Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network

Shuhua Xiao, Jiali Ma, Li Xia and Shushang Zhu

Papers from arXiv.org

Abstract: The bailout strategy is crucial to cushion the massive loss caused by systemic risk in the financial system. There is no closed-form formulation of the optimal bailout problem, making solving it difficult. In this paper, we regard the issue of the optimal bailout (capital injection) as a black-box optimization problem, where the black box is characterized as a fixed-point system that follows the E-N framework for measuring the systemic risk of the financial system. We propose the so-called ``Prediction-Gradient-Optimization'' (PGO) framework to solve it, where the ``Prediction'' means that the objective function without a closed-form is approximated and predicted by a neural network, the ``Gradient'' is calculated based on the former approximation, and the ``Optimization'' procedure is further implemented within a gradient projection algorithm to solve the problem. Comprehensive numerical simulations demonstrate that the proposed approach is promising for systemic risk management.

Date: 2022-12
New Economics Papers: this item is included in nep-cmp and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2212.05235 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2212.05235

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().