Using Multiplex Networks for Banking Systems Dynamics Modelling

Guleva, Valentina Y.; Skvorcova, Maria V.; Boukhanovsky, Alexander V.

doi:10.1016/j.procs.2015.11.031

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Previous studies have used different methods to analyze historic correlations as random matrix theory to identify the distribution of eigenvalues concerning financial correlations [6] , [7] , [8] , the partial transfer entropy to quantify the indirect influence that stock indices have on one another [9] , the approaches from information theory in exploring the uncertainty within the financial system [10] , [11] , community structure analysis [12] , multilayer network methods [13] , [14] , [15] , [16] , [17] , [18] , and filtering methods. Several authors have used network filtering methods to explain financial structures [19] , [20] , hierarchy and networks in financial markets [21] , relations between financial markets and real economy [22] , volatility [23] , interest rates [24] , stock markets [25] , [26] , [27] , [28] , future markets [29] or topological dynamics [30] to list a few.…”

Section: Introductionmentioning

confidence: 99%

An analysis of network filtering methods to sovereign bond yields during COVID-19

Pang

Granados

Chhajer

et al. 2021

Physica A: Statistical Mechanics and its Applications

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In this work, we investigate the impact of the COVID-19 pandemic on sovereign bond yields. We consider the temporal changes from financial correlations using network filtering methods. These methods consider a subset of links within the correlation matrix, which gives rise to a network structure. We use sovereign bond yield data from 17 European countries between the 2010 and 2020 period. We find the mean correlation to decrease across all filtering methods during the COVID-19 period. We also observe a distinctive trend between filtering methods under multiple network centrality measures. We then relate the significance of economic and health variables towards filtered networks within the COVID-19 period. Under an exponential random graph model, we are able to identify key relations between economic groups across different filtering methods.

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Section: Introductionmentioning

confidence: 99%

An analysis of network filtering methods to sovereign bond yields during COVID-19

Pang

Granados

Chhajer

et al. 2021

Physica A: Statistical Mechanics and its Applications

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“…With the rapid development of complex networks, such networks provide an effective tool for analyzing and solving the interaction of financial systems [27][28][29][30][31]. In the financial system, the credit association between banks and enterprises constitutes a relatively complex credit association network, which has become an important channel for credit risk and credit crisis contagion [12,[32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Double-Layer Network Model of Bank-Enterprise Counterparty Credit Risk Contagion

et al. 2020

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Banks and enterprises constitute a multilayered, multiattribute, multicriteria credit-related super network due to financial transaction behaviors, such as credit, wealth management, savings, and derivatives. Such a network has become an important channel for credit risk cross-contagion. This study constructs a two-layer network model of credit risk contagion between the bank and corporate counterparties from the perspective that banks do not withdraw loans from enterprises by considering the influence of corporate credit defaults on their counterparties under the credit linkage. This study analyzes the mechanism of influencing the evolution of bank-enterprise counterparty credit risk contagion in the two-tier network through theoretical analysis, including the following: the enterprises’ coping ability, risk preference, influence, level of interenterprise credit risk contagion and its network heterogeneity in the interenterprise credit association network, the risk prevention and control ability, business correlation degree, interbank credit risk contagion and its network heterogeneity in the interbank credit association network, the level of credit risk contagion between bank-enterprise counterparty credit association networks, and other factors in the case that banks do not withdraw loans from enterprises. In addition, this study performs a calculation experiment to analyze the characteristics of the evolution of counterparty credit risk contagion of bank and corporate counterparties under the double-layer network. The following four major conclusions can be drawn from the results. First, in the interenterprise credit-related network, the threshold of credit risk contagion rate is positively correlated with the marginal increase in risk perception and risk leveling ability of the enterprise. By contrast, such threshold is negatively correlated with the marginal decrease in the initial economic impact, leverage level, and influence of the enterprise. Moreover, the scale of corporate counterparty credit risk contagion is negatively correlated with the enterprise’s risk perception level and risk spillover ability but positively correlated with the enterprise’s initial economic shock level, the enterprise’s leverage level, and influence. Second, in the interbank credit association network, the threshold of the rate of credit risk contagion is negatively correlated with the marginal decrease in the degree of interbank business association but positively correlated with the marginal increase in the bank’s risk resistance ability and risk information processing ability. Furthermore, the scale of credit risk contagion of bank counterparties is positively correlated with the degree of interbank business association but negatively correlated with the bank’s ability to resist risks and process risk information. Third, if the heterogeneity of the credit-related network of bank-enterprise counterparties is high, then the rate threshold of credit risk contagion is high and the scale of credit risk diffusion is low. Moreover, the scale of credit risk contagion of bank counterparties is positively correlated with the marginal decrease in the degree of corporate and bank counterparties. Finally, the scale of bank counterparty credit risk contagion is a monotonically increasing convex function of the credit risk contagion rate in the enterprise credit association network and among the bank-enterprise networks.

