Bilateral exposures and systemic solvency risk

Gouriéroux, Christian; Héam, Jean-Cyprien; Monfort, Alain

doi:10.1111/j.1540-5982.2012.01750.x

Cited by 78 publications

(26 citation statements)

References 32 publications

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“…They found that a single large shock could have devastating effects on the network, but that this was highly dependent on where in the network the shock hit. Gouriéroux, Héam, and Monfort (2012) considered a model that allows interbank investment via shares (like (Elliott, Golub, and Jackson, 2014)) and lending or insurance (like (Eisenberg and Noe, 2001)). Unlike (Elliott, Golub, and Jackson, 2014), they do not introduce discontinuous failure costs.…”

Section: Previous Workmentioning

confidence: 99%

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Sensitivity and computational complexity in financial networks

Hemenway

Khanna

2017

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Abstract. Determining the causes of instability and contagion in financial networks is necessary to inform policy and avoid future financial collapse.In the American Economic Review, Elliott, Golub and Jackson proposed a simple model for capturing the dynamics of complex financial networks. In Elliott, Golub and Jackson's model, the institutions in the network are connected by linear dependencies (cross-holdings) and if any institution's value drops below a critical threshold, its value suffers an additional failure cost. This work shows that even in this simple model there are fundamental barriers to understanding the risks that are inherent in a network.First, if institutions are not required to maintain a minimum amount of self-holdings, any change in investments by a single institution can have an arbitrarily magnified influence on the net worth of the institutions in the system. This implies that if institutions have small self-holdings, then estimating the market value of an institution requires almost perfect information about every cross-holding in the system. Second, even if a regulator has complete information about all cross-holdings in the system, it may be computationally intractable to estimate the number of failures that could be caused by a small shock to the system.

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Section: Previous Workmentioning

confidence: 99%

“…The spread of risk through a financial network is known as financial contagion and has been carefully modeled and studied (Allen and Gale, 2000;Eisenberg and Noe, 2001;Gouriéroux, Héam, and Monfort, 2012;Acemoglu, Ozdaglar, and Tahbaz-Salehi, 2015;Glasserman and Young, 2014;Elliott, Golub, and Jackson, 2014).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity and computational complexity in financial networks

Hemenway

Khanna

2017

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“…We propose a systemic risk measure based on the WSV and similar financial networks as in Gourieroux et al . () and Elliott et al . (), focusing on how to capture each bank's systemic risk depending on its asset structure and its position in the network.…”

Section: Literature Reviewmentioning

confidence: 87%

“…We base the proposed systemic risk measure on a network framework similar to those in Gourieroux et al . () and Elliott et al . ().…”

Section: Introductionmentioning

confidence: 87%

Using Weighted Shapley Values to Measure the Systemic Risk of Interconnected Banks

Lin

2016

Pacific Economic Review

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A macro‐prudential approach to financial regulation should reasonably evaluate the systemic risk of individual institutions. We introduce a measure of the systemic risk of interconnected banks using the weighted Shapley value, which characterizes banks' systemic risk by capturing two characteristics: (i) a bank's risk exposure of the primitive asset structure; and (ii) its position in the interbank network. An empirical implementation reveals that the primitive asset structure and interbank connections are main drivers of each bank's systemic risk. In addition, different types of interbank linkages (debt or share) make significant differences in affecting an individual bank's systemic risk.

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“…Our framework also differs from recent efforts directed at connecting network structure and the propagation of adverse shocks, which focus on the “contagion” of catastrophic events in specific sectors, such as bank failure (e.g., Acemoglu, Ozdaglar, and Tahbaz‐Salehi (), Elliott, Golub, and Jackson (), Gouriéroux, Héam, and Monfort (), among others) over just a few time periods. In contrast, our model generates long memory even from everyday shocks, and not just through rare catastrophic events, and makes specific predictions regarding the network's spectral response within an infinite‐horizon framework.…”

Section: Introductionmentioning

confidence: 99%

Long Memory via Networking

Schennach¹

2018

Econometrica

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Many time series exhibit “long memory”: Their autocorrelation function decays slowly with lag. This behavior has traditionally been modeled via unit roots or fractional Brownian motion and explained via aggregation of heterogeneous processes, nonlinearity, learning dynamics, regime switching, or structural breaks. This paper identifies a different and complementary mechanism for long‐memory generation by showing that it can naturally arise when a large number of simple linear homogeneous economic subsystems with short memory are interconnected to form a network such that the outputs of the subsystems are fed into the inputs of others. This networking picture yields a type of aggregation that is not merely additive, resulting in a collective behavior that is richer than that of individual subsystems. Interestingly, the long‐memory behavior is found to be almost entirely determined by the geometry of the network, while being relatively insensitive to the specific behavior of individual agents.

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Bilateral exposures and systemic solvency risk

Cited by 78 publications

References 32 publications

Sensitivity and computational complexity in financial networks

Sensitivity and computational complexity in financial networks

Using Weighted Shapley Values to Measure the Systemic Risk of Interconnected Banks

Long Memory via Networking

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