IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v67y2020ics0301420719308682.html
   My bibliography  Save this article

Granger causality transmission mechanism of steel product prices under multiple scales—The industrial chain perspective

Author

Listed:
  • Qi, Yajie
  • Li, Huajiao
  • Liu, Yanxin
  • Feng, Sida
  • Li, Yang
  • Guo, Sui

Abstract

The steel industry is a basic industry of the national economy, and its product structure is complicated. Due to price differences, the prices of products of different varieties, specifications and origins affect each other and transmit each other. In addition, the steel industry covers the stages of product selection, smelting and processing. The transmission patterns between product prices at different stages also vary. At the same time, the steel industry has been confirmed to be a typical cyclical industry. Thus, our work mainly explores the transmission relationship between steel product prices under multiple timescales from the perspective of the industrial chain. First, we use discrete wavelet transform to extract the time series of 553 steel products at different timescales. Second, based on the Granger causality test, the transmission relationship between every two products is measured. Third, by constructing multiscale steel product price transmission networks, important steel products with different roles are identified. Finally, based on motif recognition, the main modes of product price transmission between different reaches of the industrial chain are summarized. The results show that as the timescale increases, the transmission between products becomes increasingly frequent and that the transmission patterns become increasingly clear. In general, there is a high probability of occurrence of important steel products in hot rolled coils, structural steel and medium and heavy plates, especially 4.75 mm and 3 mm hot rolled coils produced in Beijing and 4.75 mm hot rolled coils produced in Beijing and Zhengzhou. In addition, the transmission of steel products at different stages has undergone a process of transmission between two stages to transmission among three stages and then to transmission between two stages. In this regard, under 4–8 days, transmission within a stage and transmission among three stages is the most frequent transmission pattern. Our work will help investors and policy makers gain an in-depth understanding of the steel market and mitigate risks in investment and the policy development process.

