Optimal intervention under stress scenarios: A case of the Korean financial system

Ahn, Dohyun; Kim, Kyoung-Kuk

doi:10.1016/j.orl.2019.03.019

Cited by 16 publications

(17 citation statements)

References 14 publications

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“…", under this model corresponds to solving an NP-Hard problem. This stands in contrast to a version of the same model under fractional bailouts [AK19,Dem18]. Moreover, although thresholding individuals according to their equity is a policy that is simple to state and understand, we show that, in general, such thresholding policies can behave arbitrarily badly where networks effects are taken into account.…”

Section: Introductionmentioning

confidence: 67%

“…The work of [AK19] considers optimal intervention methods under budget constraints under the extended model of [GY15] and formulate optimization problems that minimize the systemic losses as well as minimizing the number of defaulting institutions and apply their methods on publicly available data on the Korean financial system. Their work considers fractional intervention policies which differentiates it from ours.…”

Section: Further Related Workmentioning

confidence: 99%

“…More specifically, we consider discrete interventions (i.e. a node gets the full bailout or does not) in which case the model of [AK19] can be seen as the fractional relaxation of the optimization objectives we study. Secondly, we experiment both with high-granularity financial institutions (banks) as well as view the problem from a societal lens, namely modeling the entities of the system as "societal nodes" (businesses, households, individuals).…”

Section: Further Related Workmentioning

confidence: 99%

“…not necessarily strictly increasing), there is no guarantee that the solution that will be produced by the optimization procedure will correspond to a clearing vector (still if the spectral radius of A is less than 1 the fixed point operator yields a unique solution). For that reason we consider, similarly to [AK19] the transformed objective, parametrized by > 0,…”

Section: B Transformations B1 Transforming Weakly-increasing Objectiv...mentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 67%

Section: Further Related Workmentioning

confidence: 99%

Section: Further Related Workmentioning

confidence: 99%

Section: B Transformations B1 Transforming Weakly-increasing Objectiv...mentioning

confidence: 99%

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Allocating Stimulus Checks in Times of Crisis

Papachristou,

Kleinberg

2021

Preprint

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“…Their methods are applied for small banking systems. In [1], authors cast the intervention problem in the context of the Eisenberg-Noe model [16] as a mixed integer-programming problem, and propose a notion of of -optimality to solve it approximately. They apply their methods to the Korean banking system.…”

Section: Introductionmentioning

confidence: 99%

Optimal Intervention in Economic Networks using Influence Maximization Methods

Klages‐Mundt¹,

Minca²

2021

Preprint

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We consider optimal intervention in the Elliott-Golub-Jackson network model [17] and show that it can be transformed into an influence maximization problem, interpreted as the reverse of a default cascade. Our analysis of the optimal intervention problem extends well-established targeting results to the economic network setting, which requires additional theoretical steps. We prove several results about optimal intervention: it is NP-hard and additionally hard to approximate to a constant factor in polynomial time.In turn, we show that randomizing failure thresholds leads to a version of the problem which is monotone submodular, for which existing powerful approximations in polynomial time can be applied. In addition to optimal intervention, we also show practical consequences of our analysis to other economic network problems: (1) it is computationally hard to calculate expected values in the economic network, and (2) influence maximization algorithms can enable efficient importance sampling and stress testing of large failure scenarios. We illustrate our results on a network of firms connected through input-output linkages inferred from the World Input Output Database.* We thank Sid Banerjee for helpful discussion. This paper is based on work supported by NSF CAREER award #1653354 and the Bloomberg Fellowship.

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Optimal intervention in economic networks using influence maximization methods

Klages‐Mundt

Minca

2022

European Journal of Operational Research

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Optimal intervention under stress scenarios: A case of the Korean financial system

Cited by 16 publications

References 14 publications

Allocating Stimulus Checks in Times of Crisis

Allocating Stimulus Checks in Times of Crisis

Optimal Intervention in Economic Networks using Influence Maximization Methods

Optimal intervention in economic networks using influence maximization methods

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