Systemic Importance and Risk Characteristics of Banks Based on a Multi-Layer Financial Network Analysis

被引:1
|
作者
Gao, Qianqian [1 ]
Fan, Hong [2 ]
Yu, Chengyang [2 ]
机构
[1] Shanghai Lixin Univ Accounting & Finance, Sch Financial Technol, Shanghai 201209, Peoples R China
[2] Donghua Univ, Glorious Sun Sch Business & Management, Shanghai 200051, Peoples R China
基金
中国国家自然科学基金;
关键词
systemic risk; PageRank algorithm; network; centrality; risk exposure; CONTAGION; TOPOLOGY;
D O I
10.3390/e26050378
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Domestic and international risk shocks have greatly increased the demand for systemic risk management in China. This paper estimates China's multi-layer financial network based on multiple financial relationships among banks, assets, and firms, using China's banking system data in 2021. An improved PageRank algorithm is proposed to identify systemically important banks and other economic sectors, and a stress test is conducted. This study finds that China's multi-layer financial network is sparse, and the distribution of transactions across financial markets is uneven. Regulatory authorities should support economic recovery and adjust the money supply, while banks should differentiate competition and manage risks better. Based on the PageRank index, this paper assesses the systemic importance of large commercial banks from the perspective of network structure, emphasizing the role of banks' transaction behavior and market participation. Different industries and asset classes are also assessed, suggesting that increased attention should be paid to industry risks and regulatory oversight of bank investments. Finally, stress tests confirm that the improved PageRank algorithm is applicable within the multi-layer financial network, reinforcing the need for prudential supervision of the banking system and revealing that the degree of transaction concentration will affect the systemic importance of financial institutions.
引用
收藏
页数:20
相关论文
共 50 条
  • [21] A multi-layer network approach to MEG connectivity analysis
    Brookes, Matthew J.
    Tewarie, Prejaas K.
    Hunt, Benjamin A. E.
    Robson, Sian E.
    Gascoyne, Lauren E.
    Liddle, Elizabeth B.
    Liddle, Peter F.
    Morris, Peter G.
    NEUROIMAGE, 2016, 132 : 425 - 438
  • [22] Network Security Based on GCN and Multi-Layer Perception
    Yu, Wei
    Liu, Huitong
    Song, Yu
    Wang, Jiaming
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2025, 16 (01) : 471 - 480
  • [23] Facial Expression Analysis Based on Fusion Multi-Layer Convolutional Layer Feature Neural Network
    Meng, Hao
    Yuan, Fei
    Yan, Tianhao
    FUZZY SYSTEMS AND DATA MINING VI, 2020, 331 : 43 - 51
  • [24] Trade risk transmission of global cobalt industrial chain based on multi-layer network
    Li, Yingli
    Huang, Jianbai
    Zeng, Anqi
    Zhang, Hongwei
    Resources Policy, 2024, 98
  • [25] CONGESTION RISK PROPAGATION MODEL BASED ON MULTI-LAYER TIME-VARYING NETWORK
    Huang, J. H.
    Sun, M. G.
    Cheng, Q.
    INTERNATIONAL JOURNAL OF SIMULATION MODELLING, 2021, 20 (04) : 730 - 741
  • [26] Thermal Characteristics Analysis of a Stratospheric Aerostat Based on Multi-Layer Node Model
    Deng X.
    Ma Z.
    Yang X.
    Zhu B.
    Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2020, 54 (07): : 765 - 770
  • [27] Multi-layer network embedding on scc-based network with motif
    Lu Sun
    Xiaona Li
    Mingyue Zhang
    Liangtian Wan
    Yun Lin
    Xianpeng Wang
    Gang Xu
    Digital Communications and Networks, 2024, 10 (03) : 546 - 556
  • [28] Multi-layer network embedding on scc-based network with motif
    Sun, Lu
    Li, Xiaona
    Zhang, Mingyue
    Wan, Liangtian
    Lin, Yun
    Wang, Xianpeng
    Xu, Gang
    DIGITAL COMMUNICATIONS AND NETWORKS, 2024, 10 (03) : 546 - 556
  • [29] Financial Distress Prediction based on Multi-Layer Perceptron with Parameter Optimization
    Bannany, Magdi El
    Khedr, Ahmed M.
    Sreedharan, Meenu
    Kanakkayil, Sakeena
    IAENG International Journal of Computer Science, 2021, 48 (03) : 1 - 12
  • [30] Structure Characteristics and Robustness Analysis of Multi-Layer Network of High Speed Railway and Ordinary Railway
    Sun X.-X.
    Wu Y.
    Feng X.
    Xiao J.-H.
    Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, 2019, 48 (02): : 315 - 320