The Decline of Solvency Contagion Risk

Bardoscia, Marco; Barucca, Paolo; Codd, Adam Brinley; Hill, John

doi:10.2139/ssrn.2996689

Cited by 17 publications

(22 citation statements)

References 35 publications

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“…Following the 2007–2009 financial crisis, stress tests have become an important tool to assess the stability of financial systems (Anderson, 2016). A particular concern of policy makers is to make these stress tests more macroprudential (Basel Committee on Banking Supervision, 2015b) by incorporating feedback and amplification mechanisms into the stress testing exercise (Bardoscia, Barucca, Codd, & Hill, 2017; The Bank of England, 2015). Modeling financial contagion lies at the heart of these efforts, see Glasserman and Young (2016) for a recent overview.…”

Section: Introductionmentioning

confidence: 99%

“…Examples of distress contagion models are the DebtRank model by Battiston, Puliga, Kaushik, Tasca, and Caldarelli (2012) and extensions of it such as the models by Bardoscia, Caccioli, Perotti, Vivaldo, and Caldarelli (2016) and Bardoscia et al. (2017). In all these existing models, the magnitude of contagious losses is determined by the probability of default and the recovery rate.…”

Section: Introductionmentioning

confidence: 99%

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Distress and default contagion in financial networks

Veraart

2020

Mathematical Finance

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We develop a new model for solvency contagion that can be used to quantify systemic risk in stress tests of financial networks. In contrast to many existing models, it allows for the spread of contagion already before the point of default and hence can account for contagion due to distress and mark‐to‐market losses. We derive general ordering results for outcome measures of stress tests that enable us to compare different contagion mechanisms. We use these results to study the sensitivity of the new contagion mechanism with respect to its model parameters and to compare it to existing models in the literature. When applying the new model to data from the European Banking Authority, we find that the risk from distress contagion is strongly dependent on the anticipated recovery rate. For low recovery rates, the high additional losses caused by bankruptcy dominate the overall stress test results. For high recovery rates, however, we observe a strong sensitivity of the stress test outcomes with respect to the model parameters determining the magnitude of distress contagion.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distress and default contagion in financial networks

Veraart

2020

Mathematical Finance

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“…Hurd (2016) presents a unified mathematical framework for modeling these contagion channels. Recently, the Bank of England has extended this model to analyse solvency contagion in the UK financial system (Bardoscia et al (2017)). Multiple, extensions of this model have been developed to include effects such as • Bankruptcy costs: Elsinger (2009), Rogers and Veraart (2013), Elliott et al (2014), Glasserman and Young (2015), Weber and Weske (2017),…”

Section: Introductionmentioning

confidence: 99%

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

Feinstein¹,

Pang²,

Rudloff³

et al. 2018

SIAM J. Finan. Math.

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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. Our results are a first step towards allowing regulators to quantify errors in their simulations.

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“…That paper considers the network of interbank obligations and finds the equilibrium payments. Central banks and regulators have applied the Eisenberg-Noe model to study cascading failures in the banking systems within their jurisdictions, see, e.g., [5,25,10,16,32,20,7].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Fire Sales and Borrowing in Systemic Risk

Bichuch

Feinstein

2018

SSRN Journal

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This paper provides a framework for modeling financial contagion in a network subject to fire sales and price impacts, but allowing for firms to borrow to cover their shortfall as well. We consider both uncollateralized and collateralized loans. The main results of this work are providing sufficient conditions for existence and uniqueness of the clearing solutions (i.e., payments, liquidations, and borrowing); in such a setting any clearing solution is the Nash equilibrium of an aggregation game.

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The Decline of Solvency Contagion Risk

Cited by 17 publications

References 35 publications

Distress and default contagion in financial networks

Distress and default contagion in financial networks

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

Optimization of Fire Sales and Borrowing in Systemic Risk

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