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Forecasting the Volatility of Energy Transition Metals

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Listed:
  • Andrea Bastianin
  • Xiao Li
  • Luqman Shamsudin

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," Papers 2501.16069, arXiv.org, revised Jan 2025.
  • Handle: RePEc:arx:papers:2501.16069
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    References listed on IDEAS

    as
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    More about this item

    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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