Optimal Bailouts in Diversified Financial Networks

Fuente: arXiv
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Main Authors: Dasaratha, Krishna, Venkatesh, Santosh, Vohra, Rakesh
Format: Preprint
Published: 2024
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author Dasaratha, Krishna
Venkatesh, Santosh
Vohra, Rakesh
author_facet Dasaratha, Krishna
Venkatesh, Santosh
Vohra, Rakesh
contents Widespread default involves substantial deadweight costs which could be countered by injecting capital into failing firms. Injections have positive spillovers that can trigger a repayment cascade. But which firms should a regulator bailout so as to minimize the total injection of capital while ensuring solvency of all firms? While the problem is, in general, NP-hard, for a wide range of networks that arise from a stochastic block model, we show that the optimal bailout can be implemented by a simple policy that targets firms based on their characteristics and position in the network. Specific examples of the setting include core-periphery networks.
format Preprint
id arxiv_https___arxiv_org_abs_2406_12818
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Optimal Bailouts in Diversified Financial Networks
Dasaratha, Krishna
Venkatesh, Santosh
Vohra, Rakesh
Theoretical Economics
Social and Information Networks
Widespread default involves substantial deadweight costs which could be countered by injecting capital into failing firms. Injections have positive spillovers that can trigger a repayment cascade. But which firms should a regulator bailout so as to minimize the total injection of capital while ensuring solvency of all firms? While the problem is, in general, NP-hard, for a wide range of networks that arise from a stochastic block model, we show that the optimal bailout can be implemented by a simple policy that targets firms based on their characteristics and position in the network. Specific examples of the setting include core-periphery networks.
title Optimal Bailouts in Diversified Financial Networks
topic Theoretical Economics
Social and Information Networks
url https://arxiv.org/abs/2406.12818