Skip to main content
Log in

Impact of IFRS 9 on the cost of funding of banks in Europe

  • Original Article
  • Published:
Journal of Banking Regulation Aims and scope Submit manuscript

Abstract

On implementation, IFRS 9 increases credit loss (impairment) charges and reduces after-tax profits of banks. This makes retained earnings and hence capital resources lower than what they would be under IAS 39. To maintain their capital ratios under IFRS 9, banks may choose to hold higher levels of equity capital. This paper uses a modified version of CAPM, which accounts for the low-risk anomaly (as suggested by Baker and Wurgler (Baker and Wurgler in American Economic Review 105:315–320, 2015)), to estimate the impact of this potential increase in capital levels on the cost of funding of banks in six European countries, the UK, Germany, France, Italy, Spain and Switzerland. Our results indicate that weak low-risk anomaly exists for banks’ equity in the six countries, except France. The magnitude of the anomaly varies across countries, but is generally low relative to the long-run cost of equity for banks. Due to the weak anomaly, we find a minor “day 1” impact of IFRS 9 on the cost of funding of banks in the six countries.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. International Financial Reporting Standards 9: Financial Instruments.

  2. International Accounting Standard 39: Financial Instruments: Recognition and Measurement.

  3. This is based on a naïve logic. Because it covers more loans in its scope (Stage 1 and Stage 2 loans), IFRS 9 would make impairment charges, in a given year, higher than what they would be under IAS 39. However, in certain circumstances, IFRS 9 might lead to lower impairment charges and higher profits. This could be the case in an upturn if the stock of loan loss provisions at the start of a year exceeds that required at the end of the year (due to more optimism in expectations).

  4. There are some cross-country differences in terms of tax deductibility of expenses, which may reflect on the impact of IFRS 9 on capital resources. For simplicity, our analysis abstracts from these differences.

  5. Banks could maintain their capital ratios by deleveraging and/or de-risking, rather than holding more equity capital.

  6. For example, Brigham and Ehrhardt [3]: “As leverage increases, more weight is given to low-cost debt, but equity becomes riskier. Under Modigliani–Miller assumptions, the cost of equity capital increases by exactly enough to keep the cost of capital constant” (p. 597).

  7. Authors attribute this irrational preference for shares with higher volatility to investor overconfidence (Cornell [10]), and lottery preferences Kumar [11], Barberis and Huang [12], and Bali et al. [13].

  8. Baker et al. [5] explain the limited arbitrage preventing sophisticated institutional investors from exploiting any low-risk anomaly by the following. First, shorting highly volatile shares can be hard, especially for smaller companies with small number of shares available to borrow in the market. Moreover, institutional investors do not act on their own behalf in most cases. As their customers want to ensure they can compare different investors among asset classes, the investors must perform relative to a benchmark. This benchmarking restricts their ability to exploit the low-risk anomaly.

  9. Our analysis covers the potential microeconomic costs only and does not investigate the macroeconomic costs. For instance, if banks chose to pass the higher costs to their customers, this could lead to lower lending and possibly lower output.

  10. The derivation of Eq. (6) is in Appendix.

  11. If bank debt were risky, the change in the cost of funding would be reduced to the extent that the increased level of capital reduces βd, as Eq. (7) shows. Although the assumption of riskless debt is a reasonable for banks, it does not hold true for very highly levered banks. Yet, we can drop such extreme cases, as banking regulations, which requires certain equity ratios, would prevent those cases.

  12. Stronger low-risk anomaly in debt markets and/or higher riskiness of bank debt reduce the impact of the low-risk anomaly in share markets on WACC, as Baker and Wurgler [16] indicate.

  13. Baker and Wurgler [16] indicate that the “calibration with riskless debt remains a reasonable estimate in the presence of such factors”.

  14. We estimate the average risk weight by dividing the leverage ratio by the CET1 ratio.

  15. Available at: https://www.eba.europa.eu/-/eba-updates-on-the-impact-of-ifrs-9-on-banks-across-the-eu-and-highlights-current-implementation-issues.

  16. Available at: https://www.eba.europa.eu/sites/default/documents/files/documents/10180/2087449/bb4d7ed3-58de-4f66-861e-45024201b8e6/Report%20on20IFRS%209%20impact%20and%20implementation.pdf.

  17. Available at: https://www.mazars.com/Home/News/Our-publications/Mazars-Insights/Quantified-impacts-of-IFRS-9-initial-findings.

  18. Appendix Asset betas of the banks in the sample includes more information about estimated asset betas for the banks in our sample. In that Appendix, we also re-estimate asset betas on annual basis.

  19. The weights in these portfolios (as well as those in “Robustness checks Section”) are calculated by dividing the total assets of each bank on the total assets of all banks in the portfolio, using end 2017 data.

  20. We present the estimated values of alphas and betas for the country-level portfolios, and the plots used to estimate γ values in Appendix Estimation of the magnitude of the low-risk anomaly.

  21. The debt betas of the country-level portfolios are calculated by substituting the equity and assets beta of each portfolio into Eq. (1). Their values are presented in Appendix Estimation of the magnitude of the low-risk anomaly.

  22. Appendix The impact of IFRS 9 on the cost of funding includes more details about the estimated impact of IFRS 9 on the cost of funding.

  23. The rise in asset quality transparency may increase or reduce the cost of funding by revealing the true riskiness of assets.

  24. Appendices Robustness checks; Experiment 1 – Country-level portfolios of all banksRobustness checks; Experiment 5 – Three risk-based portfolios (high, medium and low risk) include more information about γ estimation under each of the five robustness experiments.

  25. In the first experiment, we have only one portfolio comprising all German banks, in which the two banks have combined weight of 96.5%. As a result, the two banks would have a stronger contribution to the values of the alpha and beta of the portfolio and hence the estimated magnitude of the low-risk anomaly. In the baseline, the two banks affect only one of three sets of alpha and beta which have equal impacts on the estimated anomaly, making their influence on it weaker. Their influence is even weaker in the fourth experiment where the banks have equal weights in the panel.

  26. We also calculate the estimated impacts of IFRS 9 in the second case we explore in this paper (i.e. when bank debt is risky but not very sensitive to leverage). We present these impacts in Appendices The impact of IFRS 9 under Experiment 1 – Country-level portfolios of all banksThe impact of IFRS 9 under Experiment 5 – Three risk-based portfolios.

References

  1. Novotny-Farkas, Z. 2016. The interaction of the IFRS 9 expected loss approach with supervisory rules and implications for financial stability. Accounting in Europe 13 (2): 197–227.

    Article  Google Scholar 

  2. Adrian, T., and H.S. Shin. 2010. The changing nature of financial intermediation and the financial crisis of 2007–2009. Annual Review of Economics 2 (1): 603–618.

    Article  Google Scholar 

  3. Brigham, E.F., and M.C. Ehrhardt. 2014. Financial management: Theory & practice (Book Only). Cengage Learning.

    Google Scholar 

  4. Eugene, F., and K. French. 1992. The cross-section of expected stock returns. Journal of Finance 47 (2): 427–465.

    Article  Google Scholar 

  5. Baker, M., B. Bradley, and J. Wurgler. 2011. Benchmarks as limits to arbitrage: Understanding the low-volatility anomaly. Financial Analysts Journal 67 (1): 40–54.

    Article  Google Scholar 

  6. Baker, M., B. Bradley, and R. Taliaferro. 2014. The low-risk anomaly: A decomposition into micro and macro effects. Financial Analysts Journal 70 (2): 43–58.

    Article  Google Scholar 

  7. Ang, A., R.J. Hodrick, Y. Xing, and X. Zhang. 2006. The cross-section of volatility and expected returns. The Journal of Finance 61 (1): 259–299.

    Article  Google Scholar 

  8. Ang, A., R.J. Hodrick, Y. Xing, and X. Zhang. 2009. High idiosyncratic volatility and low returns: International and further US evidence. Journal of Financial Economics 91 (1): 1–23.

    Article  Google Scholar 

  9. Baker, M., Hoeyer, M. F., and Wurgler, J. (2016). The risk anomaly tradeoff of leverage (No. w22116). National Bureau of Economic Research.

  10. Cornell, B. 2009. The pricing of volatility and skewness: A new interpretation. The Journal of Investing 18 (3): 27–30.

    Article  Google Scholar 

  11. Kumar, A. 2009. Who gambles in the stock market? The Journal of Finance 64 (4): 1889–1933.

    Article  Google Scholar 

  12. Barberis, N., and M. Huang. 2008. Stocks as lotteries: The implications of probability weighting for security prices. American Economic Review 98 (5): 2066–2100.

    Article  Google Scholar 

  13. Bali, T.G., N. Cakici, and R.F. Whitelaw. 2011. Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of financial economics 99 (2): 427–446.

