IDEAS home Printed from https://ideas.repec.org/p/zbw/bubtps/283348.html
   My bibliography  Save this paper

Abschätzung des Zinseinkommens der Banken in Deutschland

Author

Listed:
  • Memmel, Christoph

Abstract

Die Entwicklung des Zinseinkommens der Banken in Deutschland wird in Szenarien abgeschätzt, und zwar für die Jahre 2023 und 2024. Es zeigt sich, dass das Zinseinkommen im Basis-Szenario im Vergleich zum Jahr 2022 zunimmt, wenn man berücksichtigt, dass die Banken an Zinsaufwendungen sparen, weil sie sehr zögerlich sind bei der Erhöhung der Zinsen für Kundeneinlagen, verglichen mit der Zinsentwicklung aus einem Modell, das auf der Vergangenheit fußt.

Suggested Citation

  • Memmel, Christoph, 2023. "Abschätzung des Zinseinkommens der Banken in Deutschland," Technical Papers 05/2023, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubtps:283348
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/283348/1/technical-paper-2023-05.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dräger Vanessa & Heckmann-Draisbach Lotta & Memmel Christoph, 2021. "Interest and credit risk management in German banks: Evidence from a quantitative survey," German Economic Review, De Gruyter, vol. 22(1), pages 63-95, February.
    2. Alexander Kempf & Christoph Memmel, 2006. "Estimating the global Minimum Variance Portfolio," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 58(4), pages 332-348, October.
    3. Busch, Ramona & Memmel, Christoph, 2021. "Why are interest rates on bank deposits so low?," Discussion Papers 46/2021, Deutsche Bundesbank.
    4. Kalkbrener, Michael & Willing, Jan, 2004. "Risk management of non-maturing liabilities," Journal of Banking & Finance, Elsevier, vol. 28(7), pages 1547-1568, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
    2. Maillet, Bertrand & Tokpavi, Sessi & Vaucher, Benoit, 2015. "Global minimum variance portfolio optimisation under some model risk: A robust regression-based approach," European Journal of Operational Research, Elsevier, vol. 244(1), pages 289-299.
    3. Frantisek Cech & Jozef Barunik, 2017. "Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns," Working Papers IES 2017/20, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2017.
    4. Oliver Entrop & Marco Wilkens & Alexander Zeisler, 2009. "Quantifying the Interest Rate Risk of Banks: Assumptions Do Matter," European Financial Management, European Financial Management Association, vol. 15(5), pages 1001-1018, November.
    5. Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
    6. Michele Costola & Bertrand Maillet & Zhining Yuan & Xiang Zhang, 2024. "Mean–variance efficient large portfolios: a simple machine learning heuristic technique based on the two-fund separation theorem," Annals of Operations Research, Springer, vol. 334(1), pages 133-155, March.
    7. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.
    8. Peter Hoffmann & Sam Langfield & Federico Pierobon & Guillaume Vuillemey, 2019. "Who Bears Interest Rate Risk?," The Review of Financial Studies, Society for Financial Studies, vol. 32(8), pages 2921-2954.
    9. Claußen, Catharina & Platte, Daniel, 2023. "Evaluating the validity of regulatory interest rate risk measures – a simulation approach," Journal of Banking & Finance, Elsevier, vol. 154(C).
    10. Thomas Holgersson & Peter Karlsson & Andreas Stephan, 2020. "A risk perspective of estimating portfolio weights of the global minimum-variance portfolio," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 59-80, March.
    11. Frahm, Gabriel & Memmel, Christoph, 2008. "Dominating estimators for the global minimum variance portfolio," Discussion Papers in Econometrics and Statistics 2/08, University of Cologne, Institute of Econometrics and Statistics.
    12. Frahm, Gabriel & Wiechers, Christof, 2011. "On the diversification of portfolios of risky assets," Discussion Papers in Econometrics and Statistics 2/11, University of Cologne, Institute of Econometrics and Statistics.
    13. Lan, Wei & Wang, Hansheng & Tsai, Chih-Ling, 2012. "A Bayesian information criterion for portfolio selection," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 88-99, January.
    14. Imre Kondor & G'abor Papp & Fabio Caccioli, 2016. "Analytic solution to variance optimization with no short-selling," Papers 1612.07067, arXiv.org, revised Jan 2017.
    15. Alexandre Adam & Hamza Cherrat & Mohamed Houkari & Jean-Paul Laurent & Jean-Luc Prigent, 2022. "On the risk management of demand deposits: quadratic hedging of interest rate margins," Annals of Operations Research, Springer, vol. 313(2), pages 1319-1355, June.
    16. Memmel, Christoph & Heckmann-Draisbach, Lotta, 2023. "Banks' net interest margin and changes in the term structure," Discussion Papers 11/2023, Deutsche Bundesbank.
    17. Kazak, Ekaterina & Pohlmeier, Winfried, 2019. "Testing out-of-sample portfolio performance," International Journal of Forecasting, Elsevier, vol. 35(2), pages 540-554.
    18. Gabriel Frahm & Tobias Wickern & Christof Wiechers, 2012. "Multiple tests for the performance of different investment strategies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(3), pages 343-383, July.
    19. Taras Bodnar & Holger Dette & Nestor Parolya & Erik Thors'en, 2019. "Sampling Distributions of Optimal Portfolio Weights and Characteristics in Low and Large Dimensions," Papers 1908.04243, arXiv.org, revised Apr 2023.
    20. Hamza Cherrat & Jean-Luc Prigent, 2023. "On the Hedging of Interest Rate Margins on Bank Demand Deposits," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 935-967, October.

    More about this item

    Keywords

    Zinsergebnis der Banken;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:bubtps:283348. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/dbbgvde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.