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 Student's $t$ distributed mixtures, and economic determinants of time-varying parameters. Monte Carlo experiments suggest that units of interest can be classified reliably into distinct components in a variety of settings. In an empirical study of 208 European banks between 2008Q1--2015Q4, we identify six business model components and discuss how these adjust to post-crisis financial developments. Specifically, bank business models adapt to changes in the yield curve.
bank business models clustering finite mixture model score-driven model low interest rates