A Dynamic Default Contagion Model: From Eisenberg-Noe to the Mean Field

Feinstein, Zachary; Søjmark, Andreas

doi:10.48550/arxiv.1912.08695

Cited by 4 publications

(4 citation statements)

References 49 publications

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“…In a similar flavour, the work of [FS19] considers a continuous-time version of the Eisenberg-Noe model and studies the default risk. The paper investigates defaults that can result either from insolvency or illiquidity, and identify the default times of nodes.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Interventions for Networked Contagions

Papachristou¹,

Banerjee²,

Kleinberg³

2022

Preprint

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We study the problem of designing dynamic intervention policies for minimizing networked defaults in financial networks. Formally, we consider a dynamic version of the celebrated Eisenberg-Noe model of financial network liabilities, and use this to study the design of external intervention policies. Our controller has a fixed resource budget in each round, and can use this to minimize the effect of demand/supply shocks in the network. We formulate the optimal intervention problem as a Markov Decision Process, and show how we can leverage the problem structure to efficiently compute optimal intervention policies with continuous interventions, and constant-factor approximations with discrete interventions. Going beyond financial networks, we argue that our model captures dynamic network intervention in a much broader class of dynamic demand/supply settings with networked inter-dependencies. To demonstrate this, we apply our intervention algorithms to a wide variety of application domains, including ridesharing, online transaction platforms, and financial networks with agent mobility; in each case, we study the relationship between node centrality and intervention strength, as well as fairness properties of the optimal interventions.

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Section: Related Workmentioning

confidence: 99%

Dynamic Interventions for Networked Contagions

Papachristou¹,

Banerjee²,

Kleinberg³

2022

Preprint

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“…In the presence of shocks [GY15], the nodes similarly become default due to external shocks that disrupt their assets. The works of [BBF18,FS19] investigate dynamic variants of the EN model, where the liabilities and assets evolve as functions of time. Our work investigates the problem from a static viewpoint, and the hardness results obviously hold for the dynamical version.…”

Section: Further Related Workmentioning

confidence: 99%

Allocating Stimulus Checks in Times of Crisis

Papachristou,

Kleinberg

2021

Preprint

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We study the problem of allocating bailouts (stimulus, subsidy allocations) to people participating in a financial network subject to income shocks. We build on the financial clearing framework of Eisenberg and Noe [EN01] that allows the incorporation of a bailout policy that is based on discrete bailouts motivated by the types of stimulus checks people receive around the world as part of COVID-19 economical relief plans. We show that optimally allocating such bailouts on a financial network in order to maximize a variety of social welfare objectives of this form is a computationally intractable problem. We develop approximation algorithms to optimize these objectives and establish guarantees for their approximation ratios. Then, we incorporate multiple fairness constraints in the optimization problems and establish relative bounds on the solutions with versus without these constraints. Finally, we apply our methodology to a variety of data, both in the context of a system of large financial institutions with real-world data, as well as in a realistic societal context with financial interactions between people and businesses for which we use semi-artificial data derived from mobility patterns. Our results suggest that the algorithms we develop and study have reasonable results in practice and outperform other network-based heuristics. We argue that the presented problem through the societal-level lens could assist policymakers in making informed decisions on issuing subsidies.

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“…This work has later been extended by considering liquid assets Chen et al (2021), heterogeneous network structures over time and early defaults Banerjee et al (2018). Other works Feinstein & Sojmark (2019), Feinstein & Søjmark (2021) propose to combine the interbank Eisenberg-Noe model and the dynamic mean field approach. Instead, Feinstein (2020) uses a continuous-time model for price-mediated contagion.…”

Section: Introductionmentioning

confidence: 99%

Clearing Payments in Dynamic Financial Networks

Calafiore¹,

Fracastoro²,

Proskurnikov³

2022

Preprint

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In this paper, we propose a novel dynamical model of clearing in a financial network, which stems from the classical Eisenberg-Noe model of financial contagion. The Eisenberg-Noe model assumes that at one point in time (say, at the end of a day), all liabilities are claimed and due simultaneously, and that the entire network of banks becomes aware of the claims and possible defaults and instantaneously agrees on the clearing payments. The motivation for the dynamic model we propose in this paper is that one may expect that if financial operations are allowed for a given number of time periods after the initial theoretical defaults, some nodes may actually recover and eventually manage to fulfill their obligations. We prove that the proposed model obeys the standard requirement known as the priority of debt claims, that is, each node either pays its liabilities in full, or it pays out all its balance. We also show that the requirements of ro-rata payments determines the solution uniquely.

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A Dynamic Default Contagion Model: From Eisenberg-Noe to the Mean Field

Cited by 4 publications

References 49 publications

Dynamic Interventions for Networked Contagions

Dynamic Interventions for Networked Contagions

Allocating Stimulus Checks in Times of Crisis

Clearing Payments in Dynamic Financial Networks

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