Credit Risk Contagion in an Evolving Network Model Integrating Spillover Effects and Behavioral Interventions

Chen, Tingqiang; Xiao, Binqing; Liu, Haifei

doi:10.1155/2018/1843792

Cited by 16 publications

(12 citation statements)

References 63 publications

(81 reference statements)

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“…Fourth, we can construct data-driven models of a discrete fractional-order game. At last, we can construct discrete fractional-order models in many scientific fields [29][30][31][32][33].…”

Section: Conclusion and Future Developmentsmentioning

confidence: 99%

A Bertrand Duopoly Game with Long-Memory Effects

et al. 2020

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Reconsidering the Bertrand duopoly game based on the concept of long short-term memory, we construct a fractional-order Bertrand duopoly game by extending the integer-order game to its corresponding fractional-order form. We build such a Bertrand duopoly game, in which both players can make their decisions with long-memory effects. Then, we investigate its Nash equilibria, local stability, and numerical solutions. Using the bifurcation diagram, the phase portrait, time series, and the 0-1 test for chaos, we numerically validate these results and illustrate its complex phenomena, such as bifurcation and chaos.

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“…Fourth, we can construct data-driven models of a discrete fractional-order game. At last, we can construct discrete fractional-order models in many scientific fields [29][30][31][32][33].…”

Section: Conclusion and Future Developmentsmentioning

confidence: 99%

A Bertrand Duopoly Game with Long-Memory Effects

et al. 2020

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“…Chen et al studied the causes, mechanisms, and control strategies of credit risk contagion among CDS counterparties and found that information asymmetry and innovation diffusion could cause credit risk contagion among CDS counterparties [24]. Later, Chen et al introduced an evolving network model of credit risk contagion in the credit risk transfer (CRT) market and described the influence and active mechanism of the spillover effects of infected investors, behaviors of investors and regulators, emotional disturbance of investors, market noise, and CRT network structure on credit risk contagion [25]. However, Li et al studied the credit risk with an affiliated guarantee relationship and found that the contagion intensity of credit risk was positively correlated with the proportion of equity [26].…”

Section: Literature Overviewmentioning

confidence: 99%

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

Qian

et al. 2020

Complexity

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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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“…Function. The heterogeneity of investors can be divided into four aspects: heterogeneous belief, heterogeneous constraint, heterogeneous income, and heterogeneous preference [35][36][37]. From the point of view of information function, considering the heterogeneity of investors, the transformation probability model of investor risk is constructed.…”

Section: Analysis Of Transformation Probability Of Investor Risk Bymentioning

confidence: 99%

“…Combined with the latest research results of behavioral finance, the degree of investor risk preference is an important factor influencing the transformation rate of investor risk [37]. The degree of investor preference promotes and inhibits risk contagion.…”

Section: Investor Risk Preferencementioning

confidence: 99%

Price Linkage Rumors in the Stock Market and Investor Risk Contagion on Bilayer‐Coupled Networks

2019

Self Cite

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Investor heterogeneities include investor risk preference, investor risk cognitive level, information value, and investor influence. From the perspective of the stock price linkage, this article constructs an SCIR contagion model of investor risk on a single-layer network. It digs out the investor risk caused by rumors in the stock market under the stock price linkage and its contagion mechanism. The function and influence of different mechanism probabilities and investor heterogeneities on the effects of risk contagion in the stock market are explored through computer simulation. Based on the SCIR contagion model of investor risk on single-layer network, we construct an SCI1I2R contagion model of investor risk on bilayer-coupled networks. Initially, the evolution mechanisms of investor risk contagion in the stock market are compared in single-layer and bilayer-coupled networks. Thereafter, the evolution characteristics and rules of investor risk contagion under different connection modes and heterogeneous mechanism probabilities are compared on bilayer-coupled networks. The results corroborate the following. (1) In the SCIR contagion model of investor risk on a single-layer network, immune failure probability and immune probability have the “global effect”. (2) Investor heterogeneities both have “global effect” and “local effect” on investor risk contagion. (3) Compared with the investor risk contagion on a single-layer network, bilayer-coupled networks can expand the investor risk contagion and have a “global enhancement” effect. (4) Among the three interlayer connection modes of the SCI1I2R model of investor risk contagion on bilayer-coupled networks, the assortative link has the effect of “local enhancement”, while the disassortative link has the effect of “local inhibition”. (5) In the SCI1I2R model of investor risk contagion on bilayer-coupled networks, heterogeneous mechanism probabilities have “global effect” and “local effect”. The research conclusion provides a theoretical basis for regulators to prevent financial risks from spreading among different investors, which is of high theoretical value and practical significance.

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Credit Risk Contagion in an Evolving Network Model Integrating Spillover Effects and Behavioral Interventions

Cited by 16 publications

References 63 publications

A Bertrand Duopoly Game with Long-Memory Effects

A Bertrand Duopoly Game with Long-Memory Effects

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

Price Linkage Rumors in the Stock Market and Investor Risk Contagion on Bilayer‐Coupled Networks

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