IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v607y2022ics0378437122007749.html
   My bibliography  Save this article

A study on China’s systemically important financial institutions based on multi-time scale causality networks

Author

Listed:
  • Hu, Yunchao
  • Lu, Guibin
  • Gao, Wenyu

Abstract

Studying systemically important financial institutions (SIFIs) becomes the research hotspots in risk management of financial markets. In this paper, we study China’s SIFIs based on the complex network modeling method on multi-time scales. First, we use complete empirical mode decomposition based on adaptive noise (CEEMDAN) and partial cross mapping (PCM) to construct multi-time scale causality networks based on the normalized daily returns of 30 institutions during 2011–2020.​ Then, the SIFIs is investigated from the perspective of connectedness (information transmission and node centrality) on multi-time scales. Finally, using the technique for order preference by similarity to an ideal solution with entropy weight method (EW-TOPSIS) to comprehensively evaluate SIFIs, and deliver a sensitivity analysis. We find that: (i) interconnectedness primarily concentrates on the inter-institution in the long-term; (ii) the evaluations of SIFIs measurement from perspective of connectedness on multi-time scale are different; (iii) the scores of SIFIs measured by multi-attribute EW-TOPSIS clarify that banks and insurers represent the SIFIs in each economic circle, and SIFIs can be evaluated according to preference for attributes.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007749
    DOI: 10.1016/j.physa.2022.128216
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122007749
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.128216?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. Wu, Shan & Tong, Mu & Yang, Zhongyi & Zhang, Tianyi, 2021. "Interconnectedness, systemic risk, and the influencing factors: Some evidence from China’s financial institutions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    2. Varotto, Simone & Zhao, Lei, 2018. "Systemic risk and bank size," Journal of International Money and Finance, Elsevier, vol. 82(C), pages 45-70.
    3. Caliskan, Hande & Cevik, Emrah I. & Kirci Cevik, Nuket & Dibooglu, Sel, 2021. "Identifying systemically important financial institutions in Turkey," Research in International Business and Finance, Elsevier, vol. 56(C).
    4. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2019. "Financial systemic risk measurement based on causal network connectedness analysis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 290-307.
    5. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    6. Su, Zhi & Xu, Fuwei, 2021. "Dynamic identification of systemically important financial markets in the spread of contagion: A ripple network based collective spillover effect approach," Journal of Multinational Financial Management, Elsevier, vol. 60(C).
    7. 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).
    8. 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).
    9. Tian, Hu & Zheng, Xiaolong & Zeng, Daniel Danjun, 2019. "Analyzing the dynamic sectoral influence in Chinese and American stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    10. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    11. Sun, Qingru & Gao, Xiangyun & Wen, Shaobo & Chen, Zhihua & Hao, Xiaoqing, 2018. "The transmission of fluctuation among price indices based on Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 36-49.
    12. Mao, Xuegeng & Yang, Albert C. & Peng, Chung-Kang & Shang, Pengjian, 2020. "Analysis of economic growth fluctuations based on EEMD and causal decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    13. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.
    14. Lu, Xinsheng & Tian, Jie & Zhou, Ying & Li, Zhihui, 2013. "Multifractal detrended fluctuation analysis of the Chinese stock index futures market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1452-1458.
    15. Zhang, Weiping & Zhuang, Xintian & Wang, Jian & Lu, Yang, 2020. "Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    16. Siyang Leng & Huanfei Ma & Jürgen Kurths & Ying-Cheng Lai & Wei Lin & Kazuyuki Aihara & Luonan Chen, 2020. "Partial cross mapping eliminates indirect causal influences," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    17. Niu, Hongli & Hu, Ziang, 2021. "Information transmission and entropy-based network between Chinese stock market and commodity futures market," Resources Policy, Elsevier, vol. 74(C).
