[1]米传民,钱媛媛.基于SEIS模型的互联网金融风险传染研究[J].南京理工大学学报(自然科学版),2019,43(06):800.[doi:10.14177/j.cnki.32-1397n.2019.43.06.019]
 Mi Chuanmin,Qian Yuanyuan.Internet financial risk contagion based on SEIS model[J].Journal of Nanjing University of Science and Technology,2019,43(06):800.[doi:10.14177/j.cnki.32-1397n.2019.43.06.019]
点击复制

基于SEIS模型的互联网金融风险传染研究()
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
43卷
期数:
2019年06期
页码:
800
栏目:
出版日期:
2019-12-31

文章信息/Info

Title:
Internet financial risk contagion based on SEIS model
文章编号:
1005-9830(2019)06-0800-07
作者:
米传民钱媛媛
南京航空航天大学 经济与管理学院,江苏 南京 211106
Author(s):
Mi ChuanminQian Yuanyuan
College of Economics and Management,Nanjing University of Aeronautics andAstronautics,Nanjing 211106,China
关键词:
互联网金融 风险传染 复杂网络方法 感染率 治愈率 潜伏率
Keywords:
internet finance risk contagion complex network method infection rate cure rate latent infection rate
分类号:
F830
DOI:
10.14177/j.cnki.32-1397n.2019.43.06.019
摘要:
针对互联网金融发展带来的金融风险增加的问题,运用复杂网络方法构建互联网金融网络,建立具有潜伏期的易感者-潜伏者-染病者-易感者(SEIS)模型,研究单因素和双因素影响下的互联网金融风险网络传播的内在规律。结果表明:在单因素变化情况下,感染率越高,风险越容易传播,提高治愈率可以有效降低风险感染率; 在双因素共同作用下,潜伏率超过0.2时转化率的上升会影响最终感染节点数量,治愈率的上升能够很好地遏制感染率上升对感染密度的影响。
Abstract:
Aiming at the increasing financial risk with the development of internet finance,an internet financial network is constructed using the complex network method,and a susceptible-exposed-infected-susceptible(SEIS)model with latency is established to study the inherent law of internet financial risk network propagation under the influence of single factor and two factors. The results show that:in the case of single factor changing,the higher the infection rate is,the easier the risk spreading is,and higher cure rate can reduce the risk of infection effectively; under the combined effect of two factors,the increase of the conversion rate can affect the number of the final infected nodes with the latent infection rate above 0.2,and the increase of the cure rate can well suppress the influence of the increase of the infection rate on the infection density.

参考文献/References:

