🤖 AI Summary
This study investigates the transmission mechanisms of financial systemic risk through multiple channels and identifies key sources of risk propagation. To this end, the authors propose a multiplex network Hawkes model that explicitly disentangles three distinct contagion channels—asset similarity, solvency, and profitability—within a unified framework by incorporating excitation weights dependent on node and edge covariates, thereby overcoming the limitations of traditional single-layer homogeneous excitation assumptions. Employing a Bayesian inference approach based on Markov chain Monte Carlo (MCMC) and leveraging large-scale credit default swap (CDS) data, the empirical analysis covers 99 European and U.S. financial institutions from 2004 to 2022. The findings reveal that systemic risk is predominantly driven by a small subset of institutions, that industry similarity constitutes the most robust asset-related linkage, and that all three channels significantly contribute to risk contagion.
📝 Abstract
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.