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Recent empirical research on systemic banking crises (henceforth, SBCs) has highlighted the existence of similar patterns across diverse episodes. SBCs are rare events. Recessions that follow SBC episodes are deeper and longer lasting than other recessions. And, more importantly for the purpose of this paper, SBCs follow credit intensive booms; “banking crises are credit booms gone wrong." Rare, large, adverse financial shocks could possibly account for the first two properties. But they do not seem in line with the fact that the occurrence of an SBC is not random but rather closely linked to credit conditions. So, while most of the existing macro-economic literature on financial crises has focused on understanding and modelling the propagation and the amplification of adverse random shocks, the presence of the third stylized fact mentioned above calls for an alternative approach, which would explain how financial imbalances build up over time and suddenly unravel.
In this model, financial crises take the form of non-linearities in the full equilibrium dynamics of the economy. They result from the pro-cyclicality of bank balance sheets that emanates from interbank market funding. Banks are assumed to be heterogeneous with respect to their intermediation skills, which gives rise to an interbank market. There are two-way relationships between the situation of the interbank market and the real economy. On the one hand, moral hazard and asymmetric information on the interbank market may generate sudden interbank market freezes, SBCs, credit crunches and, ultimately, severe recessions.
On the other hand, those frictions become more prevalent as the household accumulates assets in anticipation of severe recessions and real interest rates decrease. The typical run of events leading to a financial crisis is as follows. A sequence of favourable, non permanent, supply shocks hits the economy. The resulting increase in the productivity of capital leads to a demand-driven expansion of credit that pushes the corporate loan rate above steady state.
As productivity goes back to trend, firms reduce their demand for credit, whereas households continue to accumulate assets, thus feeding the supply of credit by banks. The credit boom then turns supply-driven and the corporate loan rate goes down, falling below steady state.
By giving banks incentives to take more risks or misbehave, too low a corporate loan rate contributes to eroding trust within the banking sector precisely at a time when banks increase in size. When counterparty fears in the interbank market rise too high, the market freezes. Ultimately, the credit boom lowers the resilience of the banking sector to shocks, making systemic crises more likely.
The model was calibrated on the business cycles in the US (post WWII) and the financial cycles in fourteen OECD countries (1870-2008), and assess its quantitative properties. The model reproduces the stylized facts associated with SBCs remarkably well. Most of the time the model behaves like a standard financial accelerator model, but once in a while - on average every forty year -| there is a banking crisis.
The larger the credit boom:
In these simulations, the recessions associated with SBCs are significantly deeper (with a 45% larger output loss) than average recessions. Overall, these results validate the role of supply-driven credit booms leading to credit busts. This result is of particular importance from a policy making perspective as it implies that systemic banking crises are predictable. The model was used to compute the k-step ahead probability of an SBC at any point in time. Fed with actual US data over the period 1960-2011, the model yields remarkably realistic results. For example, the one-year ahead probability of a crisis is essentially zero in the 1960-70s. It jumps up twice during the sample period: in 1982-3, just before the Savings & Loans crisis, and in 2007-9. Although very stylized, this model is thus also able to detect financial imbalances and predict future crises. Finally, it was analysed the sensitivity of the above results to the parameters of the model. Among other things, it was found that risk averse economies tend to be more prone to crises because more risk averse households typically accumulate more assets during booms, which amplifies credit booms.