Gai-Kapadia Model

Last updated on 26th March 2024

The Gai-Kapadia model is a model of contagion risk in financial networks, developed by the Bank of England in the following publication: Working Paper No. 383 Contagion: in financial networks

Here we present an implementation of this model in the Simudyne SDK.

Download Model Files View Demo Online

The code for this model is available as a zip download, containing a complete model as a Maven project. This project should be able to be run from any Maven or Java IDE environment.

Abstract

This model simulates contagion in financial networks. The model explores how the probability and the size of contagion in a financial system is influenced by the network's connectivity (i.e. interconnection between financial institutions). The model characterizes the financial system as a complex network of financial institutions (agents in the network) interacting with one another through their joint financial exposures (the links in the network). The agents are characterized by their assets and liabilities. Agents remain financially solvent as long as their assets are larger than their liabilities. One agent's liabilities are another agent's assets. If an agent defaults (i.e. when its liabilities become larger than its assets), this will induce losses to its connected agents, thus passing on the risk of contagious default. Thus, depending on the connectivity of the agents and their financial strength, an initial idiosyncratic default can induce a cascade of further defaults. Interestingly, this model shows that there is a complex relationship between network connectivity (i.e. average number of banks that are connected to each other, based on their joint financial exposure) and the probability and the size of default. For instance, larger connectivity generally reduces the risk of contagion (as the impact of each default only leads to low impact on each of the other institutions). However, if contagion occurs, larger portions of the network are affected. Thus, the model suggests that financial systems exhibit a robust-yet-fragile tendency: while the probability of contagion may be low, the effects can be extremely widespread when problems occur.