Bank Business Models at Zero Interest Rates - Andre Lucas; Julia Schaumburg; Bernd Schwaab (Tinbergen Institute, 2016)
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.
Keywords: C33; G21; 330; bank business models; clustering; finite mixture model; score-driven model; low interest rates