    18. Hu, Jiantao & Du, Yuxian & Mo, Hongming & Wei, Daijun & Deng, Yong, 2016. "A modified weighted TOPSIS to identify influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 73-85.
    19. Kuang, Peng-Cheng, 2021. "Measuring information flow among international stock markets: An approach of entropy-based networks on multi time-scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).
    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. Cao, Huiying & Gao, Chao & Wang, Zhen, 2023. "Ranking academic institutions by means of institution–publication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(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. 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).
    2. Wu, Shan & Tong, Mu & Yang, Zhongyi & Zhang, Tianyi, 2021. "Interconnectedness, systemic risk, and the influencing factors: Some evidence from China’s financial institutions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    3. Chuangxia Huang & Xian Zhao & Renli Su & Xiaoguang Yang & Xin Yang, 2022. "Dynamic network topology and market performance: A case of the Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1962-1978, April.
    4. Addi, Abdelhamid & Bouoiyour, Jamal, 2023. "Interconnectedness and extreme risk: Evidence from dual banking systems," Economic Modelling, Elsevier, vol. 120(C).
    5. Ling, Yu-Xiu & Xie, Chi & Wang, Gang-Jin, 2022. "Interconnectedness between convertible bonds and underlying stocks in the Chinese capital market: A multilayer network perspective," Emerging Markets Review, Elsevier, vol. 52(C).
    6. Gao, Yang & Li, Yangyang & Wang, Yaojun, 2021. "Risk spillover and network connectedness analysis of China’s green bond and financial markets: Evidence from financial events of 2015–2020," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    7. Foglia, Matteo & Addi, Abdelhamid & Angelini, Eliana, 2022. "The Eurozone banking sector in the time of COVID-19: Measuring volatility connectedness," Global Finance Journal, Elsevier, vol. 51(C).
    8. Wang, Gang-Jin & Chen, Yan & Zhu, You & Xie, Chi, 2024. "Systemic risk prediction using machine learning: Does network connectedness help prediction?," International Review of Financial Analysis, Elsevier, vol. 93(C).
    9. 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).
    10. 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).
    11. Beibei Zhang & Xuemei Xie & Chunmei Li, 2023. "How Connected Is China’s Systemic Financial Risk Contagion Network?—A Dynamic Network Perspective Analysis," Mathematics, MDPI, vol. 11(10), pages 1-19, May.
    12. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring Network Systemic Risk Contributions: A Leave-one-out Approach," LEO Working Papers / DR LEO 2608, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    13. Wang, Ze & Gao, Xiangyun & Huang, Shupei & Sun, Qingru & Chen, Zhihua & Tang, Renwu & Di, Zengru, 2022. "Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    14. Hué, Sullivan & Lucotte, Yannick & Tokpavi, Sessi, 2019. "Measuring network systemic risk contributions: A leave-one-out approach," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 86-114.
    15. Gao, Yang & Li, Yangyang & Zhao, Chengjie & Wang, Yaojun, 2022. "Risk spillover analysis across worldwide ESG stock markets: New evidence from the frequency-domain," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    16. Chen, Wei & Hou, Xiaoli & Jiang, Manrui & Jiang, Cheng, 2022. "Identifying systemically important financial institutions in complex network: A case study of Chinese stock market," Emerging Markets Review, Elsevier, vol. 50(C).
    17. Ahmad, Wasim & Tiwari, Shiv Ratan & Wadhwani, Akshay & Khan, Mohammad Azeem & Bekiros, Stelios, 2023. "Financial networks and systemic risk vulnerabilities: A tale of Indian banks," Research in International Business and Finance, Elsevier, vol. 65(C).
    18. Chen, Bin-xia & Sun, Yan-lin, 2024. "Financial market connectedness between the U.S. and China: A new perspective based on non-linear causality networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    19. Ren, Yinghua & Tan, Anqi & Zhu, Huiming & Zhao, Wanru, 2022. "Does economic policy uncertainty drive nonlinear risk spillover in the commodity futures market?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    20. Lu, Shan & Zhao, Jichang & Wang, Huiwen & Ren, Ruoen, 2018. "Herding boosts too-connected-to-fail risk in stock market of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 945-964.

    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:phsmap:v:607:y:2022:i:c:s0378437122007749. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.