Monitoring systemic risk based on dynamic thresholds

Successful implementation of macroprudential policy is contingent on the ability to identify and estimate systemic risk in real time. In this paper, systemic risk is defined as the conditional probability of a systemic banking crisis and this conditional probability is modeled in a fixed effect binary response model framework. The model structure is dynamic and is designed for monitoring as the systemic risk forecasts only depend on data that are available in real time. Several risk factors are identified and it is hereby shown that the level of systemic risk contains a predictable component w