Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/175708
Authors: 
Lucas, André
Schaumburg, Julia
Schwaab, Bernd
Year of Publication: 
2017
Series/Report no.: 
ECB Working Paper 2084
Abstract: 
We propose a novel observation-driven 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 their properties evolve over time. Changes in the yield curve predict changes in average business model characteristics.
Subjects: 
bank business models
clustering
finite mixture model
score-driven model
low interest rates
JEL: 
G21
C33
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-2806-9
Document Type: 
Working Paper

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