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

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

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.

Suggested Citation

  • Wang, Gang-Jin & Si, Hui-Bin & Chen, Yang-Yang & Xie, Chi & Chevallier, Julien, 2021. "Time domain and frequency domain Granger causality networks: Application to China’s financial institutions," Finance Research Letters, Elsevier, vol. 39(C).
    • Handle: RePEc:eee:finlet:v:39:y:2021:i:c:s1544612319311419
    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. 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).
    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. 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).

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    More about this item

    Keywords

    Causality network; Interconnectedness; Frequency domain; Time domain; Financial institutions; China;
    All these keywords.

    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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