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.
| Original language | English |
|---|---|
| Pages (from-to) | 542-555 |
| Number of pages | 14 |
| Journal | Journal of Business and Economic Statistics |
| Volume | 37 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2019 |
Funding
Lucas and Schaumburg thank the European Union Seventh Framework Programme (FP7-SSH/2007–2013, grant agreement 320270-SYRTO) for financial support. Schaumburg also thanks the Dutch Science Foundation (NWO, grant VENI451-15-022) for financial support. Parts of this article were written while Schwaab was on secondment to the ECB’s Single Supervisory Mechanism (SSM). We are particularly grateful to Klaus Düllmann, Heinrich Kick, and Federico Pierobon from the SSM. We also thank the three Referees and the Editor whose many insightful suggestions have helped us to reshape and improve the article.
| Funders | Funder number |
|---|---|
| FP7-SSH/2007 | 320270-SYRTO |
| Nederlandse Organisatie voor Wetenschappelijk Onderzoek | VENI451-15-022 |
| Seventh Framework Programme |