Efficiency and risk in European banking

TLDR: This article assess the inter-temporal relationship between bank efficiency, capital and risk in a sample of European commercial banks employing several definitions of efficiency, risk and capital and using the Granger-causality methodology in a panel data framework.

Abstract: We assess the inter-temporal relationship between bank efficiency, capital and risk in a sample of European commercial banks employing several definitions of efficiency, risk and capital and using the Granger-causality methodology in a panel data framework. Our results suggest that lower bank efficiency with respect to costs and revenues Granger-causes higher bank risk and that increases in bank capital precede cost efficiency improvements. We also find that more efficient banks eventually become better capitalized and that higher capital levels tend to have a positive effect on efficiency levels. These results are generally confirmed by a series of robustness tests. The results have potentially important implications for bank prudential supervision and underline the importance of attaining long-term efficiency gains to support financial stability objectives.

Figures

Table 1 Variables description

Table 5 Robustness check: Granger causality for the relationship among capital, efficiency (measured using profit efficiency) and credit risk in the EU-26 commercial banking

Table 3 Granger causality for the relationship among banking capital, efficiency and risk-taking in the EU-26 commercial banking

Full text

Working PaPer SerieS
no 1211 / June 2010
efficiency and
riSk in euroPean
banking
by Franco Fiordelisi,
David Marques-Ibanez
and Phil Molyneux
MACROPRUDENTIAL
RESEARCH NETWORK

WORKING PAPER SERIES
NO 1211 / JUNE 2010
In 2010 all ECB
publications
feature a motif
taken from the
€500 banknote.
EFFICIENCY AND RISK
IN EUROPEAN BANKING
by Franco Fiordelisi , David Marques-Ibanez
and Phil Molyneux
Corresponding author: Faculty of Economics, University of Rome III, Via S. D’Amico 77, 00182, Rome, Italy,
phone: +39 065 733 5672; fax: +39 065 733 5797, e-mail: fiordelisi@uniroma3.it.
Bangor Business School, University, Bangor College Road, LL572DJ, Bangor, United Kingdom,
e mail: p.molyneux@bangor.ac.uk.
Kaiserstrasse 29,
60311 Frankfurt am Main, Germany,
e-mail: david.marques@ecb.europa.eu
This paper can be downloaded without charge from http://www.ecb.europa.eu or from the Social Science
Research Network electronic library at http://ssrn.com/abstract_id=1618296.
NOTE: This Working Paper should not be reported as representing
the views of the European Central Bank (ECB).
The views expressed are those of the authors
and do not necessarily reflect those of the ECB.
1 We wwould like to thank in particular an anonymous referee for insightful and helpful comments particularly w ith regard the various estimation
issues.
The authors w ould also like to thank among others Alessandro Carretta, Francesco Cesarini, John Fell, P hilipp Hartmann, Claudia
Girardone,
Giorgio Gobbi, Marcella Lucchetta, Adrian van Rixtel, Klaus Schaeck, Roberto Violi, John Wilson and Giuseppe Z adra wwho
kindly provided
comments on earlier versions of this paper as
well as participants at seminars at the European Central Bank and
Banca
d’Italia. Special
thanks also to participants at SUERF and Banque Centrale du Luxembourg conference on "P roductivity in
the Financial Services Sector"
as w ell as participants at Associaz ione Bancaria Italiana ( ABI) and Ente Einaudi Workshop on
"European Bank Competition for their
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European Central Bank, Directorate General Research, Financial Research Division,
4
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comments. All errors and omissions as usual rest with the authors."
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MACROPRUDENTIAL
RESEARCH NETWORK

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3
ECB
Working Paper Series No 1211
June 2010
Abstract
4
Non-technical summary
5
1 Introduction
6
2 Related literature
8
3 Research hypothesis
11
4 Methodology
13
5 Variables and data
16
6 Results
19
7 Robustness tests
21
8 Conclusions
23
References
Tables
Appendix
CONTENTS
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29
35

4
ECB
Working Paper Series No 1211
June 2010
Abstract
We analyze the impact of efficiency on bank risk. We also consider whether bank capital has
an effect on this relationship. We model the inter-temporal relationships among efficiency,
capital and risk for a large sample of commercial banks operating in the European Union. We
find that reductions in cost and revenue efficiencies increase banks’ future risks thus
supporting the bad management and efficiency version of the moral hazard hypotheses. In
contrast, bank efficiency improvements contribute to shore up bank capital levels. Our
findings suggest that banks lagging behind in their efficiency levels might expect higher risk
and subdued capital positions in the near future.
Keywords: banking risk; capital; efficiency
JEL classification: G21; D24; C23; E44

Frequently asked questions

Q1. What is the main problem in the analysis of bank risk?

As bank risk is a crucial measure in their analysis the authors try to capture its maindimensions by using two major measures: the 5-year ahead cumulative Expected DefaultFrequency (EDF) for each bank calculated by Moody’s KMV and the traditional non-performing loans to total loans ratio NPL\\L. 

Q2. what is the hensen test of over-identifying restrictions?

The Hensen test of over-identifying restrictions for the GMM estimators: the null hypothesis is that instruments used are not correlated with residuals and so the over-identifying restrictions are valid. 

Q3. What is the purpose of holding additional capital buffers above theregulatory minimum?

Holding additional capital buffers above theregulatory minimum for banks with higher levels of risk aims to avoid the costs associatedwith having to issue fresh equity at short notice (Ayuso et al., 2004; Peura and Keppo, 2006). 

Q4. What is the definition of a bank’s capital adequacy?

Bank capital adequacy is measured as the equity to assets ratio (E/TA), i.e. the valueof total equity divided by the value of total assets. 

Q5. What is the null hypothesis for the GMM estimators?

The Hensen test of over-identifying restrictions for the GMM estimators: the null hypothesis is that instruments used are not correlated with residuals and so the over-identifying restrictions are valid. 

Q6. How do the authors assess the long-run effect of x over the y?

The authors also assess the ‘long-run effect’ of x over the y by testing for therestriction that the sum of all lagged coefficients is zero: a rejection of the restriction impliesthat there is evidence of a long-run effect of x on y. 

Q7. What was the main contribution to the debate?

A major contribution to the debate came from Hughes and Mester (1998, 2009) whoargued for the need to consider bank efficiency when analysing the relationship betweencapital and risk. 

Q8. What is the role of OBS items in the bank value-added analysis?

Although off-balance sheet (OBS) items may play a role in generating bank value-added, the authors omit to consider OBS items since their sample also includes small banks that do not have OBS items or data are not available in the Bankscope database. 

Q9. What is the probability of x not being caused by y?

If the probability is less than10%, then the null hypothesis that x does not Granger-cause y is rejected at the 10%significance level. 

Q10. What is the effect of the lagged capital ratio on the bank risk?

Increases in the sum of the lagged cost efficiencycoefficients temporally precede equity ratio increases and the result holds for all bank riskmeasures (i.e. EDF, EDF5Y, NPL/L) used in estimating model (4). 

Q11. What is the link between the capital ratio and the number of credit institutions?

The authors also find a positive statistically significant (at the 1% level) link between thecapital ratio and the number of credit institutions (NCI) tentatively suggesting that highcapital levels are positively linked to the number of competitors in the market (so supportingthe view that bank competition might encourage higher equity capital levels).