论文标题
最佳系统性风险救助：基于神经网络的PGO方法
Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network
论文作者
论文摘要
在金融体系中，救助策略在减轻系统风险造成的重大损失方面起着关键作用。但是，缺乏最佳救助问题的封闭形式的目标函数在解决方案中构成了重大挑战。本文概念化了最佳的救助（资本注入）问题作为黑框优化任务，其中黑匣子被建模为固定点系统，符合E-N框架，该系统与E-N框架一致，以衡量金融系统中的系统性风险。为了应对这一挑战，我们提出了一个新颖的框架，即“预测毕业生优化”（PGO）。在PGO内，该预测采用神经网络来近似和预测黑匣子所隐含的目标函数，该目标函数可以离线完成；对于在线用法，梯度步骤从此近似中得出梯度信息，优化步骤使用梯度投影算法有效地解决了问题。广泛的数值实验突出了拟议方法在管理系统风险中的有效性。
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.
