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A comparative exploration of the chaotic characteristics of Chinese and international copper futures prices

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  • Zheng, Shuxian
  • Tan, Zhanglu
  • Xing, Wanli
  • Zhou, Xuanru
  • Zhao, Pei
  • Yin, Xiuqi
  • Hu, Han

Abstract

This study used a C–C method to reconstruct the phase space of copper prices from January 2002 to December 2021, using Chinese and international copper futures price time series as a sample. It demonstrates the differences in the chaotic characteristics of two copper futures prices through recurrence plots and correlation dimensions. Recurrence complex networks have been employed to quantify the spatial structural similarity between the variables and copper prices, and the main factors influencing the structural changes in copper prices have been analysed by a systematic bifurcation mechanism. The results show that the efficiency of the copper futures market has improved gradually. The level of perfection and efficiency of the Chinese copper futures market is lower than that of the international copper futures market, and the predictability of Chinese copper futures prices is even weaker, which means that the investment in Chinese copper futures is riskier than that in the international copper futures market. Compared to other metal futures, such as aluminium and nickel, copper futures are likely to be a lower risk investment. The US dollar index, the federal funds rate and the price of gold are the main factors influencing changes in the spatial structure of copper prices in China, while political risk, crude oil and gold prices are key to shaping the chaotic character of international copper prices. A tipping point in the chaotic state of copper prices could correspond to the onset of a global financial crisis. This study can help policymakers to understand the market laws underlying prices and thus create robust and well-traded markets by controlling the chaotic characteristic structure of copper prices. In addition, the identification of chaotic state thresholds in Chinese and international copper prices can predict the occurrence of unexpected events such as financial crises, which is important to counteract financial market risks.

