Modeling financial distress propagation on customer–supplier networks

Financial networks have been the object of intense quantitative analysis during the last few decades. Their structure and the dynamical processes on top of them are of utmost importance to understand the emergent collective behavior behind economic and financial crises. In this paper, we propose a stylized model to understand the “domino effect” of distress in client–supplier networks. We provide a theoretical analysis of the model, and we apply it to several synthetic networks and a real customer–supplier network, supplied by one of the largest banks in Europe. Besides, the proposed model allows us to investigate possible scenarios for the functioning of the financial distress propagation and to assess the economic health of the full network. The main novelty of this model is the combination of two stochastic terms: an additive noise, accounting by the capability of trading and paying obligations, and a multiplicative noise representing the variations of the market. Both parameters are crucial to determining the maximum default probability and the diffusion process characteristics. Business interactions daily form a direct and weighted customer–supplier network whose structure has in-depth implications in its functioning. The financial distress of one company depends on the ability of their clients to fulfill their payments. Otherwise, a firm is not able to keep on working unless it applies for a loan. The recent financial crisis has prompted much new research on the interconnectedness of companies and the extent to which it contributes to systemic fragility. Here, we explore the interplay between network complexity and market stability in a deliberately simplified model for financial distress propagation.A.A. acknowledges support by the Ministerio de Economía y Competitividad (Grant No. FIS2015-71582-C2-1), the Generalitat de Catalunya (Grant No. 2017SGR-896), the Universitat Rovira i Virgili (Grant No. 2017PFR-URV-B2-41), and the ICREA Academia and the James S. McDonnell Foundation (Grant No. 220020325). J.J.R. acknowledges funding from the Spanish Ministry of Science and Innovation, the AEI, and FEDER (EU) under the grant PACSS (No. RTI2018-093732-B-C22), and the Maria de Maeztu program for Units of Excellence in R&D (No. MDM-2017-0711).Peer reviewe