Abstract
We analyse a sample of significant European financial intermediaries that fall under the Single Supervisory Mechanism, which is part of the existing institutional supervisory architecture of the Eurozone. Theory suggests that herding among financial intermediaries raises cross-sectional correlations and has negative implications for systemic risk. Empirically, herding behaviours are associated with clusters identifying commonalities in asset allocations and risk strategies. By adopting a novel clustering approach, we analyse whether some pre-determined classifications and criteria associated with the current supervisory framework can capture financial intermediaries’ herding behaviour. We find that simple classifications and criteria, which are less likely to be policy-biased, can be more efficient than complex ones when it comes to identifying commonalities posing the highest threats to systemic risk. The findings confirm the need for a macro- rather than micro-prudential approach to financial supervision by highlighting the importance of using a supervisory toolkit that includes indicators with a stronger cross-sectional and network dimension. Our methodology can serve as a final consistency check for quantitative-based classifications and criteria employed by supervisory authorities.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
We refer to the FIs in our sample as "significant", in line with the official labelling [3].
See the press release at: https://curia.europa.eu/jcms/upload/docs/application/pdf/2017-05/cp170054en.pdf.
More information can be found at: https://www.bis.org/bcbs/gsib/.
The Regulation (EU) No 468/2014 of the ECB, specifically mentions that: “National Competent Authorities are responsible for directly supervising the entities that are less significant, without prejudice to the ECB’s power to decide in specific cases to directly supervise such entities where this is necessary for the consistent application of supervisory standards”.
The consequences of such a behaviour were especially adverse during the 2008/09 crisis because many banks were highly leveraged and heavily reliant on short-term wholesale funding in the run-up to the crisis.
We do not characterise ‘optimal’ behaviour based on first-order conditions in a (general/partial) equilibrium model. Therefore, optimal here denotes ‘best’ or observed strategies.
To ensure potential uniformity, which can be affected by the presence of missing data in Orbis, in some few cases we use as data sources the annual reports of the FIs.
See https://www.bankingsupervision.europa.eu/banking/list/who/html/index.en.html. These types of entities are defined in accordance with Directive 2002/87/EC of the European Parliament and of the Council on financial conglomerates (that include any of the following types: credit institutions, insurance undertakings and/or investment firms).
See EBA stress test details at: https://eba.europa.eu/risk-analysis-and-data/eu-wide-stress-testing/2014 and https://eba.europa.eu/risk-analysis-and-data/eu-wide-stress-testing/2016. We do not use the EBA 2011 stress test exercise because our dataset starts in 2012 and the selection status into the stress test needs to exogenous to the FIs’ inferred behaviour. In fact, after the 2011 stress test, EBA required some banks to reach a 9% capital tier 1 by June 2012.
The full list of primary dealers is available at: https://europa.eu/efc/node/17_en.
These few exceptions might in fact be explained by other empirical findings in the literature regarding the negative impact of the previous EBA stress test exercise from 2011 [31, 40]. After the 2011 exercise, some of the participating banks were given a short time to significantly increase their capital tier 1 ratio until June 2012. Overall, this suggests capital requirements might only have short-term effects.
On average, primary dealers in our sample have assets more than 4 times larger than the rest of FIs.
The Audit report is available at: https://www.eca.europa.eu/Lists/News/NEWS1611_18/SR_SSM_EN.pdf.
References
Acharya, V.V., R. Engle, and D. Pierret. 2014. Testing macroprudential stress tests: The risk of regulatory risk weights. Journal of Monetary Economics 65: 36–53.
Acharya, V.V. 2009. A theory of systemic risk and design of prudential bank regulation. Journal of Financial Stability 5 (3): 224–255.
Acharya, V.V., and S. Steffen. 2015. The “greatest” carry trade ever? Understanding eurozone bank risks. Journal of Financial Economics 115 (2): 215–236.
Acharya, V.V., and T. Yorulmazer. 2008. Information contagion and bank herding. Journal of Money, Credit and Banking 40 (1): 215–231.
Acharya, V.V., and T. Yorulmazer. 2007. Too many to fail-An analysis of time-inconsistency in bank closure policies. Journal of Financial Intermediation 16 (1): 1–31.
Adrian, T., and M.K. Brunnermeier. 2016. CoVaR. American Economic Review 106 (7): 1705–1741.
Agoraki, M.E.K., M.D. Delis, and F. Pasiouras. 2011. Regulations, competition and bank risk-taking in transition countries. Journal of Financial Stability 7 (1): 38–48.
Allayannis, G., and E. Ofek. 2001. Exchange rate exposure, hedging, and the use of foreign currency derivatives. Journal of International Money and Finance 20 (2): 273–296.
Allen, F., A. Babus, and E. Carletti. 2012. Asset commonality, debt maturity and systemic risk. Journal of Financial Economics 104 (3): 519–534.
