We propose a novel observation-driven dynamic finite mixture model for the study of banking data. The model accommodates time-varying component means and covariance matrices, normal and t-distributed mixtures, and economic determinants of time-varying parameters. Monte Carlo experiments suggest that banks can be classified reliably into distinct components in a variety of settings. In an empirical study of 233 European banks between 2008Q1–2015Q2, we demonstrate that the global financial crisis and euro area sovereign debt crisis had a differential impact on banks with different business models. In addition, changes in long-term interest rates predict banks’ business models.
This is joint work with André Lucas (Vrije Universiteit) and Bernd Schwaab (European Central