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End of the sovereign-bank doom loop in the European Union? The Bank Recovery and Resolution Directive

A Correction to this article was published on 13 June 2018

This article has been updated

Abstract

In this paper we examine the relationship between the default risk of banks and sovereigns, i.e. the ‘doom-loop’. Specifically, we try to assess the effectiveness of the implementation of the new recovery and resolution framework in the European Union. We use a panel with daily data on European banks and sovereigns ranging from 2012 to 2016 in order to test the effects of the Bank Recovery and Resolution Directive on the two-way feedback process. We find that there was a pronounced feedback loop between banks and sovereigns from 2012 to 2014. However, after the implementation of the European Banking Union, in 2015/2016, the magnitude of the doom-loop decreased and the spillovers became not statistically significant. Furthermore, our results suggest that the implementation of the new resolution framework is a suitable candidate to explain this finding. Overall, the results are robust across several specifications.

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Change history

  • 13 June 2018

    The original version of this article unfortunately incorrectly captured all tables incorrectly.

Notes

  1. 1.

    Merler and Pisani-Ferry (2012: 204) argue that if market participants perceive that there is the risk of a bail-out, yields of government bonds are already affected.

  2. 2.

    Sgherri and Zoli (2009: 17) verify that the deterioration in fiscal positions led to an increase in the risk premiums of government bonds for most European countries. In particular, they argue that increases in debt levels of sovereigns and concerns about the solvency of national banking systems have caused this increase.

  3. 3.

    The EBA assesses the importance of banks according to several criteria. Important are: size of the balance sheet, the ratio of the size of the balance sheet to the economic activity in the home country and whether the bank has applied for financial support from other European mechanisms.

  4. 4.

    Specifically, those tools are: sales of business, bridge institutions, asset separation and a bail-in tool (European Parliament 2014, Article 37). Moreover, Denmark, Switzerland and UK decided to opt-in and apply the reforms stated in the new legislation.

  5. 5.

    See Toader (2015) for a discussion of the relationship between implicit government guarantees and the introduction of the new bank resolution framework.

  6. 6.

    Lybeck (2016) provides a critical appraisal of the future of bank bail-outs in the European Union under the Single Resolution Mechanism and the Bank Recovery Resolution Directive.

  7. 7.

    See Fig. A1 in the online appendix for a timeline of the European Banking Union.

  8. 8.

    Although Switzerland is not a member of the European Union, it has implemented a regime with similar characteristics to the BRRD. Nevertheless, this regime was implemented in different steps between 2012 and 2016, and its bail-in tool entered into force in January 2012. For this reason, UBS and Credit Suisse will not be considered for the analysis.

  9. 9.

    The MREL is only in the phase-in process, and it will be fully operational in January 2020 (ISDA 2016: 37).

  10. 10.

    We include foreign exposures of a country’s banks until we have reached 85% of the total foreign exposure, as this eliminates the need to deal with countries for which a time series of CDS premiums is not available for the sovereign or the largest banks. Then the index is normalized to 100%.

  11. 11.

    The data selection and cleaning procedure is implemented following Acharya et al. (2014: 2712).

  12. 12.

    This subdivision is useful in presenting the results and makes them comparable before and after the BRRD implementation. This selection does not drive our results; to this respect we provide estimates for a one-year rolling window over the entire period. This results justify to split the sample into yearly intervals.

  13. 13.

    The alternative approach in the literature is to implement a VAR. In this respect, the identification of a VAR model is problematic and, in order to utilize fully the cross-sectional variation, a fixed effects estimation approach seems more appropriate. Moreover, the advantage of the single equation approach is that results are easier to interpret than in a VAR.

  14. 14.

    The results of the common residual and time series diagnostics justify our exact estimation specification and can be found in the statistical appendix for the sake of space.

  15. 15.

    Since we have a large T dimension and a small N, the criticism of Nickell (1981), who points out possible biases, does not pose a problem for our estimation.

  16. 16.

    Results do not change after the inclusion of two interactions terms between bank fixed effects and the CDS market index as well as between bank fixed effects and the volatility index. This technique should be able to take into account the heterogeneity in bank characteristics. Results are provided in the online appendix, Table A1.

  17. 17.

    This finding does not change by using recursive and reversed recursive estimation methods. Furthermore, it is robust to changes in the length of the selected rolling window to three and six months.

  18. 18.

    Clearly, the bond purchase program aims at lowering the sovereign yields, that is, it affects the variable in level not in log-changes.

  19. 19.

    It is important to remark that the timetable for the implementation is different across countries, although the common deadline is 1st January 2016. To this respect, the dummy variable captures the effect of the heterogeneous implementation across countries within the period January 2015–March 2016. See Fig. 4 for the timetable of the BRRD full-implementation across countries.

  20. 20.

    As investigated by Toader (2015: 141), the decreasing value of public guarantees might work against our hypothesis. To this end, we want to recall that governments’ interventions are still possible under the BRRD resolution framework, but take place after all bail-in-able instruments are converted into new equity. This strongly reduces the likelihood and the amount of government capital injections. “This finding leads us to the conclusion that investors expect lower public support for banks from countries where efforts to implement a resolution mechanism are made. Moreover, results indicate that potential interactions between the sovereign rating and the introduction of resolution mechanism reduce significantly the expectations of public support” (Toader 2015: 142).

