Has regulatory capital made banks safer? : skin in the game vs moral hazard.
Date: 2020-07-04
Institutions: European Central Bank
Subjects: Eurosystem, bank, economic consequence, financial regulation, financial requirements, financial risk, financial solvency, holding company, own resources

Working Paper Series 

Ernest Dautović 

Has regulatory capital  
made banks safer?  
Skin in the game vs moral hazard 

No 2449 / July 2020 

Disclaimer: This 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. 

 
 
 
Abstract

The paper evaluates the impact of a phased-in introduction of capital requirements

on equity, risk-taking, and probability of default for a sample of European system-

ically important banks. Contrary to the case of a one-o(cid:11) introduction of capital

requirements, this study does not (cid:12)nd evidence of deleveraging through asset sales.

A phased-in tightening promotes adjustment to lower leverage via an increase in eq-

uity thereby improving resilience and loss absorption capacity. The higher resilience

comes at the cost of a portfolio reallocation towards riskier assets. Consistently with

models on agency costs and gambling for resurrection, the risk-taking is driven by

large and less pro(cid:12)table banks. The net impact on bank probabilities of default is

positive albeit statistically insigni(cid:12)cant, suggesting that risk-taking may crowd-out

solvency.

JEL Classi(cid:12)cation: E51, G21, G28, O52

Keywords: capital requirements, macroprudential policy, risk-taking, impact evalu-

ation, di(cid:11)erence-in-di(cid:11)erence.

ECB Working Paper Series No 2449 / July 20201Non-technical summary

The Capital Requirements Regulation (CRR) and the Capital Requirements Directive (CRD

IV) introduced a new macroprudential framework transposing the Basel III agreement in the

EU. The overarching goal of the new EU macroprudential regulation is to increase the loss

absorption capacity of banks through the introduction of a set of systemic capital requirements.

Higher capital requirement should foster the stability of the banking system and increase the

resilience of banks in time of crisis allowing them to sustain the real economy.

This study estimates the e(cid:11)ects of macroprudential capital requirements on bank capital and

risk taking by looking at the reaction of EU banks to a series of increments of macroprudential

capital requirements.

In general, banks can increase their regulatory capital ratio in three

mutually non-exclusive ways: by increasing capital, reducing the risk-weights or reducing their

assets.

The sample is composed by all G-SIB and O-SIB in the EU and Norway from 2006Q1 until

2017Q3, leading to a total number of 205 banks, divided in 14 globally-systemically impor-

tant banks (G-SIBs) and 191 other-systemically important banks (O-SIBs). The bank speci(cid:12)c

systemic macroprudential capital requirements are obtained from the European Systemic Risk

Board (ESRB) database on macroprudential policies based on the noti(cid:12)cations from the na-

tional authorities.

The paper (cid:12)nds that the EU-wide regulatory e(cid:11)ort to increase the resilience of the banking

sector has contributed to a better capitalized European (cid:12)nancial system: a one percentage

point increase in capital requirements increases CET1 capital by an average of 13 percent. The

impact is higher (17.7 percent) for banks with less than two percentage points bu(cid:11)er from the

minimum capital requirement.

However, the signi(cid:12)cant increase in capital is accompanied with a cost: banks react to a one

percentage point hike in capital requirements by increasing the average risk weights of their

portfolio by 6.1 percentage points. The highest increase in risk taking is due to medium and

large less pro(cid:12)table banks which try to achieve higher returns by substituting toward more

riskier assets and compensate thus for their lower pro(cid:12)tability. Importantly, wholesale funded

banks have a lower tendency to increase their risk pro(cid:12)le after a capital requirement increase.

In order to gauge the overall impact on banks’ probabilities of default (PD) of the two

opposing e(cid:11)ects - i.e. higher resilience achieved with increased capital versus lower resilience

ECB Working Paper Series No 2449 / July 20202arising from more risk-taking - the study estimates the impact of the policy change on PDs

extrapolated from credit ratings. The results indicate that the positive e(cid:11)ect of accumulating

more equity capital is counterbalanced by the negative substitution e(cid:11)ect toward more riskier

assets.

The countervailing e(cid:11)ect of risk taking on solvency raises the attention to the non-intended

consequences of regulatory action. The regulatory task is not a simple one, any policy change

requires a comprehensive assessment of hidden incentives behind regulatory action.

ECB Working Paper Series No 2449 / July 202031

Introduction

Capital requirements are generally seen as an e(cid:11)ective regulatory option to increase banks’

loss absorption capacity during (cid:12)nancial downturns and preserve the resilience of the (cid:12)nancial

system and the real economy (Admati and Hellwig, 2014; Jim(cid:19)enez et al., 2016). In particular,

the notion that more equity capital is indispensable to sti(cid:13)e risk-taking is at the heart of a

series of international regulatory standards, most notably the Basel Capital Accords (Thakor,

2014).

A branch the economic theory is supporting this view. Higher capital levels may curtail

risk-taking behaviour since managers and shareholders would have more skin in the game and

would have an incentive to act prudently (Merton, 1977; Furlong and Keeley, 1989; Acharya

et al., 2016; Barth and Seckinger, 2018). In addition, bank managers may have incentives to

avoid excessive risk-taking, either because they hold own career preservation motives (Benston,

1986; Saunders et al., 1990), or because more risk increases the variance of returns which can

amplify the probability of signi(cid:12)cant losses on banks’ equity (Hellmann et al., 2000; Repullo

and Suarez, 2004; Repullo, 2004). Similar arguments have been put forward by theories of the

charter value of the bank, (Marcus, 1984; Benston, 1986).

However, the principal-agent theory posits that the presence of imperfect information, which

is endemic in complex organizations such as banks, can manifest itself in moral hazard due to

distorted incentives between the principal and the agent.1 For instance, Dewatripont and Tirole

(1994) model the classic moral hazard problem with unobservable managers’ e(cid:11)ort to conclude

that banks with low leverage have an incentive to increase risk. Admati et al. (2018) show that

if a (cid:12)rm has superior information about its asset quality, shareholders would prefer to reduce

leverage by selling safer assets and retaining the riskier ones, without issuing equity. In the

context of a one-o(cid:11) tightening of leverage requirements, Gropp et al. (2018) (cid:12)nd empirical

support for assets reduction and no impact on equity; however they do not (cid:12)nd evidence of

risk-taking as measured by risk weight densities. By taking a historical perspective, Jorda et al.

(2017) conclude that higher capital ratios are more likely to be correlated with (cid:12)nancial crisis.

1Several strands of the literature have shown the consequences of the principal-agent asymmetric information
problem on risk-taking. Academic contributions range from the (cid:12)elds of economics (Stiglitz and Weiss, 1981),
(cid:12)nance (Acharya, 2009; Acharya and Naqvi, 2012), managerial (Wiseman and Gomez-Mejia, 1998), corporate
(cid:12)nance (Ross, 1973; Bolton et al., 2015; Admati et al., 2018) to law and banking regulation (Alexander, 2006).
Similarly, empirical banking literature shows a positive correlation between capital and risk-taking, (Koehn and
Santomero, 1980; Kim and Santomero, 1988; Blum, 1999) or more generally with the occurrence of (cid:12)nancial
crisis Jorda et al. (2017). A more extensive literature review is provided in Appendix A-II.

ECB Working Paper Series No 2449 / July 20204This divergence of views raises the question as to how banks adjust their balance sheet in

response to tighter regulation and whether banks’ risk-taking behaviour is a(cid:11)ected by regulatory

capital requirements. The paper sheds light on these questions by exploiting an exogenous

policy change in the macroprudential capital requirements for Global Systemically Important

Banks (G-SIBs) and Other Systemically Important Banks (O-SIBs) in the European Union

(EU). These requirements have been implemented within the context of the Basel III regulatory

overhaul. Starting in 2014, macroprudential capital instruments have been gradually phased-in

at individual bank-level and at di(cid:11)erent times across 28 EU countries. The staggered policy

implementation across time and borders allows for a research design based on a quasi-natural

experiment and a di(cid:11)erence-in-di(cid:11)erence (D-i-D) identi(cid:12)cation strategy. Throughout the paper,

the risk-taking behaviour is measured by individual bank-level risk densities, as the ratio of

risk-weighted assets over total assets.

The contribution is closely related to the empirical literature on the relationship between

bank risk-taking and leverage (Shrieves and Dahl, 1992; Jacques and Nigro, 1997; Jokipii and

Milne, 2011), the research on the adjustment mechanisms to more stringent capital require-

ments (Admati et al., 2018; Gropp et al., 2018), and the discussion on the optimal design and

sequencing of prudential regulation, (Khatkhate, 1998; Hart and Zingales, 2011; Lall, 2012).

More broadly, the paper is related to the emerging literature on the e(cid:11)ectiveness of capital-

based macrprudential policy (Repullo and Suarez, 2012; Ferrari et al., 2016; Cerutti et al., 2017;

Akinci and Olmstead-Rumsey, 2018; Cizel et al., 2019).

From the point of view of the optimal design of macroprudential policy, this paper comple-

ments the conclusions of Admati et al. (2018) and Gropp et al. (2018) by presenting evidence

that banks’ responses to more stringent leverage rules are dependent on their pace of intro-

duction. As shown by the authors, when a one-o(cid:11) leverage requirement is imposed, a costly

equilibrium emerges where banks react by reducing assets and lending to the real economy,

holding equity constant. The results presented below add to these (cid:12)ndings. When capital re-

quirements are phased-in over several years, a clear policy trade-o(cid:11) between risk and resilience

emerges. On one hand, banks comply with the regulation by raising equity capital, improving

their resilience and do not engaging in asset reduction. On the other hand, the additional

equity induces moral hazard instead of promoting skin in the game, and banks reallocate their

portfolio towards riskier assets. A detailed analysis of the heterogeneous responses to capi-

ECB Working Paper Series No 2449 / July 20205tal regulation with respect to size, pro(cid:12)tability, and funding sources complements the main

(cid:12)ndings.

The results indicate that the EU regulatory reforms have contributed to a better capitalized

European (cid:12)nancial system. In the baseline speci(cid:12)cation, a one percentage point increase in

capital requirements increases CET1 capital by an average of 13 percent. The impact is higher

(17.7 percent) when the policy tightening is more binding. When requirements are relaxed in a

downturn, the increase in highest quality CET1 capital can help the banks in supplying credit

acting counter-cyclically to sustain economic growth in bad times, see Jim(cid:19)enez et al. (2016);

Jorda et al. (2017).

The signi(cid:12)cant increase in capital is accompanied by substitution e(cid:11)ects toward more riskier

assets: banks react to a one percentage point hike in capital requirements by increasing the

average risk weights of their portfolio by 6.1 percentage points. The impact is attenuated for

small banks (4.3 p.p.). However, medium (7.3 p.p.) and large (9.6 p.p.) systemically important

banks show a signi(cid:12)cantly higher risk-taking behavior. These (cid:12)ndings indicate that risk-taking

could be arising from an intensi(cid:12)cation of agency problems as the bank grows in size. In fact,

Ang et al. (2000) shows that a diluted ownership structure, which is typical of large publicly

traded (cid:12)rms, is proportionally associated with increasing agency costs.

In terms of the overall net impact on solvency, as measured by probabilities of default, the

positive e(cid:11)ect of accumulating more equity capital is counterbalanced by the negative substitu-

tion e(cid:11)ect toward more riskier assets. In other words, risk-taking crowds-out the positive e(cid:11)ect

of greater equity.

The paper documents a substantial amount of heterogeneity across banks. Financial institu-

tions adopting the internal rating based (IRB) approach show a lower propensity to risk-taking.

This suggests the existence of a competitive advantage for IRB banks since lower risk weights

imply a lower cost of compliance to higher capital requirements. Further, the increase in risk-

taking arises in less pro(cid:12)table institutions, suggesting that gambling for resurrection motives

may underlie the substitution toward more riskier assets. The (cid:12)ndings indicate that wholesale-

funded, relative to retail-funded banks, have a lower tendency to increase their risk pro(cid:12)le. As

shown by Bruno and Shin (2015), this behavior may be a consequence of competitive advan-

tage arising from the capacity to exploit cross-border funding in regions where monetary policy

conditions are more expansionary.

ECB Working Paper Series No 2449 / July 20206The paper is organised as follows: the empirical methodology is illustrated in Section 3.

Section 4 presents the evidence for the baseline speci(cid:12)cation and Section 5 investigates het-

erogenous treatment e(cid:11)ects. The robustness to common trend assumption is presented in

Section 6, and a formal test of endogeneity is presented in Section 7. The overall impact on

the probabilities of default is described in Section 8, and conclusions are drawn in Section 9.

2 The EU macroprudential capital based regulation

One of the criticisms of earlier Basel standards for capital requirements, is the lack of empha-

sis on risks stemming from correlated exposures that may accumulate over time and increase

systemic risk (Hellwig, 1995; Acharya, 2009; Haldane and May, 2011). Basel I and II capital

standards are focused exclusively on individual portfolios without acknowledging the impor-

tance of how much these portfolios are diversi(cid:12)ed, the pattern of co-variances among individual

assets, systemic correlation of risks and interconnectedness, and/or the cost of failure of big

and more complex banks.2

Basel III standards include additional capital requirements aimed at tackling some of these

issues and add three main new bu(cid:11)ers: the Capital Conservation Bu(cid:11)er (CCoB) for build-up

of adequate bu(cid:11)ers above the minimum that can be drawn down in periods of stress, the Coun-

tercyclical Capital Bu(cid:11)er (CCyB) aimed at limiting the procyclicality of credit growth, and

additional capital bu(cid:11)ers for Global Systemically Important Banks (G-SIB) aimed at address-

ing the liquidation cost of too-big-to fail banks.3 These e(cid:11)orts notwithstanding, critics have

questioned both the lack of ambition and the design of some of the Basel III bu(cid:11)ers (Repullo

and Saurina, 2011).

In Europe, the Capital Requirements Regulation (CRR) and the Capital Requirements

Directive (CRD IV) introduced a new macroprudential framework transposing the Basel III

agreement.4 The CRDIV has been o(cid:14)cially transposed in law on 17th July 2013 and the full

2On one hand Basel I introduced risk-weighted exposures in order to force banks with more risk in their
portfolios to maintain a higher capital level, while Basel II main innovation was the introduction of the Internal
Rating Based (IRB) and the Standardized Approach (SA) models for the computation of risk-weights. For a
more detailed history of Basel capital standards and their possible de(cid:12)ciencies see for instance Brealey (2006)
and Hellwig (2010).

3In addition, Basel III introduces favourable risk-weights for OTC derivatives cleared through central counter-
parties (CCPs), and is raising the risk-weights on exposures to (cid:12)nancial institutions relative to the non-(cid:12)nancial
corporate sector, as (cid:12)nancial exposures are more highly correlated than non-(cid:12)nancial ones.

4Detailed information on the Capital Requirements Regulation and Directive can be found on the European

ECB Working Paper Series No 2449 / July 20207reform package entered into force on the 1st January 2014. The overarching goal of the new EU

regulation is to limit systemic risk in the banking sector through the introduction of a set of

Systemic macroprudential Capital Requirements (SMCR) available to national authorities to

address systemic risks, see Table 1 for a summary of the macroprudential capital requirements

in the EU.

The set of SMCR include three main capital based requirements applied at individual insti-

tution level: the Systemic Risk Bu(cid:11)er (SRB), the G-SIB bu(cid:11)er and the O-SIB bu(cid:11)er require-

ments.5 The SRB aims to address systemic risks of a long-term structural and non-cyclical

nature as for instance the accumulation of systemic risk and the degree of interconnectedness.

The O-SIB and G-SIB bu(cid:11)ers are predominantly concerned with increasing loss absorption

capacity and reducing public costs of default of bigger and complex banks.

While the economic rationale behind the diverse types of bu(cid:11)ers may di(cid:11)er in scope and

objective, all of them have to be met with an additional highest quality Common Equity Tier1

(CET1) capital as a share of risk-weighted assets (RWA). All SMCRs are applied at individual

bank-level and are speci(cid:12)cally addressed to the set of both globally and nationally systemically

important banks (SIBs).6 The list of systemically important banks is updated each year by

national authorities following EBA guidelines. The main criteria for determining a bank as

systemically important for the domestic economy are: a) size; b) importance for the economy

of the relevant Member State or the Union, capturing substitutability/(cid:12)nancial institution

infrastructure; c) complexity, including the additional complexities from cross-border activity;

d) interconnectedness of the institution or (sub-)group with the (cid:12)nancial system.7

In sum, and contrary to the Basel III capital standards, the EU package is more ambitious

since instructs Member States to designate own systemically important banks to which then

Commission website. Norway and Iceland, despite not being formally EU Member States opted for participating
in the new EU macroprudential framework for banks as established in the CRR and the CRDIV.

5The new macroprudential regulation in the EU implements also the Basel III capital conservation bu(cid:11)er
(CCoB) as well as the dynamic countercyclical capital bu(cid:11)er (CCyB). Nevertheless, the CCoB and the CCyB
are bu(cid:11)ers set at the country-level.
In the empirical framework of this paper these two requirements are
absorbed by country-quarter (cid:12)xed e(cid:11)ects and as such they are not contributing to additional variation and to
the identi(cid:12)cation of the e(cid:11)ects.

6Under the CRD IV/CRR capital framework, EU banks are required to hold a minimum amount of total
capital equal to at least 8% of RWA. The new regulation raises the minimum share of capital that has to be
of the highest quality CET1 capital from 2% to 4.5%. Additional capital until the minimum threshold of 8%
can be ful(cid:12)lled with Tier 1 minimum capital or Tier 2 minimum capital (max. 2%). As such, the new EU-wide
CRD IV/CRR minimum capital regulation places greater emphasis on the quality of capital.

7For more details cfr. the EBA Guidelines on the criteria to assess systemically important banks in the
EU. The EBA provides and maintains also an updated G-SIBs list and O-SIBs list in Europe over time on its
website.

