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Forecasting the Volatility of Energy Transition Metals

Author

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  • Andrea Bastianin

    (Department of Economics, Management, and Quantitative Methods, University of Milan and Fondazione Eni Enrico Mattei)

  • Xiao Li

    (Department of Economics, Management, and Quantitative Methods, University of Milan; Fondazione Eni Enrico Mattei and University of Pavia)

  • Luqman Shamsudin

    (Fondazione Eni Enrico Mattei and Department of Environmental Science and Policy, University of Milan)

Abstract

The transition to a cleaner energy mix, essential for achieving net-zero greenhouse gas emissions by 2050, will significantly increase demand for metals critical to renewable energy technologies. Energy Transition Metals (ETMs), including copper, lithium, nickel, cobalt, and rare earth elements, are indispensable for renewable energy generation and the electrification of global economies. However, their markets are characterized by high price volatility due to supply concentration, low substitutability, and limited price elasticity. This paper provides a comprehensive analysis of the price volatility of ETMs, a subset of Critical Raw Materials (CRMs). Using a combination of exploratory data analysis, data reduction, and visualization methods, we identify key features for accurate point and density forecasts. We evaluate various volatility models, including Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Stochastic Volatility (SV) models, to determine their forecasting performance. Our findings reveal significant heterogeneity in ETM volatility patterns, which challenge standard groupings by data providers and geological classifications. The results contribute to the literature on CRM economics and commodity volatility, offering novel insights into the complex dynamics of ETM markets and the modeling of their returns and volatilities.

Suggested Citation

  • Andrea Bastianin & Xiao Li & Luqman Shamsudin, 2025. "Forecasting the Volatility of Energy Transition Metals," Working Papers 2025.04, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2025.04
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    1. Saadaoui, Jamel & Smyth, Russell & Vespignani, Joaquin, 2025. "Ensuring the security of the clean energy transition: Examining the impact of geopolitical risk on the price of critical minerals," Energy Economics, Elsevier, vol. 142(C).
    2. Pommeret, Aude & Ricci, Francesco & Schubert, Katheline, 2022. "Critical raw materials for the energy transition," European Economic Review, Elsevier, vol. 141(C).
    3. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    4. Han, Xuyuan & Liu, Zhenya & Wang, Shixuan, 2022. "An R-vine copula analysis of non-ferrous metal futures with application in Value-at-Risk forecasting," Journal of Commodity Markets, Elsevier, vol. 25(C).
    5. Bastianin, Andrea & Casoli, Chiara & Galeotti, Marzio, 2023. "The connectedness of Energy Transition Metals," Energy Economics, Elsevier, vol. 128(C).
    6. Montero-Manso, Pablo & Athanasopoulos, George & Hyndman, Rob J. & Talagala, Thiyanga S., 2020. "FFORMA: Feature-based forecast model averaging," International Journal of Forecasting, Elsevier, vol. 36(1), pages 86-92.
    7. Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2020. "Spillovers, integration and causality in LME non-ferrous metal markets," Journal of Commodity Markets, Elsevier, vol. 17(C).
    8. repec:bla:jfinan:v:44:y:1989:i:5:p:1115-53 is not listed on IDEAS
    9. Thibault Fally & James Sayre, 2018. "Commodity Trade Matters," NBER Working Papers 24965, National Bureau of Economic Research, Inc.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Dinh, Theu & Goutte, Stéphane & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Economic drivers of volatility and correlation in precious metal markets," Journal of Commodity Markets, Elsevier, vol. 28(C).
    12. Tilmann Gneiting & Roopesh Ranjan, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 411-422, July.
    13. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    14. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    15. Christian Francq & Genaro Sucarrat, 2022. "Volatility Estimation When the Zero-Process is Nonstationary," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 53-66, December.
    16. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    17. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    18. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Modeling energy price dynamics: GARCH versus stochastic volatility," Energy Economics, Elsevier, vol. 54(C), pages 182-189.
    19. Lukas Boer & Andrea Pescatori & Martin Stuermer, 2024. "Energy Transition Metals: Bottleneck for Net-Zero Emissions?," Journal of the European Economic Association, European Economic Association, vol. 22(1), pages 200-229.
    20. Naeem, Muhammad Abubakr & Hamouda, Foued & Karim, Sitara, 2024. "Tail risk spillover effects in commodity markets: A comparative study of crisis periods," Journal of Commodity Markets, Elsevier, vol. 33(C).
    21. Bakas, Dimitrios & Triantafyllou, Athanasios, 2019. "Volatility forecasting in commodity markets using macro uncertainty," Energy Economics, Elsevier, vol. 81(C), pages 79-94.
    22. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    23. Gneiting, Tilmann & Ranjan, Roopesh, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 411-422.
    24. Diemer, Andreas & Iammarino, Simona & Perkins, Richard & Gros, Axel, 2022. "Technology, resources and geography in a paradigm shift: the case of critical and conflict materials in ICTs," LSE Research Online Documents on Economics 115103, London School of Economics and Political Science, LSE Library.
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    More about this item

    Keywords

    Critical Raw Materials; Energy Transition; Features; Volatility; Forecasting; Density forecasts;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q30 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - General
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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