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Examining the metal futures price discovery in China from multi-scale time

Author

Listed:
  • Yongguang Zhu

    (China University of Geosciences
    China University of Geosciences)

  • Ya Li

    (China University of Geosciences)

  • Yuna Gong

    (China University of Geosciences)

  • Deyi Xu

    (China University of Geosciences
    China University of Geosciences)

Abstract

Metal mineral resources are important raw materials in industrial production, and metal as an important object in the futures market, its discovery function is an important sign to measure the level of market development. The price of metal futures market has the characteristics of high-frequency data, and the mechanism of price discovery in different frequencies needs to be realized by time series decomposition method. In this paper, the complementary ensemble empirical mode decomposition with adaptive noise vector autoregressive model is constructed to re-examine the price discovery of nonferrous metal futures from the aspects of multilevel, multi-subject, and different volumes. Four typical nonferrous metals are selected for empirical research in China. The results show that price discovery exists in China's nonferrous metal futures market. Meanwhile, there are significant differences in the functional efficiency of typical metal prices under different time scales. The volume of contracts will greatly affect the efficiency of price discovery. Finally, we also find that futures prices affect spot prices, but spot prices do not affect futures prices.

Suggested Citation

  • Yongguang Zhu & Ya Li & Yuna Gong & Deyi Xu, 2024. "Examining the metal futures price discovery in China from multi-scale time," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(1), pages 173-188, March.
    • Handle: RePEc:spr:minecn:v:37:y:2024:i:1:d:10.1007_s13563-024-00430-5
    DOI: 10.1007/s13563-024-00430-5
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    References listed on IDEAS

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    1. Maggie E. C. Jones & Morten Ørregaard Nielsen & Michał Ksawery Popiel, 2014. "A fractionally cointegrated VAR analysis of economic voting and political support," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 47(4), pages 1078-1130, November.
    2. Xindan Li & Bing Zhang, 2009. "Price discovery for copper futures in informationally linked markets," Applied Economics Letters, Taylor & Francis Journals, vol. 16(15), pages 1555-1558.
    3. Sepideh Dolatabadi & Morten Ørregaard Nielsen & Ke Xu, 2015. "A Fractionally Cointegrated VAR Analysis of Price Discovery in Commodity Futures Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(4), pages 339-356, April.
    4. Isabel Figuerola‐Ferretti & Christopher L. Gilbert, 2005. "Price discovery in the aluminum market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(10), pages 967-988, October.
    5. Jeffrey J. Coulton & Tami Dinh & Andrew B. Jackson & Tom Smith, 2016. "The impact of sentiment on price discovery," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 56(3), pages 669-694, September.
    6. Chen, Xiangyu & Tongurai, Jittima, 2022. "Spillovers and interdependency across base metals: Evidence from China's futures and spot markets," Resources Policy, Elsevier, vol. 75(C).
    7. Figuerola-Ferretti, Isabel & Gonzalo, Jesús, 2010. "Modelling and measuring price discovery in commodity markets," Journal of Econometrics, Elsevier, vol. 158(1), pages 95-107, September.
    8. Qunwei Wang & Xingyu Dai & Dequn Zhou, 2020. "Dynamic Correlation and Risk Contagion Between “Black” Futures in China: A Multi-scale Variational Mode Decomposition Approach," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1117-1150, April.
    9. Xiaohang Ren & Yue Dou & Kangyin Dong & Yiying Li, 2022. "Information spillover and market connectedness: multi-scale quantile-on-quantile analysis of the crude oil and carbon markets," Applied Economics, Taylor & Francis Journals, vol. 54(38), pages 4465-4485, August.
    10. Klein, Tony & Todorova, Neda, 2021. "Night trading with futures in China: The case of Aluminum and Copper," Resources Policy, Elsevier, vol. 73(C).
    11. Wei, Yu & Bai, Lan & Li, Xiafei, 2022. "Normal and extreme interactions among nonferrous metal futures: A new quantile-frequency connectedness approach," Finance Research Letters, Elsevier, vol. 47(PB).
    12. Yang, Linghubo & Zhang, Dongxiang, 2013. "Can futures price be a powerful predictor? Frequency domain analysis on Chinese commodity market," Economic Modelling, Elsevier, vol. 35(C), pages 264-271.
    13. Isabel Figuerola-Ferretti & Christopher L. Gilbert, 2005. "Price discovery in the aluminium market," Department of Economics Working Papers 0506, Department of Economics, University of Trento, Italia.
    14. Xu, Feng & Wan, Difang, 2015. "The impacts of institutional and individual investors on the price discovery in stock index futures market: Evidence from China," Finance Research Letters, Elsevier, vol. 15(C), pages 221-231.
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    More about this item

    Keywords

    Price discovery; Nonferrous metal; CEEMDAN-VAR; Future market;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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