ECB Working Paper Series No 2449 / July 20208a wider battery of bank-speci(cid:12)c systemic capital bu(cid:11)ers may be applied. Table 1 provides an

overview of the newly introduced capital requirements in the EU.

2.1 Calibration and Setting of the Macroprudential Capital Re-

quirements

The calibration of the G-SIB bu(cid:11)er is set internationally according to the Basel G-SIB score

range for each G-SIB (BCBS, 2013). The calibration of the SRB and the O-SIB bu(cid:11)ers de-

pend on the systemic importance of the bank for the country in question and is not subject

to centralized guidelines from EBA. In other words the EBA guidelines provide criteria for

determination of systemically important banks but not have normative prescriptions in terms

of the calibration of bu(cid:11)ers. This provides leeway for national authorities to protect domestic

banks. To counter the peril of inaction bias, the ECB has developed a framework to provide a

minimum common (cid:13)oor when calibrating O-SIB bu(cid:11)ers at the national level to foster a level

playing (cid:12)eld. Above this (cid:13)oor, each country calibrates the bu(cid:11)ers using own methodologies.8

It is important to notice that not all G-SIBs or O-SIBs are subject to the SMCR as of

2017Q3: despite the EU introduction of the capital based macroprudential framework in 2014,

some national macroprudential authorities have not yet activated any of the structural SMCRs.

Under the oversight of the ESRB and the ECB, the EU regulation allows for discretion to

activate and to set the level for each O-SIB and SRB bu(cid:11)er.

On the one hand, these country divergences and the staggered implementation across coun-

tries facilitate the empirical identi(cid:12)cation problem. On the other hand, they may lead to

concerns regarding the potential endogeneity of the policy change with respect to the health of

the country banking system. These concerns are, however, alleviated by four main elements en-

shrined in the regulation: i) four EU institutions coordinated oversight contribute to refraining

from inaction bias and national favoritism,9 ii) the ECB has the power to top-up the require-

ment if considers it insu(cid:14)cient to cope with the relevant risk, or may object the decision in case

considered excessive or punitive toward foreign subsidiaries, and iii) the ESRB can issue public

8For instance, the Commission de Surveillance du Secteur Financier (CSSF), the macroprudential authority
in Luxembourg adopts "a statistical approach involving linear regression and a scaling framework with the goal
to ensure consistency between O-SIBs bu(cid:11)ers and the bu(cid:11)ers applied to G-SIBs." See CSSF noti(cid:12)cation to the
ESRB. Additional noti(cid:12)cations may be found on the ESRB website.

9It is important to notice that in order to ensure consistent macroprudential oversight across the Union, the

ESRB develops principles tailored to the Union economy and is responsible for monitoring their application.

ECB Working Paper Series No 2449 / July 20209warning and recommendations where an identi(cid:12)ed systemic risk has materialised and has not

yet been addressed and iv) the reciprocation framework allows a Member State to request a

reciprocation of a macroprudential measure.10

The (cid:12)rst provision establishes that four di(cid:11)erent EU institutions are overseeing the imple-

mentation of macroprudential bu(cid:11)ers across the EU, namely the European Systemic Risk Board

(ESRB), the EU Commission, the EBA and the ECB.11 Second, the ESRB ha the mandate

to identify and monitor systemic risk in the EU. To preserve (cid:12)nancial stability, the ESRB can

issue public warnings and recommendations to Member States where identi(cid:12)ed systemic risks

are deemed to be signi(cid:12)cant and not addressed. Moreover, the ESRB can issue con(cid:12)dential

warnings to the Heads of States in the EU Council and must monitor their follow-up.12 Third,

the ECB has top-up power for Euro Area banks, this guarantees that there is no inaction bias

toward strategically important domestic banks since the ECB can apply higher macropruden-

tial capital requirement than the one established at national level.13 With a further aim of

fostering consistency, the ECB has also developed a framework to provide a minimum common

(cid:13)oor when calibrating systemic capital requirements applied at the national level.14 Fourth,

any measure requires the approval of the ECB Governing Council, and acting on a proposal by

the EU Commission, the EU Council of ministries has the power to reject the proposed national

macroprudential measure. This provision guarantees that foreign subsidiaries are treated fairly

and equally without being a(cid:11)ected by protectionist measures.15

The validity of the research method could be further questioned if large banks shift their

assets across borders to branches or subsidiaries in order to conduct regulatory arbitrage. In this

case, the existence of spillover e(cid:11)ects may produce biased estimates. However, also this concern

is addressed within the regulatory framework which envisages the possibility of reciprocation.

10See respectively, Article 131(7) of the Capital requirements Directive IV (CRDIV), Articles 5(1) and 5(2)
of the Single Supervisory Mechanism Regulation, Article 3 of the ESRB regulation and the Recommendation
of the ESRB/2015/2 in conjunction with Article 5 of Decision ESRB/2015/4

11See fot instance, Article 131(7) of the Capital requirements Directive IV (CRDIV) which states that before
setting or resetting an O-SIB bu(cid:11)er, the competent authority shall notify the Commission, the ESRB, EBA, and
the competent microprudential supervisors of the Member States concerned one month before the publication
of the decision.

12For more information on ESRB’s tasks and powers see the related ESRB regulation.
13For the ECB top-up power and the scrutiny of the ECB on national macroprudential measures see Articles

5(1) and 5(2) of the Single Supervisory Mechanism Regulation.

14See ECB (cid:13)oor methodology for setting the capital bu(cid:11)er for an identi(cid:12)ed Other Systemically Important
Institution (O-SII). By providing a minimum (cid:13)oor, the ECB reduces national discretion in calibration of the
capital instrument and provides the basis of a discussion between the ECB and national authorities on the
overall assessment of the appropriateness of a macroprudential stance.

15This is in accordance with Article 291 of the Treaty on the Functioning of the European Union (TFEU).

ECB Working Paper Series No 2449 / July 202010This grants the power to a Member State to request a countervailing capital increase to foreign

branches, or directly across borders when risks of spillover are deemed signi(cid:12)cant. Reciprocation

should ultimately ensure the reduction of the incentive to search for regulatory arbitrage and

the enforcement of a level playing (cid:12)eld among parents, subsidiaries, and branches within and

across the borders.16

3 Empirical Methodology

3.1 Data

This study uses two main data sources to construct an integrated dataset combining bank-

level (cid:12)nancial accounts data and systemic macroprudential capital requirements. The source

of bank-level (cid:12)nancial accounts data is the commercial provider SNL Financials which collects

(cid:12)nancial accounts from (cid:12)nancial institutions around the World. More speci(cid:12)cally, (cid:12)nancial

accounts for all G-SIB and O-SIB in the EU and Norway from 2006Q1 until 2017Q3, leading

to a total number of 205 banks in the sample, with 14 G-SIBs and 191 O-SIBs. They represent

86% of total consolidated assets of EU banks in 2016 according to consolidated balance sheet

statistics.17 The list of banks sorted by total assets is presented in Appendix A-I.18

The bank speci(cid:12)c SMCRs are obtained from the ESRB database on macroprudential policies

based on the noti(cid:12)cations from the national authorities.19 Table 3 illustrates the evolution of

16The reciprocity framework is codi(cid:12)ed in two main documents: (i)Recommendation of the ESRB/2015/2;
(ii) Article 5 of Decision ESRB/2015/4. For a detailed account of the reciprocation framework in the EU consult
the dedicated ESRB web page on reciprocation, and Chapter 11 of the ESRB Handbook on operationalising
macroprudential policy. In this context, the ESRB has an important coordination role in assessing measures,
discussing cross-border e(cid:11)ects, and recommending mitigating measures, including reciprocity.

17 The sample composition of SIBs may vary from year to year due to new banks being designated as O-
SIB, or old banks not satisfying any more the requirements to be designated as O-SIBs. See European Banking
Authority Guidelines on O-SIB. For the list of G-SIB with cut-o(cid:11) date 2016Q4 consult the the Financial Stability
Board.

18One caveat to keep in mind when constructing a bank-level database over a long time period is the churning
rate of (cid:12)nancial institutions from the sample. In particular the merger of two or more (cid:12)nancial institutions
may bias the results. To limit this possibility, When a merger happens, the study sample is adjusted in order
to re(cid:13)ect this change: old entities are discontinued in the sample and a new entity is added with a separate
identi(cid:12)er as a result of the merger. To grasp the idea of sample construction in case of mergers, one examples of
recent merger episodes over the period is shortly summarised in this footnote. On 2nd Jan 2017 Nordea Bank
Denmark merged with Nordea Bank AB, see link. As a consequence of the merger, a Danish entity Nordea Real
Kredit has been identi(cid:12)ed as O-SIB by the Danish macroprudential authority. It follows also that Nordea Bank
Denmark has been removed from the O-SIB list in DK, and also from the list of O-SIBs in the study sample.
19The ESRB macroprudential database covers all changes in macroprudential regulation noti(cid:12)ed by the 28
EU countries and Norway. Noti(cid:12)cations are published on the ESRB website or disseminated through ESRB
publications. For detailed information on the national macroprudential policies in the EU cfr.: ESRB National

ECB Working Paper Series No 2449 / July 202011capital requirements for the sample of EU G-SIBs and O-SIBs from 2010Q1 until 2017Q3. In

Panel A of Table 3 we present the simple mean of the capital requirements for both treated

and non-treated banks. The (cid:12)rst row shows the phasing-in of the SMCR after 2014. From

2014 onward the SMCR CET1 capital requirements for EU G-SIBs and O-SIBs increase by an

average of 0.21 percentage points yearly.20

The average capital requirement is higher if we compute the average conditional on e(cid:11)ec-

tively treated banks. This conditional mean is shown in Panel B of Table 3 and implies an

average increase of the SMCR by 1.18 percentage points in 2017Q3. This is a substantial

increase in capital requirement, in particular for banks that are closer to the minimum re-

quirement. An inspection of the standard deviation of SMCR shows that there is a signi(cid:12)cant

variation in capital requirements. This is a welcome feature of the data since it contributes to

lower the variance of the estimated coe(cid:14)cient of interest and provide more precise estimation

of the relationship between risk-taking and capital requirements.

As explained in Section 2, an O-SIB may not have any macroprudential capital requirement

imposed if the national regulator decides not to activate the requirement for that bank. This

result that in our sample 26.34% of bank-quarter observations are not treated. In other terms,

for those banks the regulator opted not to activate the SMCR. While this is a discretionary

decision, there are substantial institutional arrangements to guarantee that the decisions are

taken objectively without favoring any national champion.

The second row of each panel of Table 3 shows the Overall Capital Requirement (OCR)

for CET1 capital, i.e. the sum of the Pillar I capital requirements and the combined macro-

prudential capital requirement. The average supply levels of CET1 capital are shown in the

third row of each panel. The di(cid:11)erence between the OCR and the supply of capital by banks

is then computed in order to derive a proxy for the stringency of the binding of the capital

requirement at bank-level (row four of Table 3). Banks’ response to higher capital requirement

are expected to be a function of the distance to the regulatory minimum, or in other words an

Policies

20Note that over the sample period, other type of country level macroprudential bu(cid:11)ers were also levied on
EU banks, as for instance the CCoB and the CCyB. The total combined yearly average of macroprudential
bu(cid:11)er averaged 0.65% from 2014 until 2017Q3. However, in this paper, the focus is exclusively on bank-level
macroprudential bu(cid:11)ers since they allow for more precise estimates and allow to control for time varying country-
level variation by including country-time (cid:12)xed e(cid:11)ects. In other terms, the country-time (cid:12)xed e(cid:11)ects absorb the
variation generated by the country-level capital requirements. Note also that there is a small average increase
(i.e. 0.01%) of the SMCR across the sample already in 2013 since in Norway the macroprudential capital
requirements where the Systemic Risk Bu(cid:11)er (SRB) was introduced in 2013Q3.

ECB Working Paper Series No 2449 / July 202012inverse function of their excess capital above the minimum requirement. In particular, banks

are expected to increase their capital supply if they are close to the regulatory minimum as

found already in Jacques and Nigro (1997) and Rime (2001).

Table 4 illustrates descriptive statistics of banks’ (cid:12)nancial accounts as extracted from the

SNL Financials database. Descriptive statistics are shown for all variables used in the subse-

quent empirical analysis, when absolute values are shown these are expressed in EUR through-

out the paper. The asset side of the balance sheet is composed by three major components:

loans gross of provisions for impairment (58.29%), securities (25.13%) and cash (15.29%).21

It is worth noting the average level of RWA over total assets, i.e.

the risk-ratio, which is

50.91%, as this measure will be a useful benchmark for the following analysis. Securities can

be further broken down by Held for Trading (HFT), Available for Sale (AFS) and Held to

Maturity (HTM). On the liabilities side the table shows the means of the main funding sources

for banks: deposits (48.5% of total assets), short and long-term wholesale funding (33.3%) and

debt (17.9%).22

Importantly, the SNL Financials database also allows to capture the extent to which sys-

temically important (cid:12)nancial institutions are interconnected with each other and the wider

(cid:12)nancial system. Excluding measures of bank interconnectedness and complexity would bias

the results due to their direct correlation with the level of the SMCR, see Section 2 above

and EBA Guidelines on the criteria to assess systemically important banks in the EU. Table

4 presents four indicators used in the paper as proxies for interconnectedness and complexity,

namely: i) interbank lending as a direct measure of interconnectedness; ii) assets held for trade

as measure of complexity and interconnection with (cid:12)nancial markets’ developments through

mark-to-market trading book accounting which directly impacts banks’ pro(cid:12)t and loss state-

ments; iii) over the counter (OTC) derivatives securities held on the balance sheet as a measure

of both complexity and interrelation with the counterparty risk in he (cid:12)nancial system; iv) cash

held at the Central Bank for interbank payments’ settlements.23

21Cash includes reserves and balances at the Central Bank, operating cash, cash and cash equivalents according
to the relevant accounting standard, i.e. "short-term, highly liquid investments that are readily convertible to
known amounts of cash and which are subject to an insigni(cid:12)cant risk of changes in value".

22The total sum of funding sources and equity is not 100% of total assets due to few missing observations

across these variables.

23Cash held at the central bank is also an important bank-level control variable for unconventional monetary
policy operations of quantitative easing whereby bank’s accounts at the ECB were credited with cash after
monetary policy operations.

ECB Working Paper Series No 2449 / July 2020133.2 Empirical Methodology

The introduction of the CRD IV/CRR regulatory framework and of the new macropruden-

tial capital requirement o(cid:11)ers an opportunity to employ an identi(cid:12)cation strategy based on a

controlled comparison by studying the e(cid:11)ect of a policy change on di(cid:11)erently a(cid:11)ected banks.

As noted in previous sections, the SMCRs are set at individual bank-level for systemically

important banks in the EU. This implies that within a country banks are subject to di(cid:11)erent

level of requirements depending on their systemic importance. This ensures cross-country and

within-country variation at bank-level, which in turn is suited for using a multi-treatment group

di(cid:11)erence-in-di(cid:11)erence identi(cid:12)cation strategy. 24 The baseline estimated equation is:

Yict = (cid:11)i + (cid:12)SM CRict + ln Xic;t(cid:0)1(cid:13) + (cid:14)ct + [(cid:30)i (cid:1) t] + uict

(1)

Where i; c and t are indicators for bank, country and time respectively and uict is the residual

unexplained term.

The outcome variables of interest are in the vector Yict and they include the capital ratio

(CET1/RWA), the logarithm of the levels of CET1 and total capital for the capital based spec-

i(cid:12)cations; the logarithm of the RWA, and its decomposition in RWA/Assets (i..e risk density)

and the logarithm of total assets for risk based speci(cid:12)cations. The policy variable of interest

is the level of the systemic macroprudential capital requirement SM CRict which is imposed

at individual bank-level and is gradually phased-in over (cid:12)ve years. Both the outcomes and

the policy variables are at time t to re(cid:13)ect a contemporanous reaction to higher capital re-

quirements. The main coe(cid:14)cient of interest in the equation is (cid:12), which can be interpreted as

the average treatment e(cid:11)ect of a one percentage point increase in capital requirement on the

outcome variable of interest.

The matrix Xict(cid:0)1 includes bank-speci(cid:12)c, time varying control variables lagged by one quar-

ter to limit simultaneity bias. The set of control variables is motivated by two main reasons.

The (cid:12)rst is to control for the EBA criteria for designating systemically important banks and

the resulting implied calibration of the SMCR, see Section 2. The EBA criteria are taken into

account using total assets (as proxy for size), loans (the importance of the bank in the (cid:12)nancial

24In this setting each bank is a group of treatment and is compared with other treated banks in terms of
intensity of treatment (i.e. di(cid:11)erent levels of capital requirement increases) and with the group of banks that
had not have any increase in the SMCR over the sample period. For details on the multi-treatment group
di(cid:11)erence-in-di(cid:11)erence estimation technique see Chapter 5.2. in Angrist and Pischke (2008).

ECB Working Paper Series No 2449 / July 202014system), interbank lending, holding of securities and OTC derivatives (the complexity and the

interconnectedness of a bank with the (cid:12)nancial system). The second is to control for struc-

tural and cyclical characteristics that may confound risk-taking or equity raising. For those

the model uses total debt and deposits (as proxy for leverage and funding), return on assets

and the cost to income ratio (cyclical and structural pro(cid:12)tability), total cash (liquidity) and

reserves held at the central bank (quantitative easing).25

In addition, the speci(cid:12)cation includes bank (cid:12)xed e(cid:11)ects, (cid:11)i, to control for time-invariant

bank heterogeneity as for instance the business model (i.e. retail vs investment bank). The

parameters (cid:14)ct are interacted country-quarter dummy variables for capturing within country

endogenous variation, for instance time varying macroeconomic e(cid:11)ects such as: credit and

economic growth, supply and demand shocks or (cid:12)scal and monetary policy changes within a

country. In a cyclical downturn, when counterparties are more likely to be downgraded than

upgraded, the resulting e(cid:11)ect is a mechanincal increase in risk weights to account for major

counterparty risk and viceversa. Moreover, during an economic upturn banks may underes-

timate risks and shift the composition of their portfolios to riskier activities (Rajan (1994);

Jokipii and Milne (2008); Shim (2013)). Therefore, the (cid:14)ct (cid:12)xed e(cid:11)ects control for time vary-

ing country-level factors that might simultaneously a(cid:11)ect the level of the SMCR and any of

the dependent variables in Yict. In addition, with respect to the use of simple macroeconomic

controls the country-quarter (cid:12)xed e(cid:11)ects ensure a lower risk of omitted variable bias.