    Article  Google Scholar 

  14. Frazzini, A., and L.H. Pedersen. 2014. Betting against beta. Journal of Financial Economics 111 (1): 1–25.

    Article  Google Scholar 

  15. Karceski, J. 2002. Returns-chasing behavior, mutual funds, and beta’s death. Journal of Financial and Quantitative analysis 37 (4): 559–594.

    Article  Google Scholar 

  16. Baker, M., and J. Wurgler. 2015. Do strict capital requirements raise the cost of capital? Bank regulation, capital structure, and the low-risk anomaly. American Economic Review 105 (5): 315–320.

    Article  Google Scholar 

  17. Arakelyan, A., and Karapetyan, A. 2014. Cost of Bank Capital: Evidence from European Banks. In European Financial Management Association 2014 Annual Meetings, June (pp. 25–28).

  18. King, M. R. 2009. The cost of equity for global banks: a CAPM perspective from 1990 to 2009. BIS Quarterly Review, September.

  19. European Systemic Risk Board (ESRB, 2017), Financial stability implications of IFRS 9, July. Available at: https://www.esrb.europa.eu/pub/pdf/reports/20170717_fin_stab_imp_IFRS_9.en.pdf

  20. Abad, J., and Suarez, J. 2017. Assessing the cyclical implications of IFRS 9-a recursive model (No. 12). ESRB Occasional Paper Series.

  21. Fatouh, M., and Giansante, S. 2020. Expected loss model and the cyclicality of bank credit losses and capital ratios. Available at SSRN 3728699.

  22. European Banking Authority (EBA, 2017), Report on results from the second EBA impact assessment of IFRS 9, July. Available at: https://www.eba.europa.eu/-/eba-updates-on-the-impact-of-ifrs-9-on-banks-across-the-eu-and-highlights-current-implementation-issues.

  23. European Banking Authority (EBA, 2018), Report on First Observations on the Impact and Implementation of IFRS 9 by EU Institutions, December. Available at: https://www.eba.europa.eu/sites/default/documents/files/documents/10180/2087449/bb4d7ed3-58de-4f66-861e-45024201b8e6/Report%20on%20IFRS%209%20impact%20and%20implementation.pdf.

  24. Mazars 2018. Quantified Impacts of IFRS 9: Initial Findings, March. Available at: https://www.mazars.com/Home/About-us/News-publications-and-media/Our-publications/IFRS-Publications/Quantified-impacts-of-IFRS-9-initial-findings.

Download references

Acknowledgement

The views expressed in this paper are those of the authors and not necessarily those of the Bank of England or its committees.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mahmoud Fatouh.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Derivation of Eq. (6)

  • Start from the definition of WACC:

    $${\text{WACC}} = e.r_{e} + \left( {1 - e} \right).r_{d}$$
  • Substitute in the values of re and rd from Eqs. (5) and (4), respectively:

    $${\text{WACC}} = e.\left[ {\gamma .\left( {\beta_{e} - 1} \right) + r_{f} + \beta_{e} .r_{p} } \right] + \left( {1 - e} \right).\left[ {r_{f} + \beta_{d} .r_{p} } \right]$$
    $$\mathop \Rightarrow \limits^{{}} {\text{WACC}} = + e.\gamma .\beta_{e} - e.\gamma + e.\beta_{e} .r_{p} + r_{f} - + \beta_{d} .r_{p} - e.\beta_{d} .r_{p}$$
    $$\mathop \Rightarrow \limits^{{}} {\text{WACC}} = r_{f} - e.\gamma + e.\beta_{e} .\left( {\gamma + r_{p} } \right) + \beta_{d} .r_{p} - e.\beta_{d} .r_{p}$$
  • Substitute in the value of βe from Eq. (2):

    $${\text{WACC}} = r_{f} - e.\gamma + \left( {\beta_{a} - \left( {1 - e} \right).\beta_{d} } \right).\left( {\gamma + r_{p} } \right) + \beta_{d} .r_{p} - e.\beta_{d} .r_{p}$$
    $$\mathop \Rightarrow \limits^{{}} {\text{WACC}} = r_{f} - e.\gamma + \gamma .\beta_{a} + r_{p} .\beta_{a} - \gamma .\left( {1 - e} \right).\beta_{d} - \left( {1 - e} \right).\beta_{d} .r_{p} + \beta_{d} .r_{p} - e.\beta_{d} .r_{p}$$
    $$\mathop \Rightarrow \limits^{{}} {\text{WACC}} = r_{f} - e.\gamma + \gamma .\beta_{a} + r_{p} .\beta_{a} - \gamma .\left( {1 - e} \right).\beta_{d} -$$
    $$\mathop \Rightarrow \limits^{{}} {\text{WACC}} = r_{f} + \gamma .\beta_{a} + r_{p} .\beta_{a} - e.\gamma - \gamma .\left( {1 - e} \right).\beta_{d}$$
    $$\mathop \Rightarrow \limits^{{}} {\text{WACC}} = r_{f} + \beta_{a} .r_{p} + \beta_{a} .\gamma - \gamma \left[ {e + \left( {1 - e} \right).\beta_{d} } \right]$$
  • For a given level of βa, Since βd is a function of e:

    $${\text{WACC}} = r_{f} + \beta_{a} .r_{p} + \beta_{a} .\gamma - \gamma \left[ {e + \left( {1 - e} \right).\beta_{d} \left( e \right)} \right]$$

Asset betas of the banks in the sample

Asset betas for UK banks

Bank

The leverage ratio

CET1 ratio

Tier-1 capital ratio

Quarterly data

Annual data

Quarterly data

Annual data

Quarterly data

Annual data

Barclays

0.03

(0.002)

0.03

(0.003)

0.10

(0.006)

0.10

(0.012)

0.09

(0.005)

0.09

(0.010)

HSBC

0.06

(0.001)

0.06

(0.003)

0.13

(0.004)

0.13

(0.007)

0.11

(0.003)

0.11

(0.007)

Lloyds

0.04

(0.002)

0.04

(0.003)

0.10

(0.007)

0.10

(0.015)

0.10

(0.005)

0.10

(0.010)

RBS

0.04

(0.002)

0.05

(0.004)

0.12

(0.007)

0.12

(0.014)

0.10

(0.005)

0.10

(0.010)

Standard Chartered

0.06

(0.002)

0.06

(0.003)

0.11

(0.004)

0.11

(0.009)

0.10

(0.004)

0.10

(0.008)

Santander UK

0.05

(0.002)

0.05

(0.003)

0.10

(0.005)

0.10

(0.009)

0.11

(0.004)

0.11

(0.008)

CYBG

0.09

(0.003)

0.09

(0.004)

0.20

(0.006)

0.19

(0.010)

0.17

(0.007)

0.15

(0.012)

Virgin Money

0.04

(0.001)

0.04

(0.002)

0.19

(0.005)

0.20

(0.015)

0.20

(0.007)

0.21

(0.018)

Metro Bank

0.08

(0.004)

0.08

(0.008)

0.23

(0.013)

0.23

(0.023)

0.19

(0.010)

0.19

(0.018)

Close Brothers

0.15

(0.004)

0.15

(0.007)

0.18

(0.003)

0.18

(0.007)

0.15

(0.003)

0.15

(0.006)

  1. Standard errors in parentheses

Asset betas for German banks

Bank

The leverage ratio

CET1 ratio

Tier-1 capital ratio

Quarterly data

Annual data

Quarterly data

Annual data

Quarterly data

Annual data

Deutsche bank

0.03

(0.001)

0.03

(0.002)

0.13

(0.003)

0.13

(0.007)

0.11

(0.003)

0.11

(0.006)

Commerzbank

0.04

(0.001)

0.04

(0.003)

0.10

(0.004)

0.10

(0.008)

0.10

(0.003)

0.10

(0.006)

Dt. Pfandbriefbank

0.05

(0.001)

0.05

(0.002)

0.21

(0.003)

0.21

(0.005)

0.18

(0.002)

0.18

(0.004)

ProCredit Holding

0.12

(0.001)

0.12

(0.003)

0.15

(0.001)

0.15

(0.002)

0.14

(0.000)

0.14

(0.000)

UmweltBank

0.04

(0.003)

0.05

(0.008)

0.05

(0.001)

0.05

(0.002)

0.09

(0.002)

0.08

(0.004)

Enercity PAR

0.36

(0.016)

0.35

(0.032)

    

Merkur Bank

0.06

(0.004)

0.06

(0.007)

0.06

(0.002)

0.06

(0.004)

0.09

(0.005)

0.09

(0.010)

Quirin privatbk

0.11

(0.007)

0.12

(0.015)

    
  1. Standard errors in parentheses

Asset betas for French banks

Bank

The leverage ratio

CET1 ratio

Tier-1 capital ratio

Quarterly data

Annual data

Quarterly data

Annual data

Quarterly data

Annual data

BNP Paribas

0.03

(0.001)

0.03

(0.001)

