Contagion in Financial Networks

Gai, Prasanna; Kapadia, Sujit

doi:10.2139/ssrn.1577043

Cited by 202 publications

(206 citation statements)

References 45 publications

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“…2 The fragility of an interconnected financial system was analyzed by Gai and Kapadia (2010), who show that the risk of systemic crises is reduced with increasing connectivity on the interbank market. At the same time, however, the magnitude of such a crisis increases.…”

Section: Introductionmentioning

confidence: 99%

The effect of the interbank network structure on contagion and common shocks

Georg¹

2013

Journal of Banking & Finance

354

179

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This paper proposes a dynamic multi-agent model of a banking system with central bank. Banks optimize a portfolio of risky investments and riskless excess reserves according to their risk, return, and liquidity preferences. They are linked via interbank loans and face stochastic deposit supply. Comparing different interbank network structures, it is shown that money-center networks are more stable than random networks. Evidence is provided that the central bank stabilizes interbank markets in the short run only. Systemic risk via contagion is compared with common shocks and it is shown that both forms of systemic risk require different optimal policy responses.

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Section: Introductionmentioning

confidence: 99%

The effect of the interbank network structure on contagion and common shocks

Georg¹

2013

Journal of Banking & Finance

354

179

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“…Such externalities resulting from counterparty risk are a major concern for regulators (Hellwig, 1995;Haldane, 2009) and network models (Allen and Gale, 2000;Boss et al, 2004;Cont and Moussa, 2010;Nier et al, 2007;Amini et al, 2010;Gai and Kapadia, 2010;Amini et al, 2012) provide an adequate framework for addressing them. Simulation studies based on network models have been extensively used by central banks for assessing contagion risk in banking systems; we refer to the pioneering work of Elsinger et al (2006a) and the survey of Upper (2011).…”

Section: Introductionmentioning

confidence: 99%

Network Structure and Systemic Risk in Banking Systems

Cont

Moussa

Santos³

2013

Handbook on Systemic Risk

224

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We present a quantitative methodology for analyzing the potential for contagion and systemic risk in a network of interlinked financial institutions, using a metric for the systemic importance of institutions: the Contagion Index.We apply this methodology to a data set of mutual exposures and capital levels of financial institutions in Brazil in 2007 and 2008, and analyze the role of balance sheet size and network structure in each institution's contribution to systemic risk. Our results emphasize the contribution of heterogeneity in network structure and concentration of counterparty exposures to a given institution in explaining its systemic importance. These observations plead for capital requirements which depend on exposures, rather than aggregate balance sheet size, and which target systemically important institutions.

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“…Sophisticated financial products further increase the complexity. These interdependencies enable amplifying positive feedback where problems in the financial system can snowball and make things worse [8,9,11,17]. Some multinational companies arrange their affairs to exploit the regulations between individual countries to minimise what they pay in tax, sometimes with governments gaming each other [30].…”

Section: Financementioning

confidence: 99%

Global Systems Science and Policy

Dum

Johnson

2017

Understanding Complex Systems

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The vision of Global Systems Science (GSS) is to provide scientific evidence and means to engage into a reflective dialogue to support policy-making and public action and to enable civil society to collectively engage in societal action in response to global challenges like climate change, urbanisation, or social inclusion. GSS has four elements: policy and its implementation, the science of complex systems, policy informatics, and citizen engagement. It aims to give policy makers and citizens a better understanding of the possible behaviours of complex social systems. Policy informatics helps generate and evaluate policy options with computer-based tools and the abundance of data available today. The results they generate are made accessible to everybody-policy makers, citizens-through intuitive user interfaces, animations, visual analytics, gaming, social media, and so on. Examples of Global Systems include epidemics, finance, cities, the Internet, trade systems and more. GSS addresses the question of policies having desirable outcomes, not necessarily optimal outcomes. The underpinning idea of GSS is not to precisely predict but to establish possible and desirable futures and their likelihood. Solving policy problems is a process, often needing the requirements, constraints, and lines of action to be revisited and modified, until the problem is 'satisficed', i.e. an acceptable compromise is found between competing objectives and constraints. Thus policy problems and their solutions coevolve much as in a design process. Policy and societal action is as much about attempts to understand objective facts as it is about the narratives that guide our actions. GSS tries to reconcile these apparently contradictory modes of operations. GSS thus provides policy makers and society guidance on their course of action rather than proposing (illusionary) optimal solutions.

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Contagion in Financial Networks

Cited by 202 publications

References 45 publications

The effect of the interbank network structure on contagion and common shocks

The effect of the interbank network structure on contagion and common shocks

Network Structure and Systemic Risk in Banking Systems

Global Systems Science and Policy

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