Abstract:
We present a network-based framework for simulating systemic risk that considers shock propagation
in banking systems. In particular, the framework allows the modeller to reflect a top-down framework
where a shock to one bank in the system affects the solvency and liquidity position of other banks,
through systemic market risks and consequential liquidity strains. We illustrate the framework with an
application using South African bank balance sheet data. Spikes in simulated assessments of systemic
risk agree closely with spikes in documented subjective assessments of this risk. This indicates that
network models can be useful for monitoring systemic risk levels. The model results are sensitive to
liquidity risk and market sentiment and therefore the related parameters are important considerations
when using a network approach to systemic risk modelling.
