Heavy-tailed distributions have been observed for various financial risks and papers have observed that these heavy-tailed distributions are power law distributions. The breakdown of a power law distribution is also seen as an indicator of a tipping point being reached and a system then moves from stability through instability to a new equilibrium. In this paper, we analyse the distribution of operational risk losses in US banks, credit defaults in US corporates and market risk events in the US during the global financial crisis (GFC). We conclude that market risk and credit risk do not follow a power law distribution, and even though operational risk follows a power law distribution, there is a better distribution fit for operational risk. We also conclude that whilst there is evidence that credit defaults and market risks did reach a tipping point, operational risk losses did not. We conclude that the government intervention in the banking system during the GFC was a possible cause of banks avoiding a tipping point.

In this paper we present and discuss a simple financial accelerator agent-based model, whose conceptual core is the interaction of heterogeneous firms and the banking system. Its simplicity notwithstanding, the model is able to replicate through simulations a large number of stylized facts concerning the shape and evolution over time of the distribution of firms' sizes, growth rates, profits, and “bad debt.”