How the ownership structures cause epidemics in financial markets: A network-based simulation model

Dastkhan, Hossein; Gharneh, Naser Shams

doi:10.1016/j.physa.2017.09.089

Cited by 16 publications

(9 citation statements)

References 29 publications

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“…Then, we evaluate the 5% VaR (Value at Risk) of the assets as infectious. The time evolution of the epidemic process is then determined by the system of Equation (10), and during the time evolution, we analyze the mean value of assets subject to systemic risk when different topological structures of the underlying network are assumed. We use the fixed recovery rate (ψ = 2) and contagion rate (r = 0.1) to emphasize the effect of the network structure on the dynamics.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…We use an exponential distribution to characterize the initial distribution of assets affected by systemic risk on each node, and we evaluate the credit risk at the 5% level by calculating the related percentile. Given this setting, we run three different simulations of the dynamics of contagion on the random network structure on the core-periphery structure and on the small-world network, in each case assuming the model of contagion (10). The basic parameters of the model of epidemic spreading, i.e., the recovery rate and the contagion rate, are the same for all network structures for the sake of a better comparison.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Basically, numerical simulations are designed to compare the spread of contagion of non-performing loans on a random network characterized by different levels of connection probability, i.e., a level of connectivity ranging from low to high and on core-periphery networks with different ratios between the core and periphery nodes. The core-periphery network structure is chosen because of its representativeness of most real financial networks (see [9,10] for some recent contributions to the scientific literature).…”

Section: Introductionmentioning

confidence: 99%

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Credit Risk Contagion and Systemic Risk on Networks

et al. 2019

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This paper proposes a model of the dynamics of credit contagion through non-performing loans on financial networks. Credit risk contagion is modeled in the context of the classical SIS (Susceptibles-Infected-Susceptibles) epidemic processes on networks but with a fundamental novelty. In fact, we assume the presence of two different classes of infected agents, and then we differentiate the dynamics of assets subject to idiosyncratic risk from those affected by systemic risk by adopting a SIIS (Susceptible-Infected1-Infected2-Susceptible) model. In the recent literature in this field, the effect of systemic credit risk on the performance of the financial network is a hot topic. We perform numerical simulations intended to explore the roles played by two different network structures on the long-term behavior of assets affected by systemic risk in order to analyze the effect of the topology of the underlying network structure on the spreading of systemic risk on the structure. Random graphs, i.e., the Erdös–Rényi model, are considered “benchmark” network structures while core-periphery structures are often indicated in the literature as idealized structures, although they are able to capture interesting, specific features of real-world financial networks. Moreover, as a matter of comparison, we also perform numerical experiments on small-world networks.

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Section: Numerical Experimentsmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Credit Risk Contagion and Systemic Risk on Networks

et al. 2019

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“…In one point of view, the systemic risk literature could be divided into networkbased and time-series systemic risk measures. Network-based studies are focused on the analysis of balance sheet exposure of firms which led to financial distresses in the financial networks (for example, see Elliott et al (2014), Dastkhan and Shams Gharneh (2016, 2018). The main obstacles of these measures could be summarized in two specific cases: (1) the required data related to the exposure of financial agents are not available and (2) the modeling of financial networks is static in most cases.…”

Section: Literature Reviewmentioning

confidence: 99%

What are the most effective and vulnerable firms in financial crisis? A network representation of CoVaR in an emerging market

Dastkhan

2019

J. Finan. Eng.

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In this paper, financial networks are applied to develop new measures of systemic risk. Using the CoVaR as a well-defined systemic risk measure, risk spillovers among different firms are estimated in Tehran Stock Exchange. Networks of systemic risk exposures are represented across time and two indices of vulnerability and systemic importance are introduced to define the most effective and vulnerable firms. The results show that the proposed network-based indices have a good performance to identify the vulnerable and systemically important firms. The results also show that when the market is in periods of financial downturn/crisis, the network interconnectedness was maximized.

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“…The rapid development and application of brain evoked potential testing technology have led to the increasing use of brain imaging technology for the researches of intertemporal decision-making. In recent years, a number of neural processing models have been proposed for neural loop systems that represent subjective values in network inter-temporal decision-making, including single-system neural processing models, dual-system neural processing models, and multinetwork mechanism neural models (Dastkhan and Gharneh, 2018;Xia et al, 2017;Zaccone et al, 2017). The financial risks caused by an enterprise's inter-temporal decision-making are affected by the attributes of the decision-making object, the traits of the decision-maker, and the decision-making context.…”

Section: Introductionmentioning

confidence: 99%

Risk Decision of Corporate Internet Financial Reporting Based on Brain Evoked Potential Testing Technology

2018

Neuroquantology

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Corporate decision makers will weigh and balance before making any decisions at different points in time. Different time and process of decision-making will lead to different degrees of risk to the financial status of the company. According to different inter-temporal decision-making financial risks, corporate decision makers will show different behavioral responses and neural changes. Based on the brain evoked potential testing technology, this study tests the behavioral performance and brain mechanism responses of the subjects under the frameworks of financial risk and zero financial risk, and explores the brain evoked potential and brain network mechanism of inter-temporal decision-making on financial risks. The experimental results show that subjects are more willing to choose the options that are nearer in time and smaller in number under the framework of risk conditions. Under the two frame conditions, the decision type has significant main effect, while the electrode has no main effect, and there is no interaction effect between the decision type and the electrode. The degree distribution, clustering coefficient and shortest path length under the two frame conditions are different, that is, the function and efficiency of brain network are different. Analysis of the key nodes by degree distribution also shows that the brain mechanism is different under the two conditions.

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How the ownership structures cause epidemics in financial markets: A network-based simulation model

Cited by 16 publications

References 29 publications

Credit Risk Contagion and Systemic Risk on Networks

Credit Risk Contagion and Systemic Risk on Networks

What are the most effective and vulnerable firms in financial crisis? A network representation of CoVaR in an emerging market

Risk Decision of Corporate Internet Financial Reporting Based on Brain Evoked Potential Testing Technology

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