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复杂系统与复杂性科学  2023, Vol. 20 Issue (1): 34-40    DOI: 10.13306/j.1672-3813.2023.01.005
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基于复杂网络演化模型的新冠危机对经济的冲击研究
路冠平, 李江平
1.上海黄金交易所,上海 200001;
2.复旦大学管理学院,上海 200433
The Economic Impact of COVID-19 Crisis Based on Complex Network Evolution Model
LU Guanping, LI Jiangping
1. Shanghai Gold Exchange,Shanghai 200001,China;
2. School of Management, Fudan University,Shanghai 200433,China
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摘要 为研究新冠危机对经济造成的非均衡、非线性冲击,建立了一个基于交易经济学理论的交易网络模型,并在其上模拟新冠危机事件冲击引发经济萧条的演化过程。研究表明:冲击影响下,盈利能力薄弱的中小交易主体将首先出现现金流危机,并通过交易网络在上下游形成危机传染;而企业经营恶化造成的信用降低,将使得经济中利率提高并与交易主体破产互相促进,导致流动性危机与债务危机交相反馈、加速企业的破产并可能引起银行业债务危机;冲击过后,经济恢复可能出现稳定恢复、缓慢衰退和二次危机3种模式。最后提出了降低危机影响的相关政策建议。
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路冠平
李江平
关键词 现金流交易网络债务危机复杂网络    
Abstract:The Covid-19 crisis impacts the economy with non-equilibrium and non-linear shocks. This paper builds a trading network model based on the theory of trading economics. Using the network model, the evolutionary procedure of the economic depression triggered by the shocks are researched. The study shows that under the impact of shocks, small and medium-sized trading agents with weak profitability will first experience cash flow crisis. Then the crisis contagion is formed in upstream and downstream through the trading network. The credit reduction caused by the business deterioration will make the interest rate in the economy increase and promote each other with the bankruptcy of trading entities. Eventually, it leads to the feedback loop in liquidity crisis and debt crisis, which accelerates the bankruptcy of enterprises and possibly causing a debt crisis in the banking sector. It is found that after the shock, the economic recovery may take three patterns: stable recovery, slow recession and secondary crisis. Finally, the paper proposes relevant policy recommendations to reduce the impact of the crisis.
Key wordscash flow    trading network    debt crisis    complex network
收稿日期: 2021-08-10      出版日期: 2023-04-19
ZTFLH:  F069  
基金资助:国家自然科学基金(61401274);中国博士后科学基金第66批面上项目(2019M661384)
通讯作者: 李江平(1981),男,湖北鄂州人,博士,讲师,主要研究方向为资产定价、资本市场开放。   
作者简介: 路冠平(1983),男,山东淄博人,博士,副研究员,主要研究方向为金融工程、复杂系统。
引用本文:   
路冠平, 李江平. 基于复杂网络演化模型的新冠危机对经济的冲击研究[J]. 复杂系统与复杂性科学, 2023, 20(1): 34-40.
LU Guanping, LI Jiangping. The Economic Impact of COVID-19 Crisis Based on Complex Network Evolution Model. Complex Systems and Complexity Science, 2023, 20(1): 34-40.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2023.01.005      或      https://fzkx.qdu.edu.cn/CN/Y2023/V20/I1/34
[1] GATTI D D, GALLEGATI M, GREENWALD B C. Business fluctuations and bankruptcy avalanches in an evolving network economy[J]. Journal of Economic Interaction and Coordination, 2009,4(2):195212.
[2] 陆磊,刘学. 违约与杠杆周期——一个带有救助的金融加速器模型[J]. 金融研究,2020(5):120.
LU L, LIU X. Default andleverage cycle: a financial accelerator model with bailout[J]. Financial Research, 2020(5): 120.
[3] BERNANKE B, GERTLER M, Gilchrist S. The Financial Accelerator in a Quantitative Business Cycle Framework[M]. North Holland, Netherland Elsevier, 1999: 13421393.
[4] GERTLER M, KARADI P. A model of unconventional monetary policy[J]. Journal of Monetary Economics, 2011, 58(1): 1734.
[5] HENRIET F, HALLEGATTE S. Assessing the consequences of natural disasters on production networks: a disaggregated approach[DB/OL].[20201202]. https://ideas.repec.org/p/ags/feemct/46657.html.
[6] INOUE H, TODO Y. Firm-level propagation of shocks through supply-chain networks[J]. Nature Sustainability, 2019(2):841847.
[7] GUAN D B, WANG D P, STEPHANE H. Global supply-chain effects of COVID-19 control measures[J]. Nature Human Behaviour, 2020(6): 577587.
[8] 王振营.交易经济学原理 (上卷)[M]. 2版. 北京:中国金融出版, 2019.
[9] 陈赟, 沈艳,王靖一.重大突发公共卫生事件下的金融市场反应[J]. 金融研究, 2020(6):2039.
CHEN Y, SHEN Y, WANG J Y, Financial market response under major public health emergencies[J]. Finance Research, 2020(6): 2039.
[10] 刘玉珍,王陈豪.行为视角下的疫情分析: 成因、影响与对策综述[J].金融研究,2020(6):119.
LIU Y Z, WANG C H. Epidemic analysis from a behavioral perspective: a review of causes, effects and countermeasures[J]. Financial Research, 2020(6):119.
[11] UPPER C. Simulation methods to assess the danger of contagion in interbank markets[J].Journal of Financial Stability, 2011,7(3):111125.
[12] SOUMA W, FUJIWARA Y, AOYAMA H. Complex networks and economics[J]. Physica A: Statistical Mechanics and Its Applications, 2003(324):396401.
[13] BARABÁSI A, BONABEAU E. “Scale-free networks”[J]. Scientific American, 2003,288(5):505.
[14] 马永强,孟子平.金融危机冲击、企业风险缓冲与政府政策选择[J].会计研究,2009(7):5258.
MA Y Q, MENG Z P. Impact offinancial crisis, enterprise risk buffer and government policy choice[J]. Accounting Research, 2009(7): 5258.
[15] 曾爱民,傅元略,魏志华.金融危机冲击、财务柔性储备和企业融资行为——来自中国上市公司的经验证据[J].金融研究,2011(10):155169.
ZENG A M, FU Y L, WEI Z H. Financialcrisis impact, financial flexible reserve and corporate financing behavior: empirical evidence from Chinese listed companies[J]. Financial Research, 2011(10): 155169.
[16] 王婷,池若楠,李佳乐.基于现金流视角的金融危机冲击程度大小的影响因素研究——以我国上市公司为例[J].中国乡镇企业会计,2013(8):2226.
WANG T, CHI R N, LI J L. A study on the influencing factors of the impact of financial crisis based on the perspective of cash flow: taking my country's listed companies as an example[J]. Accounting for China Township and Township Enterprises, 2013(8):2226.
[17] YU H, SUN C H, CHEN J. Simulating the supply disruption for the coordinated supply chain[J]. Journal of Systems Science and Systems Engineering, 2007,16 (3):323335.
[18] BOLLOBÁS B, BORGS J, CHAYES J, et al. Directed scale-free graphs[C]. Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, Baltimore, Maryland, 2003: 132139.
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