Monitoring Bank Failures in a Data-Rich Environment

This paper develops a monitoring and forecasting model for the aggregate monthly number of commercial bank failures in the U.S. We extract key sectoral predictors from the large set of macroeconomic variables proposed by McCracken and Ng (2016) and incorporate them in a hurdle negative binomial model to predict the number of monthly commercial bank failures. We uncover a strong and robust relationship between the predictor synthesizing housing industry variables and bank failures. This relationship suggests the existence of a link between developments in the housing sector and the vulnerability of commercial banks to non-performing loans increases and asset deterioration. We assess different specifications