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  • Zheng, Shuxian & Tan, Zhanglu & Xing, Wanli & Zhou, Xuanru & Zhao, Pei & Yin, Xiuqi & Hu, Han, 2022. "A comparative exploration of the chaotic characteristics of Chinese and international copper futures prices," Resources Policy, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:jrpoli:v:78:y:2022:i:c:s0301420722002380
    DOI: 10.1016/j.resourpol.2022.102790
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    as
    1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
    2. C. H. Hommes, 2001. "Financial markets as nonlinear adaptive evolutionary systems," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 149-167.
    3. Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
    4. Gordon, Richard L. & Tilton, John E., 2008. "Mineral economics: Overview of a discipline," Resources Policy, Elsevier, vol. 33(1), pages 4-11, March.
    5. Rossen, Anja, 2015. "What are metal prices like? Co-movement, price cycles and long-run trends," Resources Policy, Elsevier, vol. 45(C), pages 255-276.
    6. Phillip Crowson, 2017. "A Handbook of Primary Commodities in the Global Economy," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 30(1), pages 67-71, April.
    7. Chen, Jinyu & Zhu, Xuehong & Zhong, Meirui, 2019. "Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis," Resources Policy, Elsevier, vol. 61(C), pages 489-500.
    8. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    9. Sun, Xiaotian & Fang, Wei & Gao, Xiangyun & An, Sufang & Liu, Siyao & Wu, Tao, 2021. "Time-varying causality inference of different nickel markets based on the convergent cross mapping method," Resources Policy, Elsevier, vol. 74(C).
    10. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
    11. Zhang, Hong & Nguyen, Hoang & Vu, Diep-Anh & Bui, Xuan-Nam & Pradhan, Biswajeet, 2021. "Forecasting monthly copper price: A comparative study of various machine learning-based methods," Resources Policy, Elsevier, vol. 73(C).
    12. Panas, E., 2001. "Long memory and chaotic models of prices on the London Metal Exchange," Resources Policy, Elsevier, vol. 27(4), pages 235-246, December.
    13. C. A. Tapia Cortez & J. Coulton & C. Sammut & S. Saydam, 2018. "Determining the chaotic behaviour of copper prices in the long-term using annual price data," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-13, December.
    14. Yang, Yuying & Ma, Yan-Ran & Hu, Min & Zhang, Dayong & Ji, Qiang, 2021. "Extreme risk spillover between chinese and global crude oil futures," Finance Research Letters, Elsevier, vol. 40(C).
    15. Ahrens, W. Ashley & Sharma, Vijaya R., 1997. "Trends in Natural Resource Commodity Prices: Deterministic or Stochastic?," Journal of Environmental Economics and Management, Elsevier, vol. 33(1), pages 59-74, May.
    16. Chan, Kam C. & Fung, Hung-Gay & Thapa, Samanta, 2007. "China financial research: A review and synthesis," International Review of Economics & Finance, Elsevier, vol. 16(3), pages 416-428.
    17. Wang, Yun & Wang, Jianzhou & Wei, Xiang, 2015. "A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China," Energy, Elsevier, vol. 91(C), pages 556-572.
    18. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    19. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    20. Catherine Kyrtsou & Walter C. Labys & Michel Terraza, 2004. "Noisy chaotic dynamics in commodity markets," Empirical Economics, Springer, vol. 29(3), pages 489-502, September.
    21. Ikhlaas Gurrib, 2017. "Cross-Market Price Mechanism Between the US Copper Futures Market and a Newly Proposed Chinese Dollar Index," Springer Proceedings in Business and Economics, in: Rachid Benlamri & Michael Sparer (ed.), Leadership, Innovation and Entrepreneurship as Driving Forces of the Global Economy, chapter 0, pages 741-747, Springer.
    22. Sverdrup, Harald U. & Ragnarsdottir, Kristin Vala & Koca, Deniz, 2014. "On modelling the global copper mining rates, market supply, copper price and the end of copper reserves," Resources, Conservation & Recycling, Elsevier, vol. 87(C), pages 158-174.
    23. Lee, Junsoo & List, John A. & Strazicich, Mark C., 2006. "Non-renewable resource prices: Deterministic or stochastic trends?," Journal of Environmental Economics and Management, Elsevier, vol. 51(3), pages 354-370, May.
    24. Fan, Xinghua & Wang, Li & Li, Shasha, 2016. "Predicting chaotic coal prices using a multi-layer perceptron network model," Resources Policy, Elsevier, vol. 50(C), pages 86-92.
    25. Zhang, Chuanguo & Tu, Xiaohua, 2016. "The effect of global oil price shocks on China's metal markets," Energy Policy, Elsevier, vol. 90(C), pages 131-139.
    26. Han, Li & Romero, Carlos E. & Yao, Zheng, 2015. "Wind power forecasting based on principle component phase space reconstruction," Renewable Energy, Elsevier, vol. 81(C), pages 737-744.
    27. Baldursson, Fridrik M., 1999. "Modelling the price of industrial commodities," Economic Modelling, Elsevier, vol. 16(3), pages 331-353, August.
    28. Jiang, J. & Ma, K. & Cai, X., 2007. "Non-linear characteristics and long-range correlations in Asian stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 399-407.
    29. Roberts, Mark C., 2009. "Duration and characteristics of metal price cycles," Resources Policy, Elsevier, vol. 34(3), pages 87-102, September.
    30. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
    31. Kang, Sang Hoon & Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu & Yoon, Seong-Min, 2019. "Time-frequency co-movements between the largest nonferrous metal futures markets," Resources Policy, Elsevier, vol. 61(C), pages 393-398.
    32. Epaminondas Panas & Vassilia Ninni, 2010. "The Distribution of London Metal Exchange Prices: A Test of the Fractal Market Hypothesis," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 192-210.
    33. Liu, Donghui & Gao, Xiangyun & An, Haizhong & Qi, Yabin & Wang, Ze & Jia, Nanfei & Chen, Zhihua, 2020. "Exploring behavior changes of the lithium market in China: Toward technology-oriented future scenarios," Resources Policy, Elsevier, vol. 69(C).
    34. Renhai Hua & Baizhu Chen, 2007. "International linkages of the Chinese futures markets," Applied Financial Economics, Taylor & Francis Journals, vol. 17(16), pages 1275-1287.
    35. Mastroeni, Loretta & Vellucci, Pierluigi & Naldi, Maurizio, 2018. "Co-existence of stochastic and chaotic behaviour in the copper price time series," Resources Policy, Elsevier, vol. 58(C), pages 295-302.
    36. Shafiee, Shahriar & Topal, Erkan, 2010. "An overview of global gold market and gold price forecasting," Resources Policy, Elsevier, vol. 35(3), pages 178-189, September.
    37. Panas, Epaminondas & Ninni, Vassilia, 2000. "Are oil markets chaotic? A non-linear dynamic analysis," Energy Economics, Elsevier, vol. 22(5), pages 549-568, October.
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