0.10

(0.003)

0.10

(0.005)

0.09

(0.002)

0.09

(0.005)

Crédit Agricole

0.03

(0.001)

0.02

(0.003)

0.13

(0.006)

0.13

(0.016)

0.10

(0.005)

0.09

(0.013)

Société Générale

0.03

(0.001)

0.03

(0.001)

0.11

(0.003)

0.11

(0.007)

0.09

(0.002)

0.09

(0.005)

Natixis

0.03

(0.001)

0.03

(0.002)

0.12

(0.003)

0.12

(0.007)

0.10

(0.003)

0.10

(0.006)

Caisse Credit

0.18

(0.009)

0.18

(0.016)

0.31

(0.012)

0.31

(0.025)

0.26

(0.008)

0.26

(0.019)

CRCAM Nord CCI

0.15

(0.008)

0.15

(0.015)

0.24

(0.011)

0.26

(0.000)

0.22

(0.010)

0.23

(0.000)

Crcam Normandie

0.18

(0.009)

0.18

(0.018)

0.26

(0.014)

0.26

(0.028)

0.17

(0.011)

0.17

(0.023)

CRCAM ILLE-VIL

0.17

(0.009)

0.17

(0.017)

0.24

(0.051)

0.23

(0.132)

0.17

(0.038)

0.17

(0.099)

  1. Standard errors in parentheses

Asset betas for Italian banks

Bank

The leverage ratio

CET1 ratio

Tier-1 capital ratio

Quarterly data

Annual data

Quarterly data

Annual data

Quarterly data

Annual data

UniCredit

0.05

(0.001)

0.05

(0.002)

0.09

(0.002)

0.09

(0.004)

0.08

(0.003)

0.08

(0.006)

Intesa Sanpaolo

0.06

(0.001)

0.06

(0.003)

0.11

(0.004)

0.11

(0.009)

0.08

(0.003)

0.08

(0.007)

BANCO BPM

0.07

(0.002)

0.07

(0.004)

0.13

(0.004)

0.13

(0.008)

0.09

(0.003)

0.09

(0.007)

B. Monte dei Paschi

0.04

(0.003)

0.05

(0.005)

0.08

(0.006)

0.10

(0.009)

0.07

(0.005)

0.09

(0.005)

Unione di Banche IT

0.08

(0.002)

0.08

(0.005)

0.13

(0.003)

0.12

(0.007)

0.09

(0.002)

0.09

(0.004)

Mediobanca

0.12

(0.003)

0.12

(0.006)

0.15

(0.003)

0.15

(0.007)

0.13

(0.004)

0.13

(0.008)

BPER Banca

0.07

(0.003)

0.07

(0.006)

0.10

(0.004)

0.10

(0.009)

0.10

(0.005)

0.10

(0.010)

Credito Emiliano

0.06

(0.001)

0.06

(0.002)

0.12

(0.004)

0.12

(0.009)

0.10

(0.003)

0.10

(0.006)

Banca Poplare

0.10

(0.007)

0.09

(0.014)

0.13

(0.007)

0.13

(0.014)

0.12

(0.007)

0.12

(0.013)

Banca Piccolo

0.09

(0.002)

0.08

(0.005)

0.13

(0.003)

0.13

(0.006)

0.11

(0.003)

0.11

(0.007)

Banca Carige

0.11

(0.007)

0.11

(0.014)

0.17

(0.010)

0.17

(0.021)

0.10

(0.005)

0.10

(0.010)

Finecobank

0.03

(0.001)

0.03

(0.002)

0.35

(0.007)

0.35

(0.012)

0.22

(0.003)

0.22

(0.006)

B. Desio & Brianza

0.10

(0.004)

0.10

(0.008)

0.15

(0.005)

0.15

(0.010)

0.14

(0.005)

0.14

(0.009)

Banco di Sardegna

0.12

(0.004)

0.12

(0.007)

0.16

(0.005)

0.17

(0.010)

0.15

(0.005)

0.15

(0.010)

Dobank

0.09

(0.001)

0.10

(0.000)

0.36

(0.003)

0.36

(0.000)

0.25

(0.002)

0.25

(0.000)

Banca Finnat

0.30

(0.018)

0.30

(0.037)

0.45

(0.009)

0.45

(0.014)

0.31

(0.006)

0.31

(0.015)

Banca Profilo

0.10

(0.006)

0.10

(0.011)

0.29

(0.021)

0.29

(0.046)

0.28

(0.020)

0.29

(0.043)

  1. Standard errors in parentheses

Asset betas for Spanish banks

Bank

The leverage ratio

CET1 ratio

Tier-1 capital ratio

Quarterly data

Annual data

Quarterly data

Annual data

Quarterly data

Annual data

Santander

0.05

(0.001)

0.05

(0.002)

0.10

(0.003)

0.10

(0.006)

0.08

(0.002)

0.08

(0.005)

BBVA

0.05

(0.001)

0.05

(0.003)

0.09

(0.003)

0.09

(0.005)

0.08

(0.002)

0.08

(0.005)

CaixaBank

0.13

(0.017)

0.14

(0.037)

0.17

(0.003)

0.17

(0.005)

0.13

(0.003)

0.13

(0.005)

B. de Sabadell

0.07

(0.003)

0.07

(0.007)

0.13

(0.004)

0.13

(0.007)

0.11

(0.003)

0.11

(0.006)

Bankia

0.05

(0.006)

0.05

(0.012)

0.12

(0.013)

0.12

(0.028)

0.12

(0.005)

0.12

(0.012)

B. Popular

0.07

(0.002)

0.07

(0.004)

0.11

(0.004)

0.11

(0.009)

0.10

(0.003)

0.10

(0.006)

Caja de Ahorros

0.04

(0.021)

0.04

(0.039)

    

Bankinter

0.05

(0.001)

0.05

(0.002)

0.10

(0.004)

0.10

(0.009)

0.09

(0.003)

0.09

(0.007)

Liberbank

0.07

(0.002)

0.06

(0.005)

0.15

(0.004)

0.14

(0.010)

0.13

(0.004)

0.13

(0.009)

  1. Standard errors in parentheses

Asset betas for Swiss banks

Bank

The leverage ratio

CET1 ratio

Tier-1 Capital ratio

Quarterly data

Annual data

Quarterly data

Annual data

Quarterly data

Annual data

UBS

0.03

(0.002)

0.03

(0.004)

0.16

(0.007)

0.16

(0.015)

0.12

(0.005)

0.12

(0.010)

Schweizerische

0.12

(0.012)

0.12

(0.024)

    

Credit Suisse

0.03

(0.001)

0.03

(0.003)

0.12

(0.004)

0.12)

(0.008)

0.12

(0.005)

0.12

(0.012)

Julius Baer

0.08

(0.004)

0.08

(0.008)

0.34

(0.013)

0.35

(0.028)

0.21

(0.006)

0.20

(0.012)

B. Cnt. Vaudoise

0.09

(0.003)

0.09

(0.005)

0.20

(0.005)

0.20

(0.009)

0.21

(0.007)

0.20

(0.013)

EFG Bank

0.08

(0.005)

0.09

(0.011)

0.27

(0.012)

0.29

(0.028)

0.17

(0.005)

0.18

(0.011)

Basler KntB.

0.07

(0.006)

0.07

(0.012)

0.10

(0.009)

0.10

(0.016)

0.16

(0.005)

0.16

(0.007)

Luzerner KntB.

0.07

(0.004)

0.07

(0.007)

0.13

(0.007)

0.13

(0.013)

0.17

(0.007)

0.17

(0.013)

ST. Galler KntB.

0.09

(0.004)

0.09

(0.008)

0.17

(0.007)

0.17

(0.014)

0.16

(0.005)

0.15

(0.011)

Berner KntB.

0.07

(0.004)

0.07

(0.008)

0.17

(0.011)

0.17

(0.022)

0.21

(0.015)

0.20

(0.029)

Valiant

0.10

(0.006)

0.10

(0.011)

0.18

(0.008)

0.18

(0.017)

0.16

(0.007)

0.15

(0.014)

Graubündener

0.09

(0.004)

0.09

(0.008)

0.16

(0.008)

0.18

(0.055)

0.21

(0.011)

0.21

(0.020)

Basellandschaftlich

0.05

(0.005)

0.05

(0.009)

0.08

(0.001)

0.08

(0.001)

0.19

(0.002)

0.19

(0.002)

Vontobel

0.11

(0.008)

0.11

(0.016)

0.32

(0.012)

0.31

(0.025)

0.25

(0.012)

0.25

(0.024)

B. Cnt. Geneve

0.08

(0.003)

0.08

(0.006)

    

Thurgauer KntB.

