Systemic stress test model for shared portfolio networks

Vodenska, Irena; Dehmamy, Nima; Becker, Alexander P.; Buldyrev, Sergey V.; Havlin, Shlomo

doi:10.1038/s41598-021-82904-y

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…Battiston et al [15,16] introduce degree centrality in networks to compare different financial institutions and propose a new centrality measure DebtRank, which further extends the idea of centrality in networks, the impact of different levels of nodes on the network can be seen more clearly. Vodenska et al [17][18][19] proposed a BankRank centrality metric based on the DebtRank idea and study the debt crisis providing evidence of the contagion of the 2007 financial crisis in equity and bond markets in emerging economies around the world.…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

Yang

Javaid

2022

Entropy

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The interdependence of financial institutions is primarily responsible for creating a systemic hierarchy in the industry. In this paper, an Adaptive Hierarchical Network Model is proposed to study the problem of hierarchical relationships arising from different individuals in the economic domain. In the presented dynamically evolving network model, new directed edges are generated depending on the existing nodes and the hierarchical structures among the network, and these edges decay over time. When the preference of nodes in the network for higher ranks exceeds a certain threshold value, the equality state in the network becomes unstable and rank states emerge. Meanwhile, we select four real data sets for model evaluation and observe the resilience in the network hierarchy evolution and the differences formed by different patterns of hierarchy preference mechanisms, which help us better understand data science and network dynamics evolution.

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Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

Yang

Javaid

2022

Entropy

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“…Similarly, Vodenska et al [11] introduced a metric called BankRank for dynamic bipartite networks, and applied it to the 2011 Eurozone sovereign debt crisis. Deb-tRank and BankRank are inspired by another centrality measure known as PageRank (Page et al [12]), which sought to organize search engine results, and has since been adopted by Google.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We used the BLS productivity data for the Manufacturing industry and the non-farm business sector. We used the non-farm business productivity levels as a proxy for each industry except Manufacturing (31G), Agriculture, Forestry and Fishing (11), and Government (G). The productivity data are reported as percent changes from the previous quarter at an annualized rate.…”

Section: Aftermath and Recoverymentioning

confidence: 99%

Socio-Economic Impact of the Covid-19 Pandemic in the U.S.

Barlow

Vodenska

2021

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This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a weighted digraph G = (V,E,W) using the industry-by-industry total requirements table for 2018, provided by the Bureau of Economic Analysis (BEA). We impose an initial shock that disrupts the production capacity of one or more industries, and we calculate the propagation of production shortages with a modified Cobb–Douglas production function. For the Covid-19 case, we model the initial shock based on the loss of labor between March and April 2020 as reported by the Bureau of Labor Statistics (BLS). The industries within the network are assigned a resilience that determines the ability of an industry to absorb input losses, such that if the rate of input loss exceeds the resilience, the industry fails, and its outputs go to zero. We observed a critical resilience, such that, below this critical value, the network experienced a catastrophic cascade resulting in total network collapse. Lastly, we model the economic recovery from June 2020 through March 2021 using BLS data.

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“…Del mismo modo, Vodenska, et al (2021), elaboraron un modelo dinámico de propagación del riesgo utilizando una red bipartita enfocada a la crisis de la deuda soberana europea de 2009-2012. En adición, proponen un indicador de riesgo sistémico simple y dinámico, que mide la cantidad de daño que sufre la red bancaria por la quiebra de un banco en particular.…”

Section: Figura 1 Modelo De Red Bipartita De Activos Bancarios Con Ba...unclassified

Identificación del Riesgo Sistémico en la Banca Múltiple: un enfoque de Redes Bipartitas

Goico Castillo,

Llaugel

2023

ceyn

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El sistema financiero puede representarse como una gran red compleja en la que los bancos están interconectados entre sí a través de vínculos financieros visibles e invisibles. En este trabajo se presenta un modelo que permite analizar un sistema financiero mediante un enfoque de teoría de redes, el cual constituye una herramienta práctica de monitoreo y diagnóstico para entes reguladores al permitir identificar las vulnerabilidades que surgen a medida que los bancos se vuelven cada vez más interconectadas, un choque en una red financiera puede provocar importantes fallas en cascada en todo un sistema, en otras palabras, riesgo sistémico. Para estudiar el riesgo sistémico del sector financiero dominicano, se crea un modelo de red financiera bipartita compuesto por dos tipos de nodos, (i) bancos múltiples (o comerciales) y (ii) cartera de crédito -segmentada por sector económico-, el cual prueba la influencia de cada crédito en particular o grupo de créditos en los bancos múltiples en general, y se implementa una prueba de estrés para evaluar la vulnerabilidad de la red ante el impacto en la cartera de crédito como consecuencia de sucesos externos en los distintos sectores económicos del país.

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Systemic stress test model for shared portfolio networks

Cited by 11 publications

References 32 publications

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

Socio-Economic Impact of the Covid-19 Pandemic in the U.S.

Identificación del Riesgo Sistémico en la Banca Múltiple: un enfoque de Redes Bipartitas

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