Suggested Citation

  • Qi, Yajie & Li, Huajiao & Liu, Yanxin & Feng, Sida & Li, Yang & Guo, Sui, 2020. "Granger causality transmission mechanism of steel product prices under multiple scales—The industrial chain perspective," Resources Policy, Elsevier, vol. 67(C).
  • Handle: RePEc:eee:jrpoli:v:67:y:2020:i:c:s0301420719308682
    DOI: 10.1016/j.resourpol.2020.101674
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420719308682
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.resourpol.2020.101674?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lin, Boqiang & Wu, Ya & Zhang, Li, 2011. "Estimates of the potential for energy conservation in the Chinese steel industry," Energy Policy, Elsevier, vol. 39(6), pages 3680-3689, June.
    2. Tiwari, Aviral Kumar & Mukherjee, Zinnia & Gupta, Rangan & Balcilar, Mehmet, 2019. "A wavelet analysis of the relationship between oil and natural gas prices," Resources Policy, Elsevier, vol. 60(C), pages 118-124.
    3. Hirshleifer,Jack & Glazer,Amihai & Hirshleifer,David, 2005. "Price Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521523424.
    4. Omura, Akihiro & Todorova, Neda & Li, Bin & Chung, Richard, 2016. "Steel scrap and equity market in Japan," Resources Policy, Elsevier, vol. 47(C), pages 115-124.
    5. 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.
    6. Shahzad, Syed Jawad Hussain & Rehman, Mobeen Ur & Jammazi, Rania, 2019. "Spillovers from oil to precious metals: Quantile approaches," Resources Policy, Elsevier, vol. 61(C), pages 508-521.
    7. Gardebroek, Cornelis & Hernandez, Manuel A., 2013. "Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets," Energy Economics, Elsevier, vol. 40(C), pages 119-129.
    8. Guo, Sui & Li, Huajiao & An, Haizhong & Sun, Qingru & Hao, Xiaoqing & Liu, Yanxin, 2019. "Steel product prices transmission activities in the midstream industrial chain and global markets," Resources Policy, Elsevier, vol. 60(C), pages 56-71.
    9. Wilson, Jeffrey D., 2012. "Chinese resource security policies and the restructuring of the Asia-Pacific iron ore market," Resources Policy, Elsevier, vol. 37(3), pages 331-339.
    10. Shupei Huang & Haizhong An & Xiangyun Gao & Meihui Jiang, 2016. "The Multiscale Fluctuations of the Correlation between Oil Price and Wind Energy Stock," Sustainability, MDPI, vol. 8(6), pages 1-14, June.
    11. Benjamin H. Liebman, 2006. "Safeguards, China, and the Price of Steel," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 142(2), pages 354-373, July.
    12. Skelton, Alexandra C.H. & Allwood, Julian M., 2013. "The incentives for supply chain collaboration to improve material efficiency in the use of steel: An analysis using input output techniques," Ecological Economics, Elsevier, vol. 89(C), pages 33-42.
    13. Liu, Yanxin & Li, Huajiao & Guan, Jianhe & Liu, Xueyong & Guan, Qing & Sun, Qingru, 2019. "Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System," Resources Policy, Elsevier, vol. 60(C), pages 134-142.
    14. Li, Huajiao & An, Haizhong & Liu, Xueyong & Gao, Xiangyun & Fang, Wei & An, Feng, 2016. "Price fluctuation in the energy stock market based on fluctuation and co-fluctuation matrix transmission networks," Energy, Elsevier, vol. 117(P1), pages 73-83.
    15. Swan, Anthony & Thorpe, Sally & Hogan, Lindsay, 1999. "Australia-Japan coking coal trade: A hedonic analysis under benchmark and fair treatment pricing," Resources Policy, Elsevier, vol. 25(1), pages 15-25, March.
    16. Marckhoff, Jan & Wimschulte, Jens, 2009. "Locational price spreads and the pricing of contracts for difference: Evidence from the Nordic market," Energy Economics, Elsevier, vol. 31(2), pages 257-268, March.
    17. Huang, Xuan & An, Haizhong & Gao, Xiangyun & Hao, Xiaoqing & Liu, Pengpeng, 2015. "Multiresolution transmission of the correlation modes between bivariate time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 493-506.
    18. Sui, Guo & Li, Huajiao & Feng, Sida & Liu, Xueyong & Jiang, Meihui, 2018. "Correlations of stock price fluctuations under multi-scale and multi-threshold scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1501-1512.
    19. 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.
    20. Edward Glaeser & Wei Huang & Yueran Ma & Andrei Shleifer, 2017. "A Real Estate Boom with Chinese Characteristics," Journal of Economic Perspectives, American Economic Association, vol. 31(1), pages 93-116, Winter.
    21. Qian Liu & Huajiao Li & Feng An & Nairong Liu & Qing Guan & Jingjing Jia & Pengli An, 2018. "A Motif-Based Analysis to Reveal Local Implied Information in Cross-Shareholding Networks," Complexity, Hindawi, vol. 2018, pages 1-12, December.
    22. He, Feng & Zhang, Qingzhi & Lei, Jiasu & Fu, Weihui & Xu, Xiaoning, 2013. "Energy efficiency and productivity change of China’s iron and steel industry: Accounting for undesirable outputs," Energy Policy, Elsevier, vol. 54(C), pages 204-213.
    23. Huang, Shupei & An, Haizhong & Huang, Xuan & Wang, Yue, 2018. "Do all sectors respond to oil price shocks simultaneously?," Applied Energy, Elsevier, vol. 227(C), pages 393-402.
    24. Cashin, Paul & McDermott, C. John & Scott, Alasdair, 2002. "Booms and slumps in world commodity prices," Journal of Development Economics, Elsevier, vol. 69(1), pages 277-296, October.
    25. Chien-Chung Nieh & Hwey-Yun Yau & Ken Hung & Hong-Kou Ou & Shine Hung, 2013. "Cointegration and causal relationships among steel prices of Mainland China, Taiwan, and USA in the presence of multiple structural changes," Empirical Economics, Springer, vol. 44(2), pages 545-561, April.
    26. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
    27. Mollick, André Varella & Sakaki, Hamid, 2019. "Exchange rates, oil prices and world stock returns," Resources Policy, Elsevier, vol. 61(C), pages 585-602.
