Systemic risk of multi-layer financial network system under macroeconomic fluctuations

Abstract

As the global economy continues to integrate, COVID-19 is affecting businesses around the world, causing the financial system to become more complicated. The complicated relationship between various agents in the financial system makes potential hazards more easily transmitted. Most studies of systemic risks have focused on single-layer networks, and macroeconomic fluctuations have not been quantified in multi-layer models of financial networks. In this paper, three different macroeconomic shock scenarios (showing upward, downward, and random trends) are constructed to affect the firm’s business activities, and a multi-layer financial network model is developed to simulate systemic risk under macroeconomic fluctuations. Firms with medium and high leverage and small asset sizes, as well as banks with smaller asset sizes and fewer bank-firm credit linkages, are found to be more likely to default. The study also found that average firm leverage exhibits two inflection points, causing banks’ default probabilities to “rise, then fall, and then rise,” with the inflection point value being the lowest under the upward trend of macroeconomics. In addition, the higher the ratio of firm loans to total bank assets, the more likely the bank is to default. Appropriate loan maturity extension has also helped to reduce systemic risk, especially in light of the macroeconomic downward trend. Furthermore, improving the capital adequacy ratio can reduce the bank’s default probability under macroeconomic fluctuations.

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

“…The study shows that banks contribute to their stability by selling depreciating assets, and investment risk is an uncertainty that affects the stability of banks. In their work, Lux [ 34 ], Silva et al [ 35 ], and Gao et al [ 36 ] constructed the multi-layer financial network system based on interbank and bank–firm networks. Based on De Masi and Gallegati’s research on the actual data sets of Italy and Japan [ 37 ], Lux [ 34 ] proposed a generation algorithm for the bilateral bank–firm network and combined it with the interbank network to study risk contagion.…”

“…In addition, the network structure of the financial system plays an important role in the contagion of risk. Gao et al [ 36 ] studied the systemic risk of the multi-layer financial network system under macroeconomic fluctuations and found that firms with medium and high leverage and small asset sizes, as well as banks with smaller asset sizes and fewer bank–firm credit links, are more likely to default. On the assumption that banks do not recover loans from firms, Chen et al [ 38 ] constructed a two-layer credit risk contagion network model between banks and firm counterparties.…”