0.05

(0.000)

0.05

(0.001)

0.10

(0.001)

0.10

(0.002)

0.18

(0.001)

0.18

(0.002)

Bank CLER

0.08

(0.006)

0.08

(0.011)

0.12

(0.005)

0.13

(0.011)

0.16

(0.003)

0.16

(0.005)

B. Cnt. JURA

0.07

(0.004)

0.06

(0.008)

0.10

(0.002)

0.10

(0.005)

0.16

(0.004)

0.16

(0.009)

Zuger KntB.

0.05

(0.004)

0.05

(0.007)

0.10

(0.011)

0.10

(0.020)

0.16

(0.009)

0.15

(0.018)

Bank Linth

0.09

(0.006)

0.09

(0.011)

0.15

(0.007)

0.14

(0.014)

0.15

(0.008)

0.15

(0.015)

Glarner KntB.

0.04

(0.000)

0.04

(0.001)

0.09

(0.001)

0.09

(0.001)

0.18

(0.002)

0.18

(0.005)

Cembra Money B.

0.18

(0.002)

0.18

(0.004)

0.22

(0.002)

0.23

(0.005)

0.20

(0.002)

0.20

(0.004)

Hypothekarbank

0.09

(0.004)

0.09

(0.008)

0.14

(0.003)

0.15

(0.005)

0.17

(0.003)

0.17

(0.006)

  1. Standard errors in parentheses

Estimation of the magnitude of the low-risk anomaly

Estimated alphas and betas for the country-level portfolios

Portfolio

UK

Germany

Alpha (t value)

Beta (t value)

R 2

F-statistic

Alpha (t value)

Beta (t value)

R 2

F-statistic

Large

0.00007996

(0.48)

1.255646341

(100.95)

0.6538

10,189.98

− 0.00030123

(− 1.37)

1.16238125

(87.07)

0.5842

7580.46

Medium

0.00007535

(0.36)

1.359731891

(87.26)

0.5852

7613.78

0.00042376

(1.97)

0.22039425

(16.85)

0.0500

284.07

Small

0.00031695

(1.21)

0.756605962

(38.73)

0.2175

1499.84

0.00013307

(0.34)

0.17096583

(7.19)

0.0105

51.74

Portfolio

France

Italy

Alpha (t value)

Beta (t value)

R 2

F-statistic

Alpha (t value)

Beta (t value)

R 2

F-statistic

Large

0.00024605

(1.18)

1.24875869

(93.99)

0.6208

8833.44

− 0.00000773

(− 0.05)

1.28442512

(120.13)

0.7346

14,432.12

Medium

0.00021748

(1.02)

1.19172844

(87.50)

0.5866

7655.86

0.00003959

(0.29)

0.75748201

(83.75)

0.5736

7014.55

Small

0.00007069

(0.47)

0.21859914

(22.96)

0.0890

527.08

0.00011240

(0.66)

0.45057130

(40.50)

0.2393

1640.37

Portfolio

Spain

Switzerland

Alpha (t value)

Beta (t value)

R 2

F-statistic

Alpha (t value)

Beta (t value)

R 2

F-statistic

Large

0.00011749

(1.05)

1.28315469

(173.59)

0.8482

30,135.03

0.000101014

(0.67)

1.041655

(89.24)

0.5962

7964.32

Medium

− 0.00040377

(− 1.06)

1.62023896

(64.63)

0.4364

4176.83

0.000169958

(2.53)

0.273482

(52.54)

0.3384

2759.94

Small

0.00001484

(0.07)

0.72996240

(51.98)

0.3337

2701.78

0.000139421

(1.51)

0.173911967

(24.33)

0.0989

592.02

Portfolio weights in the country-level portfolios

 

UK

Germany

France

Port_large

HSBC

56.45%

Deutsche Bank

76.56%

BNP Paribas

55.82%

Barclays

25.37%

Commerzbank

23.44%

Crédit Agricole

44.18%

Lloyds

18.18%

    

Port_medium

RBS

41.94%

DT Pfandbriefbank

84.68%

Société Générale

68.75%

Standard Chartered

37.7%

Procredit

8.04%

Natixis

28.06%

Santander UK

17.90%

UmweltBank

5.10%

CRCAM Nord CCI

1.61%

CYBG

2.46%

Enercity PAR

2.18%

Caisse Credit

1.05%

Port_small

Virgin Money

61.59%

Merkur Bank

68.36%

Crcam Normandie

55.07%

Metro Bank

24.50%

Quirin Privatbk

31.64%

CRCAM ILLE-VIL

44.93%

Close Brothers

13.91%

    
 

Italy

Spain

Switerland

Port_large

Unicredit

40.74%

Santander

57.55%

UBS

32.69%

Intesa Sanpaolo

38.65%

BBVA

27.35%

Schweizerische

30.44%

BANCO BPM

7.74%

CaixaBank

15.10%

Credit Suisse

28.73%

B. Monte dei Paschi

6.72%

  

Julius Baer

3.53%

Unione di Banche IT

6.15%

  

B. Cnt. Vaudoise

1.64%

    

EFG Bank

1.50%

    

Basler KntB.

1.47%

Port_medium

Mediobanca

24.31%

B. de Sabadell

40.67%

Luzerner KntB.

14.76%

BPER Banca

23.77%

Bankia

38.51%

ST. Galler KntB.

13.41%

Credito Emiliano

14.04%

B. Popular

20.82%

Berner KntB.

12.05%

Banca Poplare

13.99%

  

Valiant

11.34%

Banca Piccolo

8.26%

  

Graubündener

10.54%

Banca Carige

8.06%

  

Basellandschaftlich

9.96%

Finecobank

7.58%

  

Vontobel

9.41%

    

B. Cnt. Geneve

9.33%

    

Thurgauer KntB.

9.19%

Port_small

B. Desio & Brianza

43.38%

Caja de Ahorros

45.70%

Bank CLER

24.90%

Banco di Sardegna

39.31%

Bankinter

36.91%

B. Cnt. JURA

22.14%

Dobank

6.39%

Liberbank

17.39%

Zuger KntB.

20.83%

Banca Finnat

5.64%

  

Bank Linth

9.68%

Banca Profilo

5.28%

  

Glarner KntB.

8.02%

    

Cembra Money B.

7.25%

    

Hypothekarbank

7.17%

Estimated asset and debt betas for the country-level portfolios

Beta

UK

Germany

Large banks

Medium banks

Small banks

Large banks

Medium banks

Small banks

Asset

0.050524909

0.05239728

0.065428302

0.031315585

0.058572877

0.04826616

Debt

− 0.03031258

− 0.049222917

0.021770995

− 0.025720845

0.073096031

0.066387664

Beta

France

Italy

Large banks

Medium banks

Small banks

Large banks

Medium banks

Small banks

Asset

0.030069272

0.035584653

0.178482482

0.053490445

0.087202311

0.121290348

Debt

− 0.02384

− 0.01934

0.172433

− 0.03839916

0.026569404

0.090282171

Beta

Spain

Switzerland

Large banks

Medium banks

Small banks

Large banks

Medium banks

Small banks

Asset

0.061475716

0.062376636

0.049590832

0.06177318

0.08003

0.076294297

Debt

− 0.02604737

− 0.03988731

0.001046695

− 0.032172189

0.066333459

0.070174168

Estimation of country-level gammas

figure a

The impact of IFRS 9 on the cost of funding

The impact of IFRS 9 on the cost of funding of UK banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

4.77

5.30

15.91

− 1.33

2.78

6.77

4.94

5.49

16.47

− 1.37

2.88

7.01

Mazras

2.55

2.12

10.82

− 3.18

1.26

3.83

2.63

2.20

11.20

− 3.29

1.31

3.96

Mazras (UK)

1.22

1.86

3.61

− 3.18

0.08

2.36

1.26

1.92

3.73

− 3.29

0.08

2.44

The impact of IFRS 9 on the cost of funding of German banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

6.61

7.34

22.03

− 1.84

6.17

3.85

6.76

7.51

22.53

− 1.88

6.31

3.94

Mazras

3.52

2.94

14.98

− 4.41

3.96

1.75

3.61

3.00

15.32

− 4.51

4.06

1.79

Mazras (Gr)

6.09

6.09

11.01

1.17

6.96

2.98

6.23

6.23

11.27

1.20

7.12

3.05

The impact of IFRS 9 on the cost of funding of French banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

− 1.82

− 2.02

− 6.06

0.51

− 1.70

− 1.06

− 1.86

− 2.06

− 6.19

0.52

− 1.73

− 1.08

Mazras

− 0.97

− 0.81

− 4.12

1.21

− 1.09

− 0.48

− 0.99

− 0.83

− 4.21

1.24

− 1.11

− 0.49

Mazras (Fr)

− 0.71

− 0.61

− 1.21

− 0.40

− 0.35

− 0.55

− 0.72

− 0.62

− 1.24

− 0.41

− 0.36

− 0.56

The impact of IFRS 9 on the cost of funding of Italian banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

