An Agent-Based Approach to Interbank Market Lending Decisions and Risk Implications

Abstract

Abstract:In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending market. We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 U.S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literature. A key finding of this study is that risk preferences at the individual bank level can lead to unique interbank market structures that are suggestive of the capacity with which the market responds to surprising shocks.

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

“…The fact that banks tend to keep existing relationships has also been taken into account by Liu et al. (2018) in their IMM simulations. They modeled banks as passive learning agents who may learn to select better counterparts by evaluating their pre‐existing relationships and updating relationship scores.…”

“…This interest has grown significantly, especially in recent years. One of the uses of dynamics in the field of IMM concerns includes its applications in behavioral studies of banks (Hałaj, 2018; Liu et al., 2018; Steinbacher & Steinbacher, 2015), interest rate dynamics studies (Kusnetsov & Veraart, 2019), dynamic formation of interbank networks (Kobayashi & Takaguchi, 2018; León et al., 2018), network resistance analysis (Li & He, 2011), modeling financial contagion (Erol & Ordonez, 2017), and examining the impact of different combinations of macro‐prudential and monetary policies (Popoyan et al., 2020). Another strand of future research in this field focuses on methods of implementing network dynamics, for example, theoretical derivation based on a macroscopic dynamic equation model, which can get a series of conclusions through strict theoretical proof (Jiang & Fan, 2019) and using the multi‐period approach in developing a fully dynamic model of interbank networks (Kusnetsov & Veraart, 2019).…”

Constructing banking networks under decreasing costs of link formation

“…The agent-based modeling literature has focused on replicating some of these characteristics as in the work of Gurgone et al (2018) and Liu et al (2018) and within dynamic modeling frameworks, as in Zhang et al (2018), Guleva et al (2017), Xu et al (2016), andCapponi andChen (2015). Lux (2015) introduces a simple dynamic agentbased model that, starting from a heterogeneous bank size distribution and relying on a reinforcement learning algorithm based on trust, allows the system to naturally evolve toward a core-periphery structure where core banks assume the role of mediators between the liquidity needs of many smaller banks.…”

Re-Creating Banking Networks under Decreasing Fixed Costs

“…ese empirical investigations establish a few stylized facts of interbank lending, such as a typical core-periphery structure, network sparsity, and disassortativeness. e agent-based modeling literature has focused on replicating some of these characteristics, as in the work of Gurgone et al (2018) and Liu et al (2018) and within dynamic modeling frameworks, as in Zhang et al (2018), Guleva et al (2017), Xu et al (2016), and Capponi and Chen (2015). Lux (2015) introduces a simple dynamic agent-based model that, starting from a heterogeneous bank size distribution and relying on a reinforcement learning algorithm based on trust, allows the system to naturally evolve toward a core-periphery structure where core banks assume the role of mediators between the liquidity needs of many smaller banks.…”