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Risk contagion in inter-firm credit guarantee network

Author

Listed:
  • Wang, Lei
  • Li, Shouwei
  • Wu, Chaoqun

Abstract

By introducing the dynamic change model of external investment price, this study constructs a dynamic risk contagion model of inter-firm credit guarantee network, and studies the dynamic risk contagion mechanism of inter-firm credit guarantee network. From simulation analysis, we find that the losses caused by the proportional decline of external assets price may be shared by related firms, thus reducing the possibility of firm bankruptcy. However, the total loss of guaranteed accounts payable may also directly affect related firms and accelerate the spread of bankruptcy risk. With the increase of asset price decline index, the price of external investment declines. Firms are more sensitive in the initial stage and difficult to adjust the amount of external investment in time, resulting in large losses. However, with the gradual completion of the amount adjustment of the external investment, the further loss of external investment can be reduced, and the number of bankrupt firms continues to decline. If the heterogeneity coefficient of the amount of inter-firm credit guarantee is greater, it is more likely to induce the contagion of inter-firm bankruptcy risk. Under the interaction among different credit guarantee parameters, the contagion volatility of inter-firm bankruptcy risk increases. The risk shock of internal inter-firm linkages is more significant than the risk shock of external inter-firm.

Suggested Citation

  • Wang, Lei & Li, Shouwei & Wu, Chaoqun, 2019. "Risk contagion in inter-firm credit guarantee network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    • Handle: RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119304364
    DOI: 10.1016/j.physa.2019.04.078
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    Citations

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    Cited by:

    1. Qi Yang & Yuejuan Hou & Haoran Wei & Tingqiang Chen & Jining Wang, 2022. "Nonlinear Diffusion Evolution Model of Unethical Behavior among Green Food Enterprise," Sustainability, MDPI, vol. 14(23), pages 1-22, December.
    2. Jingjing Long & Cuiqing Jiang & Stanko Dimitrov & Zhao Wang, 2022. "Clues from networks: quantifying relational risk for credit risk evaluation of SMEs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-41, December.
    3. Shan, Yuan George & Wang, Yirui & Wu, Wuqing & Zhen, Weihao, 2023. "Does the Achilles heel of guarantee networks drive financial distress?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    4. Thiago C. Silva & Diego R. Amancio & Benjamin M. Tabak, 2020. "Modeling Supply-Chain Networks with Firm-to-Firm Wire Transfers," Papers 2001.06889, arXiv.org, revised Aug 2020.
    5. Giovanni Cicceri & Giuseppe Inserra & Michele Limosani, 2020. "A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    6. Yu, Jian & Peng, Fanjia & Shi, Xunpeng & Yang, Longjian, 2022. "Impact of credit guarantee on firm performance: Evidence from China’s SMEs," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 624-636.
    7. Lv, Jiamin & Ben, Shenglin & Huang, Wenli & Xu, Yueling, 2023. "How to reduce the default contagion risk of intercorporate credit guarantee networks? Evidence from China," Emerging Markets Review, Elsevier, vol. 55(C).
    8. Wang, Lei & Li, Shouwei & Wang, Jining & Meng, Yi, 2020. "Real estate bubbles in a bank-real estate loan network model integrating economic cycle and macro-prudential stress testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).

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