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Time domain and frequency domain Granger causality networks: Application to China’s financial institutions

Author

Listed:
  • Gang-Jin Wang
  • Hui-Bin Si
  • Yang-Yang Chen
  • Chi Xie
  • Julien Chevallier

    (LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8)

Abstract

We propose Granger causality networks in the time domain and frequency domain to investigate the interconnectedness of Chinese financial institutions based on the daily returns of banks, securities, and insurers during 2011–2018. We find that the system-level interconnectedness mainly concentrates on the medium-high frequency, but individual-level interconnectedness varies across different frequencies. Dynamically, the system-level interconnectedness is consistent in the time domain and frequency domain, while this consistency in the individual-level interconnectedness does not hold, but both of them are affected by macroeconomic situations and financial events. During 2015–2016 and 2018, the system-level interconnectedness increased significantly and was at a high level.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Gang-Jin Wang & Hui-Bin Si & Yang-Yang Chen & Chi Xie & Julien Chevallier, 2021. "Time domain and frequency domain Granger causality networks: Application to China’s financial institutions," Post-Print halshs-04250263, HAL.
    • Handle: RePEc:hal:journl:halshs-04250263
    DOI: 10.1016/j.frl.2020.101662
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    Cited by:

    1. Wen, Shigang & Li, Jianping & Huang, Chuangxia & Zhu, Xiaoqian, 2023. "Extreme risk spillovers among traditional financial and FinTech institutions: A complex network perspective," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 190-202.
    2. Juan M. C. Larrosa & Emiliano M. Gutiérrez & Juan I. Uriarte & Gonzalo R. Ramírez Muñoz Toro, 2025. "Granger causality networks of price leadership in the retail tea market of Argentina," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(1), pages 50-59, February.
    3. Xiao-Li, Gong & Zhuo-Cheng, Wu & Xiong, Xiong & Wei, Zhang, 2025. "Research on sovereign credit and international banking industry tail risk contagion ----Perspective from double-layer complex network," International Review of Economics & Finance, Elsevier, vol. 99(C).
    4. Wang, Gang-Jin & Xiong, Lu & Zhu, You & Xie, Chi & Foglia, Matteo, 2022. "Multilayer network analysis of investor sentiment and stock returns," Research in International Business and Finance, Elsevier, vol. 62(C).
    5. Nguyen, Anh T.H. & Le, Thanh T., 2025. "In bank runs and market stress, it matters how networks impact: Exploring the financial connectedness in Vietnam," Finance Research Letters, Elsevier, vol. 72(C).
    6. Kamrul Hasan Tuhin & Ashadun Nobi & Mahmudul Hasan Rakib & Jae Woo Lee, 2025. "Long short-term memory autoencoder based network of financial indices," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.
    7. Tian, Maoxi & Guo, Fei & Niu, Rong, 2022. "Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    8. Li, Mengting & Ma, Xiaofu & Jia, Junsheng & Zhu, Chen, 2025. "Risk spillovers between the financial market and macroeconomic sectors under mixed-frequency information: A frequency domain perspective," International Review of Economics & Finance, Elsevier, vol. 99(C).
    9. Gong, Xiao-Li & Liu, Jian-Min & Xiong, Xiong & Zhang, Wei, 2022. "Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network," International Review of Financial Analysis, Elsevier, vol. 84(C).
    10. Li, Zixuan & Long, Shaobo & Xu, Xiang, 2025. "Dynamic spillovers between global financial stress and uncertainties: Evidence from quantile connectedness," International Review of Economics & Finance, Elsevier, vol. 103(C).
    11. Ren, Yinghua & Zhao, Wanru & You, Wanhai & Zhu, Huiming, 2022. "Multiscale features of extreme risk spillover networks among global stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    12. Hu, Yunchao & Lu, Guibin & Gao, Wenyu, 2022. "A study on China’s systemically important financial institutions based on multi-time scale causality networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    13. Ke, Rui & Shen, Anni & Yin, Man & Tan, Changchun, 2024. "The cross-sector risk contagion among Chinese financial institutions: Evidence from the extreme volatility spillover perspective," Finance Research Letters, Elsevier, vol. 63(C).
    14. Roberto Esposti, 2025. "Investigating commodity price interdependence with Granger causality networks," Working Papers 498, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

    More about this item

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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