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Bank Business Models at Zero Interest Rates

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  • André Lucas
  • Julia Schaumburg
  • Bernd Schwaab

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.

Suggested Citation

  • André Lucas & Julia Schaumburg & Bernd Schwaab, 2019. "Bank Business Models at Zero Interest Rates," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 542-555, July.
    • Handle: RePEc:taf:jnlbes:v:37:y:2019:i:3:p:542-555
    DOI: 10.1080/07350015.2017.1386567
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    References listed on IDEAS

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    1. Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andr� Lucas, 2014. "Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 898-915, December.
    2. Manganelli, Simone & Altunbas, Yener & Marqués-Ibáñez, David, 2011. "Bank risk during the financial crisis: do business models matter?," Working Paper Series 1394, European Central Bank.
    3. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
    4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    5. Abbassi, Puriya & Iyer, Rajkamal & Peydró, José-Luis & Tous, Francesc R., 2016. "Securities trading by banks and credit supply: Micro-evidence from the crisis," Journal of Financial Economics, Elsevier, vol. 121(3), pages 569-594.
    6. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    7. André Lucas & Bernd Schwaab & Xin Zhang, 2014. "Conditional Euro Area Sovereign Default Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 271-284, April.
    8. Acharya, Viral V. & Steffen, Sascha, 2015. "The “greatest” carry trade ever? Understanding eurozone bank risks," Journal of Financial Economics, Elsevier, vol. 115(2), pages 215-236.
    9. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    10. Lucas, André & Zhang, Xin, 2016. "Score-driven exponentially weighted moving averages and Value-at-Risk forecasting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 293-302.
    11. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    12. Abbassi, Puriya & Iyer, Rajkamal & Peydró, José-Luis & Tous, Francesc R., 2016. "Securities trading by banks and credit supply: Micro-evidence from the crisis," Journal of Financial Economics, Elsevier, vol. 121(3), pages 569-594.
    13. Leopoldo Catania, 2016. "Dynamic Adaptive Mixture Models," Papers 1603.01308, arXiv.org, revised Jan 2023.
    14. Rungporn Roengpitya & Nikola Tarashev & Kostas Tsatsaronis, 2014. "Bank business models," BIS Quarterly Review, Bank for International Settlements, December.
    15. Svensson, Lars E O, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992-4," CEPR Discussion Papers 1051, C.E.P.R. Discussion Papers.
    16. Beltratti, Andrea & Stulz, René M., 2012. "The credit crisis around the globe: Why did some banks perform better?," Journal of Financial Economics, Elsevier, vol. 105(1), pages 1-17.
    17. Iyer, Rajkamal & Peydró, José-Luis & Abbassi, Puriya & Tous, Francesc, 2015. "Securities Trading by Banks and Credit Supply: Micro-Evidence," CEPR Discussion Papers 10480, C.E.P.R. Discussion Papers.
    18. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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