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 |
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Bank Business Models at Zero Interest Rates. / Lucas, André; Schaumburg, Julia; Schwaab, Bernd.
In: Journal of Business and Economic Statistics, Vol. 37, No. 3, 2019, p. 542-555.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Bank Business Models at Zero Interest Rates
AU - Lucas, André
AU - Schaumburg, Julia
AU - Schwaab, Bernd
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85047929519&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047929519&partnerID=8YFLogxK
U2 - 10.1080/07350015.2017.1386567
DO - 10.1080/07350015.2017.1386567
M3 - Article
VL - 37
SP - 542
EP - 555
JO - Journal of Business and Economic Statistics
JF - Journal of Business and Economic Statistics
SN - 0735-0015
IS - 3
ER -