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

TL;DR

This paper introduces a novel neural network-based PGO framework to optimize systemic risk bailouts in financial systems, addressing the challenge of unknown objective functions with a black-box approach.

Contribution

It develops a new PGO method combining neural network prediction and gradient-based optimization for systemic risk management.

Findings

Effective in managing systemic risk through numerical experiments

Neural network approximation enables offline objective estimation

Gradient projection algorithm efficiently solves the bailout optimization

Abstract

In the financial system, bailout strategies play a pivotal role in mitigating substantial losses resulting from systemic risk. However, the lack of a closed-form objective function to the optimal bailout problem poses significant challenges in its resolution. This paper conceptualizes the optimal bailout (capital injection) problem as a black-box optimization task, where the black box is modeled as a fixed-point system consistent with the E-N framework for measuring systemic risk in the financial system. To address this challenge, we propose a novel framework, "Prediction-Gradient-Optimization" (PGO). Within PGO, the Prediction employs a neural network to approximate and forecast the objective function implied by the black box, which can be completed offline; For the online usage, the Gradient step derives gradient information from this approximation, and the Optimization step uses a gradient projection algorithm to solve the problem effectively. Extensive numerical experiments highlight the effectiveness of the proposed approach in managing systemic risk.