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The economic importance of rare earth elements volatility forecasts

Author

Listed:
  • Juliane Proelss

    (John Molson School of Business - Concordia University [Montreal])

  • Denis Schweizer

    (John Molson School of Business - Concordia University [Montreal])

  • Volker Seiler

    (XJTLU - Xi’an Jiaotong-Liverpool University)

Abstract

We compare the suitability of short-memory models (ARMA), long-memory models (ARFIMA), and a GARCH model to describe the volatility of rare earth elements (REEs). We find strong support for the existence of long-memory effects. A simple long-memory ARFIMA (0, d, 0) baseline model shows generally superior accuracy both in- and out-of-sample, and is robust for various subsamples and estimation windows. Volatility forecasts produced by the baseline model also convey material forward-looking information for companies in the REEs industry. Thus, an active trading strategy based on REE volatility forecasts for these companies significantly outperforms a passive buy-and-hold strategy on both an absolute and a risk-adjusted return basis.

Suggested Citation

  • Juliane Proelss & Denis Schweizer & Volker Seiler, 2019. "The economic importance of rare earth elements volatility forecasts," Post-Print hal-02983233, HAL.
    • Handle: RePEc:hal:journl:hal-02983233
    DOI: 10.1016/j.irfa.2019.01.010
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    1. repec:hal:wpaper:halshs-04126172 is not listed on IDEAS
    2. Guo, Qing & Mai, Zishan, 2024. "How do seasonal, significant events, and policies affect China's REE export prices? Based on deep learning perspective," Resources Policy, Elsevier, vol. 96(C).
    3. Zhang, Hongwei & Wei, Shiyao & Guo, Yaoqi & Gao, Wang, 2024. "Analyzing the interconnection between rare earth market and green economy: Time-varying effects of trade policy uncertainty," Resources Policy, Elsevier, vol. 97(C).
    4. Konstantinos Komnitsas, 2020. "Social License to Operate in Mining: Present Views and Future Trends," Resources, MDPI, vol. 9(6), pages 1-15, June.
    5. Madaleno, Mara & Taskin, Dilvin & Dogan, Eyup & Tzeremes, Panayiotis, 2023. "A dynamic connectedness analysis between rare earth prices and renewable energy," Resources Policy, Elsevier, vol. 85(PB).
    6. Hanif, Waqas & Mensi, Walid & Gubareva, Mariya & Teplova, Tamara, 2023. "Impacts of COVID-19 on dynamic return and volatility spillovers between rare earth metals and renewable energy stock markets," Resources Policy, Elsevier, vol. 80(C).
    7. Zhou, Mei-Jing & Huang, Jian-Bai & Chen, Jin-Yu, 2022. "Time and frequency spillovers between political risk and the stock returns of China's rare earths," Resources Policy, Elsevier, vol. 75(C).
    8. Considine, Jennifer & Galkin, Phillip & Hatipoglu, Emre & Aldayel, Abdullah, 2023. "The effects of a shock to critical minerals prices on the world oil price and inflation," Energy Economics, Elsevier, vol. 127(PB).
    9. Seiler, Volker, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 160-179.
    10. Gao, Yang & Liu, Xiaoyi, 2024. "Time and frequency spillovers and drivers between rare earth and energy, metals, green, and agricultural markets," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    11. Seiler, Volker, 2021. "China-to-FOB price transmission in the rare earth elements market and the end of Chinese export restrictions," Energy Economics, Elsevier, vol. 102(C).
    12. Depraiter, Lisa & Goutte, Stephane, 2023. "The role and challenges of rare earths in the energy transition," Resources Policy, Elsevier, vol. 86(PB).
    13. Caner Özdurak & Veysel Ulusoy, 2020. "Spillovers from the Slowdown in China on Financial and Energy Markets: An Application of VAR–VECH–TARCH Models," IJFS, MDPI, vol. 8(3), pages 1-17, August.
    14. Song, Ying & Bouri, Elie & Ghosh, Sajal & Kanjilal, Kakali, 2021. "Rare earth and financial markets: Dynamics of return and volatility connectedness around the COVID-19 outbreak," Resources Policy, Elsevier, vol. 74(C).
    15. Zheng, Biao & Zhang, Yuquan & Chen, Yufeng, 2021. "Asymmetric connectedness and dynamic spillovers between renewable energy and rare earth markets in China: Evidence from firms’ high-frequency data," Resources Policy, Elsevier, vol. 71(C).
    16. Reboredo, Juan C. & Ugolini, Andrea, 2020. "Price spillovers between rare earth stocks and financial markets," Resources Policy, Elsevier, vol. 66(C).
    17. Cheilas, Panagiotis & Christou, Tryfonas & Karkalakos, Sotiris & Kottaridi, Constantina & Michaelides, Panayotis G., 2025. "Rare earth elements and the US renewable economy: A causality exploration between critical materials and clean energy," Resources Policy, Elsevier, vol. 101(C).
    18. Hau, Liya & Zhu, Huiming & Yu, Yang & Yu, Dongwei, 2022. "Time-frequency coherence and quantile causality between trade policy uncertainty and rare earth prices: Evidence from China and the US," Resources Policy, Elsevier, vol. 75(C).
    19. Salim, Hengky & Sahin, Oz & Elsawah, Sondoss & Turan, Hasan & Stewart, Rodney A., 2022. "A critical review on tackling complex rare earth supply security problem," Resources Policy, Elsevier, vol. 77(C).

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices

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