Systemic risk components in a network model of contagion

Staum, Jeremy; Feng, Mingbin; Liu, Ming

doi:10.1080/0740817x.2015.1110650

Cited by 15 publications

(5 citation statements)

References 16 publications

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“…It triggers a substantial body of investigation on the relationship between systemic risk and network topology. We refer the reader to Gai and Kapadia (2010), Liu and Staum (2010), Gai et al (2011), Haldane and May (2011), Staum et al (2016, Acemoglu et al (2015), , and Elliott et al (2014) for more details about the recent developments in this direction. Another line of research, represented by Shleifer and Vishny (1997), Gromb and Vayanos (2003), Allen and Gale (2004), Gârleanu and Pedersen (2007), and Brunnermeier and Pedersen (2009), primarily focuses on the contagious effect of asset price.…”

Section: Related Literaturementioning

confidence: 99%

“…The matrix of I − P −1 will be referred to as the network multiplier below, because it captures the above amplification mechanism through the liability network. Such notion is well known in the network literature (see, e.g., Staum et al 2016). We will compare it with the liquidity effect developed below in the next section.…”

Section: Net Worth Systemic Resilience and Market Liquiditymentioning

confidence: 99%

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An Optimization View of Financial Systemic Risk Modeling: Network Effect and Market Liquidity Effect

Chen

Liu

Yao

2016

Operations Research

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Financial institutions are interconnected directly by holding debt claims against each other (the network channel), and they are also bound by the market when selling assets to raise cash in distressful circumstances (the liquidity channel). The goal of our study is to investigate how these two channels of risk interact to propagate individual defaults to a systemwide catastrophe. We formulate a constrained optimization problem that incorporates both channels of risk, and exploit the problem structure to generate the solution (to the clearing payment vector) via a partition algorithm. Through sensitivity analysis, we are able to identify two key contributors to financial systemic risk, the network multiplier and the liquidity amplifier, and to discern the qualitative difference between the two, confirming that the market liquidity effect has a great potential to cause systemwide contagion. We illustrate the network and market liquidity effects-in particular, the significance of the latter-in the formation of systemic risk with data from the European banking system. Our results contribute to a better understanding of the effectiveness of certain policy interventions. In addition, our algorithm can be used to pin down the changes of the net worth (marked to market) of each bank in the system as the spillover effect spreads, so as to estimate the extent of contagion, and to provide a metric of financial resilience as well. Our framework can also be easily extended to incorporate the effect of bankruptcy costs.

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Section: Related Literaturementioning

confidence: 99%

Section: Net Worth Systemic Resilience and Market Liquiditymentioning

confidence: 99%

An Optimization View of Financial Systemic Risk Modeling: Network Effect and Market Liquidity Effect

Chen

Liu

Yao

2016

Operations Research

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“…This network can describe the complex relationship between financial subjects in contagion effects. For example, Staum et al (2016) develop the theory of systemic risk attribution by using the Shapley and Aumann-Shapley values to show how systematic risk attribution works in a complex network model of contagion with interlocking balance sheets. In their model, contagion spreads through the network when a firm that borrows defaults and the default of this firm results in the default of the lender.…”

Section: Introductionmentioning

confidence: 99%

Reduced-form models of correlated default timing: a systematic literature review

Nguyen

Zhou²

2022

JAL

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PurposeThis paper aims to provide an overview, a classification of existing research groups for correlated default models using a reduced-form method and an identification of future research opportunities in the field.Design/methodology/approachA systematic literature review is used for the identification, selection, evaluation and synthesis of relevant literature using keywords regarding the reduced-form default models in the Web of Science database. The authors also add articles from cross-referencing and expert recommendations to the literature. HistCite™ program is used to generate a citation map of the literature.FindingsThe results show that reduced-form correlated default risk models are developing towards modelling credit risk with both observable and unobservable variables. The frailty correlated default model at the firm level is still a potential research field.Originality/valueThis is the first paper systematically reviewing the research on reduced-form models of default timing.

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“…The use of risk measurement methods has been studied recently by Acharya et al (2017); Adrian and Brunnermeier (2008); Jonghe (2010); Staum et al (2016). Authors often use stock data because this is an easily accessible source of information on the market.…”

Section: Introductionmentioning

confidence: 99%

Systemic Risk in Vietnam Stock Market

Hung

2019

Asian Economic and Financial Review

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Systemic risk is one of the issues currently being paid attention to in ensuring the stability and sustainability of the global financial system in general and the securities market of countries in particular. The paper studied the systemic risk of enterprises listed on Ho Chi Minh City Stock Exchange in the period from the first quarter of 2010 to the second quarter of 2017. The authors have applied the VaR and CoVaR method to compare the loss level of businesses to the systemic risk of the whole market upon an unstable event. The study also found a disadvantage of using VaR in measuring systemic risk in that it was still "individual" and "single" and didn't consider the spread among various entities in the market. In addition, the sensitivity of listed companies varied under normal and volatile conditions. The results showed that CoVaR is a more suitable measure in considering the contribution level of companies to the systemic risk of the whole market. Calculated results were proposed as an indicator for investors and market managers in order to limit systemic risks in the future. Contribution/ Originality:This study is one of very few studies which have applied VaR and CoVaR methods to measure and rank system risks of listed companies in Vietnam. Based on the quantile regression, the study also found that the disadvantage of using VaR in measuring systemic risk was that it was still "individual" and "single" and didn't consider the spread among various entities in the market. In addition, the sensitivity of listed companies varies under normal and volatile conditions. CoVaR is a more appropriate method of assessing the contribution to systemic risks of the whole market.

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Systemic risk components in a network model of contagion

Cited by 15 publications

References 16 publications

An Optimization View of Financial Systemic Risk Modeling: Network Effect and Market Liquidity Effect

An Optimization View of Financial Systemic Risk Modeling: Network Effect and Market Liquidity Effect

Reduced-form models of correlated default timing: a systematic literature review

Systemic Risk in Vietnam Stock Market

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