Evaluating systemic risk using bank default probabilities in financial networks

Souza, S. R.; Silva, Thiago Christiano; Tabak, Benjamin Miranda; Guerra, Solange Maria

doi:10.1016/j.jedc.2016.03.003

Cited by 54 publications

(29 citation statements)

References 42 publications

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“…The derivative financial assets and the financial assets that are trading or available for sale. However, according to the measurements of the ∆CoVaRs based on the data from financial markets, the large SOE banks do not always have the highest contributions to systemic risk, which is consistent with the conclusions drawn by [39,40]. Furthermore, some JOI banks and even some CCB banks contribute more to the systemic risk of China's banking industry than the SOE banks, whose sizes are larger than those of the JOI and CCB banks.…”

Section: Complexitysupporting

confidence: 83%

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Discovering Systemic Risks of China's Listed Banks by CoVaR Approach in the Digital Economy Era

Jiang

Zhang

2020

Mathematics

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The world has entered the digital economy era. As a developing country, China's banking industry plays an important role in the financial industry, and its size ranks first in the world. Therefore, it is of great significance to study the systemic risks of China's banks in the digital economy era. We first compare the traditional indicator approach and the market-based approach theoretically, and Conditional Value at Risk (CoVaR) model, a market-based approach, is considered to be an efficient way to discover systemic risk in different perspectives. Based on static and dynamic models, we evaluate the contributions of sixteen China's listed banks to the systemic risk. Furthermore, we model bank exposures, extend the models by considering extreme circumstance, and incorporate the effects of Fintech and non-bank financial institutions. The results show the levels of systemic risks and the corresponding systemic importance rankings vary in different time periods. We find that the contributions of some small banks to systemic risk are even higher than some big banks during the sample period. Moreover, the big banks face less risks than most of the small banks when the banking system is in distress. We make suggestions for improving financial supervision and maintaining financial stability.

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Section: Complexitysupporting

confidence: 83%

“…BJYH ranks 1st, with a CoVaR of 55. 39 26.94) in 2015 is 1.28 times the historical highest value (JTYH in 2013, 21.10), and the highest ∆CoVaR (HXYH, 37.53) in 2015 is 1.78 times that in 2013. Furthermore, the lowest ∆CoVaR (ZSYH, 26.94) is 4.07 times the historical lowest ∆CoVaR (PAYH in 2012, 6.61).…”

Section: Sample Descriptionmentioning

confidence: 90%

Discovering Systemic Risks of China's Listed Banks by CoVaR Approach in the Digital Economy Era

Jiang

Zhang

2020

Mathematics

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“…These structures have not been fully investigated in the financial literature. Notable exceptions are represented by de Souza et al (2016), Puliga et al (2016) and Bargigli and Gallegati (2013). 2 A case in point to better understand the role of the community structure in a network is provided by epidemiology.…”

Section: Introductionmentioning

confidence: 99%

Financial Bridges and Network Communities

2018

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Financial crises and systemic risks proved to play central roles as shock transmitters to the real economy, threatening the stability of the economic and financial systems. These phenomena have boosted a massive interest in the literature which deeply investigated systemic risk and contagion channels on the financial systems.Generally, systemic risk may arise as the interactions among financial institutions and markets which, consequently, lead to financial crises. A stylized fact that occurs often in real networks is the presence of a group of nodes which share common properties or play a similar role within the network. These community structures have been recognized also in finance with the presence of key nodes (community bridges) linked through short-cuts to otherwise separated communities. The case in point to better understand the role of the community structure in a network is provided by epidemiology. A parallelism with the financial stability indicates that the mitigation and prevention of the spread in infectious diseases (financial contagion) can be attained by seeking actively to immunize the super-spreaders. However, the presence of a community structure significantly affects the dynamic of the disease: immunization interventions focusing on nodes strongly linked with other communities (community bridges) are in this case more effective than the ones which aim to the highly connected nodes in the whole network. The reason is that community bridges are more relevant in spreading out contagion with respect to the nodes with fewer inter-community connections in the group: with the latter, contagion may stop before spreading out to the other communities. Hence, in a network with a community structure, classical connectedness measures can lead to the misidentification of a given SIFI at least in two cases: i) a financial institution shows a lower total degree with respect to the other nodes in the community, but a higher degree to the nodes belonging to other communities (false negative); ii) a financial institution shows a higher total degree with respect to the other nodes in the community, but a lower degree to the nodes belonging to other communities (false positive). Therefore, also in a financial network, the node immunization through the identification of highly connected nodes may not be effective in a network with community structure.On this ground, the aim of this paper is to investigate the topology of the financial networks focusing on ...

