We introduce the Multiplex Network Hawkes model, which extends the network Hawkes framework of Linderman & Adams (2014) by allowing multiple excitation layers whose weights depend on observed edge and node covariates. We use the model to investigate how contagion in financial networks is affected by different transmission channels. The multiplex structure separates channel-specific contributions within a single inferred transmission network, allowing candidate propagation mechanisms to be compared directly rather than being absorbed into one homogeneous excitation layer. Covariate-dependent excitation allows us to investigate sources of transmission. We make posterior inference about the inferred directed network and its excitation dynamics using an MCMC sampler. The application uses a broad cross-industry credit default swap (CDS) dataset of 99 North American and European firms, including banks, insurers and non-financial firms over 2004-2022. We evaluate three candidate contagion channels associated with asset similarity, solvency and profitability. The results indicate sparse contagion pathways, with systemic-risk transmission concentrated in outward flows from a small number of influential institutions rather than in mutual feedback between institutions. The channel results show that industry similarity is the most consistently supported asset-similarity effect, while aggregate layer contributions indicate that asset-similarity, solvency and profitability channels all contribute to inferred excitation.

翻译：我们提出多层级网络霍克斯模型，该模型扩展了Linderman和Adams（2014）的网络霍克斯框架，通过允许权重依赖于观测到的边和节点协变量的多个激励层来实现。我们利用该模型研究金融网络中传染如何受到不同传输渠道的影响。多层级结构在单一推断的传输网络中分离出渠道特定贡献，使得候选传播机制能够直接比较，而非被吸收到一个同质激励层中。依赖协变量的激励使我们能够探究传播的源头。我们通过MCMC采样器对推断的有向网络及其激励动态进行后验推断。应用部分使用了包含99家北美和欧洲公司（包括银行、保险公司和非金融企业）的广泛跨行业信用违约互换（CDS）数据集，时间跨度为2004年至2022年。我们评估了与资产相似性、偿付能力和盈利能力相关的三种候选传染渠道。结果表明，传染路径稀疏，系统性风险传递集中在少数有影响力机构的外向流动上，而非机构间的相互反馈。渠道结果显示，行业相似性是最持续获得支持的资产相似性效应，而总体层面的贡献表明，资产相似性、偿付能力和盈利能力渠道均对推断出的激励有贡献。