A version of Equation 1 is augmented with the bank speci(cid:12)c trends (cid:30)i (cid:1) t, this is for a

robustness test of the common trends assumption.

In fact, the inclusion of trends at the

level of the change of the policy variable should be a standard robustness test for di(cid:11)-in-di(cid:11)

speci(cid:12)cations, see Wolfers (2006) and Angrist and Pischke (2008).26

3.3 Threats to Identi(cid:12)cation and Solutions

Throughout the paper, the identi(cid:12)cation of a more robust casual impact is facilitated exploiting

the (cid:13)exibility of a cross-country bank-level panel. The advantages of a bank-level cross-country

dataset are manifold. Firstly, a cross-country bank-level panel allows for a robust method

25All the control variables in Xict(cid:0)1 are scaled by total assets to limit heteroscedasticity, except of course

total assets which are included using the natural logarithm.

26Notice that matching methods are often more intuitive but regression techniques are more straightforward
to implement, this is true in particular when covariates are continuously distributed and matching on continuous
covariates would requires strati(cid:12)cation or pairing as in our case (Cochran, 1968; Angrist, 1998)

ECB Working Paper Series No 2449 / July 202015to control for time varying macroeconomic variation which is absorbed by country-time (cid:12)xed

e(cid:11)ects. Second, the paper exploits the granular bank-level data to measure banks intercon-

nectedness with the (cid:12)nancial system through interbank lending and OTC derivatives. This

is an essential for delivering unbiased estimates since by regulation the calibration of capital

requirements is a function of banks’ interconnectedness with the broader (cid:12)nancial system and

its operational complexity. Third, in the bank-level panel utilised in this paper it is possible to

control for the quantitative-easing e(cid:11)ects due to non-conventional monetary policy by including

excess reserves held at the Central Bank. Quantitative easing may in(cid:13)uence banks ability to

raise equity since they would have a more stable source of alternative funding, omitting the

quantitative easing channel may thus bias the estimates. Fourth, in contrast to single country

bank-level studies, it can increase the external validity of the results. Finally, in comparison to

macro country-level panels, a bank-level dataset improves on the granularity of available data

fostering the study of heterogeneous e(cid:11)ects across banks.

Notwithstanding these advantages, threats to the identi(cid:12)cation of causal e(cid:11)ects may still

arise from violations of one of one of the following assumptions of the D-i-D technique:

i)

banks do not anticipate the change, i.e.

the common trend assumption is violated, ii) the

policy change is exogenous, iii) there are no spillovers across borders.

The failure of the common trend assumption due to anticipating behavior by control group

banks is a standard threat for the identi(cid:12)cation. Section 6 discusses those threats and presents

two robustness tests. The (cid:12)rst test of the common trend assumption controls for bank-level

trends in the regression as in Wolfers (2006). The second test investigates the presence of

announcement e(cid:11)ects of the policy and is akin to Alpert (2016). Both tests present robust

evidence on the absence of diverging pre-treatment e(cid:11)ects across the treated and the control

group.

The exogeneity of the policy change may not be warranted because capital requirements

are set by national macroprudential authorities and are not centralised at EU level. This leaves

discretion to national regulators and could cause the introduction of these requirements to be

endogenous if the Member State acts strategically to protect (weaker) domestic banks. If this

is the case then capital requirements may not be e(cid:11)ective as planned because of Member States

inaction bias. The paper investigates the potential presence of policy-maker protective behavior

and inaction bias in Section 7 without (cid:12)nding evidence supporting endogenous behavior. This

ECB Working Paper Series No 2449 / July 202016is not surprising since several arrangements enshrined in EU banking regulation are speci(cid:12)cally

aimed at eliminating national biases with respect to macroprudential risks, as described in

Section 2.

Concerns on spillovers are addressed by using consolidated (cid:12)nancial accounts of EU banks

which eliminate the possibility of arbitrage. The use of consolidated data is warranted also

because macroprudential capital requirements are levied at a group consolidated level.

In

a nutshell, the robustness tests provided in the paper, in conjunction with other provisions

established in the EU banking regulation, address the concerns on the validity of the research

method due to endogeneity.

3.3.1 Bank Risk Measure

In terms of measurement, one of the most important elements of the analysis presented in this

paper is the measurement of risk-taking behaviour which must be able to identify individual

bank speci(cid:12)c risk-taking. One way to approach this problem is to look directly at the intrinsic

risk stemming from the combination and composition of the portfolios on the assets side of

banks’ balance sheet. This is the approach followed by the Basel Committee on Banking

Supervision (BCBS) under the Basel II rules on risk sensitive capital requirements.

But how can banks adjust their CET1 ratio after an increase in the regulatory capital

requirement? A look at the CET1 ratio can help discerning the e(cid:11)ects:

CET 1Ratio =

CET 1
RWaAsseta

(2)

where CET1 is the amount of Common Equity Tier 1 capital held by the bank, RW rep-

resents the non-negative risk weight speci(cid:12)c to asset a, and Asset is the amount of nominal

exposure in asset a. As Equation 2 shows, banks can increase their regulatory capital ratio

in three mutually non-exclusive ways: by increasing capital, reducing the risk-weights or re-

ducing their assets. A bank can raise capital by either issuing new shares and/or not paying

dividends to its shareholders to retain earnings. The newly issued shares and retained earnings

increase the CET1 ratio, provided that the bank does not increase its risk-weighted assets.

Alternatively, holding equity constant, the management of the bank can reduce risk-weighted

assets, either through shifting assets composition towards exposures with lower risk-weights

such as government securities, or reducing assets, that is by reducing lending, selling securities,

ECB Working Paper Series No 2449 / July 202017impaired loans or other assets.27

From Equation 2 a logical approach for the measurement of bank speci(cid:12)c risk-taking be-

haviour lies in the Basel II rules on risk-sensitiveness of assets. The ratio of RWA over total

assets, for simplicity risk density, is a natural measure of bank risk-taking behaviour if we keep

(cid:12)xed the risk weights measurement approach. The risk density provides the average risk the

bank’s portfolio according to the risk-weight associated with each asset. It has the advantage

of being a simple and very intuitive measure of bank risk-taking, (Berger, 1995), even if the

appropriateness of risk-weights has been questioned in the literature, (Hellwig, 2010). A further

bene(cid:12)t of using the risk density is that it takes into account the deterioration of the quality

of a credit portfolio, as already noted by Shrieves and Dahl (1992) and Jacques and Nigro

(1997). In fact, regulatory provisions foresee higher risk-weights for non performing exposures

and impaired assets.28

In addition, the risk density indicator is a decision variable within the reaction function

of banks’ risk-management and its decision making process to changes in capital requirements

determined by the regulator. The response of the risk management is exclusively determined by

strategic decision and thus more apt to measure risk-taking behaviour than market based risk

measures such as CDS spreads which are usually measures of solvency. In addition, the latter

are of minor interest since they are external to the decision making of the banks management,

and are a mere re(cid:13)ection on how the (cid:12)nancial markets judge the probability of the bank being

solvent when payments are due.29

A further advantage of the use of internal risk-weights is that they provide for a timely and

not delayed response when an increase in capital requirements occurs. Banks (cid:12)nd issuing new

capital generally more expensive than issuing debt or retain earnings, and they do not (cid:12)nd

pro(cid:12)table reducing their assets in order not to hamper their returns. As such, either increasing

27Note that this paper does not look at alternative supervisory measures aimed at increasing the risk-weighted
capital ratio, as for instance the CRR Art. 458 measure on the (cid:13)oor of risk-weights to be applied to speci(cid:12)c
exposures such as real estate. Despite having a similar e(cid:11)ect on the overall risk-weighted capital requirements
this provision is fundamentally di(cid:11)erent since constraints banks’ reaction to a speci(cid:12)c channel of adjustment.

28Some authors suggest to use directly non-performing loans (NPLs) as proxies for risk-taking since granting
high-return, but high-risk, loans underlines a risk-taking propensity. However, NPLs would not be entirely apt
to our task due to lags in their accounting rules, they are recognized as non-performing starting on the 90th day
past due (depending on the type of asset and the accounting classi(cid:12)cation) implying a di(cid:14)cult identi(cid:12)cation
problem for the econometrician regarding the timing of the impact.

29It is also important to notice that the scope of application of CDS pricing is very limited in our sample since
CDS prices are generally available only for some of the large systemic banks. In our sample, this translates to
only 49 banks with available CDS prices.

ECB Working Paper Series No 2449 / July 202018CET1 or shrinking the balance sheet earlier than necessary is not an e(cid:14)cient allocation of

resources. Similarly, the costs of regulatory sanctions for non-compliance ensure there is no

lag in the reaction. The absence of lags or delays in the reaction of bank managers in setting

their preferred risk level entails that risk-taking can be readily discernible in regular (cid:12)nancial

statements.

Some notes of caution in using the risk density are due. Banks using internal rating based

approach (IRB) to set risk-weights on their portfolio have a competitive advantage with respect

to banks using standardized approach (SA), (Praet, 2004; Tschemernjak, 2004; Haselmann

and Wahrenburg, 2016). The competitive advantage arises because IRB banks use their own

empirical models to estimate more appropriately market and credit risk, while SA banks use

the one-size-(cid:12)t-all risk weights de(cid:12)ned in the regulation which are generally more stringent.

This study controls for this heterogeneity using bank-level (cid:12)xed e(cid:11)ects in all speci(cid:12)cations (the

risk weights measurement approach is generally sticky in time), in addition it explores the

extent of the competitive advantage of IRB banks by interacting the macroprudential capital

requirement with an IRB dummy in Section 4.

A (cid:12)nal note of caution is warranted since, by construction, the risk density identi(cid:12)es risk

stemming predominantly from on-balance sheet exposures, while the risk associated with o(cid:11)-

balance sheet exposures transactions are not entirely captured in this metric. In the transpo-

sition of Basel standards in the EU, the CRR asserts that o(cid:11)-balance sheet items are treated

like on-balance sheet exposures and shall be risk-weighted, however the calculation method

implies a lower risk weight for o(cid:11)-balance sheet exposures.30 Since o(cid:11)-balance sheet items are

unobserved in the dataset, we can only try to form an educated guess on the bias arising from

omitted variable. Table 2 shows the direction of omitted variable bias given the correlation

patterns between the omitted variable, the treatment variable and the dependent variable.

Due to the preferential regulatory treatment for o(cid:11)-balance sheet assets, it is likely that banks

arbitrage and react to higher capital requirement by shifting some of their risky assets to unob-

served o(cid:11)-balance sheet positions implying a positive correlation between a hike in regulatory

30Not all activities of the banks can be moved o(cid:11)-balance sheet, o(cid:11)-balance sheet items are typically exclusively
those not owned by or not a direct obligation of the bank, for instance securitised activities and operating leases
are the most common o(cid:11)-balance items, others are credit conversion factors which calculate the amount of a
free credit line or guarantees. The key di(cid:11)erence between o(cid:11)-balance and on-balance sheet exposures relates to
the calculation method of the exposure value that should be risk-weighted. The de(cid:12)nition and calculation of
the exposure value of o(cid:11)-balance sheet items is detailed in CRR Article 166 for the IRB approach and CRR
Article 111 for the SA.

ECB Working Paper Series No 2449 / July 202019capital and o(cid:11)-balance sheet activity. In addition, due to lower risk weights of o(cid:11)-balance sheet

exposures, the (cid:12)2 coe(cid:14)cient in Table 2 should be lower than zero. It follows that the estimates

on the impact of capital requirements on risk-taking behaviour may result downward biased.

This downward bias may underestimate the real risk-taking behaviour in our estimates, prob-

ably even more for more complex institutions which have a higher capacity to transfer assets

o(cid:11)-balance sheet.

4 Results

By presenting the (cid:12)rst set of results, the paper acknowledges that banks tend to maintain a

capital bu(cid:11)er on top of the regulatory minima as a signal of (cid:12)nancial health to the markets, to

attract funding and to minimize supervisory interference (Shrieves and Dahl, 1992; Jacques and

Nigro, 1997). Moreover, microprudential supervisors encourage banks to maintain an additional

voluntary bu(cid:11)er on top of the requirements, this indicates the adequate level of capital to be

maintained in order to withstand stressed situations.31 This study sorts banks by how binding

is the new capital requirement. To this end, an indicator of the distance from the OCR is

interacted with the change in the systemic macroprudential capital requirement.32

4.1 Capital: Baseline and binding distance interaction

Before investigating the relative importance of the moral hazard versus the skin in the game

channels, it is instructive to understand how e(cid:11)ective the capital requirement is at increasing

the banks’ capital. This section provides evidence on whether banks increase the numerator

of the capital ratio and, as a consequence, whether their solvency is strengthened. Table 5

illustrates the results of the impact of an increase in the Systemic macroprudential Capital

Requirements (SMCR) on three measures of capital: the risk-weighted CET1 ratio, the volume

of available CET1 capital and the volume of supplied total capital. All regressions follow the

speci(cid:12)cation in Equation 1, and include quarterly varying country-time (cid:12)xed e(cid:11)ects. The (cid:12)rst

31In the Banking Union framework, this is regulated via an additional Pillar 2 Guidance which is calibrated
on the basis of the adverse scenario in the supervisory stress tests. see ECB description of the Pillar 2 Guidance
32In turn, levels of desired capital may depend on external factors such as the macroeconomic environment,
the market interest rate, the degree of tax di(cid:11)erentials between debt and equity (cid:12)nancing Schepens (2016), as
well as the degree of regulatory pressure. In a bank-level empirical setting, the country-level features can be
controlled for in speci(cid:12)cation 1 via country-time (cid:12)xed-e(cid:11)ects, these help to absorb the bias in the estimates
arising from country-level speci(cid:12)c in(cid:13)uences.

ECB Working Paper Series No 2449 / July 202020columns (1)-(3) present the baseline estimates, while Columns (4)-(6) di(cid:11)erentiate the impact

by the cushion banks maintain from the OCR.

Column (1) shows how the resulting average impact on the risk-weighted capital ratio is

not statistically signi(cid:12)cant. For the average bank, and without categorizing banks by distance

from the minimum requirement, the impact of the SMCR is not strictly binding. This result

is however not distinguishing by the bu(cid:11)er banks maintain on top of regulatory minima.33

Column (4) indicates that once we include in the regressions dummies for distance, and their

interaction with the SMCR, a one percentage point increase in the capital requirement induces

an increase of the CET1 ratio by 0.83 percentage points, providing evidence that signi(cid:12)cant

European banks are e(cid:11)ectively constrained by the regulatory change. The interaction e(cid:11)ect is

not statistically di(cid:11)erent for the group of banks with a more than two percentage points excess

capital with respect to the minimum requirement. The absence of negative sign for non capital

constrained banks, provides evidence that the reaction to the hike in capital requirement is

widespread. This (cid:12)nding is in line with the notion that banks have a preference to maintain a

desired, or target level of capital, above the minimum to assuage market pressure and reduce

supervisory interference, (Shrieves and Dahl, 1992; Jacques and Nigro, 1997).

But how this increase in the ratio occurs? In Column (2), the focus shifts to the amount

of CET1 capital, i.e. the numerator of the CET1 ratio. On average, a one percentage point

higher SMCR yields CET1 to increase by 8.9 percent.

In Column (5), we di(cid:11)erentiate the

impact by distance to OCR: the result highlights again that banks with a relative shortage of

capital have almost a double e(cid:11)ect (17.7 percent) with respect to the average impact in Column

(2). The coe(cid:14)cient for the reference group of banks with less than 2 p.p. of CET1 bu(cid:11)er above

the minimum, translates in a 17.7 percent increase of CET1 ratio for a 1 p.p. rise in capital

requirements. This positive impact provides evidence of the direct bene(cid:12)ts of capital based

macroprudential regulation in the EU. The reforms promoted widespread increase in capital

levels across the sample of systemically important banks, and in particular for banks with lower

loss absorption capacity, increasing capital for banks with lower bu(cid:11)ers and therefore improving

the overall resilience of the system. Signi(cid:12)cant banks with capital in excess of the minimum

regulatory threshold have somewhat weaker, but still strong e(cid:11)ects, in terms of the magnitude

33In the following note that the estimation sample is composed by 137 signi(cid:12)cant banks, the distribution
of the OCR distance variable in the estimation sample has mean 8.7 percentage points and median at 7.8
percentage points. Similar results are obtained with di(cid:11)erent break-down of the distance from OCR, the results
are available from the author.

ECB Working Paper Series No 2449 / July 202021of CET1 capital increase.

Column (3) and Column (6) corroborate the results on CET1 capital when including ad-

ditional Tier1 capital and Tier2 instruments in the numerator. The net impact is attenuated

since the bulk of the increase is borne by CET1 capital, a natural consequence of the SMCR

requirement. The induced higher levels of capital ratios mean a greater loss absorption capacity

for European banks when the next (cid:12)nancial crisis hits, Jim(cid:19)enez et al. (2016).

4.2 Risk: Baseline and binding distance interaction

This section presents the (cid:12)rst results on the skin in the game versus moral hazard channels. As

summarized in Appendix A-II, a branch of the banking literature shows how more regulated

banks can have risk-taking incentives due to the negative e(cid:11)ect of higher capital requirements

on bank pro(cid:12)ts.34 On the other hand, the skin in the game argument postulates that for banks

with higher capital ratios there is an incentive by bank managers to avoid excessive risk-taking

since more risk increases the variance of returns with higher probability of signi(cid:12)cant losses

on banks’ equity.35 This section shed lights on the capital requirements and risk-incentive

relationship using the risk density as a measure for riskier assets, and interacting the SMCR

with the distance from the OCR in order to study the interaction of risk-taking with the supply

of regulatory capital.

