A dynamic approach merging network theory and credit risk techniques to assess systemic risk in financial networks

Abstract

The interconnectedness of financial institutions affects instability and credit crises. To quantify systemic risk we introduce here the PD model, a dynamic model that combines credit risk techniques with a contagion mechanism on the network of exposures among banks. A potential loss distribution is obtained through a multi-period Monte Carlo simulation that considers the probability of default (PD) of the banks and their tendency of defaulting in the same time interval. A contagion process increases the PD of banks exposed toward distressed counterparties. The systemic risk is measured by statistics of the loss distribution, while the contribution of each node is quantified by the new measures PDRank and PDImpact. We illustrate how the model works on the network of the European Global Systemically Important Banks. For a certain range of the banks’ capital and of their assets volatility, our results reveal the emergence of a strong contagion regime where lower default correlation between banks corresponds to higher losses. This is the opposite of the diversification benefits postulated by standard credit risk models used by banks and regulators who could therefore underestimate the capital needed to overcome a period of crisis, thereby contributing to the financial system instability.

Dynamically induced cascading failures in power grids

“…In this way, an initial local shock produces a sequence of multiple failures in a domino mechanism which may finally affect a substantial part of the network. Cascading failures occur in transportation systems 45 , 46 , in computer networks 47 , in financial systems 48 , but also in supply networks 23 . When, for some either exogenous or endogenous reason, a line of a supply network fails, its load has to be somehow redistributed to the neighboring lines.…”

Credit Risk Diffusion in Supply Chain Finance: A Complex Networks Perspective

“…According to the above analysis of the credit risk diffusion of supply chain finance, the model for the diffusion of credit risk in supply chain finance is established below. The traditional credit risk contagion model studies the relationship between credit risk transmission between two or a few individuals, the model calculation is very complex and research focuses on one-way transmission between individuals, and cannot reveal the dynamic process of credit risk contagion and diffusion [3,12,28]. The SIS model under the complex network theory is a classic virus infection model and has recently been widely applied to credit risk research [12,14].…”

“…The traditional credit risk contagion model studies the relationship between credit risk transmission between two or a few individuals, the model calculation is very complex and research focuses on one-way transmission between individuals, and cannot reveal the dynamic process of credit risk contagion and diffusion [3,12,28]. The SIS model under the complex network theory is a classic virus infection model and has recently been widely applied to credit risk research [12,14]. As a model for studying a large-scale risk diffusion phenomenon, the SIS infection model has several characteristics that can meet the needs of this research: first, it can be applied to large-scale network environments; second, the model has simple and easy-to-use features in case a large number of calculation processes make research work impossible; third, there is no clear requirement for the direction of credit risk transmission in the network; fourth, the model reflects the dynamic evolution characteristics of the credit risk contagion process in the network.…”

“…On the basis of the original portfolio credit risk model, Anagnostou [11] included the systemic risk factors that affect the whole credit system, then built an extended model of investment credit risk and a credit pressure transmission network. Petrone [12] introduced a dynamic model to quantify the systemic risk by incorporating credit risks with various transmission mechanisms in the bank credit risk network and obtaining the distribution in terms of potential losses through multi-cycle Monte Carlo simulations. It can be seen that while the network model has been used in many studies, few studies have focused on the network characteristic of the supply chain financial network to study credit risk diffusion.…”

The Effect of Interfirm Financial Transactions on the Credit Risk of Small and Medium-Sized Enterprises

“…In a networked economy, the failure of one firm can have a snowball effect, causing failure of other companies, and in extreme cases causing an avalanche of failures, known as a bankruptcy chain (Delli Gatti et al, 2006;Jacobson and von Schedvin, 2015). Hurd (2016) provided an exposition on mathematical stochastic models of contagion and cascades in the context of systemic risk and recent work proposes models that are specific to the case of credit risk (Petrone and Latora, 2018).…”