Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach

•This study provides an early-warning measure of banks’ systemic vulnerabilities, estimates systemic credit risk, and forecasts it out of sample.•It is applied to a set of European banking groups and their affiliates in Luxembourg.•The framework contributes to the macroprudential literature with a method to monitor systemic credit risk in advance over a couple of years.•The framework also allows measuring the other two sources of systemic risk, i.e., systemic risk due to common distress and to contagion.•The methodology links the systemic risk measures with the state of the economy to extract its driving forces and thus facilitates policy decisions.

This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks’ default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in the three forms categorized by the European Central Bank: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks; and (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust out-of-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.