Allen, F., and D. Gale. 2000. Comparing Financial Systems. MIT Press.
Allen, F., and X. Gu. 2018. The interplay between regulations and financial stability. Journal of Financial Services Research 53 (2): 233–248.
Asmild, M., and K. Matthews. 2012. Multi-directional efficiency analysis of efficiency patterns in Chinese banks 1997–2008. European Journal of Operational Research 219 (2): 434–441.
Athanasoglou, P.P., S.N. Brissimis, and M.D. Delis. 2008. Bank-specific, industry-specific and macroeconomic determinants of bank profitability. Journal of International Financial Markets, Institutions and Money 18 (2): 121–136.
Beck, T., A., Demirguc-Kunt, and O. Merrouche. 2013. Islamic vs. conventional banking: Business model, efficiency and stability. Journal of Banking & Finance 37 (2): 433–447.
Berger, A. N., and D. B. Humphrey. 1992. Measurement and efficiency issues in commercial banking. In Griliches, Z. (Ed.) Output Measurement in the Service Sectors. National Bureau of Economic Research, pp. 245–300.
Billio, M., M. Getmansky, A.W. Lo, and L. Pelizzon. 2012. Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics 104 (3): 535–559.
Blei, S., and B. Ergashev. 2014. Asset commonality and systemic risk among large banks in the United States. In Economics working paper 2014–3.
Brissimis, S. N., M. D. Delis, and N. I., Papanikolaou. 2008. Exploring the nexus between banking sector reform and performance: Evidence from newly acceded EU countries. Journal of Banking and Finance 32 (12): 2674–2683.
Cai, J., F. Eidam, A. Saunders, and S. Steffen. 2018. Syndication, interconnectedness, and systemic risk. Journal of Financial Stability 34: 105–120.
Carvallo, O., and A. Kasman. 2005. Cost efficiency in the Latin American and Caribbean banking systems. Journal of International Financial Markets, Institutions and Money 15 (1): 55–72.
Chinazzi, M., G., Fagiolo, J. A., Reyes, and S. Schiavo. 2013. Post-mortem examination of the international financial network. Journal of Economic Dynamics and Control 37 (8): 1692–1713.
Cosimano, T. F., and D. S. Hakura. 2011. Bank behavior in response to Basel III: A cross-country analysis. In IMF working paper 11/119.
Drake, L., and M. J. B. Hall. 2003. Efficiency in Japanese banking: An empirical analysis. Journal of Banking and Finance 27 (5): 891–917.
De Grauwe, P., Y. Ji, and A. Steinbach. 2017. The EU debt crisis: Testing and revisiting conventional legal doctrine. International Review of Law and Economics 51: 29–37.
De Jonghe, O., L. Baele, and R.V. Vennet. 2007. Does the stock market value bank diversification? Journal of Banking and Finance 31 (7): 1999–2023.
Farne, M., and A. Vouldis. 2017. Business models of the Banks in the Euro Area. In European Central Bank, ECB Working Paper 2070.
Farhi, E., and J. Tirole. 2012. Collective moral hazard, maturity mismatch, and systemic bailouts. American Economic Review 102 (1): 60–93.
Fiordelisi, F., O. Ricci, and F.S. Lopes. 2017. The unintended consequences of the launch of the single supervisory mechanism in Europe. Journal of Financial and Quantitative Analysis 52 (6): 2809–2836.
Furfine Craig H. 2003. Interbank exposures: Quantifying the risk of contagion. Journal of Money, Credit and Banking 35 (1): 111–128.
Garcia-Herrero, A., S. Gavila, and D. Santabarbara. 2009. What explains the low profitability of Chinese banks? Journal of Banking and Finance 33 (11): 2080–2092.
Gropp, R., T. Mosk, S. Ongena, and C. Wix. 2018. Banks response to higher capital requirements: Evidence from a quasi-natural experiment. Review of Financial Studies 32 (1): 266–299.
Hassan, M.K. 1993. The off-balance sheet banking risk of large US commercial banks. The Quarterly Review of Economics and Finance 33 (1): 51–59.
Humphrey, D.B., and L.B. Pulley. 1997. Bank’s responses to deregulation: Profits, technology, and efficiency. Journal of Money, Credit and Banking 29 (1): 73–93.
Ibragimov, R., D. Jaffee, and J. Walden. 2011. Diversification disasters. Journal of Financial Economics 99 (2): 333–348.
IMF. 2007. Do market risk management techniques amplify systemic risk? Global Financial Stability Report (Chapter 2). International Monetary Fund, Washington. https://www.elibrary.imf.org/abstract/IMF082/08560-9781589066762/08560-9781589066762/ch02.xml?rskey=dMkPX8&result=6&redirect=true. Publish October 2007.
Laeven, M. L., L. Ratnovski, and H. Tong. 2014. Bank size and systemic risk. International Monetary Fund. IMF Staff Discussion Note No. 14.