1.55

1.73

5.18

− 0.43

1.45

0.90

1.60

1.78

5.33

− 0.44

1.49

0.93

Mazras

0.83

0.69

3.52

− 1.04

0.93

0.41

0.85

0.71

3.62

− 1.07

0.96

0.42

Mazras (It)

2.45

2.45

3.52

1.38

1.51

1.77

2.52

2.52

3.62

1.42

1.56

1.83

The impact of IFRS 9 on the cost of funding of Spanish banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

4.56

5.07

15.22

− 1.27

4.26

2.66

4.69

5.21

15.62

− 1.30

4.37

2.73

Mazras

2.43

2.03

10.35

− 3.04

2.74

1.21

2.50

2.08

10.62

− 3.12

2.81

1.24

Mazras (Sp)

3.70

2.59

8.11

1.52

3.02

2.35

3.80

2.66

8.33

1.56

3.10

2.41

The impact of IFRS 9 on the cost of funding of Swiss banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

0.69

0.77

2.31

− 0.19

0.65

0.40

0.71

0.79

2.36

− 0.20

0.66

0.41

Mazras

0.37

0.31

1.57

− 0.46

0.42

0.18

0.38

0.31

1.60

− 0.47

0.42

0.19

Mazras (Sw)

0.32

0.32

0.63

0.00

0.45

0.12

0.32

0.32

0.64

0.00

0.46

0.12

Robustness checks; Experiment 1—Country-level portfolios of all banks

Portfolio weights in the country-level portfolios

UK

Germany

France

Port all banks

HSBC

40.07%

Deutsche Bank

73.86%

BNP Paribas

36.36%

Barclays

18.01%

Commerzbank

22.62%

Crédit Agricole

28.77%

Lloyds

12.90%

DT Pfandbriefbank

2.91%

Société Générale

23.62%

RBS

11.73%

Procredit

0.28%

Natixis

9.64%

Standard Chartered

10.54%

UmweltBank

0.18%

CRCAM Nord CCI

0.55%

Santander UK

5.01%

Enercity PAR

0.08%

Caisse Credit

0.36%

CYBG

0.69%

Merkur Bank

0.06%

Crcam Normandie

0.28%

Virgin Money

0.65%

Quirin Privatbk

0.03%

CRCAM ILLE-VIL

0.23%

Metro Bank

0.26%

    

Close Brothers

0.15%

    

Italy

Spain

Switzerland

Port all banks

Unicredit

35.09%

Santander

44.54%

UBS

29.37%

Intesa Sanpaolo

33.28%

BBVA

21.17%

Schweizerische

27.34%

BANCO BPM

6.67%

CaixaBank

11.69%

Credit Suisse

25.81%

B. Monte dei Paschi

5.79%

B. de Sabadell

6.73%

Julius Baer

3.17%

Unione di Banche IT

5.30%

Bankia

6.38%

B. Cnt. Vaudoise

1.47%

Mediobanca

3.04%

B. Popular

3.45%

EFG Bank

1.35%

BPER Banca

2.98%

Caja de Ahorros

2.76%

Basler KntB

1.32%

Credito Emiliano

1.76%

Bankinter

2.23%

Luzerner KntB

1.16%

Banca Poplare

1.75%

Liberbank

1.05%

ST. Galler KntB

1.06%

Banca Piccolo

1.03%

  

Berner KntB

0.95%

Banca Carige

1.01%

  

Valiant

0.89%

Finecobank

0.95%

  

Graubündener

0.83%

B. Desio & Brianza

0.59%

  

Basellandschaftlich

0.79%

Banco di Sardegna

0.53%

  

Vontobel

0.74%

Dobank

0.09%

  

B. Cnt. Geneve

0.74%

Banca Finnat

0.08%

  

Thurgauer KntB

0.72%

Banca Profilo

0.07%

  

Bank CLER

0.57%

    

B. Cnt. JURA

0.50%

    

Zuger KntB

0.47%

    

Bank Linth

0.22%

    

Glarner KntB

0.18%

    

Cembra Money B

0.17%

    

Hypothekarbank

0.16%

Estimated alphas and equity, asset, and debt betas for the country-level portfolios

 

UK

Germany

France

Italy

Spain

Switzerland

Alpha

0.0000768

0.0002959

0.000231294

0.0000008

0.0000656

− 0.00001078

Beta

1.2830039

1.1582509

1.218080276

1.2097841

1.2062280

0.9636282

Asset beta

0.050846592

0.032287016

0.032719773

0.058629095

0.060906783

0.063543099

Debt beta

− 0.034698153

− 0.02501598

− 0.021628174

− 0.029854501

− 0.019983026

− 0.020235869

Gamma (annual)

− 0.0006785

− 0.0046848

− 0.002654983

− 0.0000097

− 0.0007957

− 0.000741

Robustness checks; Experiment 2—Two portfolios (large and small banks)

Portfolio weights in the country-level portfolios

 

UK

Germany

France

Port_larger

HSBC

42.97%

Deutsche Bank

74.11%

BNP Paribas

36.95%

Barclays

19.31%

Commerzbank

22.69%

Crédit Agricole

29.24%

Lloyds

13.84%

DT Pfandbriefbank

2.92%

Société Générale

24.01%

RBS

12.58%

Procredit

0.28%

Natixis

9.80%

Standard Chartered

11.31%

    

Port_smaller

Santander UK

74.12%

UmweltBank

52.98%

CRCAM Nord CCI

38.61%

CYBG

10.17%

Enercity PAR

22.69%

Caisse Credit

25.32%

Virgin Money

9.67%

Merkur Bank

16.63%

Crcam Normandie

19.86%

Metro Bank

3.85%

Quirin Privatbk

7.70%

CRCAM ILLE-VIL

16.21%

Close Brothers

2.18%

    
 

Italy

Spain

Switzerland

Port_larger

Unicredit

37.37%

Santander

52.94%

UBS

31.28%

Intesa Sanpaolo

35.45%

BBVA

25.16%

Schweizerische

29.12%

BANCO BPM

7.10%

CaixaBank

13.89%

Credit Suisse

27.48%

B. Monte dei Paschi

6.16%

B. de Sabadell

8.00%

Julius Baer

3.38%

Unione di Banche IT

5.64%

  

B. Cnt. Vaudoise

1.57%

Mediobanca

3.24%

  

EFG Bank

1.43%

BPER Banca

3.17%

  

Basler KntB

1.41%

Credito Emiliano

1.87%

  

Luzerner KntB

1.24%

    

ST. Galler KntB

1.13%

    

Berner KntB

1.01%

    

Valiant

0.95%

Port_smaller

Banca Poplare

28.73%

Bankia

40.19%

Graubündener

13.62%

Banca Piccolo

16.97%

B. Popular

21.73%

Basellandschaftlich

12.88%

Banca Carige

16.55%

Caja de Ahorros

17.40%

Vontobel

12.17%

Finecobank

15.56%

Bankinter

14.06%

B. Cnt. Geneve

12.06%

B. Desio & Brianza

9.63%

Liberbank

6.62%

Thurgauer KntB

11.88%

Banco di Sardegna

8.72%

  

Bank CLER

9.31%

Dobank

1.42%

  

B. Cnt. JURA

8.28%

Banca Finnat

1.25%

  

Zuger KntB

7.79%

Banca Profilo

1.17%

  

Bank Linth

3.62%

    

Glarner KntB

3.00%

    

Cembra Money B

2.71%

    

Hypothekarbank

2.68%

Estimated alphas and equity, asset and debt betas for the country-level portfolios

 

UK

Germany

France

Larger banks

Smaller banks

Larger banks

Smaller banks

Larger banks

Smaller banks

Alpha

0.0000733

0.0001471

− 0.0002981

0.0003290

0.0002327

0.0001005

Beta

1.2870241

0.9868373

1.1611747

0.1669420

1.2324249

0.2017739

Asset beta

0.050628584

0.053857134

0.031988451

0.122202714

0.030716631

0.171380168

Debt beta

− 0.035117426

− 0.01188665

− 0.025246076

0.116977523

− 0.022728645

0.166799842

Gamma

− 0.00061539

− 0.001578

0.00032

 