[1] Watts D J,Strogatz S H. Collective dynamics of ‘small-world’ networks[J]. Nature,1998,393(6684):440-442.
[2]Barabási A L,Albert R,Jeong H. Mean-field theory for scale-free random networks[J]. Physica A:Statistical Mechanics & Its Applications,1999,272(1-2):173-187.
[3]王子丰,周晔. 中美上市银行高维网络结构特征与系统性风险传染[J]. 金融经济学研究,2018,33(4):35-45.
Wang Zifeng,Zhou Ye. Characteristics of high-dimensional network structures and systemic risk infection in Chinese and US-listed banks[J]. Financial Economics Research,2018,33(4):35-45.
[4]吴念鲁,徐丽丽,苗海宾. 我国银行同业之间流动性风险传染研究——基于复杂网络理论分析视角[J]. 国际金融研究,2017(7):34-43.
Wu Nianlu,Xu Lili,Miao Haibin. Research on inter-bank liquidity risk contagion of China—From the perspective of complex network theory[J]. Studies of International Finance,2017(7):34-43.
[5]姚登宝. 基于银行间网络的流动性风险传染机制研究[J]. 安徽大学学报(哲学社会科学版),2017,41(4):130-137.
Yao Dengbao. Research on the mechanism of liquidity risk transmission based on interbank network[J]. Journal of Anhui University(Philosophy and Social Sciences Edition),2017,41(4):130-137.
[6]Georg C P. The effect of the interbank network structure on contagion and common shocks[J]. Journal of Banking and Finance,2013,37(7):2216-2228.
[7]Matesanza D,Ortega G J. Sovereign public debt crisis in Europe:A network analysis[J]. Physica A:Statistical Mechanics and Its Applications,2015,436(15):756-766.
[8]Axel G,Luitgard A. A Bayesian methodology for systemic risk assessment in financial network[J]. Management Science,2016,63(12):4428-446.
[9]Hamed A,Andreea M. Inhomogeneous financial networks and contagious links[J]. Operations Research,2016,64(5):1109-1120.
[10]刘超,郝丹辉,唐孝文,等. 基于复杂网络的金融风险跨市场传导机制研究——以金融危机时期(2007~2009年)数据为例[J]. 运筹与管理,2018,27(8):155-161,181.
Liu Chao,Hao Danhui,Tang Xiaowen,et al. A study of cross-market financial risks contagion mechanism based on complex network theory:For data around financial crisis(2007~2009)[J]. Operations Research and Management Science,2018,27(8):155-161,181.
[11]王克达,庞晓波,王姗姗. 金融危机对全球股票市场的传染研究:基于复杂网络分析方法[J]. 世界经济研究,2018(4):28-39,135.
Wang Keda,Pang Xiaobo,Wang Shanshan. The contagion of financial crisis to global stock market based on complex network approach[J]. World Economy Studies,2018(4):28-39,135.
[12]张婷,米传民. 基于超网络的互联网金融均衡问题研究[J]. 复杂系统与复杂性科学,2016,13(2):36-43.
Zhang Ting,Mi Chuanmin. Study on internet financial equilibrium problem based on supernetwork[J]. Complex Systems and Complexity Science,2016,13(2):36-43.
[13]米传民,李丹丹,张婷,等. 考虑社交网络和互联网金融的金融市场超网络均衡研究[J]. 中国管理科学,2018,26(12):56-65.
Mi Chuanmin,Li Dandan,Zhang Ting,et al. Study on financial market supernetwork equilibrium considering social network and internet finance[J]. Chinese Journal of Management Science,2018,26(12):56-65.
[14]Xu Runjie,Mi Chuanmin,Mierzwiak R,et al. Complex network construction of Internet finance risk[J]. Physica A:Statistical Mechanics and its Applications,2020,540(2):122930-122948.
[15]Garas A,Argyrakis P,Rozenblat C,et al. Worldwide spreading of economic crisis[J]. New Journal of Physics,2010,12(2):185-188.
[16]Haldane A G. Rethinking the financial network[J]. Springer Fachmedien Wiesbaden,2013(53):243-278.
[17]Toivanen M. Contagion in the interbank network:An epidemiological approach[J]. SSRN Electronic Journal,2013(19):1-43.
[18]胡志浩,李晓花. 复杂金融网络中的风险传染与救助策略——基于中国金融无标度网络上的SIRS模型[J]. 财贸经济,2017,38(4):101-114.
Hu Zhihao,Li Xiaohua. Contagion and bailout strategy in complex financial network—SIRS model on the Chinese scale-free financial network[J]. Finance & Trade Economics,2017,38(4):101-114.
[19]姚林华. 基于动物传染病模型的我国银行间风险传染效应研究[J]. 当代金融研究,2018(5):95-105.
Yao Linhua. Study on risk contagion effect of the banking based on the SIR epidemic model[J]. Journal of Contemporary Financial Research,2018(5):95-105.
[20]曾志坚,吴汪洋. 贸易渠道视角下的金融危机传染研究:基于复杂网络与SIRS模型[J]. 湖南大学学报(社会科学版),2018,32(3):87-93.
Zeng Zhijian,Wu Wangyang. Research on contagion of financial crisis from the perspective of trade channel:Based on complex network and SIRS model[J]. Journal of Hunan University(Social Sciences),2018,32(3):87-93.
[21]张子振,储煜桂. 一类具有阶段结构的时滞生态流行病模型周期解[J]. 南京理工大学学报,2018,42(6):756-762.
Zhang Zizhen,Chu Yugui. Periodic solutions of delayed eco-epidemiological model with stage structure[J]. Journal of Nanjing University of Science and Technology,2018,42(6):756-762.
[22]庞晓波,王姗姗,陈守东. 欧债危机对全球及中国传染性的测度分析——基于复杂网络的模拟研究[J]. 世界经济研究,2015(12):35-46,124-125.
Pang Xiaobo,Wang Shanshan,Chen Shoudong. Measurement analysis of European debt crisis’s infects on global economies and China based on simulation of complex networks[J]. World Economy Studies,2015(12):35-46,124-125.
[23]王姗姗,庞晓波. 金融危机的贸易网络和金融网络传染性比较——基于欧债危机的模拟研究[J]. 浙江社会科学,2016(11):18-27,156.Wang Shanshan,Pang Xiaobo. Simulation of contagion in financial crisis by network[J]. Zhejiang Social Sciences,2016(11):18-27,156.
[24]崔玉美,陈姗姗,傅新楚. 几类传染病模型中基本再生数的计算[J]. 复杂系统与复杂性科学,2017,14(4):14-31.Cui Yumei,Chen Shanshan,Fu Xinchu. The thresholds of some epidemic models[J]. Complex Systems and Complexity Science,2017,14(4):14-31.
[25]孙峥,李宝成. 有治愈的非线性传染力SIS模型渐近性分析[J]. 南京理工大学学报,2003,27(S1):81-83.
Sun Zheng,Li Baocheng. Asymptotically analysis of the curability nonlinear infectivity model[J]. Journal of Nanjing University of Science and Technology,2003,27(S1):81-83.

备注/Memo

备注/Memo:
收稿日期:2019-09-26 修回日期:2019-11-12
基金项目:国家社会科学基金(17BGL055); 江苏省研究生教育教学改革课题(JGLX19_014); 南京航空航天大学研究生教育教学改革研究项目(2018YJXGG17)
作者简介:米传民(1976-),男,博士,副教授,主要研究方向:金融风险管理,E-mail:michuanmin@163.com。
引文格式:米传民,钱媛媛. 基于SEIS模型的互联网金融风险传染研究[J]. 南京理工大学学报,2019,43(6):800-806.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2019-12-31