scholarly journals The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

2021 ◽  
pp. 101407
Author(s):  
Chengguo Zhao ◽  
Li Meng ◽  
Jun Wang ◽  
Shujian Ma
Keyword(s):  
Credit Risk ◽  
Time Lag ◽  
Seir Model ◽  
P2p Lending ◽  
Credit Risk Contagion

scholarly journals An SEIR Model for Assessment of Current COVID-19 Pandemic Situation in the UK

2020 ◽  
Author(s):  
Peiliang Sun ◽  
Kang Li
Keyword(s):  
Seasonal Influenza ◽  
Time Lag ◽  
Seir Model ◽  
Infected People ◽  
Proposed Model ◽  
Lag Effect ◽  
Novel Coronavirus ◽  
The Uk ◽  
Specific Symptoms ◽  
Limited Testing

AbstractThe ongoing COVID-19 pandemic spread to the UK in early 2020 with the first few cases being identified in late January. A rapid increase in confirmed cases started in March, and the number of infected people is however unknown, largely due to the rather limited testing scale. A number of reports published so far reveal that the COVID-19 has long incubation period, high fatality ratio and non-specific symptoms, making this novel coronavirus far different from common seasonal influenza. In this note, we present a modified SEIR model which takes into account the time lag effect and probability distribution of model states. Based on the proposed model, it is estimated that the actual total number of infected people by 1 April in the UK might have already exceeded 610,000. Average fatality rates under different assumptions at the beginning of April 2020 are also estimated. Our model also reveals that the R0 value is between 7.5–9 which is much larger than most of the previously reported values. The proposed model has a potential to be used for assessing future epidemic situations under different intervention strategies.

scholarly journals Credit Risk Contagion Based on Asymmetric Information Association

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Shanshan Jiang ◽  
Hong Fan ◽  
Min Xia
Keyword(s):  
Asymmetric Information ◽  
Credit Risk ◽  
Financial Market ◽  
Risk Attitude ◽  
Individual Risk ◽  
Complex Network Theory ◽  
Structure Information ◽  
The Individual ◽  
Credit Risk Contagion ◽  
Information Association

The study of the contagion law of credit risk is very important for financial market supervision. The existing credit risk contagion models based on complex network theory assume that the information between individuals in the network is symmetrical and analyze the proportion of the individuals infected by the credit risk from a macro perspective. However, how individuals are infected from a microscopic perspective is not clear, besides the level of the infection of the individuals is characterized by only two states: completely infected or not infected, which is not realistic. In this paper, a credit risk contagion model based on asymmetric information association is proposed. The model can effectively describe the correlation among individuals with credit risk. The model can analyze how the risk individuals are infected in the network and can effectively reflect the risk contagion degree of the individual. This paper further analyzes the influence of network structure, information association, individual risk attitude, financial market supervision intensity, and individual risk resisting ability on individual risk contagion. The correctness of the model is verified by theoretical deduction and numerical simulation.

scholarly journals AN EVOLVING NETWORK MODEL OF CREDIT RISK CONTAGION IN THE FINANCIAL MARKET

2016 ◽  
Vol 23 (1) ◽  
pp. 22-37 ◽  
Author(s):  
Tingqiang CHEN ◽  
Jianmin HE ◽  
Xindan LI
Keyword(s):  
Theoretical Analysis ◽  
Risk Aversion ◽  
Credit Risk ◽  
Network Model ◽  
Financial Market ◽  
Network Size ◽  
Clustering Coefficient ◽  
The Other ◽  
Credit Risk Contagion ◽  
Average Fitness

This paper introduces an evolving network model of credit risk contagion containing the average fitness of credit risk contagion, the risk aversion sentiments, and the ability of resist risk of credit risk holders. We discuss the effects of the aforementioned factors on credit risk contagion in the financial market through a series of theoretical analysis and numerical simulations. We find that, on one hand, the infected path distribution of the network gradually increases with the increase in the average fitness of credit risk contagion and the risk aversion sentiments of nodes, but gradually decreases with the increase in the ability to resist risk of nodes. On the other hand, the average fitness of credit risk contagion and the risk aversion sentiments of nodes increase the average clustering coefficient of nodes, whereas the ability to resist risk of nodes decreases this coefficient. Moreover, network size also decreases the average clustering coefficient.

scholarly journals Associated Credit Risk Contagion with Incubatory Period: A Network-Based Perspective

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kai Xu ◽  
Jianming Mo ◽  
Qian Qian ◽  
Fengying Zhang ◽  
Xiaofeng Xie ◽  
...  
Keyword(s):  
Field Theory ◽  
Credit Risk ◽  
Mean Field Theory ◽  
Mean Field ◽  
Stable State ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
The Mean ◽  
Credit Risk Contagion

Associated credit risk is a kind of credit risk among the associated credit entities formed by credit-related entities. Focusing on this hot topic of associated credit risk and the relevant contagion and considering the latent entities and their incubatory period, this paper builds an infectious dynamic model to describe the associated credit risk contagion of associated credit entities based on the mean-field theory of complex networks. Firstly, this paper analyzes the stable state of the associated credit risk contagion in the associated entity network, considering the latent entities and their incubatory period. Secondly, from the perspective of complex network and considering the incubatory period, a SHIS model is built to reveal how the incubatory period influences associated credit risk contagion. Finally, the sensitivity of some parameters is analyzed in the Barabási–Albert (BA) scale-free network. The results show the following: (i) the contagion threshold of associated credit risk is related to the incubatory period of latent entities, the recovery rate and infectivity of infected entities, and the newborn rate of credit entities; (ii) the infectious rate of infected entities, the mortality rate of credit entities, and the important factors stated in (i) are all significantly correlated with the density of infected entities.

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