Financial Contagion in Large, Inhomogeneous Stochastic Interbank Networks

Abstract

We consider the fraction of nodes that default in large, stochastic, inhomogeneous financial networks following an initial shock to the system. Results for deterministic sequences of networks are generalized to stochastic networks to account for interbank lending relationships that change frequently. A general class of inhomogeneous stochastic networks is proposed for use in systemic risk research, and we illustrate how results that hold for Erdős–Rényi networks can be generalized to the proposed network class. The network structure of a system is determined by interbank lending behavior which may vary according to the relative sizes of the banks. We then use the results of the paper to illustrate how network structure influences the systemic risk inherent in large banking systems.

Interbank money market concerns and actors’ strategies—A systematic review of 21st century literature

“…Studies by Walters et al. (2019) revealed that connectivity, combined with other network characteristics, potentially affects the system’s strengthening or weakening through the network’s tiering level. Also, Simaan et al.…”

“…Finding innovative methods for building more efficient and accurate models to identify interbank asset classes within banks’ balance sheets (Souza, 2016) remains a research opportunity in this field. Central bank —At present, the vast majority of models developed from the interbank market by researchers lack the central bank as a significant, influential player, making those models far from what is happening in the real world. Hence, many scholars suggest the inclusion of a central bank in their future models as the provider of external safety nets, which acts as the “lender of last resort” and intervene in the market in the form of bailouts or liquidity injections (e.g., long‐term refinancing against collateral) (Barroso et al., 2016; Iori et al., 2006; Ladley, 2013; León et al., 2018; Lux, 2015; Smaga et al., 2018; Walters et al., 2019). In order to study networks of interbank loans connections that are as realistic as possible, it is worthwhile for other researchers to introduce the central bank as an active interbank market player in their upcoming research. Liquidity —Many researchers have identified the relationship between declining liquidity and several concerns such as the network structure, network stability, and systemic risk in the interbank market (Acemoglu et al., 2015; Iori et al., 2006; Lee, 2013; Taylor & Williams, 2009) as future goals of their research.…”

“…The first would like to theoretically and/or empirically examine the effects of a large number of variables on contagion. The most important of these variables are changes in the volume of liquid assets (Lee, 2013; Yao et al., 2016), implicit and explicit government guarantees (Craig & Ma, 2018), specific policies of the central bank (Popoyan et al., 2020), targeted shocks (Walters et al., 2019), semi‐complete and incomplete network structures (Battiston et al., 2012; Erol & Ordonez, 2017; Tabak et al., 2014), different types of interbank loans (e.g., repurchase agreements or nonperforming loans), differences in nature and the business of financial institutions participating in the market, as well as the complex maturity structure of interbank debt (Acemoglu et al., 2015; Leventides et al., 2019). Identifying and introducing new contagion channels and examining the application of existing models in dealing with these channels (Hübsch & Walther, 2017; Li et al., 2015; Silva et al., 2016) is another thread of research in this field.…”

“…Central bank —At present, the vast majority of models developed from the interbank market by researchers lack the central bank as a significant, influential player, making those models far from what is happening in the real world. Hence, many scholars suggest the inclusion of a central bank in their future models as the provider of external safety nets, which acts as the “lender of last resort” and intervene in the market in the form of bailouts or liquidity injections (e.g., long‐term refinancing against collateral) (Barroso et al., 2016; Iori et al., 2006; Ladley, 2013; León et al., 2018; Lux, 2015; Smaga et al., 2018; Walters et al., 2019). In order to study networks of interbank loans connections that are as realistic as possible, it is worthwhile for other researchers to introduce the central bank as an active interbank market player in their upcoming research.…”

“…They identified a nonlinear relationship between connectivity and contagious defaults. Studies by Walters et al (2019) revealed that connectivity, combined with other network characteristics, potentially affects the system's strengthening or weakening through the network's tiering level. Also, Simaan et al (2020), who considered IMM as a money supply chain, stated that the less the connectivity between clusters and the more the connectivity within clusters in the network is, the more serious the network would be threatened.…”