Italy

Spain

Switzerland

Larger banks

Smaller banks

Larger banks

Smaller banks

Larger banks

Smaller banks

Alpha

0.00000727

− 0.00013123

− 0.00011929

0.00023372

0.00010589

0.00014364

Beta

1.24804504

0.53569811

1.28019335

0.81680636

1.00893967

0.26167914

Asset beta

0.056412825

0.092762623

0.062326689

0.053376969

0.062677288

0.076878554

Debt beta

− 0.035436746

0.060169662

− 0.024298119

0.001485163

− 0.023759572

0.07271048

Gamma

0.00048594

− 0.001906

− 0.000126

Robustness checks; Experiment 3—Individual banks

Estimated alphas and equity and debt betas for the banks in the sample

UK

Germany

France

Bank

alpha

beta

Debt beta

Bank

alpha

beta

Debt beta

Bank

alpha

beta

Debt beta

HSBC

0.00008

1.09932

− 0.0111

Deutsche Bank

− 0.00025

1.17299

− 0.0216

BNP Paribas

0.00026

1.27322

− 0.0292

Barclays

0.00017

1.51455

− 0.0572

Commerzbank

− 0.00047

1.12773

− 0.0386

Crédit Agricole

0.00016

1.26633

− 0.0194

Lloyds

− 0.00004

1.37979

− 0.0471

DT Pfandbriefbank

0.00029

0.90382

0.0014

Société Générale

0.00020

1.32362

− 0.0318

RBS

− 0.00001

1.41945

− 0.0504

Procredit

0.00016

0.39605

0.0826

Natixis

0.00026

0.95796

0.0007

Standard Chartered

0.00012

1.35097

− 0.0522

UmweltBank

0.00056

0.22280

0.0376

CRCAM Nord CCI

0.00019

0.16923

0.1773

Santander UK

− 0.00002

1.24457

− 0.0368

Enercity PAR

0.00023

0.11894

0.4716

Caisse Credit

0.00004

0.24266

0.1317

CYBG

0.00065

1.20838

− 0.0104

Merkur Bank

0.00022

0.11971

0.0527

Crcam Normandie

0.00007

0.25942

0.1699

Virgin Money

0.00010

1.09359

− 0.0092

Quirin Privatbk

− 0.00006

0.31178

0.0882

CRCAM ILLE-VIL

0.00005

0.21185

0.1689

Metro Bank

0.00093

0.96725

0.0183

        

Close Brothers

0.00026

0.75103

0.0737

        

Italy

Spain

Switzerland

Bank

alpha

beta

Debt beta

Bank

alpha

beta

Debt beta

Bank

alpha

beta

Debt beta

Unicredit

− 0.00002

1.36470

− 0.0472

Santander

− 0.00012

1.32701

− 0.0422

UBS

0.00000

1.41137

− 0.0526

Intesa Sanpaolo

0.00021

1.30379

− 0.0332

BBVA

− 0.00007

1.29228

− 0.0437

Schweizerische

− 0.00039

0.22801

0.1003

BANCO BPM

− 0.00027

1.08182

− 0.0111

CaixaBank

− 0.00021

0.94533

0.0776

Credit Suisse

0.00009

1.54761

− 0.0543

B. Monte dei Paschi

− 0.00098

1.04188

− 0.0430

B. de Sabadell

0.00006

0.79288

0.0251

Julius Baer

− 0.00009

1.27647

0.0014

Unione di Banche IT

− 0.00009

1.18964

− 0.0357

Bankia

0.00080

1.02161

− 0.0205

B. Cnt. Vaudoise

− 0.00018

0.48601

0.0549

Mediobanca

0.00023

0.98904

− 0.0131

B. Popular

0.00057

0.95058

0.0188

EFG Bank

0.00017

1.18931

0.0345

BPER Banca

0.00005

0.69652

0.0237

Caja de Ahorros

0.00049

0.15187

0.0368

Basler KntB

− 0.00008

0.20926

0.0683

Credito Emiliano

0.00028

0.90151

0.0019

Bankinter

− 0.00023

0.94737

− 0.0103

Luzerner KntB

− 0.00022

0.19452

0.0641

Banca Poplare

0.00009

0.48475

0.0693

Liberbank

0.00016

1.11085

− 0.0252

ST. Galler KntB

− 0.00024

0.34775

0.0686

Banca Piccolo

− 0.00049

0.65780

0.0492

    

Berner KntB

− 0.00005

0.17191

0.0649

Banca Carige

− 0.00073

0.62540

0.0570

    

Valiant

− 0.00015

0.26786

0.0885

Finecobank

0.00087

0.80613

0.0085

    

Graubündener

− 0.00021

0.13455

0.0856

B. Desio & Brianza

0.00010

0.45969

0.0761

    

Basellandschaftlich

− 0.00011

0.11594

0.0513

Banco di Sardegna

0.00010

0.45969

0.0869

    

Vontobel

− 0.00025

0.83050

0.0602

Dobank

0.00004

0.39038

0.0609

    

B. Cnt. Geneve

− 0.00001

0.22866

0.0657

Banca Finnat

0.00052

0.46678

0.2818

    

Thurgauer KntB

− 0.00019

0.20788

0.0413

Banca Profilo

0.00022

0.82608

0.0260

    

Bank CLER

0.00001

0.18114

0.0720

        

B. Cnt. JURA

− 0.00024

0.15583

0.0604

        

Zuger KntB

− 0.00017

0.19025

0.0488

        

Bank Linth

− 0.00010

0.13753

0.0864

        

Glarner KntB

− 0.00058

0.25530

0.0342

        

Cembra Money B

− 0.00036

0.54831

0.0971

        

Hypothekarbank

− 0.00018

0.16363

0.0832

Robustness checks; Experiment 4—Panels of banks in each country

Estimated alphas and equity, asset and debt betas for the country-level panels

 

UK

Germany

France

Italy

Spain

Switzerland

Alpha

0.0001006

− 0.0000377

0.0001587

− 0.000018

− 0.00000796

− 0.0001355

Beta

1.247605

0.554894

0.713867

0.82079

0.989824

0.425921

Asset beta

0.050846592

0.032287016

0.032947678

0.058629095

0.060906783

0.063543099

Debt beta

− 0.032240524

0.005690286

0.00151519

0.000046

− 0.004699176

0.029813365

Gamma (annual)

− 0.001016

− 0.000212

0.001386

− 0.000251

− 0.001957

− 0.000590

Robustness checks; Experiment 5—Three risk-based portfolios (high, medium and low risk)

Portfolio weights in the country-level portfolios

 

UK

Germany

France

Port_high-risk

Barclays

42.23%

Deutsche Bank

76.56%

Société Générale

39.38%

RBS

27.50%

Commerzbank

23.44%

BNP Paribas

60.62%

Lloyds

30.26%

    

Port_medium-risk

Standard Chartered

18.72%

DT Pfandbriefbank

85.92%

Crédit Agricole

73.67%

Santander UK

8.89%

Procredit

8.15%

Natixis

24.68%

CYBG

1.22%

Quirin Privatbk

0.75%

Crcam Normandie

0.72%

HSBC

71.17%

UmweltBank

5.17%

Caisse Credit

0.92%

 

Virgin Money

61.59%

Merkur Bank

42.28%

CRCAM ILLE-VIL

29.56%

 

Metro Bank

24.50%

Enercity PAR

57.72%

CRCAM Nord CCI

70.44%

 

Close Brothers

13.91%

    
 

Italy

Spain

Switzerland

Port_high-risk

Unicredit

40.74%

Santander

66.72%

Credit Suisse

41.57%

Intesa Sanpaolo

38.65%

BBVA

31.71%

UBS

47.31%

Unione di Banche IT

6.15%

Liberbank

1.57%

Julius Baer

5.11%

BANCO BPM

7.74%

  

EFG Bank

2.17%

B. Monte dei Paschi

6.72%

  

Vontobel

1.20%

    

Cembra Money B

0.27%

    

B. Cnt. Vaudoise

2.37%

Port_medium-risk

Mediobanca

28.08%

Bankia

52.89%

ST. Galler KntB

3.12%

Credito Emiliano

16.21%

B. Popular

28.60%

Valiant

2.64%

Banca Profilo

0.66%

Bankinter

18.50%

Glarner KntB

0.54%

Finecobank

8.75%

  

B. Cnt. Geneve

2.17%

BPER Banca

27.45%

  

Schweizerische

80.67%

Banca Piccolo

9.54%

  

Basler KntB

3.90%

Banca Carige

9.31%

  

Thurgauer KntB

2.14%

    

Luzerner KntB

3.43%

    

Zuger KntB

1.40%

Port_low-risk

Banca Poplare

57.75%

CaixaBank

55.18%

Bank CLER

14.11%

Banca Finnat

2.52%

B. de Sabadell

31.79%

Berner KntB

23.62%

B. Desio & Brianza

19.35%

Caja de Ahorros

13.03%

Hypothekarbank

4.06%

Banco di Sardegna

17.53%

  

B. Cnt. JURA

12.55%

Dobank

2.85%

  

Bank Linth

5.49%

    

Graubündener

20.65%

    

Basellandschaftlich

19.52%

Estimated asset and debt betas for the country-level portfolios

 

UK

Germany

High-risk banks

Med-risk banks

Low-risk banks

High-risk banks

Med-risk banks

Low-risk banks

Alpha

0.000053285

0.000092101

0.000316948

− 0.000301226

0.000531524

0.000238925

Beta

1.447610925

1.157368055

0.756605962

1.162381247

0.239129952

0.125855111

Asset beta

0.037659642

0.060558091

0.065428302

0.031315585

0.05230876

0.230783285

Debt beta

− 0.052418256

− 0.0203645

0.021770995

− 0.025720845

0.041558909

0.257840929

Gamma

− 0.000392571

− 0.00067

    
 