Table 6 presents the estimates. Columns (1)-(3) investigate the e(cid:11)ect without distinguishing

banks by their distance from the overall capital requirement. Column (1) shows the estimates

on the impact on the combined risk weighted assets, columns (2) and (3) presents the impact

on the decomposition of RWA in risk density and total assets. The results suggest that, on

average, banks have a signi(cid:12)cant tendency to increase their RWA after a tightening of the

capital requirements. In particular, in Column (2), the impact stems from higher risk-taking,

as the composition of the asset side of banks’ balance sheets tilts toward more riskier assets.

The risk-taking behaviour manifests in considerably higher risk densities (RWA/Assets), with

a one percentage point hike in capital requirements being associated with 6.9 percentage points

increase of the risk density. The estimates are signi(cid:12)cant at 1 percent con(cid:12)dence level and are

indicative of the existence of a risk-taking channel of capital adequacy requirements, raising

concerns on the non-intended consequences and perverse e(cid:11)ects of capital based regulation.

34See for instance Koehn and Santomero (1980); Kim and Santomero (1988); Blum (1999).
35See for instance Hellmann et al. (2000); Repullo and Suarez (2004); Repullo (2004)

ECB Working Paper Series No 2449 / July 202022To understand better the magnitude of this impact, recall that the average risk density level

in the sample is 50.9 percent (see Table 4). In other words, a one percentage point increase

in the SMCR could shift the average risk density to 57.8 percent. This is an economically

signi(cid:12)cant amount and, as noted in Appendix A-II, the qualitative impact is consistent with

previous theoretical and empirical work. Moreover, we can try to extrapolate this impact to

the average EU systemically important bank, we can compare how much this risk-taking relates

to the e(cid:11)ective increase in capital requirement occurred during the observation period. Table

3 shows that over the four years between 2014 and 2017, the SMCR increase on average in

the sample of systemically important EU banks by 0.87 percentage points. A simple linear

approximation would thus entail an average increase of risk weights by 6 percentage points, i.e.

0.87 multiplied by 6.9 the coe(cid:14)cient of Column (2).

The second part of Table 6 tests the hypothesis of a non-linear relationship between capital

requirement and risk-taking, (Hellmann et al., 2000; Repullo and Suarez, 2004; Repullo, 2004).

In particular, Jokipii and Milne (2011) (cid:12)nd that banks with a lower management bu(cid:11)er above

the minimum capital requirement reduce their risk while banks with a higher bu(cid:11)er increase

risk. Contrary to the predictions of this strand of the literature, the evidence in Columns

(4)-(6) does not show signs of a decreasing risk-taking behaviour by more capitalized banks.

The interaction terms in the speci(cid:12)cation are not signi(cid:12)cant at standard con(cid:12)dence levels, i.e.

irrespective of their level of capital supply, banks have similar propensities to take on more risk

after a hike in capital demand.

The results of this section present a clear banks’ tendency to react to more capital by shifting

the portfolio toward riskier assets. The predisposition to take on more risk can be interpreted

as evidence that the moral hazard channel is stronger than the skin in the game channel of

capital regulation. Potential losses to equity holders arising from greater risk-taking are not

the main driver of risk management decisions in the adjustment process. The positive aspect

of the new regulation is that banks react by increasing the amount for capital even if this does

not restrain them from taking on more risk.

ECB Working Paper Series No 2449 / July 2020235 Heterogeneity

5.1 Heterogeneity by Size and IRB

This section investigates how size and internal rating based approach a(cid:11)ect the relationship

between capital requirements and risk-taking behaviour. Bank size is measured using total

assets, with small banks classi(cid:12)ed as those having less than EUR 20 billions in total assets,

medium banks de(cid:12)ned as banks with assets between EUR 20 to EUR 100 billions and large

banks have more than EUR 100 billions in total assets.36

Further, banks are distinguished by their risk weights measurement approach in order to

gauge whether more sophisticated (cid:12)nancial institutions can successfully circumvent the risk-

weighting system and present lower risk-weights on their books. The indicator variable for

the IRB approach is constructed from SNL Financials where the risk weights measurement

framework is provided and the dummy takes value one if the bank is using either the advanced

or the foundation IRB.37 A priori, we expect a positive correlation between size and IRB, due

to resource constraints smaller banks may not have the required human capital to design and

deploy the IRB approach which is more demanding in terms of modelling skills. In our sample,

size and IRB have a positive pairwise Paerson correlation coe(cid:14)cient of 0.31, this correlation is

signi(cid:12)cant at one percent signi(cid:12)cance level. Table 7 presents the evidence on the impact of a hike

in capital requirements for capital indicators, while Table 8 shows the estimates for measures

of risk. All regressions include bank-level controls, bank (cid:12)xed e(cid:11)ects and country-quarter (cid:12)xed

e(cid:11)ects, the latter control for time varying macroeconomic heterogeneity.

36The classi(cid:12)cation of banks follows a division of the sample in three approximately equal parts in order not
to lose observations and hence statistical power when performing heterogeneity e(cid:11)ects, see Table A-I. For the
smaller banks, this subdivision is also in line with the EU Banking Union criteria to distinguish Least Signi(cid:12)cant
Institutions (i.e. total assets < EUR 30 billions) and Signi(cid:12)cant Institutions (total assets > EUR 30 billions).
Other thresholds for size have been tested and results do not alter the conclusions presented in this section.
Regressions by other categorizations are available from the author.

37There are two versions of the IRB approaches. The Advanced (A-IRB) is the most sophisticated of two
credit risk modelling approaches agreed by regulators in 2004. It allows banks to calculate the probability of
default (PD) for a loan, as well as the exposure at the point of default and the resulting losses. Its simpler
cousin, the foundation IRB, only allows PD to be modelled. In the following we consider a dummy one for
banks using either the A-IRB or the foundation IRB approaches, or a mixture of the two. The dummy is set to
zero for purely standardized approaches (SA).

ECB Working Paper Series No 2449 / July 2020245.1.1 Impact on Capital by Size and IRB

Following the results in Section 4.1, Table 7 adopts the speci(cid:12)cation with a dummy variable

for distance which is 1 if the distance from the OCR is greater than two percentage points,

for the sake of space and according to the results of Table 5 the dummy for distance takes

on only two values.38 The estimates on the impact of capital do not present strong evidence

of hetereogeneous impact by size or risk weights measurement framework. The interaction

with the distance from the overall capital requirement is likewise not signi(cid:12)cant. The evidence

on capital raising from Table 7 leads to conclude that there is no signi(cid:12)cant heterogeneous

behaviour between small and big banks or between banks adopting the IRB or the SA.

5.1.2

Impact on Risk by Size and IRB

The results related to the risk-taking behaviour are more informative. Table 8 illustrates the

outcome of the regression without di(cid:11)erentiating by distance from the OCR since risk-taking

behaviour does not appear to be related to the bu(cid:11)er of capital the bank maintains on top

of the minimum requirement, this was shown in Table 6. Columns (1)-(3) present the results

by bank size while Columns (4)-(6) illustrate the estimates for banks with IRB and for the

interaction of size and IRB.

Column (1) of Table 8 indicates that RWA are increasing by approximately seven percent for

a one percentage point increase of capital requirements. The impact on RWA does not appear

signi(cid:12)cantly di(cid:11)erent between smaller and bigger banks. In Column (2), RWA are divided by

total assets to obtain the risk density. The evidence indicates a clear increase of the impact

on the risk density as the size of the banks increases. Banks with total assets above EUR 20

billions tend to take on more risk compared to small banks, approximately a two percentage

point more for every percentage point increase in capital requirements. Column (3) con(cid:12)rms

further that banks do not decrease their assets size signi(cid:12)cantly following an increase in capital

requirements.39

In Column (4)-(6) we augment the speci(cid:12)cation with the risk weight measurement framework

38Same categories for the breakdown of distance from OCR as in the previous section, as well as other
categories of size of the bank have been experimented, the results are similar in terms of both magnitude and
statistical signi(cid:12)cance, they are available from the author.

39In a separate set of regressions the interaction with size is tested by interacting a dummy for G-SIBs with
the SMCR, these results do not have a signi(cid:12)cant interaction term, this is likely due to the fact that variation
of treatment status is not su(cid:14)cient enough among the G-SIBs, see Appendix A-I

ECB Working Paper Series No 2449 / July 202025represented by the indicator variable for IRB, which takes the value one if the bank is using

the internal rating based approach. Column (4) reveals that the increase in RWA is driven

exclusively by banks with assets greater than EUR 100 billions. The coe(cid:14)cient for large banks

increased to eighteen percent following a one percentage point hike in capital requirements.

The impact for smaller banks is no longer signi(cid:12)cant. More telling is the impact for banks

with more than EUR 100 billions in assets using the IRB approach, they have lower risk-taking

by (cid:12)fteen percent with respect to large banks relying on the SA. The marginal impact of one

percentage point increase of the SMCR on large IRB banks is a two percent increase in RWA.

Column (5) of Table 8 takes a closer look by netting out the confounding e(cid:11)ect of the RWA

ratio denominator. The (cid:12)rst and the forth row of Column (5) con(cid:12)rm that even smaller banks

take more risks following a rise in capital requirements while the IRB approach for small banks

does not bring signi(cid:12)cant bene(cid:12)t in curtailing their risk density. For smaller banks, a one

percentage point increase in capital requirements induces an increase of the risk density by 4.3

percentage points. More interestingly, the second and the third row of Column (5) suggests that

there is a positive relation between risk-taking behaviour and bank size. As the size of the bank

increases, and with it its systemic importance, the risk-taking behaviour is more accentuated.

A one percentage point increase in capital requirements is associated with a 7.3 percentage

points increase of the risk density for medium banks, and with a 9.6 percentage points increase

for large banks, both at 5% signi(cid:12)cance level. These results are consistent with the presence

and intensi(cid:12)cation of agency costs as the (cid:12)nancial institutions becomes larger with a more

fragmented shareholders base. In fact, as pointed out by Ang et al. (2000), agency problems

are directly proportional with the dilution of the ownership structure, and it can be inferred

that in large publicly traded banks these are substantial.

Can IRB banks reduce the observed risk-taking behaviour by using the more advanced

approach to measure risk weights? The last row of Table 8 presents the results. For medium

banks, the interaction coe(cid:14)cient between size and IRB is negative but is not statistically

signi(cid:12)cant. Large IRB banks with total assets above EUR 100 billions have a signi(cid:12)cantly

lower propensity to augment their risk density when capital requirements are incremented, the

point estimate is 3.6 percentage points lower relative to SA banks of same size. This implies

that large IRB banks present a reduced risk exposure from a supervisory perspective, suggesting

overall lower levels of risk-taking than large SA banks. To what extent this risk reduction is

ECB Working Paper Series No 2449 / July 202026real and e(cid:11)ective, or is just the result of manipulating the risk weights in their own favor, it is

impossible to discern with the data used in this paper.40 Nevertheless, the evidence establishes

the presence of a competitive advantage for IRB banks since lower risk weights imply a lower

cost of compliance to a hike of regulatory capital requirements.41

5.2 Heterogeneity on Risk by Pro(cid:12)tability, Funding and Leverage

This section explores further the heterogenous impacts of a change in capital requirements on

bank risk-taking behaviour by looking at three measures of bank performance: net interest

income as a proxy for pro(cid:12)tability, wholesale funding as a proxy for inherent liquidity risk, and

the leverage ratio as a measure of bank capitalization.

5.2.1 Pro(cid:12)tability and risk-taking

The low interest rate environment which characterised the past decade shrinks the interest

income margin of banks and increases pressure on their pro(cid:12)tability. The literature has shown

convincingly this link in both theoretical and empirical contributions, (Samuelson, 1945; Han-

cock, 1985; Borio et al., 2017). Therefore, less pro(cid:12)table banks may take more risk in order to

compensate for the reduced pro(cid:12)tability. This argument is strictly intertwined with the propo-

nents of the charter value theory of the bank, as summarized in Appendix A-II, which support

the skin in the game argument whereby banks have more incentives to operate conservatively

when the amount of equity is at risk, (Marcus, 1984; Benston, 1986).

If the above arguments are true, more pro(cid:12)table banks should have a less risk-taking be-

haviour when faced with a capital increase. To test this proposition, the speci(cid:12)cation is aug-

mented including an interaction of the SMCR and an indicator dummy of net interest income

(NII) as a proxy for pro(cid:12)tability. NII is de(cid:12)ned as interest income less interest expense before

provisions for loan losses, and hence is a direct measure of return stemming from interest rate.

Table 9 presents the results for pro(cid:12)tability in Columns (1)-(2). The dummy for pro(cid:12)tability is

40The marginal e(cid:11)ect for large IRB banks is however still greater than the small banks with assets lower than

EUR 20 billions.

41These result raise the question whether very big and sophisticated banks, the G-SIBs and their subsidiaries
across Europe, are driving this behaviour. Unfortunately, all 14 G-SIB in our sample are sophisticated enough
to adopt the IRB approach for risk weights measurement, this lack of variation does not permit to test this
hypothesis. A solution is to use a higher threshold for the size of the very large banks and as a proxy rule we
de(cid:12)ned the threshold of EUR 300 billions for the very big banks in the EU, but even in this case the required
variation in the IRB variable was not su(cid:14)cient to obtain the estimates, see Table A-I. Only three banks with
assets greater than EUR 300 billions adopt the SA and 29 use the IRB.

ECB Working Paper Series No 2449 / July 202027switched on if net interest income is above median (NII = 1), reporting the estimates for more

pro(cid:12)table banks.

The evidence suggest that there is a greater tendency to take on more risk by the cluster

of less pro(cid:12)table banks, i.e. when the dummy for pro(cid:12)table banks is turned on (NII = 1). The

interaction coe(cid:14)cient of small banks with a net interest income above the median has a positive

albeit insigni(cid:12)cant magnitude. More interestingly, pro(cid:12)table medium and large banks have a

signi(cid:12)cantly lower propensity to increase risk with respect to similarly sized less pro(cid:12)table

banks. Medium banks with above median net interest income decrease their risk density by 1.6

percentage points (s.d. 0.741) less than same sized banks with below median NII. The same

compensatory pattern in risk-taking is observed for large pro(cid:12)table banks, they decrease the

risk density by 2.07 percentage points (s.d. 0.925) with respect large banks with below median

NII. For both median and large banks, a test of the sum of the coe(cid:14)cients for above and below

median NII banks, fails to reject the null.42

These results con(cid:12)rm the fact that more pro(cid:12)table banks have a less aggressive risk-taking

behaviour when faced with a capital increase, and indicate that most of the increase in risk-

taking associated with size is related to less pro(cid:12)table institutions. This leads to the conclusion

that the increase in risk-taking associated with bank size is due to less pro(cid:12)table banks and

the tendency to gamble for resurrection.

5.2.2 Wholesale funding and risk-taking

This section introduces a link between liquidity risk and the risk-taking behaviour. In general,

banks with a greater reliance on market funding are more prone to liquidity runs in times of

crisis, (Rajan, 2006; Brunnermeier, 2009).43 wholesale-funded banks have to frequently rollover

large amounts of funds which makes them particularly vulnerable when market or interbank

liquidity dries up. In addition, Huang and Ratnovski (2011) show how on the supply side of

wholesale funding the (cid:12)nanciers do not have incentives to conduct costly monitoring of banks

since they may withdraw on short notice based on negative news signals, exacerbating further

the risk of a potential bunk run. This inherent liquidity risk residing in wholesale-funded banks

poses a threat to their stability.

42For median banks the null hypothesis of the linear combination 2.158-1.634=0 has a p-value=0.63 and fails
to reject the null. For large banks the tested linear combination is 2.842-2.067=0 with a resulting p-value=0.48
43For instance, Shin (2009) notes that in the Northern Rock bank run case, wholesale funding plummeted by

more than 50%, from 26.7 billion pounds in June to 11.5 billion pounds in December 2007.

ECB Working Paper Series No 2449 / July 202028Based on the evidence provided in previous sections the question arises as to whether

wholesale-funded banks recognize the inherent liquidity risk of their funding model when re-

acting to a hike in regulatory capital or not. If not, the consequences of an increase in capital

requirement from a systemic standpoint may be even more worrisome since higher risk-taking

is more likely to lead to negative news signals and subsequent bunk runs.

The evidence on the interaction between wholesale funding model and risk-taking behaviour

following an increment of the SMCR is provided in Column (3)-(4) of Table 9. wholesale-funded

banks are coded with a dummy being one when the ratio of wholesale funding, short and long

term, over total assets is greater than the median. The (cid:12)rst three rows corroborate the results

observed in previous speci(cid:12)cations. Medium and large banks have more than two percentage

points higher reaction than smaller banks, corroborating that agency costs may be a driver of

this di(cid:11)erence. The results change signi(cid:12)cantly when the SMCR increase is interacted with size

and a dummy representing wholesale-funded banks (WHS=1). Smaller wholesale-funded banks

show a further increase in the risk density by 1.34 percentage points with respect to similar

size non wholesale-funded banks. On the contrary, medium and large wholesale-funded banks

decrease their risk density by the same amount of their risk increase after the hike in capital

requirements.

This reduction compensates the propensity to rise the risk density associated with medium

and large banks and indicates that the increase of the risk density is largely driven by medium

and large retail-funded banks. The results suggest that wholesale-funded banks have a lower

incentive to increase the riskiness of their portfolio when faced with a capital requirement hike.

