Systemic Risk Analysis of Multi-Layer Financial Network System Based on Multiple Interconnections between Banks, Firms, and Assets

Abstract

Global financial systems are increasingly interconnected, and risks can spread more easily, potentially causing systemic risks. Research on systemic risk based on multi-layer financial networks is relatively scarce, and studies usually focus on only one type of risk. This paper develops a model of the multi-layer financial network system based on three types of links: firm-bank credit, asset-bank portfolio, and interbank lending, which simulates systemic risk under three risk sources: firm credit default, asset depreciation, and bank bankruptcy. The impact of the multi-layer financial network structure, default risk threshold, and bank asset allocation strategy is further explored. It has been shown that the larger the risk shock, the greater the systemic risk under different risk sources, and the risk propagation cycle tends to rise and then decline. As centralized nodes in the multi-layer financial network system, bank nodes may play both blocking and propagation roles under different risk sources. Furthermore, the multi-layer financial network system is most susceptible to bank bankruptcy risk, followed by firm credit default risk. Further research indicates that increasing the average degree of firms in the bank–firm credit network, the density of the bank-asset portfolio network, and the bank capital adequacy ratio all contribute to reducing systemic risk under the three risk sources. Additionally, the more assets a bank holds in a single market, the more vulnerable it is to the risks associated with that market.

Systemic Importance and Risk Characteristics of Banks Based on a Multi-Layer Financial Network Analysis

“…It is usually only possible to obtain the total interbank lending and interbank borrowing from the bank's balance sheet. Referring to Lux [8] and Gao [26], this paper constructs an interbank lending linkage matrix based on a probability function related to the bank's assets. According to this paper, the probability of an interbank lending relationship between bank i and bank k is as follows:…”

“…This paper collects the actual data of the Chinese banking system and listed firms in 2021. Firstly, this paper estimates China's multi-layer financial network system by estimating the interbank lending network, the bank-firm credit network, and the bank-asset portfolio network based on the multi-layer financial network model proposed by Gao [26]. Based on the statistical data of China's multi-layer financial networks, this paper also analyzes the characteristics of single-layer networks and multi-layer financial networks.…”

Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy

“…However, in most cases, interactions are dyadic in nature and therefore can be represented by traditional networks (Skardal et al, 2021). The use of multiplex networks for financial analysis tasks is described in detail in (Bardoscia et al, 2021;del Rio-Chanona et al, 2020;Serguieva, 2016;Brummitt and Kobayashi, 2015;Cao et al, 2021;Xie et al, 2022;Gao, 2022;Aldasoro and Alves, 2018;Squartini et al, 2018;del Rio-Chanona et al, 2020), and higher-order networks in (Stavroglou et al, 2019;Jackson and Pernoud, 2021;Saha et al, 2022;Battiston et al, 2016;Huremovic et al, 2020;Franch et al, 2022;Bartesaghi et al, 2022;Han et al, 2022).…”