Interbank Contagion: An Agent-Based Model Approach to Endogenously Formed Networks

Abstract

The potential impact of interconnected financial institutions on interbank financial systems is a financial stability concern for central banks and regulators. In examining how financial shocks propagate through contagion effects, we argue that endogenous individual bank choices are necessary to properly consider how losses develop as the interbank lending network evolves. We present an agent-based model to endogenously reconstruct interbank networks based on 6,600 banks' decision rules and behaviors reflected in quarterly balance sheets. We compare the results of our model to the results of a traditional stationary network framework for contagion. The model formulation reproduces dynamics similar to those of the 2007-09 financial crisis and shows how bank losses and failures arise from network contagion and lending market illiquidity. When calibrated to post-crisis data from 2011-14, the model shows the U.S. banking system has reduced its likelihood of bank failures through network contagion and illiquidity, given a similar stress scenario.

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

“…To compare the network structures, we conducted 100 experiments with the same number of agents (see Table 4). An interesting finding is that the intelligent agents generate a network structure that is very close to our previous study [21].…”

“…However, banks may refuse to accept requests from other banks in the market even though they have capacity. The banks utilize an S-shaped function, p(S total ), to assess the chance to lend to each specific borrower, which is a revised form but the same assumption in [21] S total relation size…”

Making economics more useful: how technological eclecticism could help

“…For instance, European simulation-based publications include Galliani and Zedda (2015) and Benczur et al (2016) (2015) on the use of Monte Carlo simulations to monitor hedge fund risks; and, Paddrik and Young (2017), Paddrik et al (2016aPaddrik et al ( , 2016b, Cetina, Rajan, and Paddrik (2016), and Flood and Korenko (2015) on supervisory stress testing. Other working and staff papers published by the OFR, including Liu et al (2016), Bookstaber and Paddrik (2015), Paddrik et al (2014), Bookstaber, Paddrik, and Tivnan (2014), and Bookstaber (2012) assess systemic risk using agent-based modelling.…”

Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

“…The low-frequency data with daily, weekly, monthly, quarterly or annual sampling frequency can not accurately measure the whole-day volatility information [ 42 ]. Nowadays, more and more scholars have realized that the high-frequency data with the frequency of hours, minutes or even shorter includes the rich information of asset price, and it has been intensively studied in applied finance risk management [ 43 , 44 , 45 , 46 ]. On the other hand, with the unexpected changes of macroeconomic conditions, international events and economic policy in recent years, financial markets are increasingly volatile [ 47 ].…”