Analysis of linkage fluctuation in time series data of nickel futures price index


This paper explores the variation pattern of nickel futures prices using the daily closing levels of the nickel futures price index of the London Futures Exchange and the Shanghai Futures Exchange. The data coarse-graining method is employed to transform the continuous time series data of price index changes into symbols {P, N, M}, which are slid through continuous windows to form the modalities of price index linkage fluctuations. By treating the modalities as nodes and the transformations between them as edges, a weighted directed complex network is constructed to represent the linked volatility of the LME and SHFE nickel futures indices time series. The complex network is applied to analyse the network characteristics and obtain the inner pattern of the linked fluctuations. The results show that the complex network of time series linked volatility of the LME and SHFE nickel futures indices exhibits a power-law nature, with closely linked subgroups formed within it. And the mode transitions within these subgroups follow certain patterns. This paper also identifies core positioned modes and important intermediate modes that reflect the dynamics of nickel prices in reality. The method presented in this paper may be extended to related fields and has good applicability.

Keyword : nickel futures price index, coarse-graining method, time series data, linkage fluctuation, complex network, positioned modes, intermediate modes

How to Cite
Chen, X., Huo, G., & Cao, G. (2023). Analysis of linkage fluctuation in time series data of nickel futures price index. Journal of Business Economics and Management, 24(4), 712–731.
Published in Issue
Nov 8, 2023
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Abraham, B., & Ledolter, J. (1983). Statistical methods for forecasting. Wiley.

An, H., Du, Q., & Dong, Z. (2015). Research on the fluctuation range of single variable time series based on complex networks. Journal of Systems Science and Mathematical Sciences, 35(02), 158–169.

An, S., Gao, X., An, H., An, F., Sun, Q., & Liu, S. (2020). Windowed volatility spillover effects among crude oil prices. Energy, 200, Article 117521.

Armstrong, J. S. (1985). Long range forecasting: From crystal ball to computer (2nd ed.). Wiley.

Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.

Chen, X.-d. (2013). Research on volatility characteristics of fuel oil futures price index in China. Commercial Research, 55(11), 157–163.

Dong, X., An, H., & Dong, Z. (2018). Evolution analysis of price linkage effect in the international futures market of non-ferrous metals: Case of copper, aluminum and zinc. Complex Systems and Complexity Science, 15(4), 50–59.

Dong, X., An, F., Dong, Z., Wang, Z., Jiang, M., Yang, P., & An, H. (2021). Optimization of the international nickel ore trade network. Resources Policy, 70, Article 101978.

Dubal, D. P., Gomez-Romero, P., Sankapal, B. R., & Holze, R. (2015). Nickel cobaltite as an emerging material for supercapacitors: An overview. Nano Energy, 11, 377–399.

Fildes, R., & Lusk, E. J. (1984). The choice of a forecasting model. Omega, 12(5), 427–435.

Fu, T. C. (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1), 164–181.

Guo, S., Li, H., An, H., Sun, Q., Hao, X., & Liu, Y. (2019). Steel product prices transmission activities in the midstream industrial chain and global markets. Resources Policy, 60, 56–71.

Guohua, Y., Elshkaki, A., & Xiao, X. (2021). Dynamic analysis of future nickel demand, supply, and associated materials, energy, water, and carbon emissions in China. Resources Policy, 74, Article 102432.

Hill, G., & Fildes, R. (1984). The accuracy of extrapolation methods; an automatic Box–Jenkins package Sift. Journal of Forecasting, 3(3), 319–323.

Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS ONE, 9(6), Article e98679.

Kaufmann, R. K., & Ullman, B. (2009). Oil prices, speculation, and fundamentals: Interpreting causal relations among spot and futures prices. Energy Economics, 31(4), 550–558.

Lee, Y. H., Hu, H. N., & Chiou, J. S. (2010). Jump dynamics with structural breaks for crude oil prices. Energy Economics, 32(2), 343–350.

Li, H., Ren, H., An, H., Ma, N., & Yan, L. (2021). Multiplex cross-shareholding relations in the global oil & gas industry chain based on multilayer network modeling. Energy Economics, 95, Article 105130.

Long, Y. S., Jia, Z., & Wang, Y. Y. (2018). Coarse graining method based on generalized degree in complex network. Physica A: Statistical Mechanics and its Applications, 505, 655–665.

Mahalakshmi, G., Sridevi, S., & Rajaram, S. (2016, January). A survey on forecasting of time series data. In 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16) (pp. 1–8). IEEE.

Maslyuk, S., & Smyth, R. (2009). Cointegration between oil spot and future prices of the same and different grades in the presence of structural change. Energy Policy, 37(5), 1687–1693.

Meade, N., & Smith, I. M. (1985). ARARMA vs ARIMA – a study of the benefits of a new approach to forecasting. Omega, 13(6), 519–534.

Murdock, B. E., Toghill, K. E., & Tapia-Ruiz, N. (2021). A perspective on the sustainability of cathode materials used in lithium-ion batteries. Advanced Energy Materials, 11(39), Article 2102028.

Pal, D., & Mitra, S. K. (2015). Asymmetric impact of crude price on oil product pricing in the United States: An application of multiple threshold nonlinear autoregressive distributed lag model. Economic Modelling, 51, 436–443.

Pandit, S. M., & Wu, S. M. (1983). Time series and system analysis with applications. Wiley.

Ronald, S. B. (1992). Structural holes: The social structure of competition. Harvard University Press.

Su, C., Geng, Y., Zeng, X., Gao, Z., & Song, X. (2023). Uncovering the features of nickel flows in China. Resources, Conservation and Recycling, 188, Article 106702.

Sun, T., Wang, D., Qian, Z., Fu, Y., Chen, Z., & Lou, D. (2015). A preliminary review of the metallogenic regularity of nickel deposits in China. Acta Geologica Sinica – English Edition, 89(4), 1375–1397.

Thury, G., & Anderson, I. O. D. (1980). Time series analysis: Theory and practice 1: Modeling private consumer expenditure in Austria by intervention analysis. Armsterdam, North-Holland.

Wang, X., Wang, A., Zhong, W., Zhu, D., & Wang, C. (2022). Analysis of international nickel flow based on the industrial chain. Resources Policy, 77, Article 102729.

Wang, X. Q., Wu, T., Zhong, H., & Su, C. W. (2023). Bubble behaviors in nickel price: What roles do geopolitical risk and speculation play?. Resources Policy, 83, Article 103707.

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.

Wen, S., An, H., Chen, Z., & Liu, X. (2017). Driving factors of interactions between the exchange rate market and the commodity market: A wavelet-based complex network perspective. Physica A: Statistical Mechanics and its Applications, 479, 299–308.

Zeng, L., Jia, Z., & Wang, Y. (2019). Extraction algorithm for optimal coarse-grained networks on complex networks. International Journal of Modern Physics C, 30(11), Article 1950081.

Zhao, Y., Gao, X., An, H., Xi, X., Sun, Q., & Jiang, M. (2020). The effect of the mined cobalt trade dependence Network’s structure on trade price. Resources Policy, 65, Article 101589.

Zheng, S., Zhou, X., Zhao, P., Xing, W., Han, Y., Hao, H., & Luo, W. (2022). Impact of countries’ role on trade prices from a nickel chain perspective: Based on complex network and panel regression analysis. Resources Policy, 78, Article 102930.

Zhou, L., Gong, Z. Q., Zhi, R., & Feng, G. L. (2011). Influence of time delay on global temperature correlation. Acta Physica Sinica, 20(8), 380–387.