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“…Notable exceptions are represented by De Souza et al (2016), Puliga et al (2016) and Bargigli and Gallegati (2013). Bargigli and Gallegati (2013) investigate credit communities on a Japanese bank-firm weighted and directed bipartite network finding a strengthening of Japanese communities over time.…”

Section: Introductionmentioning

confidence: 99%

“…Bargigli and Gallegati (2013) investigate credit communities on a Japanese bank-firm weighted and directed bipartite network finding a strengthening of Japanese communities over time. Puliga et al (2016) find through an accounting network that regional bank communities change weakening geographically boundaries while De Souza et al (2016) identify Brazilian banking communities through inter-banking exposures and find that a large part of them includes non-large banks.…”

Section: Introductionmentioning

confidence: 99%

Financial Bridges and Network Communities

2017

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Financial crises and systemic risks proved to play central roles as shock transmitters to the real economy, threatening the stability of the economic and financial systems. These phenomena have boosted a massive interest in the literature which deeply investigated systemic risk and contagion channels on the financial systems.Generally, systemic risk may arise as the interactions among financial institutions and markets which, consequently, lead to financial crises. A stylized fact that occurs often in real networks is the presence of a group of nodes which share common properties or play a similar role within the network. These community structures have been recognized also in finance with the presence of key nodes (community bridges) linked through short-cuts to otherwise separated communities. The case in point to better understand the role of the community structure in a network is provided by epidemiology. A parallelism with the financial stability indicates that the mitigation and prevention of the spread in infectious diseases (financial contagion) can be attained by seeking actively to immunize the super-spreaders. However, the presence of a community structure significantly affects the dynamic of the disease: immunization interventions focusing on nodes strongly linked with other communities (community bridges) are in this case more effective than the ones which aim to the highly connected nodes in the whole network. The reason is that community bridges are more relevant in spreading out contagion with respect to the nodes with fewer inter-community connections in the group: with the latter, contagion may stop before spreading out to the other communities. Hence, in a network with a community structure, classical connectedness measures can lead to the misidentification of a given SIFI at least in two cases: i) a financial institution shows a lower total degree with respect to the other nodes in the community, but a higher degree to the nodes belonging to other communities (false negative); ii) a financial institution shows a higher total degree with respect to the other nodes in the community, but a lower degree to the nodes belonging to other communities (false positive). Therefore, also in a financial network, the node immunization through the identification of highly connected nodes may not be effective in a network with community structure.On this ground, the aim of this paper is to investigate the topology of the financial networks focusing on the detection of financial communities and community bridges to overcome the weakness of classical connectedness measure. We denote these communities as the Systemically Important Financial Communities (SIFC) defined as a group of nodes that belong to the community with the highest inter-connectivity density of the network. In this regard, we propose measures of connectedness to describe the inter-and intra community connectivity in financial networks. In the empirical analysis, we investigate the European financial system from 1996 to 2013 including all the f...

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Evaluating systemic risk using bank default probabilities in financial networks

Cited by 54 publications

References 42 publications

Discovering Systemic Risks of China's Listed Banks by CoVaR Approach in the Digital Economy Era

Discovering Systemic Risks of China's Listed Banks by CoVaR Approach in the Digital Economy Era

Financial Bridges and Network Communities

Financial Bridges and Network Communities

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