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Dynamic interdependence of industrial metal price returns: Evidence from wavelet multiple correlations

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  • Tweneboah, George

Abstract

This study employs the recently developed wavelet multiple correlations and cross-correlations based on the maximal overlap discrete transform estimator popularized by Fernandez-Macho (2012) to investigate the interdependence and spill-over linkages among six industrial metals for the period January 2000 to June 2018. We establish that the structure of interdependence in industrial metals markets has been non-homogeneous across timescales. From the wavelet multiple cross-correlations analysis, there is evidence of intense and positive degree of integration at all frequencies in the period up to the 2008 global financial crisis and the period after. It was further revealed that, in the period up to December 2010, copper assumes the potential to act as market leader in all wavelet scales except one, which is led by aluminium. In the post-crisis era, zinc emerges as the potential market leader in all wavelet scales except at the lowest scale, led by copper. By considering the pattern of correlation among industrial metals at different timescales and investment horizons, our results highlight important implications for policy, portfolio diversification, and risk management strategies.

Suggested Citation

  • Tweneboah, George, 2019. "Dynamic interdependence of industrial metal price returns: Evidence from wavelet multiple correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
  • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119306971
    DOI: 10.1016/j.physa.2019.121153
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    Citations

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    Cited by:

    1. Liang Wang & Xianyan Xiong & Ziqiu Cao, 2023. "Time-frequency volatility spillovers between Chinese renminbi onshore and offshore markets during the COVID-19 crisis," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    2. Aikins Abakah, Emmanuel Joel & Gil-Alana, Luis A. & Tripathy, Trilochan, 2022. "Stochastic structure of metal prices: Evidence from fractional integration non-linearities and breaks," Resources Policy, Elsevier, vol. 78(C).
    3. Gaete, Michael & Herrera, Rodrigo, 2023. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," Journal of Commodity Markets, Elsevier, vol. 32(C).
    4. Gong, Xu & Xu, Jun & Liu, Tangyong & Zhou, Zicheng, 2022. "Dynamic volatility connectedness between industrial metal markets," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    5. Su, Chi-Wei & Wang, Xiao-Qing & Zhu, Haotian & Tao, Ran & Moldovan, Nicoleta-Claudia & Lobonţ, Oana-Ramona, 2020. "Testing for multiple bubbles in the copper price: Periodically collapsing behavior," Resources Policy, Elsevier, vol. 65(C).
    6. Siaw Frimpong, 2022. "On the Macroeconomic Conditions of West African Economies to External Uncertainty Shocks," Risks, MDPI, vol. 10(7), pages 1-27, July.
    7. Mensi, Walid & Nekhili, Ramzi & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Quantile dependencies between precious and industrial metals futures and portfolio management," Resources Policy, Elsevier, vol. 73(C).
    8. Nekhili, Ramzi & Sultan, Jahangir & Mensi, Walid, 2021. "Co-movements among precious metals and implications for portfolio management: A multivariate wavelet-based dynamic analysis," Resources Policy, Elsevier, vol. 74(C).
    9. Al-Yahyaee, Khamis Hamed & Rehman, Mobeen Ur & Wanas Al-Jarrah, Idries Mohammad & Mensi, Walid & Vo, Xuan Vinh, 2020. "Co-movements and spillovers between prices of precious metals and non-ferrous metals: A multiscale analysis," Resources Policy, Elsevier, vol. 67(C).
    10. Yu, Hui & Ding, Yinghui & Sun, Qingru & Gao, Xiangyun & Jia, Xiaoliang & Wang, Xinya & Guo, Sui, 2021. "Multi-scale comovement of the dynamic correlations between copper futures and spot prices," Resources Policy, Elsevier, vol. 70(C).
    11. Ghazani, Majid Mirzaee & Khosravi, Reza & Caporin, Massimiliano, 2023. "Analyzing interconnection among selected commodities in the 2008 global financial crisis and the COVID-19 pandemic," Resources Policy, Elsevier, vol. 80(C).

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