Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/149470
Authors: 
Lucas, Andre
Schaumburg, Julia
Schwaab, Bernd
Year of Publication: 
2016
Series/Report no.: 
Tinbergen Institute Discussion Paper 16-066/IV
Abstract: 
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.
Subjects: 
bank business models
clustering
finite mixture model
score-driven model
low interest rates
JEL: 
C33
G21
Document Type: 
Working Paper

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