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“…Finalmente, con una mayor aproximación a esta tesis están las propuestas de redes financieras multicapas que permiten identificar desde diferentes aproximaciones la estructura y dinámica de los sistemas financieros. Guleva et al (2015), usando modelos dinámicos, miden la duración de los enlaces con sus longitudes y, a partir de esto, construyen capas y la longitud de los enlaces que cambia en cada interacción. El trabajo de Poledna et al (2015) muestra que la limitación de centrarse en una sola capa puede subestimar el riesgo sistémico e incorporan cuatro capas diferentes para el mercado mexicano.…”

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Emergencia global del ecosistema financiero computacional

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El ecosistema financiero está compuesto por agentes e interacciones en un sistema abierto que genera segundo a segundo miles de interacciones entre agentes humanos, no humanos y exobiológicos. Al ser los sistemas financieros parte de la complejidad social y como resultado de una creciente transformación tecnológica, esta tesis hace referencia a una forma transdisciplinar de pensar que ayude a resolver las preguntas sobre la evolución del ecosistema y de buscar las respuestas en la sabiduría colectiva de un grupo de ciencias y no en el egoísmo y las verdades absolutas de la disciplinariedad. Esta tesis acude a los sistemas complejos, a la ciencia de redes, a las ciencias computacionales, a la biología evolutiva, a la topología, a la geometría, a la historia, a la economía, a la sociología y a la ficción para identificar los futuros posibles que tiene el ecosistema financiero al incorporar las transformaciones tecnológicas, la simbiosis con el ecosistema tecnológico y la sinapsis de las inteligencias natural y artificial. Es entender la emergencia de un ecosistema computacional como resultado de una simbiogénesis y una vida computacional no conocida. Pero también, de una fusión científica pues, con la articulación de varias ciencias, se logran simular las interacciones que no son visibles en la escala humana, ya que con la sinapsis de las inteligencias será fácil interactuar en microtiempos y microespacios, reduciendo las diferencias temporales y espaciales que nos han acompañado. ¿Cuál es el futuro de los agentes financieros? ¿Cómo será la interacción entre los agentes financieros? ¿Por qué la transformaciones y convergencias tecnológicas cambiarán al ecosistema? En las próximas páginas se conocerá sobre la evolución de los agentes financieros desde una perspectiva histórica hasta la simulación computacional de los futuros posibles del ecosistema. Es así como se articulan fuentes históricas, modelos matemáticos, argumentos sociológicos y filosóficos, teorías de la física, guiones cinematográficos, simulaciones, código computacional e imaginación para identificar la emergencia global del ecosistema financiero computacional. Un ecosistema en el que las aglomeraciones digitales serán la forma de interactuar entre agentes humanos, no humanos y exobiológicos, pero también de coevolucionar hacia una nueva forma de interactuar en Gaia, una nueva Gaia. El lector debe estar preparado para aprender de los mecanismos que identifican a los agentes financieros, de la dinámica del ecosistema, de los procesos de transmisión, difusión y transición de la tecnología, de la simbiosis de los ecosistemas, de la sinapsis de las inteligencias y la simbiogénesis de un nuevo tipo de agentes financieros, siempre en el marco de la ciencia de redes, pues los agentes se moverán indistintamente en el ecosistema y entender la distancia entre ellos será fundamental. También para alternar en diferentes dimensiones temporales y espaciales, pues como sistema abierto, esta tesis explora su curiosidad y si quiere profundizar un hecho o una narración específica deberá salir de la tesis o buscar en su interior los senderos que lo lleven a encontrar las respuestas.

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Using Multiplex Networks for Banking Systems Dynamics Modelling

Cited by 8 publications

References 7 publications

An analysis of network filtering methods to sovereign bond yields during COVID-19

An analysis of network filtering methods to sovereign bond yields during COVID-19

Double-Layer Network Model of Bank-Enterprise Counterparty Credit Risk Contagion

Emergencia global del ecosistema financiero computacional

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