    28. Wen, Shaobo & An, Haizhong & Huang, Shupei & Liu, Xueyong, 2019. "Dynamic impact of China's stock market on the international commodity market," Resources Policy, Elsevier, vol. 61(C), pages 564-571.
    29. Husain, Shaiara & Tiwari, Aviral Kumar & Sohag, Kazi & Shahbaz, Muhammad, 2019. "Connectedness among crude oil prices, stock index and metal prices: An application of network approach in the USA," Resources Policy, Elsevier, vol. 62(C), pages 57-65.
    30. Woo, Wing Thye, 2019. "China's soft budget constraint on the demand-side undermines its supply-side structural reforms," China Economic Review, Elsevier, vol. 57(C).
    31. Fernandez, Viviana, 2007. "Wavelet- and SVM-based forecasts: An analysis of the U.S. metal and materials manufacturing industry," Resources Policy, Elsevier, vol. 32(1-2), pages 80-89.
    32. Lin, Boqiang & Wang, Xiaolei, 2014. "Exploring energy efficiency in China׳s iron and steel industry: A stochastic frontier approach," Energy Policy, Elsevier, vol. 72(C), pages 87-96.
    33. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Wen, Shaobo & Hao, Xiaoqing, 2017. "The multiscale impact of exchange rates on the oil-stock nexus: Evidence from China and Russia," Applied Energy, Elsevier, vol. 194(C), pages 667-678.
    34. Arık, Evren & Mutlu, Elif, 2014. "Chinese steel market in the post-futures period," Resources Policy, Elsevier, vol. 42(C), pages 10-17.
    35. Roberts, Mark C., 2009. "Duration and characteristics of metal price cycles," Resources Policy, Elsevier, vol. 34(3), pages 87-102, September.
    36. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    37. Bildirici, Melike E. & Turkmen, Ceren, 2015. "Nonlinear causality between oil and precious metals," Resources Policy, Elsevier, vol. 46(P2), pages 202-211.
    38. Jerrett, Daniel & Cuddington, John T., 2008. "Broadening the statistical search for metal price super cycles to steel and related metals," Resources Policy, Elsevier, vol. 33(4), pages 188-195, December.
    39. Kyoungsu Kim & Seok Lim, 2019. "Price discovery and volatility spillover in spot and futures markets: evidences from steel-related commodities in China," Applied Economics Letters, Taylor & Francis Journals, vol. 26(5), pages 351-357, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cheng Xin & Kailin Ji & Hao Chang & Yang Li & Ya-Qiong Liu, 2022. "Price Co-Movement between Electrical Equipment and Metal Commodities—A Time-Frequency Analysis," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    2. Jia, Nanfei & An, Haizhong & Gao, Xiangyun & Liu, Donghui & Chang, Hao, 2023. "The main transmission paths of price fluctuations for tungsten products along the industry chain," Resources Policy, Elsevier, vol. 80(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Qingru & Gao, Xiangyun & An, Haizhong & Guo, Sui & Liu, Xueyong & Wang, Ze, 2021. "Which time-frequency domain dominates spillover in the Chinese energy stock market?," International Review of Financial Analysis, Elsevier, vol. 73(C).
    2. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
    3. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    4. 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.
    5. Chen, Peng & He, Limin & Yang, Xuan, 2021. "On interdependence structure of China's commodity market," Resources Policy, Elsevier, vol. 74(C).
    6. Liu, Yanxin & Li, Huajiao & Guan, Jianhe & Liu, Xueyong & Guan, Qing & Sun, Qingru, 2019. "Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System," Resources Policy, Elsevier, vol. 60(C), pages 134-142.
    7. Rubaszek, Michał & Karolak, Zuzanna & Kwas, Marek, 2020. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 65(C).
    8. 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).
    9. Vasyl Golosnoy & Anja Rossen, 2018. "Modeling dynamics of metal price series via state space approach with two common factors," Empirical Economics, Springer, vol. 54(4), pages 1477-1501, June.
    10. Claudio-Quiroga, Gloria & Gil-Alana, Luis A. & Maiza-Larrarte, Andoni, 2023. "Mineral prices persistence and the development of a new energy vehicle industry in China: A fractional integration approach," Resources Policy, Elsevier, vol. 82(C).
    11. Hu, Rui & Zhang, Qun, 2015. "Study of a low-carbon production strategy in the metallurgical industry in China," Energy, Elsevier, vol. 90(P2), pages 1456-1467.
    12. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    13. Li, Huajiao & Ren, Huijun & An, Haizhong & Ma, Ning & Yan, Lili, 2021. "Multiplex cross-shareholding relations in the global oil & gas industry chain based on multilayer network modeling," Energy Economics, Elsevier, vol. 95(C).
    14. Chen, Ying & Zhu, Xuehong & Li, Hailing, 2022. "The asymmetric effects of oil price shocks and uncertainty on non-ferrous metal market: Based on quantile regression," Energy, Elsevier, vol. 246(C).
    15. 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).
    16. Jiang, Zhuhua & Yoon, Seong-Min, 2020. "Dynamic co-movement between oil and stock markets in oil-importing and oil-exporting countries: Two types of wavelet analysis," Energy Economics, Elsevier, vol. 90(C).
    17. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    18. Ehrlich, Lars G., 2018. "What drives nickel prices: A structural VAR approach," HWWI Research Papers 186, Hamburg Institute of International Economics (HWWI).
    19. Johnson A. Oliyide & Oluwasegun B. Adekoya & Muhammad A. Khan, 2021. "Economic policy uncertainty and the volatility connectedness between oil shocks and metal market: An extension," International Economics, CEPII research center, issue 167, pages 136-150.
    20. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:67:y:2020:i:c:s0301420719308682. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.