This may be due to several factors, in particular can be interpreted as a strategic need to

reduce the publicly observed risk density in view of the already riskier funding model. A further

interpretation may be related to pro(cid:12)tability and the results in Column (3)-(4) on wholesale

funding can be reconciled with the evidence provided in the previous section on pro(cid:12)tability and

risk-taking in Columns (1)-(2). Recent literature has shown that wholesale-funded banks have

a competitive advantage in a low interest rate environment since they can shift their funding

globally towards regions where monetary policy conditions are looser and exploit thus cross-

border funding to limit the negative pressure on pro(cid:12)tability due to low interest rates, (Bruno

and Shin, 2015). It follows that wholesale banks are on average more pro(cid:12)table than banks

relying on standard deposit funding, this is described in Figure 1 for our sample of G-SIBs and

ECB Working Paper Series No 2449 / July 202029O-SIBs in the EU, and therefore have lower incentives to increase their riskiness to compensate

for lower interest income.

5.2.3 Leverage and risk-taking

This section investigates the link between the leverage ratio and risk-taking. The relationship is

expected to be positive according to the previous contributions by Furlong and Keeley (1989),

Dewatripont and Tirole (1994) and Jokipii and Milne (2011). Leverage is measured following the

Bank of International Settlements de(cid:12)nition by dividing Tier 1 capital by the bank’s average

total consolidated assets (i.e. the sum of the exposures of all assets and non-balance sheet

items).44

In addition, a dummy variable is de(cid:12)ned for above (LR=1) or below (LR=0) the

median leverage ratio in the sample. The evidence for the interlinkages between leverage and

risk-taking subject to a regulatory capital increase is presented in Columns (5)-(6) of Table 9.

While the (cid:12)rst three rows con(cid:12)rm again the incremental impact of regulatory capital on the

risk-taking behaviour by bank size, the heterogeneous impact by above median leverage ratio is

not statistically signi(cid:12)cant. The sign of the interaction coe(cid:14)cients for medium and large banks

hints at a negative relationships, however the estimates are rather noisy suggesting an absence

of relationship between risk-taking and leverage ratio following an increase in regulatory capital

requirement. This result is consistent with the evidence presented for the baseline regression

for risk-taking presented in Table 6 where the impact was broken down by the distance from

the minimum overall capital requirement. Despite the distance from OCR being a risk-sensitive

measure of capital due to the use of risk-weighted assets at the denominator, it is positively

correlated (correlation coe(cid:11). = 0.35) with the non-risk-based leverage ratio measure and has a

similar economic interpretation.

6 Robustness: Common Trends

The failure of the common trend assumption due to diverging behavior between treated and con-

trol banks is a standard threat for the identi(cid:12)cation of Di(cid:11)erence-in-Di(cid:11)erence (DiD) empirical

strategies, (Angrist and Pischke, 2008). In the current setting the common trend assumption

implies that the risk-taking behaviour of banks would be same in the absence of treatment. This

44For more detailed information on the Basel III leverage ratio consult the documentation provided on the

BIS website

ECB Working Paper Series No 2449 / July 202030section presents the evidence of two alternative tests of the common trend assumption for DiD

regressions. The (cid:12)rst test controls for bank speci(cid:12)c trends in the speci(cid:12)cation and estimates

them on a longer time dimension in the panel; the second interacts a treatment dummy with

a dummy for the announcement period to investigates for diverging behavior between treated

and control groups prior to the phasing-in of the SMCR.45

6.1 Bank speci(cid:12)c trends and a longer T

The (cid:12)rst test studies the in(cid:13)uence of confounding pre-existing trends at the level of the policy

variable by including a bank speci(cid:12)c trend in the model as in Wolfers (2006). If banks’ trends

are not controlled for, and treated and control banks have diverging trend, then the estimates

of the impact of capital requirements may su(cid:11)er of bias due to the confounding e(cid:11)ect on them

induced by the diverging trends across the two groups.46

It is important to note another reason why including bank speci(cid:12)c trends in the regression

is necessary for unbiased identi(cid:12)cation. When pre-existing bank trends are correlated with

both the change in capital requirements (i.e. main regressor of interest) and the risk-taking

behaviour of banks (i.e. the dependent variable), the inclusion of bank speci(cid:12)c trends in the

model ensures that the estimated coe(cid:14)cient on the variable of interest is not a(cid:11)ected by omitted

trend bias, (Wolfers, 2006).

Formally, to test for the robustness of the inclusion of bank trends the baseline model is

augmented with bank speci(cid:12)c trends represented by the product (cid:30)i (cid:1) t where i is the indicator

for banks and t stands for the time dimension (i.e. quarters):

Yict = (cid:11)i + (cid:12)SM CRict + ln Xic;t(cid:0)1(cid:13) + (cid:30)i (cid:1) t + (cid:14)ct + uict

(3)

If the impact of the increment in capital requirements on the risk density is not statistically

45It is useful to note the di(cid:11)erence between the standard two-way DiD setting and the multi-group setting.
In the two groups setting, uniformly treated and non-treated groups are compared and the uniform treatment
variables is a dummy (1/0). In the multi-group DiD setting the intensity of treatment varies across the treated
group: in this paper these are represented by heterogeneous capital requirements across banks, i.e. the treatment
variable is not a simple dummy but varies across banks. In addition, in this study the time dimension is not
constituted by only two periods (after/before) as in the standard two-way DiD approach, instead, each quarter
can have progressively stricter treatment intensity per bank introducing thus a more dynamic multi-period
treatment.

46Note that trends in risk-taking behaviour may diverge because of several reasons: for instance structural
changes in bank business models or because of the formation of expectations on future regulation as for example
the introduction of new rules within Basel IV that may a(cid:11)ect strategic portfolio allocation of banks.

ECB Working Paper Series No 2449 / July 202031signi(cid:12)cant after including bank-level trends then the evidence presented should be interpreted

with caution.

In that case, it is very likely that divergent trends would a(cid:11)ect the (cid:12)ndings,

i.e. the increment in capital requirements would have happened predominantly in banks where

already a rising risk density was being implemented by bank management.

The (cid:12)rst evidence for the (cid:12)rst test on common trends is illustrated in Table 10. The Table is

split in two parts, Columns (1)-(3) present the results for capital to be compared with baseline

regression without bank speci(cid:12)c trend as in Table 5; Columns (4)-(6) presents the results for

risk-taking behaviour to be compared with baseline Table 6. After including bank speci(cid:12)c

trends the statistical signi(cid:12)cance of the coe(cid:14)cients remains unchanged for both capital and

risk-taking. The magnitude of the coe(cid:14)cients for the level of CET1 and total capital are

slightly smaller than in the baseline regressions. Similarly, the coe(cid:14)cient on the risk density

decreases after including bank trends, the impact of higher capital requirement on risk-taking

results halved to 3.1 percentage point increase for a one percentage point increase of the SMCR.

While this is a considerable reduction of risk-taking, it is a symptom that bank speci(cid:12)c trends

play a signi(cid:12)cant role in the estimates driving down the overall results.47

For the second part of the evidence on the bank speci(cid:12)c trends, it is important to notice that

the estimated trends may depend on the length of the time series. As shown in Wolfers (2006),

controlling for bank speci(cid:12)c trends only works well when there is a su(cid:14)cient sample period

available before the treatment period commences. As such, the estimated trends in Equation

3 may require more observations to be properly (cid:12)tted to the data.48 The test is therefore

repeated extending the estimation sample to begin in 2006 rather than in 2010. This allows to

estimate bank speci(cid:12)c trends on a full (cid:12)nancial cycle starting in 2006 before the (cid:12)nancial crisis

and ending with the introduction in 2014 of macroprudential capital bu(cid:11)ers.

The second part of the evidence is presented in Figure 3. From left to right in each plot,

the dots represent di(cid:11)erent (cid:12)s estimated when increasing progressively the starting period of

the sample by one year and shrinking thus the available observations for the estimation of

the trend. The (cid:12)rst column of Figure 3 presents the evidence for capital variables with one

plot each for CET1 ratio, CET1 capital and total capital. The second column illustrates the

47Note that the coe(cid:14)cient of determination R-squared is considerably higher since data now explain a greater
portion of the variation of dependent variables. As common when trends at the policy variable are included
standard errors are bigger implying a higher p-value.

48The problem is exacerbated when there is a structural break in the pre-existing trend of the outcome variable

as it is likely to have happened after the 2007-8 (cid:12)nancial crisis as illustrated for capital levels in Figure 2.

ECB Working Paper Series No 2449 / July 202032evidence for risk with one plot for RWA, the risk density and total assets. Vertical bars show

con(cid:12)dence intervals for every estimated (cid:12).

By looking at the results, it is con(cid:12)rmed that the assumption of common trends is robust

also to di(cid:11)erent lengths and starting years of the sample which allow for a better (cid:12)t of pre-

existing trends to the data. Consistently with the previous results, the risk density and the

RWA show similar estimated coe(cid:14)cients as in Table 10. The level of CET1 is however not

always signi(cid:12)cant when the trend is allowed to be computed prior to 2010, even if the failure

to accept is due to few decimals of a percentage points, indicating that the results for the level

of CET1 may su(cid:11)er marginally from the non holding of the common trend assumption.

6.2 Announcement e(cid:11)ects

A further method to test for the presence of diverging trends prior to the implementation of

the policy is to look at announcement e(cid:11)ects. The announcement of a change in the capital

requirements policy may itself lead to strategic reactions by banks invalidating the common

trend assumption. The European Commission anticipated publicly the intention to strengthen

its capital framework for systemically important banks in September 2009, when it introduced

the possibility to increase macroprudential capital bu(cid:11)ers in good times to be released in a

downturn.49 This change of paradigm may have induced banks to anticipate their reaction to

the capital increase before the implementation in 2014 and may confound the previous (cid:12)ndings.

In order to test formally for the impact during the announcement period this section relies on

an event analysis framework akin to Alpert (2016). In the following set-up, the announcement

period dummy D(Announcement) is de(cid:12)ned as one for the period from 2009Q4, the (cid:12)rst period

since the announcement, until 2013Q4, the last quarter before the phasing-in of the policy.

During the announcement period, large European banks may already expect to be charged

with higher capital requirement and may have an incentive to send positive signals to the market

by increase their capital supply. However, being e(cid:11)ectively levied a capital requirement and

hence assigned a treatment status happens only after 2014 and is a discretionary choice of the

regulator. The treatment status is exogenous to banks’ expectations which are predetermined

49The proposed changes were introduced under the Commission Directive 2009/111/EC of the European
Parliament and of the Council of 16 September 2009 amending Directives 2006/48/EC, 2006/49/EC and
2007/64/EC as regards banks a(cid:14)liated to central institutions, certain own funds items, large exposures, super-
visory arrangements and crisis management. A copy of this directive may be found at this link.

ECB Working Paper Series No 2449 / July 202033to the regulators’ decision.

In other terms, during the announcement period banks are not

aware of what would be the regulators revealed preferences in 2014. This set-up ensures that

banks cannot self-select in the treatment group since the decision to levy a capital requirement

on a speci(cid:12)c bank is exogenous to their expectations. As such, in the following, the treatment

status D(T reated) is de(cid:12)ned equal to one if the bank has been subject to a positive SMCR in

any quarter after the phasing-in of the policy from 2014q1 until 2017Q3.

Formally, variants of the following DiD equation are estimated:

lnYict = (cid:11)i + (cid:12)SM CRict + !D(Announcement) (cid:3) D(T reated) + ln Xict(cid:0)1(cid:13)+

(4)

+ (cid:17)D(Announcement) + (cid:18)D(T reated) + (cid:14)ct + uict

This basic strategy compares deviations from trends of capital and risk-taking between a

treatment and a control group of banks during the announcement period, the coe(cid:14)cient of

interest is the ! of the interaction term D(Announcement) (cid:3) D(T reated). If the coe(cid:14)cient

is statistically signi(cid:12)cant then the trend deviations across the treated and control groups are

diverging and the common trend assumption across the two groups would not hold.

The results are presented in Figure 4 for the level of CET1 capital and in Figure 5 when

the dependent variable is the risk density. The left panels of each (cid:12)gure depict the ! coe(cid:14)cient

of the interaction term D(Announcement) (cid:3) D(T reated) for di(cid:11)erent starting periods of the

estimation sample similar to the reasoning of Table 3. The right panels of each (cid:12)gure plot the

(cid:12) coe(cid:14)cient for the level of the SM CRict as speci(cid:12)ed in Equation 4.

For either capital or risk indicators, the evidence indicates that there is generally no statis-

tically signi(cid:12)cant di(cid:11)erence in the reaction of treatment banks during the announcement period

and in comparison to the non-treated group. For the risk density, and only when the sample

begins in 2006, the impact is signi(cid:12)cant at the ten percent con(cid:12)dence level. The impact of

the SMCR in the implementation period, i.e. after 2014, remains in line with the coe(cid:14)cient

found in the baseline Tables 5 and 6. In other words, the impact of the SMCRs is not absorbed

or curtailed by the introduction of a dummy that captures the announcement period and we

can conclude that the estimated positive relationship between higher capital requirements and

higher capital and risk-taking is robust to the inclusion of announcement periods.50

50Alternative later periods may be considered as the beginning of the new macroprudential framework in the
EU as for instance since the EU Commission public consultation on the new CRD-IV in 2010Q2. Nevertheless,
similar results to those presented in Figures 4 and 5 are obtained when the announcement period is set in

ECB Working Paper Series No 2449 / July 2020347 Robustness: Endogeneity

The exogeneity of the policy change may not be warranted even if the change in the SMCR is

external to the bank decision making process. A variable of interest that is external, but not

exogeneous, will not yield consistent estimates of the parameter of interest, see Deaton (2010).

Macroprudential capital requirements are set for each bank individually at national level by

its own macroprudential authority. This leaves discretion to national regulators and could cause

the introduction of macroprudential capital requirements to be endogenous if the Member State

behaves strategically and wants to protect (weaker) domestic banks. If it does, then undercap-

italised banks may have a favorable treatment and the level of capital will be determining the

SMCR introducing reverse causality. As described earlier, signi(cid:12)cant regulatory provisions are

aimed at ensuring that this does not happen, nevertheless a test for this possibility is warranted.

This section presents a simple procedure to test for the possibility of endogeneity by looking

if the outcome variables of interest are correlated with the SMCR exploiting the within variation

of the (cid:12)xed e(cid:11)ect estimator. The following equation is estimated:

SM CRict = (cid:11)i +  Yict (cid:3) D2010Q1(cid:0)2013Q4 + ln Xict(cid:0)1(cid:13) + [(cid:30)i (cid:1) t]

(5)

+ (cid:31)D2010Q1(cid:0)2013Q4 + (cid:16)Yict + (cid:14)ct + uict

In Equation 5, the systemic macroprudential capital requirement is regressed separately on

the series of outcomes of interest Yict representing in turn the capital and risk variables used as

dependent variables throughout the paper. The dummy variable D2010Q1(cid:0)2013Q4 is turned on in

the period prior to the commencement of the phasing-in of the SMCR in the EU, that is prior

to 2014. The speci(cid:12)cation controls for bank-level characteristics ln Xict(cid:0)1 and a bank trend (cid:30)i (cid:1) t

is included to control for diverging trends across treated and control groups, as before, (cid:11)i are

bank (cid:12)xed e(cid:11)ects and (cid:14)ct are country-time (cid:12)xed e(cid:11)ects.

The main coe(cid:14)cient of interest in the equation is the coe(cid:14)cient of the interaction term

Yict (cid:3)D2010Q1(cid:0)2013Q4 estimating the relationship between pre-determined outcome variables prior

to the treatment period and the realised capital requirement after 2014. If the coe(cid:14)cient   is

signi(cid:12)cant then bank capital situation prior to the phasing-in may have in(cid:13)uenced the setting

of the SMCR questioning the exogeneity assumption.

If this is not the case, then results

2010Q2. Results are available from the author. The document of the consultation is available at this link.

ECB Working Paper Series No 2449 / July 202035will substantiate the assumption on the absence of strategic targeting by policy makers. For

simultaneity bias then it is useful to observe the (cid:16) coe(cid:14)cient of Yict.

Results are presented in Table 11 for capital and in Table 12 for risk. In both tables any

sign of correlation between outcome variables and the SMCR vanishes as bank speci(cid:12)c trends

are included in the regression. For capital, in Table 11 Columns (1)-(2) the CET1 ratio is not

related with the SMCR even without bank trends. The level of CET1 in Columns (3), however,

presents negative relation prior to 2014, as a potential sign of favouritism towards weaker banks.

In the robustness check, this disappears once bank trends are controlled for in Column (4). For

risk, in Table 12 Columns (1), (3) and (5) may suggest endogeneity, this however is not robust

to inclusion of bank speci(cid:12)c trends in Columns (2), (4) and (6). Importantly, for the risk density

which is the main variable of interest, the simultaneous bias does not appear to be a problem

even when bank trend are not controlled for in Column (3). Overall, the results indicate once

more the importance of bank speci(cid:12)c trend in the estimation and the relevance of the tests

presented and discussed in Section 6.1.

A similar result is presented in Table 13. The table presents a simple regression of the

average of the SMCR by bank after 2014 on the average prior to 2014 of the bank-level outcome

variables. The aim is to investigate whether the average of the outcome variables before 2014

correlates with the regulation after 2014.

If there is positive correlation then is likely that

the SMCR is endogenous since bank capital and risk-taking prior to the implementation of

the macroprudential capital requirements would have a(cid:11)ected the discretionary decision of

national competent authorities in setting the capital requirement. Note that by taking bank-

level averages prior and post the start of the implementation phase the bank-level trend is

controlled for by construction.

Results in Table 13 con(cid:12)rm that there is no statistical relationship between the capital

and risk position of the bank prior to 2014 and the subsequently levied capital requirements.

