  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.
