The liquidity regulation of banks in Pillar 1 of the Basel framework does not consider longer-term funding cost risks of different bank business models. Therefore, we assemble a data set of balance sheet positions including maturities and use the method of Value-Liquidity-at-Risk to explore 118 European retail, wholesale, and trading banks. When examining liquidity-induced equity risks, trigged by exemplary rating shifts, we find that retail banks bear significantly lower funding cost risks than wholesale and trading banks. Consequently, a prudential regulation, which simultaneously considers the funding cost risk and the diversification of the banking system, is recommended.
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For example, the German Savings Bank Association with over 400 member banks or the German Cooperative Financial Network with about 1000 members display no statements in their annual reports about maturities of securities portfolios. The Deutsche Bank started to disclose the term structure of covered bonds, but not for the examined years and not for securities held as assets.
This assumption might neglect different maturity structures of bank’s securities portfolios. As a robustness check, the time buckets in Table 3 and "Appendix 6" are mirrored for the calculation of alternative funding gaps, e.g., in 2000, 7% (instead of 33%) of all debt securities have a maturity of up to 1 year, respectively, 33% (instead of 7%) of securities have a maturity over 10 years. As a result, the mean VLaR-Ratios in the baseline scenario (see Table 5) increase for all banks to − 0.02% (prior − 0.01%) and for wholesale banks to − 0.04% (− 0.02%), remain the same for retail banks (0.01%), and decrease for trading banks to − 0.03% (− 0.07%). If only the maturity structure of securities held as assets are mirrored, the VLaR-Ratios remain at −0.01% for all banks, increase for wholesale banks (− 0.03%), and decrease for retail banks (0.02%) and for trading banks (− 0.01%). Overall, the necessary underlying assumption might underestimate the latter results of some wholesale banks and overestimate the results of some retail and trading banks in the sample.
The data are provided as part of a cooperation with the University of Applied Sciences Kiel.
The results for the Gaussian and modified methodical concepts are presented in "Appendix 7". The tendencies of the findings, e.g., the differences between business models, are comparable to the results of the historical concept.
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We are grateful to Klaus Beckmann, Robert Fiedler, Lars Grosstueck, Sven Klinner, Jan-Hendrik Meier, Stefan Okruch, Stefan Prigge, Christian Schaeffler, Stefan Schoenherr, Aline Taenzer, Christoph Weldam, and participants of the Claussen-Simon Graduate Centre at HSBA for helpful comments and discussions. We would particularly like to thank the University of Applied Sciences Kiel for the cooperation and the provided market data. The paper benefited from the comments and remarks of two anonymous referees. We also like to thank Goetz Greve, the Claussen-Simon Foundation, and the Association of Friends and Sponsors of the HSBA.
See Table 7.
Retail bank: gross loans ≥ 50% (of balance sheet total net of derivatives) with customer deposits ≥ 50%, or gross loans ≥ 35% with customer deposits ≥ wholesale debt and interbank borrowing, and investment activities < 20%.
Wholesale bank: gross loans ≥ 50% with wholesale debt and interbank borrowing ≥ customer deposits, or gross loans ≥ 35% with wholesale debt and interbank borrowing ≥ customer deposits, and investment activities < 20%.
Trading bank: investment activities ≥ 20%, or if interbank lending and investment activities ≥ gross loans.
For borderline decisions, additional (internal) data can be used to allocate banks, e.g., strategic orientation, financial and common reporting data, domestic characteristics, as well as recovery and resolution planning .
See Table 8.
See Table 9.
See Table 21.
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Grossmann, D., Scholz, P. The golden rule of banking: funding cost risks of bank business models. J Bank Regul 20, 174–196 (2019). https://doi.org/10.1057/s41261-018-0080-5
- Bank business models
- Funding cost risk
- Liquidity requirements
- Value Liquidity Expected Shortfall