France

Italy

High- risk banks

Med-risk Banks

Low-risk banks

High-risk banks

Med-risk banks

Low-risk banks

Alpha

0.000238905

0.000171013

0.000039424

− 0.000006257

0.000033190

0.000093498

Beta

1.293069018

1.043939989

0.233554649

1.284346691

0.805673277

0.462209120

Asset beta

0.03262408

0.030009791

0.175915945

0.053490445

0.085838369

0.10728338

Debt beta

− 0.030219279

− 0.009061907

0.169488404

− 0.038393305

0.01815138

0.079069368

Gamma

0.000182

− 0.00012

    
 

Spain

Switzerland

High- risk banks

Med-risk banks

Low-risk banks

High-risk banks

Med-risk banks

Low-risk banks

Alpha

0.000106622

− 0.000275516

0.000423736

− 0.000026080

0.000350786

0.000121414

Beta

1.315207924

0.934885601

0.564517588

1.434843764

0.230105858

0.146499937

Asset beta

0.048840802

0.054352481

0.102663175

0.036382071

0.112068411

0.073790854

Debt beta

− 0.042651085

− 0.003570594

0.071119136

− 0.046077797

0.092574689

0.068892649

Gamma

− 0.000416038

0.0501%

    

The impact of IFRS 9 under Experiment 1—Country-level portfolios of all banks

The impact of IFRS 9 on the cost of funding of UK banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

3.05

3.39

10.18

− 0.85

1.78

4.33

3.16

3.51

10.53

− 0.88

1.84

4.48

Mazras

1.63

1.36

6.92

− 2.04

0.81

2.45

1.68

1.40

7.16

− 2.11

0.84

2.53

Mazras (UK)

0.78

1.19

2.31

− 2.04

0.05

1.51

0.81

1.23

2.39

− 2.11

0.05

1.56

The impact of IFRS 9 on the cost of funding of German banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

21.08

23.42

70.27

− 5.86

19.68

12.27

21.61

24.01

72.03

− 6.00

20.17

12.58

Mazras

11.24

9.37

47.78

− 14.05

12.65

5.58

11.52

9.60

48.98

− 14.41

12.97

5.72

Mazras (Gr)

19.44

19.44

35.14

3.75

22.19

9.50

19.93

19.93

36.01

3.84

22.75

9.74

The impact of IFRS 9 on the cost of funding of French banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

11.95

13.27

39.82

− 3.32

11.15

6.95

12.21

13.56

40.69

− 3.39

11.39

7.10

Mazras

6.37

5.31

27.08

− 7.96

7.17

3.16

6.51

5.42

27.67

− 8.14

7.32

3.23

Mazras (Fr)

4.65

3.98

7.96

2.65

2.30

3.62

4.75

4.07

8.14

2.71

2.35

3.69

The impact of IFRS 9 on the cost of funding of Italian banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

0.04

0.05

0.15

− 0.01

0.04

0.03

0.04

0.05

0.15

− 0.01

0.04

0.03

Mazras

0.02

0.02

0.10

− 0.03

0.03

0.01

0.02

0.02

0.10

− 0.03

0.03

0.01

Mazras (Fr)

0.07

0.07

0.10

0.04

0.04

0.05

0.07

0.07

0.10

0.04

0.04

0.05

The impact of IFRS 9 on the cost of funding of Spanish banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

3.58

3.98

11.93

− 0.99

3.34

2.08

3.65

4.06

12.17

− 1.01

3.41

2.13

Mazras

1.91

1.59

8.12

− 2.39

2.15

0.95

1.95

1.62

8.28

− 2.43

2.19

0.97

Mazras (Sp)

2.90

2.03

6.37

1.19

2.37

1.84

2.96

2.07

6.49

1.22

2.42

1.88

The impact of IFRS 9 on the cost of funding of Swiss banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

3.33

3.70

11.11

− 0.93

3.11

1.94

3.40

3.78

11.34

− 0.94

3.17

1.98

Mazras

1.78

1.48

7.56

− 2.22

2.00

0.88

1.81

1.51

7.71

− 2.27

2.04

0.90

Mazras (Fr)

1.52

1.52

3.04

0.00

2.15

0.56

1.55

1.55

3.10

0.00

2.19

0.57

The impact of IFRS 9 under Experiment 2—Two portfolios (large and small banks)

The impact of IFRS 9 on the cost of funding of UK banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

2.77

3.08

9.23

− 0.77

1.61

3.93

2.86

3.18

9.54

− 0.80

1.67

4.06

Mazras

1.48

1.23

6.28

− 1.85

0.73

2.22

1.53

1.27

6.49

− 1.91

0.76

2.30

Mazras (UK)

0.71

1.08

2.09

− 1.85

0.05

1.37

0.73

1.11

2.16

− 1.91

0.05

1.41

The impact of IFRS 9 on the cost of funding of German banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

 

Mean

Median

Max

Min

99.9% Conf. Intrv.

 

EBA [23]

7.10

7.89

23.67

− 1.97

6.63

4.13

7.28

8.09

24.26

− 2.02

6.79

4.24

Mazras

3.79

3.16

16.10

− 4.73

4.26

1.88

3.88

3.23

16.50

− 4.85

4.37

1.93

Mazras (Gr)

6.55

6.55

11.84

1.26

7.48

3.20

6.71

6.71

12.13

1.29

7.66

3.28

The impact of IFRS 9 on the cost of funding of French banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

− 1.44

− 1.60

− 4.81

0.40

− 1.35

− 0.84

− 1.47

− 1.64

− 4.91

0.41

− 1.37

− 0.86

Mazras

− 0.77

− 0.64

− 3.27

0.96

− 0.87

− 0.38

− 0.79

− 0.65

− 3.34

0.98

− 0.88

− 0.39

Mazras (Fr)

− 0.56

− 0.48

− 0.96

− 0.32

− 0.28

− 0.44

− 0.57

− 0.49

− 0.98

− 0.33

− 0.28

− 0.45

The impact of IFRS 9 on the cost of funding of Italian banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

− 2.19

− 2.43

− 7.29

0.61

− 2.04

− 1.27

− 2.25

− 2.50

− 7.50

0.63

− 2.10

− 1.31

Mazras

− 1.17

− 0.97

− 4.96

1.46

− 1.31

− 0.58

− 1.20

− 1.00

− 5.10

1.50

− 1.35

− 0.60

Mazras (Sp)

− 3.45

− 3.45

− 4.96

− 1.94

− 2.13

− 2.50

− 3.55

− 3.55

− 5.10

− 2.00

− 2.19

− 2.57

The impact of IFRS 9 on the cost of funding of Spanish banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

8.58

9.53

28.59

− 2.38

8.01

4.99

8.75

9.72

29.17

− 2.43

8.17

5.09

Mazras

4.58

3.81

19.44

− 5.72

5.15

2.27

4.67

3.89

19.84

− 5.83

5.25

2.32

Mazras (Sp)

6.96

4.86

15.25

2.86

5.67

4.42

7.10

4.96

15.56

2.92

5.79

4.51

The impact of IFRS 9 on the cost of funding of Swiss banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

 

Mean

Median

Max

Min

99.9% Conf. Intrv.

 

EBA [23]

0.57

0.63

1.89

− 0.16

0.53

0.33

0.58

0.64

1.93

− 0.16

0.54

0.34

Mazras

0.30

0.25

1.29

− 0.38

0.34

0.15

0.31

0.26

1.31

− 0.39

0.35

0.15

Mazras (Sp)

0.26

0.26

0.52

0.00

0.37

0.09

0.26

0.26

0.53

0.00

0.37

0.10

The impact of IFRS 9 under Experiment 3—Individual banks

The impact of IFRS 9 on the cost of funding of UK banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

6.82

7.57

22.72

− 1.89

3.97

9.67

7.05

7.83

23.50

− 1.96

4.10

9.99

Mazras

3.64

3.03

15.45

− 4.54

1.80

5.47

3.76

3.13

15.98

− 4.70

1.87

5.65

Mazras (UK)

1.74

2.65

5.15

− 4.54

0.11

3.37

1.80

2.74

5.33

− 4.70

0.12

3.48

The impact of IFRS 9 on the cost of funding of German banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

5.66

6.28

18.85

− 1.57

5.28

3.29

5.79

6.43

19.30

− 1.61

5.40

3.37

Mazras

3.02

2.51

12.82

− 3.77

3.39

1.50

3.09

2.57

13.13

− 3.86

3.47

1.53

Mazras (Gr)

5.22

5.22

9.43

1.01

5.95

2.55

5.34

5.34

9.65

1.03

6.10

2.61

The impact of IFRS 9 on the cost of funding of French banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

 

Mean

Median

Max

Min

99.9% Conf. Intrv.

