Monte Carlo Simulations: A Case Study of Systemic Risk Modelling

In this paper, we present a case study of systemic risk modelling using Monte Carlo simulations. Specifically, we analyse the link between the global financial system and sovereign debt crises. In order to this, we construct an agent-based network model of an artificial financial system allowing us to analyse the effects of government support on systemic stability and feedback loops of risk transfer back into the system. Consequently, the model is calibrated to real-world data using a unique dataset put together from multiple sources and tested with various parameter settings in Monte Carlo simulations. Based on these simulations we present relevant policy recommendations for decision makers anticipating future bank bail-outs.