Lehar, A. 2005. Measuring systemic risk: A risk management approach. Journal of Banking and Finance 29 (10): 2577–2603.
León, C., C. Machado, and M. Sarmiento. 2018. Identifying central bank liquidity super-spreaders in interbank funds networks. Journal of Financial Stability 35: 75–92.
Lin, E.M.H., E.W. Sun, and M.T. Yu. 2018. Systemic risk, financial markets, and performance of financial institutions. Annals of Operations Research 262 (2): 579–603.
Mésonnier, J.S., and A. Monks. 2014. Did the EBA capital exercise cause a credit crunch in the euro area? International Journal of Central Banking 11 (3): 75–117.
Mistrulli, P. E. (2011). Assessing financial contagion in the interbank market: Maximum entropy versus observed interbank lending patterns. Journal of Banking & Finance 35 (5): 1114–1127.
Navaretti, G.B., G. Calzolari, A.F. Pozzolo, and M.T.T. de Daverio. 2019. Few large with many small: Banks size distribution and cross-border financial linkages. Journal of Financial Services Research 56 (3): 229–258.
Patro, D.K., M. Qi, and X. Sun. 2013. A simple indicator of systemic risk. Journal of Financial Stability 9 (1): 105–116.
Perotti, E. C., and Suarez. J. 2002. Last bank standing: What do I gain if you fail? European Economic Review 46 (9): 1599–1622.
Philippas, D., S. Papadamou, and I. Tomuleasa. 2019. The role of leverage in quantitative easing decisions: Evidence from the UK. The North American Journal of Economics and Finance 47: 308–324.
Sealey, C.W., and J.T. Lindley. 1977. Inputs, outputs, and theory of production cost at depository financial institutions. Journal of Finance 32 (4): 1251–1266.
Smimou, K., and W. Khallouli. 2016. On the intensity of liquidity spillovers in the Eurozone. International Review of Financial Analysis 48: 388–405.
Tsionas, M.G., and D. Philippas. 2021. Measures of global sensitivity in linear programming: Applications in banking sector. Annals of Operations Research. https://doi.org/10.1007/s10479-021-03980-x.
Tziogkidis, P., K. Matthews, and D. Philippas. 2018. The effects of sector reforms on the productivity of Greek banks: A step-by-step analysis of the pre-Euro era. Annals of Operations Research 266 (1–2): 531–549.
Wagner, W. 2008. The homogenization of the financial system and financial crises. Journal of Financial Intermediation 17 (3): 330–356.
Wagner, W. 2010. Diversification at financial institutions and systemic crises. Journal of Financial Intermediation 19 (3): 373–386.
Acknowledgements
We would like to thank the Editor(s) and the anonymous reviewers for helpful comments and suggestions that streamlined the discussions and the analysis presented in the paper. We are also grateful to the participants at the 56th Meeting of the Euro Working Group of Commodities and Financial Modeling (EWGCFM, 2015) in Dubai, and at the 25th Annual Conference of the Multinational Finance Society (2018) in Budapest, as well as to Prof. Konstantinos (Costas) Siriopoulos for his many comments and suggestions. All remaining errors are our own. The codes and the data will be provided upon reasonable requests from researchers.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
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.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Appendices
Appendix A
This Appendix presents the estimation results of our clustering analysis along the input dimension. Each table presents on the first column the year of the data drawn from Orbis. On the next three columns, it displays the BIC, Chi-square and p value associated with the identified clusters. The fifth column displays the label of the clusters (e.g. A, B, etc.), while the next columns display the number of FI that belong to each cluster, according to the “exogenous type” which is indicated on the table heading (Tables 2, 3, 4, 5, 6).
Appendix B
This Appendix presents the estimation results of our clustering analysis along the output dimension. Each table presents on the first column the year of the data drawn from Orbis. On the next three columns, it displays the BIC, Chi-square and p value associated with the identified clusters. The fifth column displays the label of the clusters (e.g. A, B, etc.), while the next columns display the number of FI that belong to each cluster, according to the “exogenous type” which is indicated on the table heading (Tables 7, 8, 9, 10, 11).
Appendix C
This Appendix presents the estimation results of our clustering analysis along the risk dimension. Each table presents on the first column the year of the data drawn from Orbis. On the next three columns, it displays the BIC, Chi-square and p value associated with the identified clusters. The fifth column displays the label of the clusters (e.g. A, B, etc.), while the next columns display the number of FI that belong to each cluster, according to the “exogenous type” which is indicated on the table heading (Tables 12, 13, 14, 15, 16).
Rights and permissions
About this article
Cite this article
Philippas, D., Dragomirescu-Gaina, C., Leontitsis, A. et al. Built-in challenges within the supervisory architecture of the Eurozone. J Bank Regul 24, 15–39 (2023). https://doi.org/10.1057/s41261-021-00183-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1057/s41261-021-00183-z