Column (1) indicates that there is some positive relation between the average CET1 ratio before

2014 and the later SMCR, however this relation is positive suggesting that stronger (and not

weaker) capitalised banks may have been subject to harsher capital requirements. Nevertheless

this relationship is not robust to the inclusion of country dummies. Similarly, from Column (3)

to (8) there is no systematically strong evidence of capital and risk prior to 2014 in(cid:13)uencing the

setting of the SMCR post policy implementation. The absence of signi(cid:12)cant correlation is an

ECB Working Paper Series No 2449 / July 202036encouraging sign since it shows that SMCR were not systematically levied on less capitalised

banks or banks with higher risk densities.

8 Solvency: Probability of Default

This section explores the impact of a hike in capital requirements on the solvency of (cid:12)nancial

institutions. As presented in Section 4, the tightening of the capital requirements has two

opposing e(cid:11)ects: i) results of Section 4.1 would suggest that higher capital requirements would

make banks more solvent and, consequently, reduce their probability of default; ii) at the same

time, the (cid:12)ndings in Section 4.2 indicate that higher capital requirements may lead to moral

hazard and increased risk-taking, thus weakening banks solvency. Hence, the net impact of the

two opposing e(cid:11)ects on banks’ probability of default is ambiguous.

In order to shed light on which of the two opposing e(cid:11)ects on solvency is stronger, this section

uses credit ratings as a gauge of banks’ probabilities of default. The advantage of using credit

ratings is that regulatory action is not directly taken into account but only (cid:12)nancial performance

and solvency is assessed, a stringent regulation is encompassed in the evaluation only to the

extent that banks react to higher capital requirement. Nevertheless, a caveat is that they are

provide only probabilities of default through the (cid:12)nancial cycle and may have lags in their

setting. The default probabilities are extrapolated from bank issuer ratings provided by three

major rating agencies.51 The probabilities of default are obtained by mapping and converting of

alphanumeric ratings using publicly available conversion tables on rating agencies websites. The

constructed distance to default variable informs about the solvency of a bank by estimating the

default probability over the next, two, three, four and (cid:12)ve years. It provides timely information

re(cid:13)ecting current market perception, and summarises market-wide information on the drivers

of default probability.

Similarly to Section 3.2, the phasing-in of SMCR in the EU is used as a tool for a controlled

comparison whereby di(cid:11)erent institutions across Europe are subject to heterogeneous intensity

of capital requirements. However, this section departs fundamentally from the previous esti-

mates since the dependent variable is now part of the reaction function of market agents to

higher capital requirements and not a reaction of the bank itself. Results are presented in Table

14. The table presents in each column the evidence for a di(cid:11)erent probability of default horizon.

51The rating agencies are Fitch, Moody’s and S&Ps

ECB Working Paper Series No 2449 / July 202037Estimates are broken down by the size of the bank. Notice that the sample size decreased due

to the limited availability of ratings for some banks with respect to previous speci(cid:12)cations, the

consequence is that results should be interpreted with caution for external consistency.52

The evidence suggests that the market reaction to higher capital requirements is bringing

some bene(cid:12)ts to medium and large banks in terms of reduced probability of default but only

relative to smaller banks. The relative impact is slightly greater for banks with total assets

above EUR 100 millions, and increasing in the probability of default horizon for both medium

and large banks. Depending on the maturity horizon of the probability of default, medium

and large banks have a lower probability of default with respect to small banks, i.e. 1.3-2.0

percentage points lower for a one percentage point increase in capital requirements. This result

suggests that, relative to small banks, for medium and large banks the e(cid:11)ect of the increase in

CET1 capital is stronger than the risk-taking channel.

Nevertheless, the marginal e(cid:11)ects for medium and large banks, while having a negative sign,

is not statistically signi(cid:12)cant as shown in the second panel of Table 14. For instance for the

one year horizon, the marginal e(cid:11)ect of a one percentage point increase in capital requirements

for large banks is -0.938 (st.dev. 0.900). While this indicates that the rating agencies may tend

to assess the capital increase channel to be stronger, this assessment cannot be statistically

corroborated. Similar results are obtained for the marginal e(cid:11)ects at di(cid:11)erent time horizons

and for medium sized banks.53

The evidence on leads to conclude that the increase of capital requirements does not im-

prove banks’ probability of default in absolute terms.

In other words, the positive e(cid:11)ect of

accumulating more equity capital is counterbalanced by the negative substitution e(cid:11)ect toward

more riskier assets, the overall net e(cid:11)ect on solvency is zero. This raises a concern for the policy

maker since the improved resilience achieved by demanding higher capital requirements can be

crowded-out by an increase in risk-taking.

52An alternative market based measure of banks’ solvency are CDS prices. Nevertheless, contrary to the
ratings, the scope of application of CDS pricing is very limited in our sample since CDS prices are generally
available only for some of the large systemic banks. In our sample, this translates to 49 banks with available
CDS prices which is much less than the number of clusters in previous regressions. The use of CDS prices would
thus create a sample composition bias relative to previous estimates.

53This is further con(cid:12)rmed by a baseline regression of the probability of default without the dummy for size,
and those broken down by the distance from the OCR, the net interest income and wholesale funding dummies.
All of them do not have statistically signi(cid:12)cant results at standard con(cid:12)dence levels, these speci(cid:12)cations are
available from the author.

ECB Working Paper Series No 2449 / July 2020389 Conclusions

The paper presents empirical evidence on the reaction of systemically important EU banks to

a phased-in capital requirement regulation. Contrary to one-o(cid:11) leverege requirement exercises,

the evidence indicates that the impact contributed to a substantial increase of equity capital

in the EU banking sector. As such, the resilience of European systemically important banks

increased. At the same time, some unintended consequences appear. In a phased-in set up, the

increased resilience comes at the cost of a higher incentive to exploit moral hazard by banks,

which shift the asset composition towards more risky assets.

The (cid:12)ndings add to more recent evidence on EU capital exercises Gropp et al. (2018) which

is based on a one-o(cid:11) capital increase. Their contribution does not (cid:12)nd evidence of a increase in

bank equity but a substantial deleveraging e(cid:11)ect through lending reduction. As a complement,

this paper concludes that the pace of introduction of capital requirements is fundamental if

we want to fully understand the banks’ responses. On one side, a one-o(cid:11) fast shock to capital

requirements may have negative consequences on the real economy; on the other side, a more

gradual approach leaves time for banks to plan their funding needs promoting an increase in

equity, but also risk-taking.

The paper documents that risks-taking is particularly relevant for less pro(cid:12)table and large

banks, suggesting that gambling for resurrection and agency costs may be promote moral

hazard. At the same time, banks adopting the IRB approach mitigate substantially the increase

in risk-taking, and wholesale-funded banks have lower risk-taking which may be explained by

a strategic need to reduce the observable risk in view of their already fragile funding model.

The paper then investigates the net e(cid:11)ect of improved resilience at the cost of higher risk-

taking on the probability of default of banks as measured by external ratings. It documents

that the positive e(cid:11)ect of accumulating more equity capital is counterbalanced by the negative

substitution e(cid:11)ect toward more riskier assets.

In other words, the increased risk-taking is

perceived to compensate the positive results of higher capital, such that the overall marginal

e(cid:11)ect on banks’ probabilities of default results insigni(cid:12)cant.

This raises the question as to how regulation should aim at constraining bank’s risk-taking

behaviour. While this is not in the scope of this paper, the main message is that the pace of

introduction of capital requirement has an important role when assessing how banks react to

leverage restrictions. If the objective is to increase high quality bank capital such as equity,

ECB Working Paper Series No 2449 / July 202039then a phased-in approach can be optimal. The caveat is that the improved absorption capacity

may induce banks to exploit moral hazard and increase risk-taking.

To limit the risk-taking behavior, the regulator can contemplate, for instance, to introduce

a cap on the risk weight densities as a tool to limit the incentives for moral hazard. The cap

on risk-weight densities would be automatically complemented by the leverage ratio if banks

would try to increase their assets with low risk-weight investments such as sovereign or corporate

bonds. Future research is planned to assess this interplay with a rigorous model.

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ECB Working Paper Series No 2449 / July 202047Figure 1: Pro(cid:12)tability and Wholesale Funding

Note: the bar chart show some pro(cid:12)tability measures broken down by above and below median
wholesale funding reliance. For pro(cid:12)tability it is used net overall income, net interest income (NII) and
total interest income. A standard test of mean di(cid:11)erence is run separately for the three income variables
in a pooled panel. They are regressed on a dummy for wholesale funding above median. For net
interest income and interest income the estimated (cid:12) coe(cid:14)cients are both signi(cid:12)cant and respectively
0.45 (s.d. 0.091) and 1.67 (s.d. 0.182) where standard errors are robust to heteroschedasticity and
serial correlation. The coe(cid:14)cient on mean di(cid:11)erence for net income is 0.04 (s.e. 0.035) and thus not
statistically signi(cid:12)cant.

01234Bln.Below median WSFAbove median WSFNet incomeNIIInterest incomeECB Working Paper Series No 2449 / July 202048Figure 2: The longer term trend of Capital

Note: The plot illustrates the evolution of the average CET1 and Total capital levels across the
systemically important banks in the EU. The red line illustrates the average level of the SMCR across
the EU G-SIBs and O-SIBs.

0.511.522.5% RWA81012141618Blns.2009q12011q12013q12015q12017q1CET1Total CapitalMacroprudential Capital Req., rhsECB Working Paper Series No 2449 / July 202049Figure 3: Robustness: Bank Trends and Longer T

Note: The (cid:12)gure shows the evolution of the (cid:12) coe(cid:14)cient for the level of the SMCR in equation 3 when
the estimation sample is progressively reduced by one year. All models have bank trends included
in the speci(cid:12)cation. On the horizontal axis every point represents the staring year of the respective
estimation sample, for each sample the last quarter is 2017Q3. Moving to the right of each plot the
sample period shrinks by one year each time and hence there are less observations available to compute
bank-level trends. On the y-axis, the coe(cid:14)cients represent the impact of a hike in the macroprudential
capital requirement. Vertical bars represent con(cid:12)dence interval at 10% signi(cid:12)cance level. Standard
errors are clustered at bank-level and robust for serial correlation and heteroschedasticity.

-0.29-0.34-0.41-0.37-1.00-0.500.500.00Sample begins at yeary2006y2007y2008y2009CET1Ratio0.080.070.060.060.050.100.150.00Sample begins at yeary2006y2007y2008y2009CET10.080.080.070.080.050.100.150.00Sample begins at yeary2006y2007y2008y2009Tot. Capital0.070.080.070.070.050.100.150.00Sample begins at yeary2006y2007y2008y2009RWA4.114.154.483.912.004.006.008.000.00Sample begins at yeary2006y2007y2008y2009RWA/Assets-0.01-0.01-0.01-0.01-0.03-0.02-0.010.010.00Sample begins at yeary2006y2007y2008y2009Tot. AssetsECB Working Paper Series No 2449 / July 202050Figure 4: Robustness: Announcement e(cid:11)ect of EU macroprudential policy on CET1 Capital

Note: The graph shows the evolution of ! and (cid:12) coe(cid:14)cients of equation 4 when the dependent variable
is the level of CET1 Capital. The estimation sample is progressively reduced by one year, notice that
for each sample the last quarter used in all regressions is 2017Q3. The announcement period is
represented by a dummy for the period between 2009Q4-2013Q4, that is since the publication of
the EU Commission Directive 2009/111/EC of the European Parliament and of the Council of 16
September pre-announcing a change in macroprudential regulation in the EU. Vertical bars represent
con(cid:12)dence interval at 10% signi(cid:12)cance level. Standard errors are clustered at bank-level and robust
for serial correlation and heteroschedasticity. Similar results are obtained when the regressions control
for bank speci(cid:12)c trends, these results are available from the author.

0.070.060.030.020.090.090.090.09-0.100.100.200.00Sample begins at yearSample begins at yeary2006y2007y2008y2009y2006y2007y2008y2009D(announcement period)*D(Treated)SMCRCoefficientCET1 CapitalECB Working Paper Series No 2449 / July 202051Figure 5: Robustness: Announcement e(cid:11)ect of EU macroprudential policy on the risk density

Note: The graph shows the evolution of ! and (cid:12) coe(cid:14)cients of equation 4 when the dependent
variable is the risk density (i.e. RWA/Assets). The estimation sample is progressively reduced by one
year, notice that for each sample the last quarter used in all regressions is 2017Q3. The announcement
period is represented by a dummy for the period between 2009Q4-2013Q4, that is since the publication
of the EU Commission Directive 2009/111/EC of the European Parliament and of the Council of 16
September pre-announcing a change in macroprudential regulation in the EU. Vertical bars represent
con(cid:12)dence interval at 10% signi(cid:12)cance level. Standard errors are clustered at bank-level and robust
for serial correlation and heteroschedasticity. Similar results are obtained when the regressions control
for bank speci(cid:12)c trends, these results are available from the author.

2.571.74-0.40-2.297.517.386.916.71-5.005.0010.000.00Sample begins at yearSample begins at yeary2006y2007y2008y2009y2006y2007y2008y2009D(announcement period)*D(Treated)SMCRCoefficientRWA/AssetsECB Working Paper Series No 2449 / July 202052Table 1: Macroprudential Capital Requirements in Europe

Note: The table summarises the four macroprudential capital requirements introduced in EU in 2014.
The CCoB and the CCyB are country-level capital requirements levied on all banks within a country.
The capital requirement for systemically important banks and the SRB bu(cid:11)er are applied at bank-
level. MS stands for EU Member States, Norway, despite not being an EU Member State implemented
the EU capital based macroprudential regulation.

Buffer CRD Article Level Scope Capital conservation buffer (CCoB)  Art. 129 The objective is to conserve the bank’s capital. Mandatory capital buffer equal to 2.5% of RWAs, this implies a minimum CET1 ratio requirement is 7% Country-level  (i.e. same for all banks within MS) Counter-cyclical  Capital buffer (CCyB)  Art. 130, 135-140 The purpose of this buffer is to counteract the effects of the economic cycle. Buffer rate calibrated on MS credit-to-GDP gap. G-SIB and O-SIB Systemically Important Banks buffer (SIB) Art. 131  For banks that are identified by the relevant authority as systemically important: 1 ≤ x ≤ 3.5% of RWAs for G-SII 0 ≤ x ≤ 2.0% of RWAs for O-SII Bank-level  (i.e. set at bank level within MS) Systemic risk buffer (SRB) Art. 133 and 134 To prevent and mitigate long term non-cyclical systemic or macro-prudential risks: 0 ≤ x ≤ 5.0% of RWA Above 5% the MS must be authorized by Commission ECB Working Paper Series No 2449 / July 202053Table 2: Direction of Omitted Variable Bias

The table illustrates the sign of the bias due to omitted variable in a simple bivariate model
where x1 is the treatment variable and x2 is the omitted variable:

y = (cid:12)1x1 + (cid:12)2x2 + u

Corr(x1; x2 > 0) Corr(x1; x2 < 0)

(cid:12)2 > 0

(cid:12)2 < 0

Bias > 0

Bias < 0

Bias < 0

Bias > 0

ECB Working Paper Series No 2449 / July 202054Table 3: Descriptive statistics by year: Capital, Requirements and Distance

The table summarize the evolution over time of simple means (Panel A), means conditional on treat-
ment (Panel B) and means for non-treated banks (Panel C) o: the level of the SMCR, the level of the
OCR, the level of the CET1 ratio, the distance from the OCR and the level of the total capital ratio.
Note that the OCR for the control group increases after 2014 due to phasing in of the CCoB and
CCyB bu(cid:11)ers which change at country level and are absorbed in the equation 1 by the country-time
(cid:12)xed e(cid:11)ects. Standard deviations in parenthesis.

Panel A: Simple Means:

SMCR (%)

Overall CET1 Req. (OCR) (%)

CET1 Ratio (%)

Distance from OCR (%)

Tot. Capital Ratio (%)

2010

2011

2012

2013

2014

2015

2016

2017

0
(0)
4.500
(0)
10.70
(2.872)
7.190
(6.295)
15.26
(7.175)

0
(0)
4.500
(0)
11.51
(3.314)
8.043
(6.536)
15.92
(8.050)

0
(0)
4.500
(0)
12.58
(3.848)
8.523
(6.432)
16.12
(7.288)

0.014
(0.170)
4.533
(0.384)
13.90
(4.752)
9.806
(6.935)
17.44
(8.015)

0.149
(0.618)
5.174
(1.414)
14.90
(5.372)
9.993
(6.192)
17.83
(6.800)

0.442
(1.032)
5.800
(2.014)
16.16
(6.895)
10.33
(6.622)
19.08
(7.584)

0.656
(1.070)
6.510
(1.824)
17.09
(6.998)
10.50
(6.620)
20.17
(8.692)

0.870
(1.100)
7.124
(1.705)
17.58
(7.415)
10.47
(7.270)
20.83
(10.59)

Observations

820

820

820

820

820

820

820

615

Panel B: Means Conditional on Treatment

SMCR (%)

Overall CET1 Cap. Req. (OCR) (%)

CET1 Ratio (%)

Distance from OCR (%)

Tot. Capital Ratio (%)

0
(0)
4.500
(0)
11.36
(2.858)
7.416
(4.408)
15.50
(5.077)

0
(0)
4.500
(0)
11.93
(2.921)
8.052
(4.612)
15.96
(5.508)

0
(0)
4.500
(0)
12.81
(3.278)
8.676
(4.352)
16.21
(4.972)

0.020
(0.199)
4.545
(0.447)
14.16
(4.179)
9.803
(4.726)
17.26
(5.149)

0.203
(0.712)
5.349
(1.564)
15.07
(5.327)
9.938
(5.556)
17.80
(5.761)

0.600
(1.163)
6.067
(2.203)
16.39
(7.264)
10.27
(6.904)
19.15
(7.609)

0.892
(1.160)
6.849
(1.967)
17.15
(7.301)
10.24
(6.766)
20.08
(8.090)

1.184
(1.130)
7.509
(1.809)
17.63
(7.029)
10.24
(6.847)
20.76
(7.988)

Observations

604

604

604

604

604

604

604

453

Panel C: Means Non-treated Banks

SMCR (%)

Overall CET1 Cap. Req. (OCR) (%)

CET1 Ratio (%)

Distance from OCR (%)

Tot. Capital Ratio (%)

0
(0)
4.500
(0)
8.668
(1.752)
6.505
(10.05)
14.52
(11.44)

0
(0)
4.500
(0)
10.26
(4.026)
8.020
(10.20)
15.82
(12.93)

0
(0)
4.500
(0)
11.88
(5.175)
8.084
(10.29)
15.85
(11.61)

0
(0)
4.500
(0)
13.20
(5.996)
9.812
(10.85)
17.95
(13.03)

0
(0)
4.685
(0.656)
14.46
(5.482)
10.14
(7.657)
17.94
(9.143)

0
(0)
5.056
(1.042)
15.54
(5.795)
10.48
(5.828)
18.90
(7.529)

0
(0)
5.565
(0.778)
16.96
(6.205)
11.17
(6.192)
20.42
(10.13)

0
(0)
6.046
(0.563)
17.47
(8.244)
11.02
(8.196)
21.01
(15.51)

Observations

216

216

216

216

216

216

216

162

ECB Working Paper Series No 2449 / July 202055Table 4: Descriptive statistics: EU G-SIB and O-SIB Financial Accounts

The table summarizes descriptive statistics of the balance sheet variables used in the paper. The
sample is the sample of designated Systemically Important Banks (SIB) in the EU as described in
section 3.1. For each variable the simple mean, standard deviation, the median, the 25th and 75th
quintiles and the maximum are shown. Time period: 2006Q1-2017Q3. The data source is SNL
Financials.

