Contagion in Financial Networks

Glasserman, Paul; Young, Peyton

doi:10.2139/ssrn.2681392

Cited by 29 publications

(17 citation statements)

References 144 publications

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“…Our research suggests that one cannot impose such arbitrary regularities, and instead one has to think carefully on how heterogeneity in balance sheets and heterogeneity in network structure co-evolve. This point was made by Glasserman and Young (2016) as well. In this paper, we do not model the balance sheets.…”

Section: Resultsmentioning

confidence: 83%

The Formation of a Core-Periphery Structure in Heterogeneous Financial Networks

2016

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Recent empirical evidence suggests that financial networks exhibit a core-periphery network structure. This paper aims at giving an explanation for the emergence of such a structure using network formation theory. We propose a simple model of the overnight interbank lending market, in which banks compete for intermediation benefits. Focusing on the role of bank heterogeneity, we find that a core-periphery network cannot be unilaterally stable when banks are homogeneous. A core-periphery network structure can form endogenously, however, if we allow for heterogeneity among banks in size. Moreover, size heterogeneity may arise endogenously if payoffs feed back into bank size.

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Section: Resultsmentioning

confidence: 83%

The Formation of a Core-Periphery Structure in Heterogeneous Financial Networks

2016

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“…However, such dynamics can be important when we consider particular applications. 25 Moreover, as we have already argued above, we might be able to eliminate some of the less appealing stable networks (e.g. those where each agent's expected net payo¤ is negative) as dynamically unstable.…”

Section: Distribution Of Shocksmentioning

confidence: 89%

“…For example, as Chai (1993) suggests while it is true that exposure to the risk of in…ltration keeps the size of resistance groups small, these groups can be further protected by having a hierachical structure where every person is in contact with no more that three other members (one above and two below). 4 For excellent literature reviews see Acemoglu et al 2017a, Babus and Allen (2009) and Glasserman and Young (2016). 5 For a network approach to market freezes see Gabrieli and Georg (2014).…”

Section: Contagion In Social and Economic Networkmentioning

confidence: 99%

Contagion in Stable Networks

Bougheas

2017

SSRN Journal

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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. Terms of use: Documents in Contagion in Stable Networks AbstractWe study the formation of networks in environments where agents derive benefits from other agents directly linked to them but suffer losses through contagion when any agent on a path connected to them is hit by a shock. We first consider networks with undirected links (e.g. epidemics, underground resistance organizations, trade networks) where we find that stable networks are comprised of completely connected disjoint subnetworks. Then, we consider networks with directed links and we find that the completely connected network is stable, although, its exact structure, and thus contagion implications, is sensitive to parameter values for costs and benefits. Lastly, we introduce aggregate externalities (e.g. fire sales for the case of financial networks) and we find that stable networks can be asymmetric, connected but not completely connected, thus capturing the main features of inter-industry and financial networks. JEL-Codes: C720, D850.

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“…Some studies are dedicated to optimizing scenario selection, and defining probability distributions of the numerous intertwined driving variables across asset classes. For two recent reviews see 3,28 and for a mathematical approach to worst case scenario selection see 29 . However, we were not able to find any previous research on reverse stress testing in interbank networks that investigates systemic risk.…”

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confidence: 99%

Reverse Stress Testing Interbank Networks

Grigat

Caccioli

2017

SSRN Journal

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We reverse engineer dynamics of financial contagion to find the scenario of smallest exogenous shock that, should it occur, would lead to a given final systemic loss. This reverse stress test can be used to identify the potential triggers of systemic events, and it removes the arbitrariness in the selection of shock scenarios in stress testing. We consider in particular the case of distress propagation in an interbank market, and we study a network of 44 European banks, which we reconstruct using data collected from banks statements. By looking at the distribution across banks of the size of smallest exogenous shocks we rank banks in terms of their systemic importance, and we show the effectiveness of a policy with capital requirements based on this ranking. We also study the properties of smallest exogenous shocks as a function of the parameters that determine the endogenous amplification of shocks. We find that the size of smallest exogenous shocks reduces and that the distribution across banks becomes more localized as the system becomes more unstable.Systemic risk -the risk associated with the occurrence of a catastrophic breakdown of the financial systemarises endogenously from interactions between the participants that operate in financial markets. Because some types of interactions between financial institutions (in the following banks for brevity) can be modeled in terms of dynamical processes on networks, a growing body of literature has focused on the study of contagion and distress propagation in financial networks [1][2][3] . This research began in the year 2000 with the work of Allen and Gale, who showed that the topology of financial networks influences financial contagion 4 . Many different algorithms have since then been developed to model the propagation of distress between banks under different assumptions as well as to study the relation between the structure of a financial network and its stability (see for instance 3,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] ). In this respect, significant progress has been made in the identification of the main drivers of financial contagion and in the design of new stress test frameworks that, at odds with standard micro-prudential tools, do account for interactions between banks 13,[21][22][23][24][25][26] . While the focus of research carried out so far has been mainly that of developing models to understand how exogenous shocks are amplified by the endogenous dynamics of the system, here we look at the reverse problem. We compute the time trajectories of smallest shocks that need to affect banks to produce a final loss of equity larger than a given threshold, which we therefore refer to as worst case shocks. The solution of this reverse problem is useful to identify stress scenarios whose occurrence would lead to systemic events, thus identifying the vulnerabilities of a financial system.At the level of individual institutions, reverse stress testing is a regulatory requirement in the United Kingdom (UK) and the European Union. The...

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Contagion in Financial Networks

Cited by 29 publications

References 144 publications

The Formation of a Core-Periphery Structure in Heterogeneous Financial Networks

The Formation of a Core-Periphery Structure in Heterogeneous Financial Networks

Contagion in Stable Networks

Reverse Stress Testing Interbank Networks

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