Contagion in Financial Systems: A Bayesian Network Approach

Chong, Carsten; Klüppelberg, Claudia

doi:10.1137/17m1116659

Cited by 19 publications

(6 citation statements)

References 53 publications

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“…Financial networks also experience some change over time, however, and to make statements about the resilience of possible scenarios of the financial system in future it might not be advisable to simply consider today's observed network. In [15] for example, the authors develop a structural default model and use a Bayesian network approach to derive formulas for the joint default and survival probability that can be computed explicitly. Although not directly related to a default cascade, another structural stochastic model that describes interbank lending was proposed in [24] and modified/extended in [14,33].…”

Section: Introductionmentioning

confidence: 99%

Managing Default Contagion in Inhomogeneous Financial Networks

Detering¹,

Meyer‐Brandis²,

Παναγιώτου³

et al. 2019

SIAM J. Finan. Math.

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The aim of this paper is to quantify and manage systemic risk caused by default contagion in the interbank market. We model the market as a random directed network, where the vertices represent financial institutions and the weighted edges monetary exposures between them. Our model captures the strong degree of heterogeneity observed in empirical data and the parameters can easily be fitted to real data sets. One of our main results allows us to determine the impact of local shocks, where initially some banks default, to the entire system and the wider economy. Here the impact is measured by some index of total systemic importance of all eventually defaulted institutions. As a central application, we characterize resilient and non-resilient cases. In particular, for the prominent case where the network has a degree sequence without second moment, we show that a small number of initially defaulted banks can trigger a substantial default cascade. Our results complement and extend significantly earlier findings derived in the configuration model where the existence of a second moment of the degree distribution is assumed. As a second main contribution, paralleling regulatory discussions, we determine minimal capital requirements for financial institutions sufficient to make the network resilient to small shocks. An appealing feature of these capital requirements is that they can be determined locally by each institution without knowing the complete network structure as they basically only depend on the institution's exposures to its counterparties.

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Section: Introductionmentioning

confidence: 99%

Managing Default Contagion in Inhomogeneous Financial Networks

Detering¹,

Meyer‐Brandis²,

Παναγιώτου³

et al. 2019

SIAM J. Finan. Math.

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“…There is no borrowing in this case, as it will be assumed that it is imprudent to lend money to a fundamentally insolvent bank. Including a notion of bankruptcy costs as in, e.g., [28,13], it is assumed that for the duration of the crisis being studied, no obligations will be paid by any fundamentally insolvent banks, i.e. p i = 0 for any bank in Case I.…”

Section: Optimal Tradeoff Between Fire Sales and Borrowingmentioning

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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“…BNs enjoy applications to numerous fields, but the focus of the current paper is on fields related to economics applications, such as production economics [5], macroeconomics [6] and environmental resource economics [7]. Applications of BNs can also be found in financial econometrics [8], banking and finance [9], credit scoring [10], insurance [11] and customer service [12], to name a few. Despite the plethora of applications of BNs, not many BN algorithms exist, and most importantly, fewer are publicly available in free software environments, such as the statistical software R. The Max-Min Hill Climbing (MMHC) [13] is an example of a widely used BN learning algorithm (The relevant paper is one of the classic papers in the Artificial Intelligence field, and has received more than 1870 citations according to scholar.google.com as of July 2022.)…”

Section: Introductionmentioning

confidence: 99%

The FEDHC Bayesian Network Learning Algorithm

Tsagris

2022

Mathematics

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The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software R is prohibitively expensive, and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, which can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show that it is computationally efficient, and that it produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software R.

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Contagion in Financial Systems: A Bayesian Network Approach

Cited by 19 publications

References 53 publications

Managing Default Contagion in Inhomogeneous Financial Networks

Managing Default Contagion in Inhomogeneous Financial Networks

Optimization of Fire Sales and Borrowing in Systemic Risk

The FEDHC Bayesian Network Learning Algorithm

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