Capital Position:

CET1 Ratio(%)
Distance from OCR (%)
Tot. Capital Ratio(%)
Leverage Ratio (%)

Risk:

RWA (bln.)
RWA/Assets(%)

Assets:

Mean

Std.dev.

p25

p50

p75

Max.

13.64
8.34
16.58
7.60

6.06
6.56
7.87
4.07

10.20
4.84
12.28
4.66

12.60
7.29
15.00
6.86

15.70
10.14
18.30
9.80

74.93
82.36
111.64
32.98

70.37
50.06

139.15
21.41

5.54
33.14

19.26
50.29

60.89
64.91

1129.63
261.02

Tot. Assets (bln.)
Gross Loans/Assets(%)
Net Loans/Assets (%)
Securities Holdings/Assets (%)
Total Cash/Assets(%)

196.15
58.29
54.71
25.13
15.29

379.97
19.62
19.06
16.92
11.86

8.58
48.08
44.77
13.86
6.94

39.19
62.57
59.03
22.15
12.23

198.37
71.59
67.53
32.22
20.58

2506.28
121.83
105.21
99.56
93.64

Securities Holdings:

Securities Held for trading/Assets (%)
Securities Available for Sale/Assets (%)
Securities Held to Maturity/Assets (%)

7.55
9.34
2.68

Funding Structure:

Deposits/Assets (%)
Total Wholesale Funding/Assets(%)
Debt/Assets (%)

Interconnectedness:

Loans to Banks/Assets (%)
Tot. HFT Assets/Assets (%)
Securities OTC derivatives/Assets (%)
Total cash balance at C.B./Assets (%)

48.45
33.23
17.92

10.01
8.34
5.56
5.34

9.50
8.29
5.63

22.96
21.95
18.13

11.23
10.63
10.01
6.08

1.17
2.59
0.00

4.15
8.13
0.24

10.08
13.88
2.51

31.46
16.69
4.62

51.25
29.29
13.08

66.51
45.48
24.69

3.07
1.23
0.37
1.13

6.21
4.22
1.84
3.09

12.95
11.40
6.12
7.19

65.40
57.99
89.18

98.31
95.82
95.82

92.65
67.32
74.30
43.88

Pro(cid:12)tability:
ROA (%)
Cost/Income(%)

0.32
58.64

1.14
21.87

0.10
47.83

0.31
56.14

0.71
65.60

6.56
390.50

ECB Working Paper Series No 2449 / July 202056Table 5: The Impact on Capital

The table summarises the baseline reduced form speci(cid:12)cation of the change in systemic macropru-
dential capital requirements (SMCR) on bank capital in Columns (1)-(3). Columns (4)-(6) present
the heterogenous impact by bank distance from the overall CET1 capital requirements (OCR). All
dependent variables that are measured in levels, i.e. CET1 capital in columns (2) and (5) and total
capital in columns (3) and (6), are transformed using natural logarithms. Bank-level control variables
are as speci(cid:12)ed in equation 1. Time period: 2010Q1-2017Q3. Standard errors are shown in parenthesis
clustered at bank-level, robust to heteroscedasticity and serial correlation. FE stands for (cid:12)xed-e(cid:11)ects.
Stars indicate statistical signi(cid:12)cance levels: *** p<0.01, ** p<0.05, * p<0.10.

Non-Binding

(1)
CET1 Ratio
(p.p.)

(2)
CET1
(ln)

(3)

(4)

Tot. Capital CET1 Ratio

(ln)

(p.p.)

Binding

(5)
CET1
(ln)

(6)
Tot. Capital
(ln)

SMCR

-0.054
(0.359)

0.089
(0.027)***

0.081
(0.027)***

0.834
(0.402)**

0.177
(0.036)***

0.116
(0.042)***

SMCR (cid:2)
2pp< OCR distance <5pp

SMCR (cid:2)
5pp< OCR distance <10pp

SMCR (cid:2)
OCR distance >10pp

Bank Controls
Bank FE
Country-quarter FE

Obs.
N. clusters
R2

yes
yes
yes

3174
137
0.688

yes
yes
yes

3174
137
0.672

yes
yes
yes

3174
137
0.663

-0.143
(0.215)

0.003
(0.214)

0.087
(0.232)

yes
yes
yes

3173
137
0.800

-0.042
(0.022)*

-0.047
(0.024)*

-0.053
(0.024)**

yes
yes
yes

3173
137
0.763

-0.003
(0.026)

-0.012
(0.031)

-0.013
(0.031)

yes
yes
yes

3173
137
0.695

ECB Working Paper Series No 2449 / July 202057Table 6: The Impact on Risk

The table illustrates the baseline reduced form speci(cid:12)cation of the change in systemic macroprudential
capital requirements (SMCR) on banks’ risk-taking and assets in Columns (1)-(3). Columns (4)-(6)
show the heterogenous impact by bank distance from the overall CET1 capital requirements (OCR).
All dependent variables that are measured in levels, i.e. CET1 capital in column (2) and Risk-weighted
Assets in column (3), are transformed using natural logarithms. Bank-level control variables are as
speci(cid:12)ed in equation 1. Time period: 2010Q1-2017Q3. Standard errors are shown in parenthesis
clustered at bank-level, robust to heteroscedasticity and serial correlation. FE stands for (cid:12)xed-e(cid:11)ects.
Stars indicate statistical signi(cid:12)cance levels: *** p<0.01, ** p<0.05, * p<0.10.

Non-Binding

(1)
RWA
(ln)

(2)

(3)

RWA/Assets Tot. Assets

(p.p.)

(ln)

(4)
RWA
(ln)

Binding

(5)

(6)

RWA/Assets Tot. Assets

(p.p.)

(ln)

SMCR

0.101
(0.023)***

6.873
(1.388)***

-0.007
(0.008)

0.065
(0.026)**

6.073
(1.455)***

SMCR(cid:2)
2pp< OCR distance <5pp

SMCR(cid:2)
5pp< OCR distance <10pp

SMCR(cid:2)
OCR distance >10pp

Bank Controls
Bank FE
Country-quarter FE

Obs.
N. clusters
R2

yes
yes
yes

3277
137
0.749

yes
yes
yes

3277
137
0.646

yes
yes
yes

3277
137
0.875

0.002
(0.009)

0.011
(0.011)

0.013
(0.011)

yes
yes
yes

3195
137
0.768

-0.139
(0.419)

-0.219
(0.533)

0.242
(0.523)

yes
yes
yes

3195
137
0.677

-0.016
(0.012)

-0.002
(0.006)

0.005
(0.005)

0.004
(0.005)

yes
yes
yes

3195
137
0.875

ECB Working Paper Series No 2449 / July 202058Table 7: The Impact on Capital: the role of bank size and internal rating approach (IRB)

The table summarises the reduced form speci(cid:12)cation of the change in systemic macroprudential capital
requirements (SMCR) on bank capital. Columns (1)-(3) present the heterogenous impact by bank
size and distance from the overall CET1 capital requirements (OCR). Columns (4)-(6) show the
heterogeneous impact by IRB and distance from the OCR. Bank-level control variables are as speci(cid:12)ed
in equation 1. Time period: 2010Q1-2017Q3. Standard errors are shown in parenthesis clustered at
bank-level, robust to heteroscedasticity and serial correlation. FE stands for (cid:12)xed-e(cid:11)ects. Stars
indicate statistical signi(cid:12)cance levels: *** p<0.01, ** p<0.05, * p<0.10.

(1)
CET1 Ratio
(p.p.)

0.214
(0.680)

0.131
(0.463)

0.701
(3.232)

-0.506
(0.655)

Size

(2)
CET1
(ln)

0.133
(0.049)***

-0.027
(0.024)

0.458
(0.503)

-0.043
(0.062)

(3)

(4)

Tot. Capital CET1 Ratio

(ln)

(p.p.)

IRB

(5)
CET1
(ln)

(6)
Tot. Capital
(ln)

-0.006
(0.543)

0.164
(0.503)

0.149
(0.046)***

0.099
(0.049)**

-0.049
(0.035)

-0.025
(0.038)

0.057
(0.056)

-0.006
(0.031)

0.013
(0.420)

-0.055
(0.070)

-0.683
(3.213)

-0.455
(0.504)

0.006
(0.421)

-0.300
(0.622)

0.011
(0.056)

0.065
(0.068)

SMCR

SMCR(cid:2)
OCR distance >2pp

SMCR(cid:2)
20bln.< Tot.Ass <100bln

SMCR(cid:2)
Tot.Ass>100bln.

SMCR(cid:2)
OCR distance >2pp (cid:2)
20bln.< Tot.Ass <100bln

SMCR(cid:2)
OCR distance >2pp (cid:2)
Tot.Ass>100bln.

SMCR(cid:2) IRB

SMCR(cid:2)
OCR distance >2pp (cid:2) IRB

Bank Controls
Bank FE
Country-quarter FE

Obs.
N. clusters
R2

yes
yes
yes

3312
144
0.692

yes
yes
yes

3312
144
0.711

yes
yes
yes

3312
144
0.662

-0.229
(0.495)

0.051
(0.555)

yes
yes
yes

3298
143
0.696

-0.075
(0.056)

0.065
(0.056)

yes
yes
yes

3298
143
0.706

-0.100
(0.069)

0.097
(0.070)

yes
yes
yes

3298
143
0.656

ECB Working Paper Series No 2449 / July 202059Table 8: The Impact on Risk: the role of bank size and internal rating approach (IRB)

The table illustrates the estimates of a reduced form speci(cid:12)cation for the impact of the change in
systemic macroprudential capital requirements (SMCR) on banks’ risk-taking and assets. Columns
(1)-(3) show the heterogenous impact by bank size. Columns (4)-(6) show the heterogenous impact
by bank size and IRB. Variables measured in levels, i.e. risk-weighted Assets and total assets, are
transformed using natural logarithms. Bank-level control variables are as speci(cid:12)ed in equation 1. Time
period: 2010Q1-2017Q3. Standard errors are shown in parenthesis clustered at bank-level, robust
to heteroscedasticity and serial correlation. FE stands for (cid:12)xed-e(cid:11)ects. Stars indicate statistical
signi(cid:12)cance levels: *** p<0.01, ** p<0.05, * p<0.10.

Size

(2)

(3)

RWA/Assets Tot.Assets

(p.p.)

(ln)

(1)
RWA
(ln)

Size and IRB Banks

(4)
RWA
(ln)

(5)

(6)

RWA/Assets Tot.Assets

(p.p)

(ln)

SMCR

SMCR(cid:2)
20bln.< Tot.Ass <100bln.

SMCR(cid:2)
Tot.Ass>100bln.

SMCR(cid:2) IRB

SMCR(cid:2) IRB (cid:2)
20bln.< Tot.Ass <100bln.

SMCR(cid:2) IRB (cid:2)
Tot.Ass >100bln.

0.071
(0.036)*

3.944
(1.731)**

0.020
(0.019)

0.022
(0.024)

1.864
(1.073)*

2.079
(1.149)*

-0.004
(0.018)

-0.002
(0.010)

0.066
(0.043)

0.030
(0.024)

4.345
(1.904)**

2.963
(1.224)**

-0.001
(0.011)

0.181
(0.049)***

5.324
(2.149)**

0.001
(0.014)

-0.009
(0.031)

-0.034
(0.901)

-1.498
(1.469)

-0.157
(0.042)***

-3.595
(1.802)**

Bank Controls
Bank FE
Country-quarter FE

Obs.
N. clusters
R2

yes
yes
yes

3277
137
0.750

yes
yes
yes

3277
137
0.648

yes
yes
yes

3277
137
0.878

yes
yes
yes

3277
137
0.756

yes
yes
yes

3277
137
0.655

-0.020
(0.019)

-0.006
(0.010)

0.004
(0.018)

0.008
(0.004)*

0.008
(0.010)

-0.002
(0.016)

yes
yes
yes

3277
137
0.878

ECB Working Paper Series No 2449 / July 202060Table 9: Pro(cid:12)tability, Funding and Leverage

The table illustrates the reduced form speci(cid:12)cation for the impact of the change in systemic macropru-
dential capital requirements (SMCR) on banks’ risk-taking. Columns (1)-(2) show the heterogenous
impact by bank pro(cid:12)tability as measured with a dummy equal to one if the bank has above the sample
median net interest income (NII). Columns (3)-(4) show the heterogenous impact by wholesale funding
(WSF) as captured by a dummy equal to one for above median WSF. Columns (5)-(6) illustrate the
impact by bank leverage as measured by a dummy above median for the ratio of Tier1 capital on total
assets (LR). Bank-level control variables are as speci(cid:12)ed in equation 1. Time period: 2010Q1-2017Q3.
Standard errors are shown in parenthesis clustered at bank-level, robust to heteroscedasticity and
serial correlation. FE stands for (cid:12)xed-e(cid:11)ects. Stars indicate statistical signi(cid:12)cance levels: *** p<0.01,
** p<0.05, * p<0.10.

NII

Wholesale Funding

Leverage Ratio

(1)
RWA
(ln)

(2)
RWA/Assets
(p.p.)

(3)
RWA
(ln)

(4)

(5)
RWA/Assets RWA RWA/Assets
(ln)

(p.p.)

(p.p.)

(6)

SMCR

SMCR(cid:2)
>20bln.< Tot.Ass <100bln.
SMCR(cid:2)
Tot.Ass>100bln.

SMCR(cid:2) NII

SMCR(cid:2) NII (cid:2)
>20bln.< Tot.Ass <100bln.
SMCR(cid:2) NII (cid:2)
Tot.Ass >100bln.

SMCR(cid:2) WSF

SMCR(cid:2) WSF (cid:2)
>20bln.< Tot.Ass <100bln.
SMCR(cid:2) WSF (cid:2)
Tot.Ass >100bln.

SMCR(cid:2) LR

SMCR(cid:2) LR (cid:2)
>20bln.< Tot.Ass <100bln.
SMCR(cid:2) LR (cid:2)
Tot.Ass >100bln.

Bank Controls
Bank FE
Country-quarter FE

Obs.
N. clusters
R2

0.066
(0.032)**
0.022
(0.018)
0.027
(0.027)

3.699
(1.341)***
2.101
(0.897)**
2.310
(1.104)**

0.052
(0.032)
0.030
(0.024)
0.033
(0.026)

3.943
(1.389)***
2.199
(1.226)*
2.605
(1.141)**

0.070
(0.029)**
0.026
(0.017)
0.055
(0.025)**

0.018
(0.013)
-0.029
(0.013)**
-0.055
(0.020)***

4.296
(1.262)***
2.158
(0.815)***
2.842
(0.993)***

1.142
(0.741)
-1.634
(0.761)**
-2.067
(0.925)**

0.011
(0.011)
-0.059
(0.025)**
-0.060
(0.025)**

1.339
(0.633)**
-2.124
(1.132)*
-2.110
(0.994)**

0.026
(0.027)
-0.014
(0.027)
-0.021
(0.027)

yes
yes
yes

2794
142
0.748

0.456
(1.328)
-0.451
(1.355)
-1.010
(1.396)

yes
yes
yes

2794
142
0.644

yes
yes
yes

2794
142
0.747

yes
yes
yes

2794
142
0.644

yes
yes
yes

2713
142
0.747

yes
yes
yes

2713
142
0.649

ECB Working Paper Series No 2449 / July 202061Table 10: Bank Trends

The table presents the reduced form speci(cid:12)cation augmented with bank speci(cid:12)c trends to control for
the presence of diverging trend across banks. SMCR stands for the level in the systemic macropruden-
tial capital requirement. Columns (1)-(3) present the estimates for the impact on capital di(cid:11)erentiated
by the distance from the OCR. Columns (4)-(5) show the evidence for risk and assets without di(cid:11)er-
entiating by distance from OCR since risk and capital are not sensitive to distance from OCR, see 6.
Bank-level control variables are as speci(cid:12)ed in equation 1. Time period: 2010Q1-2017Q3. Standard
errors are shown in parenthesis clustered at bank-level, robust to heteroscedasticity and serial correla-
tion. FE stands for (cid:12)xed-e(cid:11)ects. Stars indicate statistical signi(cid:12)cance levels: *** p<0.01, ** p<0.05,
* p<0.10.

(1)
CET1 Ratio
(p.p.)

Capital

(2)
CET1
(ln)

(3)
Tot. Capital
(ln)

(4)
RWA
(ln)

Risk

(5)

(6)

RWA/Assets Tot. Assets

(p.p.)