 

EBA [23]

1.38

1.53

4.60

− 0.38

1.29

0.80

1.41

1.56

4.69

− 0.39

1.31

0.82

Mazras

0.74

0.61

3.12

− 0.92

0.83

0.36

0.75

0.63

3.19

− 0.94

0.84

0.37

Mazras (Fr)

0.54

0.46

0.92

0.31

0.27

0.42

0.55

0.47

0.94

0.31

0.27

0.43

The impact of IFRS 9 on the cost of funding of Italian banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

1.89

2.10

6.29

− 0.52

1.76

1.10

1.94

2.16

6.47

− 0.54

1.81

1.13

Mazras

1.01

0.84

4.27

− 1.26

1.13

0.50

1.03

0.86

4.40

− 1.29

1.16

0.51

Mazras (It)

2.98

2.98

4.27

1.68

1.84

2.15

3.06

3.06

4.40

1.72

1.89

2.21

The impact of IFRS 9 on the cost of funding of Spanish banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

4.53

5.03

15.09

− 1.26

4.23

2.64

4.61

5.12

15.36

− 1.28

4.30

2.68

Mazras

2.41

2.01

10.26

− 3.02

2.72

1.20

2.46

2.05

10.44

− 3.07

2.76

1.22

Mazras (Sp)

3.67

2.57

8.05

1.51

2.99

2.33

3.74

2.61

8.19

1.54

3.05

2.37

The impact of IFRS 9 on the cost of funding of Swiss banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

1.76

1.96

5.88

− 0.49

1.65

1.03

1.75

1.95

5.84

− 0.49

1.64

1.02

Mazras

0.94

0.78

4.00

− 1.18

1.06

0.47

0.93

0.78

3.97

− 1.17

1.05

0.46

Mazras (Sw)

0.80

0.80

1.61

0.00

1.14

0.29

0.80

0.80

1.60

0.00

1.13

0.29

The impact of IFRS 9 under Experiment 4—Panels of banks in each country

The impact of IFRS 9 on the cost of funding of UK banks (in basis points)

Sample

Riskless Debt

Risky Debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

4.57

5.08

15.24

− 1.27

2.66

6.48

4.72

5.25

15.74

− 1.31

2.75

6.69

Mazras

2.44

2.03

10.37

− 3.05

1.21

3.67

2.52

2.10

10.70

− 3.15

1.25

3.79

Mazras (UK)

1.17

1.78

3.46

− 3.05

0.08

2.26

1.21

1.84

3.57

− 3.15

0.08

2.33

The impact of IFRS 9 on the cost of funding of German banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

0.95

1.06

3.18

− 0.26

0.89

0.55

0.95

1.05

3.16

− 0.26

0.88

0.55

Mazras

0.51

0.42

2.16

− 0.64

0.57

0.25

0.51

0.42

2.15

− 0.63

0.57

0.25

Mazras (Gr)

0.88

0.88

1.59

0.17

1.00

0.43

0.87

0.87

1.58

0.17

1.00

0.43

The impact of IFRS 9 on the cost of funding of French banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

− 6.24

− 6.93

− 20.78

1.73

− 5.82

− 3.63

− 6.23

− 6.92

− 20.75

1.73

− 5.81

− 3.62

Mazras

− 3.33

− 2.77

− 14.13

4.16

− 3.74

− 1.65

− 3.32

− 2.77

− 14.11

4.15

− 3.74

− 1.65

Mazras (Fr)

− 2.42

− 2.08

− 4.16

− 1.39

− 1.20

− 1.89

− 2.42

− 2.08

− 4.15

− 1.38

− 1.20

− 1.88

The impact of IFRS 9 on the cost of funding of Italian banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

1.13

1.26

3.77

− 0.31

1.05

0.66

1.13

1.26

3.77

− 0.31

1.05

0.66

Mazras

0.60

0.50

2.56

− 0.75

0.68

0.30

0.60

0.50

2.56

− 0.75

0.68

0.30

Mazras (It)

1.78

1.78

2.56

1.00

1.10

1.29

1.78

1.78

2.56

1.00

1.10

1.29

The impact of IFRS 9 on the cost of funding of Spanish banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

8.81

9.79

29.36

− 2.45

8.22

5.13

8.85

9.83

29.50

− 2.46

8.26

5.15

Mazras

4.70

3.91

19.97

− 5.87

5.28

2.33

4.72

3.93

20.06

− 5.90

5.31

2.34

Mazras (Sp)

7.14

4.99

15.66

2.94

5.83

4.54

7.18

5.01

15.73

2.95

5.85

4.56

The impact of IFRS 9 on the cost of funding of Swiss banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

2.66

2.95

8.85

− 0.74

2.48

1.55

2.58

2.86

8.59

− 0.72

2.41

1.50

Mazras

1.42

1.18

6.02

− 1.77

1.59

0.70

1.37

1.15

5.84

− 1.72

1.55

0.68

Mazras (Sw)

1.21

1.21

2.42

0.00

1.71

0.44

1.17

1.17

2.35

0.00

1.66

0.43

The impact of IFRS 9 under Experiment 5—Three risk-based portfolios

The impact of IFRS 9 on the cost of funding of UK banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

4.42

4.91

14.73

− 1.23

2.57

6.27

4.57

5.07

15.22

− 1.27

2.66

6.48

Mazras

2.36

1.96

10.02

− 2.95

1.17

3.54

2.44

2.03

10.35

− 3.04

1.21

3.66

Mazras (UK)

1.13

1.72

3.34

− 2.95

0.07

2.18

1.17

1.78

3.45

− 3.04

0.08

2.26

The impact of IFRS 9 on the cost of funding of German banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

7.52

8.36

25.07

− 2.09

7.02

4.38

7.69

8.55

25.65

− 2.14

7.18

4.48

Mazras

4.01

3.34

17.05

− 5.01

4.51

1.99

4.10

3.42

17.44

− 5.13

4.62

2.04

Mazras (Gr)

6.94

6.94

12.53

1.34

7.92

3.39

7.10

7.10

12.82

1.37

8.10

3.47

The impact of IFRS 9 on the cost of funding of French banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

− 2.05

− 2.27

− 6.82

0.57

− 1.91

− 1.19

− 2.09

− 2.32

− 6.96

0.58

− 1.95

− 1.21

Mazras

− 1.09

− 0.91

− 4.64

1.36

− 1.23

− 0.54

− 1.11

− 0.93

− 4.73

1.39

− 1.25

− 0.55

Mazras (Fr)

− 0.80

− 0.68

− 1.36

− 0.45

− 0.39

− 0.62

− 0.81

− 0.70

− 1.39

− 0.46

− 0.40

− 0.63

The impact of IFRS 9 on the cost of funding of Italian banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

1.34

1.49

4.46

− 0.37

1.25

0.78

1.38

1.53

4.59

− 0.38

1.28

0.80

Mazras

0.71

0.59

3.03

− 0.89

0.80

0.35

0.73

0.61

3.12

− 0.92

0.83

0.36

Mazras (It)

2.11

2.11

3.03

1.19

1.30

1.53

2.17

2.17

3.12

1.22

1.34

1.57

The impact of IFRS 9 on the cost of funding of Spanish banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

4.68

5.20

15.61

− 1.30

4.37

2.73

4.75

5.28

15.83

− 1.32

4.43

2.76

Mazras

2.50

2.08

10.61

− 3.12

2.81

1.24

2.53

2.11

10.76

− 3.17

2.85

1.26

Mazras (Sp)

3.80

2.65

8.33

1.56

3.10

2.41

3.85

2.69

8.44

1.58

3.14

2.44

The impact of IFRS 9 on the cost of funding of Swiss banks (in basis points)

Sample

Riskless debt

Risky debt, low sensitivity to leverage

Mean

Median

Max

Min

99.9% Conf. Intrv.

Mean

Median

Max

Min

99.9% Conf. Intrv.

EBA [23]

2.25

2.51

7.52

− 0.63

2.10

1.31

2.24

2.49

7.47

− 0.62

2.09

1.31

Mazras

1.20

1.00

5.11

− 1.50

1.35

0.60

1.20

1.00

5.08

− 1.49

1.35

0.59

Mazras (Sw)

1.03

1.03

2.05

0.00

1.45

0.38

1.02

1.02

2.04

0.00

1.44

0.37

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fatouh, M., Bock, R. & Ouenniche, J. Impact of IFRS 9 on the cost of funding of banks in Europe. J Bank Regul 24, 115–145 (2023). https://doi.org/10.1057/s41261-021-00177-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/s41261-021-00177-x

Keywords

JEL Classification

Navigation