2] Gustafsson R, Dutta A, Bouri E, 2022, Are Energy Metals Hedges or Safe Havens for Clean Energy Stock Returns? Energy, 244: 122708.
[3] Igeland P, El Yadini M, Uddin GS, et al., 2026, The Relationship Between Energy Metal Price Volatility and Clean Energy Assets. International Journal of Finance & Economics, 31(2): 1735–1753.
[4] Shao L, Zhang H, 2020, The Impact of Oil Price on the Clean Energy Metal Prices: A Multi-Scale Perspective. Resources Policy, 68: 101730.
[5] Ding Q, Huang J, Chen J, 2023, Time-Frequency Spillovers and the Determinants Among Fossil Energy, Clean Energy and Metal Markets. The Energy Journal, 44(2): 259–286.
[6] Magzumov Z, Kumral M, 2024, Application of Wavelet Coherence and Connectedness Approaches to Unearth Nickel Price Dynamics. Mining, Metallurgy & Exploration, 41(6): 2901–2919.
[7] Hou J, Wang P, Borjigin S, 2026, Dependence and Risk Spillover Effects Between Clean Energy Stocks and Related Assets: An Empirical Study Based on Asymmetric W-TVP-VAR Model. Applied Economics, 58(24): 4763–4794.
[8] Xu T, Wang L, Chen T, et al., 2025, China’s Energy Risk Spillover Networks Under Major Events and External Uncertainty Shocks: An Analysis Based on LASSO-VAR-DY and TVP-SV-VAR Models. Systems, 13(11): 1037.
[9] Bi Z, Wu Q, Luo L, 2026, Climate Policy Uncertainty and the Co-Movement Between Clean Energy and Metal Markets: A Spillover Perspective. Resources Policy, 112: 105818.
[10] Aydoğdu A, 2024, Volatility Spillovers of Global Economic Policy Uncertainty and Fear Index Among Cryptocurrencies: A Wavelet-Based DCC-GARCH Approach. Journal of Applied Microeconometrics, 4(1): 13–29.
[11] Gupta R, Hammoudeh S, Modise MP, et al., 2014, Can Economic Uncertainty, Financial Stress and Consumer Sentiments Predict US Equity Premium? Journal of International Financial Markets, Institutions and Money, 33: 367–378.
[12] Antonakakis N, Chatziantoniou I, Gabauer D, 2020, Refined Measures of Dynamic Connectedness Based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4): 84.
[13] Diebold FX, Yilmaz K, 2012, Better to Give Than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28(1): 57–66.
[14] Yang L, Cai XJ, Hamori S, 2017, Does the Crude Oil Price Influence the Exchange Rates of Oil-Importing and Oil-Exporting Countries Differently? A Wavelet Coherence Analysis. International Review of Economics & Finance, 49: 536–547.
[15] Shrestha A, Kakinaka M, 2023, Nexus Between Renewable Energy Certificates and Electricity Prices in India: Evidence From Wavelet Coherence Analysis. Renewable Energy, 204: 836–847.
[16] Liu R, He L, Chen L, et al., 2025, How Climate Risks Relate to Chinese Green Finance Markets in Time-Frequency Domains? A Consideration of Extreme Market Conditions. Journal of Cleaner Production, 487: 144596.
[17] Kumah SP, Mensah JO, 2022, Are Cryptocurrencies Connected to Gold? A Wavelet-Based Quantile-in-Quantile Approach. International Journal of Finance & Economics, 27(3): 3640–3659.
[18] Ghosh B, Pham L, Gubareva M, et al., 2023, Energy Transition Metals and Global Sentiment: Evidence From Extreme Quantiles. Resources Policy, 86: 104170.
[19] Restrepo N, Ceballos JC, Uribe JM, 2023, Risk Spillovers of Critical Metals Firms. Resources Policy, 86: 104135.
[20] Zhu Y, Gong Y, Yang L, et al., 2025, The Rise of Clean Energy Markets: Evidence From Frequency-Domain Spillover Effects Between Critical Metals and Energy Markets. Energy Economics, 141: 108126.
[21] Li H, Li Y, Zhang H, 2023, The Spillover Effects Among the Traditional Energy Markets, Metal Markets and Sub-Sector Clean Energy Markets. Energy, 275: 127384.
[22] Su H, Wu Q, Zhou N, et al., 2025, Dynamic Spillover of Upstream Primary Metals and By-Product Metals Market and Its Impact From the Downstream New Energy Vehicles Market. Mineral Economics, 38(1): 1–19.
[23] Watari T, McLellan BC, Ogata S, et al., 2018, Analysis of Potential for Critical Metal Resource Constraints in the International Energy Agency’s Long-Term Low-Carbon Energy Scenarios. Minerals, 8(4): 156.