  21. 21.

    Data on the PSPP by country are available only at monthly frequency from the ECB’s website. Since Sweden and Denmark do not belong to the Euro Area, the inclusion of the PSPP omits them from the sample.

  22. 22.

    Data on debt to GDP are collected from the ECB’s statistical data warehouse.

  23. 23.

    For the sake of space and for a better comparison and interpretation of the results at monthly frequency between the two channels, results are placed in the appendix.

  24. 24.

    Data at monthly frequency for channel 2 is only one third (428) of the data available for channel 1 (1256). This is due to the reduction of the cross-sectional data: N - the number of countries - is 9, while N - the number of banks - is 26.

  25. 25.

    Moreover, results are not driven by the economic recovery. Table A2 in the online appendix reports the estimates for both channels at daily frequency. This robustness check verifies that those countries experiencing an economic recovery before the BRRD introduction were still prone to the doom loop.

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Acknowledgments

We wish to thank Matthias Raddant, Harmen Lehment, Olivier Godart, Eugenio Caverzasi, Jamel Saadaoui and the participants to the 7th ICEEE (Messina, 2017), DIW Macroeconometric Workshop (Berlin, 2016), 4th Workshop in “Macro Banking and Finance” Sapienza University (Rome, 2016), Economics, Economic Policies and Sustainable Growth in the Wake of the Crisis Conference (Ancona, 2016), and the seminar at Kiel Institute for the World Economy, University of Verona, and University of Potsdam for helpful comments and suggestions. Furthermore, we thank the contributions of two anonymous referees on a previous version of the paper. Finally, we would like to emphasize our gratitude to the Kiel Institute for the World Economy for the opportunity to develop this paper in a highly stimulating environment. None of the above are responsible for errors in the paper.

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Correspondence to Giovanni Covi.

Additional information

The original version of this article was revised: All tables are corrected.

Electronic supplementary material

Fig. A1
figure5

Timeline of the European Banking Union. This figure shows the steps taken for the European Banking Union’s implementation: the European Stability Mechanism (ESM), the Basel III requirements (CRD IV), the Single Supervisory Mechanism (SSM), the European Fiscal Compact (EFC), the Deposit Guarantee Scheme Directive (DGSD), the Bank Recovery and Resolution Directive (BRRD), the Single Resolution Fund (SRF), and ultimately the Single Resolution Mechanism (SRM). (PNG 2.05 mb)

Table A1
figure6

Robustness: controlling for interaction terms. This table examines the robustness of the results by reporting the coefficients after having controlled for interaction terms, respectively between bank fixed effects and the CDS index - iTraxx Europe - as well as bank fixed effects and the volatility index - VDAX. Standard errors are clustered at the bank level and bootstrapped by 200 replications. ***, **, and * indicates statistical significance at the 1%, 5%, and 10% level, respectively. The regression specification is given by: Δ log (BANK CDSijt) = αi + ζt + βΔ log (SOV CDSjt) + ωΔ log (BANK CDSit − 1) + γΔ log (FXjt) + φΔ log (SPit) + δΔ log (Xt) + [αi ∗ Δ (Xt)] + εijt. (PNG 2.95 mb)

Table A2
figure7

Robustness: time fixed effect specification - economic recovery. This table examines the robustness of the time fixed effects specification by reporting the coefficients after having controlled for those countries that were experiencing an economic recovery during 2012, 2013, 2014. Respectively the countries excluded in the estimation are: Italy, Portugal and Spain; Italy and Spain; Sweden. Standard errors are clustered at the bank level and bootstrapped by 200 replications. ***, **, and * indicates statistical significance at the 1%, 5%, and 10% level, respectively. The regression specification for channel 1 is given by: Δ log (BANK CDSijt) = αi + ζt + β Δ log (SOV CDSjt) + ω Δ log (BANK CDSit − 1) + γΔ log (FXjt) + φΔ log (SPit) + δΔ log (Xt) + εijt. The regression specification for channel 2 is given by: Δ log (SOV CDSit) = αi + ζt + βΔ log (FSDit) + ωΔ log (SOC CDSit − 1) + δΔ log (Xt) + εit (PNG 2.58 mb)

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Appendix

Appendix

Table 7 Bank CDS and sovereign CDS explanatory power: time and bank/country fixed effects. This table shows the effect of sovereign credit risk on bank credit risk and vice versa at monthly frequency, before and after the implementation of the BRRD

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Covi, G., Eydam, U. End of the sovereign-bank doom loop in the European Union? The Bank Recovery and Resolution Directive. J Evol Econ 30, 5–30 (2020). https://doi.org/10.1007/s00191-018-0576-2

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Keywords

  • Financial stability
  • Sovereign bail-out
  • Bail-in tool
  • Doom loop
  • European Banking Union
  • Bank Recovery and Resolution Directive

JEL classification

  • E44
  • E58
  • G01
  • G18
  • G21
  • G28
  • L51