An agent-based approach to modelling long-term systemic risk in networks of interacting banks

Abstract

The recent banking crisis has led to a spate of literature investigating the concept of systemic risk, aiming to understand the stability of specific financial systems and how contagion can spread through them following stress events. However, the primary focus of this literature has been on static networks, rather than dynamic systems that evolve over time and are shaped by participant interactions. Such a long-term focus is necessary to fully understand how systems will react to policy changes.

This thesis analyses two banking systems that are subject to systemic risk, but also feature both micro-level contagion dynamics and strategic interactions between participants. The first is the large value payment system CHAPS, in which participating banks face a strategic decision for how to make their payments in an optimal manner. The second is the relationship between the resolution of insolvent banks and system efficiency, including whether the moral hazard effect created by bank bailouts causes the system to evolve suboptimally. Both systems are analysed using agent-based models with respect to a long term \social welfare" measure that balances bank profitability with the costs caused by contagion.

The models generate results that would not be possible through a static analysis of the systems without adaptive banks. The payment system is shown to operate below its social optimum, as banks do not endogenise the systemic risk externalities caused by strategies that appear optimal at an individual level. This leads to insufficient liquidity in the system and the queuing of non-priority payments in an inefficient manner.

In the insolvency model, a policy of regulatory intervention shapes bank risk-taking over the long term, with the short term gains of a bailout leading over time to excessive bank leverage, a higher number of insolvencies and reduced social welfare. A targeted strategy of only bailing out specific institutions that are Too-Big-To-Fail also reduces long term system efficiency.

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