Using Counterfactual Simulations to Assess the Danger of Contagion in Interbank Markets

Researchers at central banks increasingly turn to counterfactual simulations to estimate the danger of contagion owing to exposures in the interbank loan market. The present paper summarises the findings of such simulations, provides a critical assessment of the modelling assumptions on which they are based, and discusses their use in financial stability analysis. On the whole, such simulations suggest that contagious defaults are unlikely, but cannot be fully ruled out, at least in some countries. If contagion does take place, then it could lead to the breakdown of a substantial fraction of the banking system, thus imposing high costs to society. However, when interpreting these results, one has to bear in mind the potential bias caused by the very strong assumptions underlying the simulations. While robustness tests indicate that the models might be able to correctly predict whether or not contagion could be an issue and, possibly, also identify critical institutions, they are less suited for stress testing or for the analysis of policy options in crises, primarily due to their lack of behavioural foundations. Going forward, more work is needed on how to attach probabilities to the individual scenarios and on the microfoundations of the models