Department of Financial and Risk Engineering, New York University, New York, USA.
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada.
Sci Rep. 2023 Mar 9;13(1):3990. doi: 10.1038/s41598-023-30710-z.
In highly connected financial networks, the failure of a single institution can cascade into additional bank failures. This systemic risk can be mitigated by adjusting the loans, holding shares, and other liabilities connecting institutions in a way that prevents cascading of failures. We are approaching the systemic risk problem by attempting to optimize the connections between the institutions. In order to provide a more realistic simulation environment, we have incorporated nonlinear/discontinuous losses in the value of the banks. To address scalability challenges, we have developed a two-stage algorithm where the networks are partitioned into modules of highly interconnected banks and then the modules are individually optimized. We developed a new algorithms for classical and quantum partitioning for directed and weighed graphs (first stage) and a new methodology for solving Mixed Integer Linear Programming problems with constraints for the systemic risk context (second stage). We compare classical and quantum algorithms for the partitioning problem. Experimental results demonstrate that our two-stage optimization with quantum partitioning is more resilient to financial shocks, delays the cascade failure phase transition, and reduces the total number of failures at convergence under systemic risks with reduced time complexity.
在高度互联的金融网络中，单个机构的倒闭可能会引发其他银行倒闭。通过调整贷款、持有股份和其他连接机构的负债方式，可以减轻这种系统性风险，防止倒闭的连锁反应。我们正在通过尝试优化机构之间的联系来解决系统性风险问题。为了提供更现实的模拟环境，我们在银行价值的非线性/不连续损失中加入了非线性/不连续损失。为了解决可扩展性挑战，我们开发了一种两阶段算法，将网络划分为高度互联的银行模块，然后单独优化这些模块。我们为有向加权图的经典和量子分区开发了新的算法（第一阶段），以及一种针对系统风险环境的具有约束的混合整数线性规划问题的新方法（第二阶段）。我们比较了分区问题的经典和量子算法。实验结果表明，我们的带有量子分区的两阶段优化在面对金融冲击时更具弹性，延迟了级联故障相变，并在降低时间复杂度的情况下减少了系统风险收敛时的总故障数。
