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.
Highlights
Banks are highly heterogenous, differing widely in terms of size, complexity, organization, activities, funding choices, and geographical reach
We identify six business model components and discuss how they adjust to post-crisis regulatory and financial developments, including changes in the yield curve
This section investigates the ability of our score-driven dynamic mixture model to simultaneously i) correctly classify a data set into distinct components, and ii) recover the dynamic cluster means over time
Summary
Banks are highly heterogenous, differing widely in terms of size, complexity, organization, activities, funding choices, and geographical reach. As long-term interest rates decrease, banks on average (across all business models) grow larger, hold more assets in trading portfolios to offset declines in loan demand, hold more sizeable derivative books, and, in some cases, increase leverage and decrease funding through customer deposits Each of these effects – increased size, leverage, complexity, and a less stable funding base – are intuitive, and potentially problematic from a financial stability perspective. Catania (2016) proposes a score-driven dynamic mixture model which is related to ours His modeling framework is different in that the main focus is on the modeling of conditional asset return distributions over time, rather than on classifying a large cross-section.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
