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Network features of sector indexes spillover effects in China: A multi-scale view


  • Feng, Sida
  • Huang, Shupei
  • Qi, Yabin
  • Liu, Xueyong
  • Sun, Qingru
  • Wen, Shaobo


The spillover effects among sectors are of concern for distinct market participants, who are in distinct investment horizons and concerned with the information in different time scales. In order to uncover the hidden spillover information in multi-time scales in the rapidly changing stock market and thereby offer guidance to different investors concerning distinct time scales from a system perspective, this paper constructed directional spillover effect networks for the economic sectors in distinct time scales. The results are as follows: (1) The “2–4 days” scale is the most risky scale, and the “8–16 days” scale is the least risky one. (2) The most influential and sensitive sectors are distinct in different time scales. (3) Although two sectors in the same community may not have direct spillover relations, the volatility of one sector will have a relatively strong influence on the other through indirect relations.

Suggested Citation

  • Feng, Sida & Huang, Shupei & Qi, Yabin & Liu, Xueyong & Sun, Qingru & Wen, Shaobo, 2018. "Network features of sector indexes spillover effects in China: A multi-scale view," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 461-473.
  • Handle: RePEc:eee:phsmap:v:496:y:2018:i:c:p:461-473
    DOI: 10.1016/j.physa.2017.12.091

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    References listed on IDEAS

    1. Majdoub, Jihed & Mansour, Walid, 2014. "Islamic equity market integration and volatility spillover between emerging and US stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 452-470.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Nazlioglu, Saban & Soytas, Ugur & Gupta, Rangan, 2015. "Oil prices and financial stress: A volatility spillover analysis," Energy Policy, Elsevier, vol. 82(C), pages 278-288.
    4. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Huang, Xuan, 2015. "Identifying the multiscale impacts of crude oil price shocks on the stock market in China at the sector level," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 13-24.
    5. Zhu, Huiming & Guo, Yawei & You, Wanhai & Xu, Yaqin, 2016. "The heterogeneity dependence between crude oil price changes and industry stock market returns in China: Evidence from a quantile regression approach," Energy Economics, Elsevier, vol. 55(C), pages 30-41.
    6. Karali, Berna & Ramirez, Octavio A., 2014. "Macro determinants of volatility and volatility spillover in energy markets," Energy Economics, Elsevier, vol. 46(C), pages 413-421.
    7. 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.
    8. Pin-te Lin, 2013. "Examining volatility spillover in Asian REIT markets," Applied Financial Economics, Taylor & Francis Journals, vol. 23(22), pages 1701-1705, November.
    9. Reboredo, Juan C., 2014. "Volatility spillovers between the oil market and the European Union carbon emission market," Economic Modelling, Elsevier, vol. 36(C), pages 229-234.
    10. 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.
    11. Jammazi, Rania & Reboredo, Juan C., 2016. "Dependence and risk management in oil and stock markets. A wavelet-copula analysis," Energy, Elsevier, vol. 107(C), pages 866-888.
    12. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2016. "Gold, oil, and stocks: Dynamic correlations," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 186-201.
    13. Giuseppe Buccheri & Stefano Marmi & Rosario N. Mantegna, 2013. "Evolution of correlation structure of industrial indices of US equity markets," Papers 1306.4769,
    14. Chester Curme & H. Eugene Stanley & Irena Vodenska, 2015. "Coupled Network Approach To Predictability Of Financial Market Returns And News Sentiments," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(07), pages 1-26, November.
    15. Tiwari, Aviral Kumar & Oros, Cornel & Albulescu, Claudiu Tiberiu, 2014. "Revisiting the inflation–output gap relationship for France using a wavelet transform approach," Economic Modelling, Elsevier, vol. 37(C), pages 464-475.
    16. Garcia, René & Tsafack, Georges, 2011. "Dependence structure and extreme comovements in international equity and bond markets," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1954-1970, August.
    17. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2011. "Structural changes and volatility transmission in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4317-4324.
    18. Mai, Yong & Chen, Huan & Meng, Lei, 2014. "An analysis of the sectorial influence of CSI300 stocks within the directed network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 235-241.
    19. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    20. Hassan, Syed Aun & Malik, Farooq, 2007. "Multivariate GARCH modeling of sector volatility transmission," The Quarterly Review of Economics and Finance, Elsevier, vol. 47(3), pages 470-480, July.
    21. Yanan Li & David E. Giles, 2015. "Modelling Volatility Spillover Effects Between Developed Stock Markets and Asian Emerging Stock Markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 20(2), pages 155-177, March.
    22. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2010. "Topological properties of stock market networks: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3240-3249.
    23. Liu, Xueyong & An, Haizhong & Li, Huajiao & Chen, Zhihua & Feng, Sida & Wen, Shaobo, 2017. "Features of spillover networks in international financial markets: Evidence from the G20 countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 265-278.
    24. Liu, Xueyong & An, Haizhong & Huang, Shupei & Wen, Shaobo, 2017. "The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH–BEKK model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 374-383.
    25. Dewandaru, Ginanjar & Masih, Rumi & Masih, A. Mansur M., 2016. "Contagion and interdependence across Asia-Pacific equity markets: An analysis based on multi-horizon discrete and continuous wavelet transformations," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 363-377.
    26. Khalfaoui, R. & Boutahar, M. & Boubaker, H., 2015. "Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis," Energy Economics, Elsevier, vol. 49(C), pages 540-549.
    27. Brida, Juan Gabriel & Matesanz, David & Seijas, Maria Nela, 2016. "Network analysis of returns and volume trading in stock markets: The Euro Stoxx case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 751-764.
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    Cited by:

    1. Yin, Kedong & Liu, Zhe & Jin, Xue, 2020. "Interindustry volatility spillover effects in China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    2. Fu Qiao & Yan Yan, 2020. "How does stock market reflect the change in economic demand? A study on the industry-specific volatility spillover networks of China's stock market during the outbreak of COVID-19," Papers 2007.07487,
    3. Wu, Fei & Zhang, Dayong & Zhang, Zhiwei, 2019. "Connectedness and risk spillovers in China’s stock market: A sectoral analysis," Economic Systems, Elsevier, vol. 43(3).
    4. Wang, Ze & Gao, Xiangyun & An, Haizhong & Tang, Renwu & Sun, Qingru, 2020. "Identifying influential energy stocks based on spillover network," International Review of Financial Analysis, Elsevier, vol. 68(C).
    5. Zhang, Weiping & Zhuang, Xintian & Wu, Dongmei, 2020. "Spatial connectedness of volatility spillovers in G20 stock markets: Based on block models analysis," Finance Research Letters, Elsevier, vol. 34(C).
    6. Liu, Xueyong & Jiang, Cheng, 2020. "The dynamic volatility transmission in the multiscale spillover network of the international stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    7. An, Pengli & Li, Huajiao & Zhou, Jinsheng & Li, Yang & Sun, Bowen & Guo, Sui & Qi, Yajie, 2020. "Volatility spillover of energy stocks in different periods and clusters based on structural break recognition and network method," Energy, Elsevier, vol. 191(C).
    8. An, Sufang & Gao, Xiangyun & An, Haizhong & An, Feng & Sun, Qingru & Liu, Siyao, 2020. "Windowed volatility spillover effects among crude oil prices," Energy, Elsevier, vol. 200(C).
    9. Zhang, Weiping & Zhuang, Xintian & Lu, Yang & Wang, Jian, 2020. "Spatial linkage of volatility spillovers and its explanation across G20 stock markets: A network framework," International Review of Financial Analysis, Elsevier, vol. 71(C).


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