Network models and financial stability

Nier, Erlend; Yang, Jing; Yorulmazer, Tanju; Alentorn, Amadeo

doi:10.1016/j.jedc.2007.01.014

Cited by 767 publications

(479 citation statements)

References 30 publications

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“…However, they do not analyse the effects of network structure on the dynamics of contagion. 6 Nier et al (2007) also simulate the effects of unexpected shocks in financial networks, though they do not distinguish the probability of contagion from its potential spread and their results are strictly numerical-they do not consider the underlying analytics of the complex (random graph) network that they use. Recent work by May & Arinaminpathy (2010) uses analytic mean-field approximations to offer a more complete explanation of their findings and also contrasts their results with those presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

Contagion in financial networks

2010

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This paper develops an analytical model of contagion in financial networks with arbitrary structure. We explore how the probability and potential impact of contagion is influenced by aggregate and idiosyncratic shocks, changes in network structure and asset market liquidity. Our findings suggest that financial systems exhibit a robust-yet-fragile tendency: while the probability of contagion may be low, the effects can be extremely widespread when problems occur. And we suggest why the resilience of the system in withstanding fairly large shocks prior to 2007 should not have been taken as a reliable guide to its future robustness.

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Section: Introductionmentioning

confidence: 99%

Contagion in financial networks

2010

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“…By employing basic concepts developed in network theory, such as percolation, assortativity and clustering, those studies attempt to extract the necessary conditions for a stable financial system by conducting simulations under various patterns of network topology [4][5][6][7][8]. The basic idea behind these studies is that a financial network must be robust enough to withstand default cascades.…”

Section: Introductionmentioning

confidence: 99%

Network versus portfolio structure in financial systems

Kobayashi

2013

Eur. Phys. J. B

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Abstract. The question of how to stabilize financial systems has attracted considerable attention since the global financial crisis of -2009. Recently, Beale et al. [Proc. Natl. Acad. Sci. USA 108, 12647 (2011] demonstrated that higher portfolio diversity among banks would reduce systemic risk by decreasing the risk of simultaneous defaults at the expense of a higher likelihood of individual defaults. In practice, however, a bank default has an externality in that it undermines other banks' balance sheets. This paper explores how each of these different sources of risk, simultaneity risk and externality, contributes to systemic risk. The results show that the allocation of external assets that minimizes systemic risk varies with the topology of the financial network as long as asset returns have negative correlations. In the model, a well-known centrality measure, PageRank, reflects an appropriately defined "infectiveness" of a bank. An important result is that the most infective bank needs not always to be the safest bank. Under certain circumstances, the most infective node should act as a firewall to prevent large-scale collective defaults. The introduction of a counteractive portfolio structure will significantly reduce systemic risk.

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“…Moreover, extensions of this model have been developed to include effects such as fire sales (e.g. Cifuentes et al (2005), Nier et al (2007), Gai and Kapadia (2010), Chen et al (2016), Amini et al (2016a,b), Weber and Weske (2017), Feinstein (2017a), Feinstein and El-Masri (2017), Feinstein (2017b), Di Gangi et al (2015)), cross-ownership (e.g. Elsinger (2009), Elliott et al (2014), Weber and Weske (2017)), bankruptcy costs (e.g.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of the Eisenberg-Noe Clearing Vector to Individual Interbank Liabilities

et al. 2017

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We quantify the sensitivity of the Eisenberg-Noe clearing vector to estimation errors in the bilateral liabilities of a financial system. The interbank liabilities matrix is a crucial input to the computation of the clearing vector. However, in practice central bankers and regulators must often estimate this matrix because complete information on bilateral liabilities is rarely available. As a result, the clearing vector may suffer from estimation errors in the liabilities matrix. We quantify the clearing vector's sensitivity to such estimation errors and show that its directional derivatives are, like the clearing vector itself, solutions of fixed point equations. We describe estimation errors utilizing a basis for the space of matrices representing permissible perturbations and derive analytical solutions to the maximal deviations of the Eisenberg-Noe clearing vector. This allows us to compute upper bounds for the worst case perturbations of the clearing vector. Moreover, we quantify the probability of observing clearing vector deviations of a certain magnitude, for uniformly or normally distributed errors in the relative liability matrix.Applying our methodology to a dataset of European banks, we find that perturbations to the relative liabilities can result in economically sizeable differences that could lead to an underestimation of the risk of contagion. Importantly, our results allow regulators to bound the error of their simulations.

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Network models and financial stability

Cited by 767 publications

References 30 publications

Contagion in financial networks

Contagion in financial networks

Network versus portfolio structure in financial systems

Sensitivity of the Eisenberg-Noe Clearing Vector to Individual Interbank Liabilities

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