(ln)

SMCR

-0.265
(0.371)

0.064
(0.037)*

0.074
(0.028)***

0.061
(0.029)**

3.115
(1.555)**

-0.008
(0.012)

Bank Controls
Bank FE
Country-quarter FE
Bank Trends

Obs.
N. clusters
R2

yes
yes
yes
yes

3312
144
0.804

yes
yes
yes
yes

3312
144
0.778

yes
yes
yes
yes

3312
144
0.791

yes
yes
yes
yes

3312
144
0.845

yes
yes
yes
yes

3312
144
0.824

yes
yes
yes
yes

3312
144
0.898

ECB Working Paper Series No 2449 / July 202062Table 11: Endogeneity: Capital

The table shows the relation between the level of the Systemic Macroprudential Capital Requirement
(SMCR) and the main the main outcome variables used in the capital equation. A dummy representing
the period prior to the phasing-in of the SMCR (2010Q1-2013Q4) is interacted with the main outcomes
of interest for capital. Columns (2), (4) and (6) presents the estimates with bank speci(cid:12)c trends.
Bank-level control variables are as speci(cid:12)ed in equation 1. Overall time period: 2010Q1-2017Q3.
Standard errors are shown in parenthesis clustered at bank-level, robust to heteroscedasticity and
serial correlation. FE stands for (cid:12)xed-e(cid:11)ects. Stars indicate statistical signi(cid:12)cance levels: *** p<0.01,
** p<0.05, * p<0.10.

D2010Q1(cid:0)2013Q4

CET1 Ratio

D2010Q1(cid:0)2013Q4 (cid:2) CET1 Ratio

ln(CET1)

D2010Q1(cid:0)2013Q4 (cid:2) ln(CET1)

ln(Tot. Capital)

D2010Q1(cid:0)2013Q4 (cid:2) ln(Tot. Capital)

(1)

(2)

(3)

(4)

(5)

(6)

SMCR

-1.420
(0.161)***

-2.612
(1.561)*

-0.484
(0.480)

-2.859
(1.621)*

-0.576
(0.488)

-2.302
(1.749)

0.002
(0.004)
-0.005
(0.006)

-0.000
(0.002)
-0.002
(0.002)

0.145
(0.075)*
-0.059
(0.027)**

0.028
(0.039)
0.014
(0.016)

0.126
(0.076)
-0.056
(0.027)**

0.045
(0.036)
0.014
(0.015)

Bank FE
Country-quarter FE
Bank Trends

Obs.
N. clusters
R2

yes
yes

3334
144
0.991

yes
yes
yes

3334
144
0.996

yes
yes

3330
144
0.991

yes
yes
yes

3330
144
0.996

yes
yes

3405
144
0.991

yes
yes
yes

3405
144
0.996

ECB Working Paper Series No 2449 / July 202063Table 12: Endogeneity: Risk

The table shows the relation between the level of the Systemic Macroprudential Capital Requirement
(SMCR) and the main outcome variables used in risk equation. A dummy representing the period
prior to the phasing-in of the SMCR (2010Q1-2013Q4) is interacted with the main outcomes of interest
for risk-taking. Columns (2), (4) and (6) presents the estimates with bank speci(cid:12)c trends. Bank-level
control variables are as speci(cid:12)ed in equation 1. Overall time period: 2010Q1-2017Q3. Standard errors
are shown in parenthesis clustered at bank-level, robust to heteroscedasticity and serial correlation.
FE stands for (cid:12)xed-e(cid:11)ects. Stars indicate statistical signi(cid:12)cance levels: *** p<0.01, ** p<0.05, *
p<0.10.

D2010Q1(cid:0)2013Q4

ln(RWA)

D2010Q1(cid:0)2013Q4 (cid:2) ln(RWA)

RWA/Assets

D2010Q1(cid:0)2013Q4 (cid:2) RWA/Assets

ln(Tot.Assets)

D2010Q1(cid:0)2013Q4 (cid:2) ln(Tot.Assets)

(1)

(2)

(3)

(4)

(5)

(6)

SMCR

-0.560
(0.441)

-2.940
(1.646)*

-1.640
(0.163)***

-2.606
(1.598)

0.074
(0.608)

-3.019
(1.624)*

0.214
(0.118)*
-0.051
(0.023)**

0.065
(0.057)
0.018
(0.015)

0.005
(0.003)
0.003
(0.002)**

0.003
(0.002)
-0.000
(0.001)

-0.006
(0.041)
-0.079
(0.030)***

-0.050
(0.036)
0.021
(0.018)

Bank FE
Country-quarter FE
Bank Trends

Obs.
N. clusters
R2

yes
yes

3423
144
0.991

yes
yes
yes

3423
144
0.996

yes
yes

3423
144
0.991

yes
yes
yes

3423
144
0.996

yes
yes

3573
144
0.990

yes
yes
yes

3573
144
0.996

ECB Working Paper Series No 2449 / July 202064Table 13: Endogeneity: Averaging pre and post treatment periods

The table presents a simple OLS regression of the change in the bank-level SMCR averaged for
the period 2014-2017Q3 on the bank-level averages of outcome variables for the period prior to the
implementation of the macroprudential policy 2010Q1-2013Q4. The aim is to investigate whether the
average of the outcome variables before 2014 correlates with the regulation after 2014. If yes, this may
suggest that weaker bank prior to the phasing-in were protected by national authorities. Bank-level
control variables are also averaged prior to 2014 and are the ones as speci(cid:12)ed in equation 1. Columns
(2), (4), (6) and (8) presents the estimates with bank speci(cid:12)c trends. Standard errors are shown
in parenthesis robust to heteroscedasticity and serial correlation. FE stands for (cid:12)xed-e(cid:11)ects. Stars
indicate statistical signi(cid:12)cance levels: *** p<0.01, ** p<0.05, * p<0.10.

Average bank-level SMCR post 2014

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

CET 1Ratioi;2010Q1(cid:0)2013Q4

0.049
(0.021)**

0.011
(0.007)

ln(CET 1)i;2010Q1(cid:0)2013Q4

ln(RW A)i;2010Q1(cid:0)2013Q4

RW A=Ass:i;2010Q1(cid:0)2013Q4

0.274
(0.231)

-0.024
(0.057)

-0.278
(0.229)

-0.169
(0.106)

-0.007
(0.006)

-0.005
(0.003)*

Bank-level controls
Country FE

yes

yes
yes

yes

yes
yes

yes

yes
yes

yes

yes
yes

Obs.
R2

138
0.292

138
0.968

138
0.264

138
0.967

138
0.263

138
0.968

138
0.265

138
0.969

ECB Working Paper Series No 2449 / July 202065Table 14: Impact on the Probability of Default

The table presents the baseline reduced form speci(cid:12)cation for the impact of the change in the SMCR on
the probability of default as inferred from banks’ ratings. Bank-level control variables are as speci(cid:12)ed
in equation 1. Time period: 2010Q1-2017Q3. Standard errors are shown in parenthesis and are
clustered at bank-level, robust to heteroscedasticity and serial correlation. FE stands for (cid:12)xed-e(cid:11)ects.
Stars indicate statistical signi(cid:12)cance levels: *** p<0.01, ** p<0.05, * p<0.10.

Probability of Default Horizon

5yrs
(p.p.)

4yrs
(p.p.)

3yrs
(p.p.)

2yrs
(p.p.)

1yr
(p.p.)

1.346
(1.044)

1.293
(1.042)

1.161
(1.028)

0.930
(0.981)

0.533
(0.776)

-1.843
(0.833)**

-1.840
(0.835)**

-1.811
(0.831)**

-1.717
(0.803)**

-1.345
(0.654)**

-2.011
(0.893)**

-1.999
(0.895)**

-1.960
(0.888)**

-1.868
(0.858)**

-1.471
(0.699)**

yes
yes
yes

1969
87
0.451

yes
yes
yes

1969
87
0.451

yes
yes
yes

1969
87
0.446

yes
yes
yes

1969
87
0.433

yes
yes
yes

1969
87
0.396

SMCR

SMCR(cid:2)
>20bln.< Tot.Ass <100bln.

SMCR(cid:2)
Tot.Ass>100bln.

Bank Controls
Bank FE
Country-quarter FE

Obs.
N. clusters
R2

Marginal E(cid:11)ects

SMCR(cid:2)
>20bln.< Tot.Ass <100bln.

SMCR(cid:2)
Tot.Ass>100bln.

-0.497
(1.100)

-0.665
(1.183)

-0.548
(1.101)

-0.706
(1.182)

-0.650
(1.094)

-0.799
(1.172)

-0.787
(1.052)

-0.938
(1.126)

-0.811
(0.840)

-0.938
(0.900)

ECB Working Paper Series No 2449 / July 202066A-I Online Appendix: List of Banks

Table A-I: List of GSIBs and O-SIBs with more than EUR 300 billions in total assets as of
2016Q4

Number Country GSI-OSI

Bank Name

Euro Area Total Assets

IRB

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

UK
FR
DE
FR
UK
FR
ES
UK
UK
UK
IT
NL
FR
FR
ES
IT
SE
UK
UK
UK
DE
NL
DE
DK
UK
UK
NL
UK
ES
UK
UK
DE

GSI
GSI
GSI
GSI
GSI
GSI
GSI
OSI
GSI
OSI
GSI
GSI
OSI
GSI
GSI
OSI
GSI
GSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI

HSBC Holdings Plc
BNP Paribas SA
Deutsche Bank AG
Credit Agricole SA
Barclays Bank Plc
Societe Generale SA
Banco Santander, SA
Lloyds Banking Group Plc
Royal Bank of Scotland Group Plc
Goldman Sachs International
UniCredit SpA
ING Groep N.V.
Credit Mutuel Group
BPCE SA
Banco Bilbao Vizcaya Argentaria, SA
Intesa Sanpaolo SpA
Nordea Bank AB (publ)
Standard Chartered Plc
J.P. Morgan Capital Holdings Ltd.
Nomura Europe Holdings plc
DZ BANK AG Deutsche Zentral-Genossenschaftsbank
Rabobank
Commerzbank AG
Danske Bank A/S
Morgan Stanley & Co. International Plc
Merrill Lynch International
ABN AMRO Group NV
Santander UK Plc
CaixaBank, SA
Citigroup Global Markets Ltd.
Credit Suisse International
UniCredit Bank AG

No
Yes
Yes
Yes
No
Yes
Yes
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
Yes
Yes
Yes
No
No
No
Yes
No
Yes
No
No
Yes

2251961725
2076959000
1590546000
1524232000
1421778818
1382241000
1339124751
957795606
935382435
885924120
859532774
845081000
793522000
765069000
731855527
725100000
615659000
613193354
555986552
548007616
509447000
498468992
480450000
468501389
401416677
395201226
394482000
355038592
347927262
327705506
315163664
302090000

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
n/a
0
1
1
1
0
1
1
1
1

ECB Working Paper Series No 2449 / July 202067Table A-II: Banks with total assets between EUR 100 and EUR 300 billions in total assets as
of 2016Q4

Number Country GSI-OSI

Bank Name

Euro Area Total Assets

IRB

33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66

BE
BE
SE
SE
UK
NO
DE
FI
FR
SE
ES
DE
AT
ES
DK
BE
DE
DE
DE
NL
IT
BE
ES
DE
AT
FI
DE
NL
IE
UK
AT
DK
AT
DE

OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI

BNP Paribas Fortis SA
KBC Group NV
Svenska Handelsbanken AB (publ)
Skandinaviska Enskilda Banken AB (publ.)
Nationwide Building Society
DNB Bank ASA
Landesbank Baden-Wurttemberg
Nordea Pankki Suomi Oyj
La Banque Postale, SA
Swedbank AB (publ)
Banco de Sabadell, SA
Bayerische Landesbank
Erste Group Bank AG
Bankia, SA
Nykredit Realkredit A/S
Bel(cid:12)us Banque SA
NORD/LB Norddeutsche Landesbank Girozentrale
Landesbank Hessen-Thuringen Girozentrale
ING-DiBa AG
NV Bank Nederlandse Gemeenten
Banca Monte dei Paschi di Siena SpA
ING Belgie NV
Banco Popular Espanol, SA
NRW.BANK
Rai(cid:11)eisen Zentralbank osterreich AG
OP Financial Group
Volkswagen Financial Services AG
SNS REAAL NV
Governor and Company of the Bank of Ireland
Credit Suisse Securities (Europe) Ltd.
Rai(cid:11)eisen Bank International AG
Nordea Bank Danmark A/S
UniCredit Bank Austria AG
Landesbank Berlin Holding AG

Yes
Yes
No
No
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes

297790000
275200000
274321632
273597716
263401902
258682038
243620000
238775000
229577420
224900662
212507719
212150000
208227070
190167459
188360510
176720926
174797000
165164000
157553000
154000000
153178466
150418720
147925728
142065678
134846575
133747000
130148000
124806000
123129000
112791235
111863845
108970440
105785411
102437000

1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
0
1
1
1
0
1
1
0
1
1
1
1
1
0
1
1
1

ECB Working Paper Series No 2449 / July 202068Table A-III: Banks with total assets between EUR 20 and EUR 100 billions in total assets as
of 2016Q4

Number Country GSI-OSI

Bank Name

Euro Area Total Assets

IRB

67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115

IE
DE
PT
DE
DE
DE
GR
DK
GR
NO
PT
GR
GR
PL
PT
LU
UK
IE
NO
LU
PT
LU
LU
LU
CZ
AT
PL
CZ
AT
PT
BE
HU
BE
CZ
PL
FI
IE
PL
FI
BE
IE
PL
AT
IE
CZ
LU
CY
PT
DK

Allied Irish Banks, Plc
Landwirtschaftliche Rentenbank
Caixa Geral de Depositos, SA

OSI
OSI
OSI
OSI Westdeutsche Genossenschafts-Zentralbank AG
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI

DekaBank Deutsche Girozentrale
HSH Nordbank AG
Piraeus Bank SA
Jyske Bank A/S
National Bank of Greece SA
Nordea Bank Norge ASA
Banco Comercial Portugus, SA
Eurobank Ergasias SA
Alpha Bank AE
Powszechna Kasa Oszczednosci Bank Polski SA
Novo Banco, SA
Deutsche Bank Luxembourg SA
UBS Ltd.
Citibank Europe Plc
Kommunalbanken AS
CACEIS Bank Luxembourg SA
Santander Totta, SGPS SA
BGL BNP Paribas SA
Banque et Caisse d’Epargne de l’Etat, Luxembourg
Societe Generale Bank & Trust SA
ceskoslovenska obchodn banka, a.s.
Bank fur Arbeit und Wirtschaft und Osterreichische Postsparkasse AG
Bank Polska Kasa Opieki SA
ceska spoitelna, a.s.
Rai(cid:11)eisenlandesbank Oberosterreich AG
Banco BPI, SA
Bank of New York Mellon SA/NV
OTP Bank Nyrt.
Argenta Spaarbank NV
Komercn banka, a.s.
Bank Zachodni WBK SA
Kuntarahoitus Oyj
Ulster Bank Ireland DAC
mBank SA
Danske Bank Oyj
AXA Bank Belgium SA
DEPFA BANK Plc
ING Bank slaski SA
Rai(cid:11)eisenlandesbank Niederosterreich-Wien AG
Permanent TSB Group Holdings Plc
UniCredit Bank Czech Republic and Slovakia, a.s.
Banque Internationale a Luxembourg SA
Bank of Cyprus Public Company Ltd.
Caixa Economica Montepio Geral, caixa economica bancaria, SA
DLR Kredit A/S

Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
No
No
Yes
Yes
Yes
No
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No

95622000
95045800
93547313
89794496
85954700
84365000
81500534
78902758
78531000
73744593
71264811
66393000
64872266
64851280
52332672
51787398
47624329
46729176
46082260
46081972
44991681
44980200
43468625
42187856
40177083
39743000
39562822
39473826
39385129
38284652
36427299
36291787
36156329
34151966
34086447
34052186
30694000
30372081
28962100
27994508
27596000
26678248
25404784
23601000
23503916
23148659
22171935
21345909
20944292

0
1
1
1
1
1
1
1
1
1
0
n/a
n/a
1
1
1
1
1
1
0
1
0
0
0
0
1
1
0
0
1
1
1
1
0
1
0
1
1
1
0
0
1
1
1
0
0
1
0
0

ECB Working Paper Series No 2449 / July 202069Table A-IV: Banks with total assets lower than EUR 20 billions in total assets as of 2016Q4

Number Country GSI-OSI

Bank Name

Euro Area Total Assets

IRB

116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159

IE
DK
HR
PL
PL
AT
PL
BE
RO
SK
CY
SK
AT
PL
SI
CZ
RO
RO
SK
MT
HR
BG
PL
EE
HU
HU
AT
CY
HR
SK
RO
AT
LT
RO
LT
MT
CY
HU
HU
HU
HU
RO
BG
EE

OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI

UniCredit Bank Ireland Plc
Sydbank A/S
Zagrebacka banka d.d.
Bank BG BNP Paribas SA
Bank Millennium SA
HYPO NOE Landesbank fur Niederosterreich und Wien AG
Getin Noble Bank SA
Euroclear Bank SA/NV
Banca Comerciala Romana SA
Slovenska Sporitelna, a.s.
Cyprus Cooperative Bank Ltd.
Vseobecna uverova banka, a.s.
Sberbank Europe AG
Rai(cid:11)eisen Bank Polska SA
Nova Ljubljanska banka d.d., Ljubljana
Rai(cid:11)eisenbank a.s.
Banca Transilvania SA
BRD-Groupe Societe Generale SA
Tatra banka, a.s.
Bank of Valletta Plc
Privredna Banka Zagreb d.d.
UniCredit Bulbank AD
Bank Handlowy w Warszawie SA
Swedbank AS
K&H Bank Zrt.
UniCredit Bank Hungary Zrt.
Oberosterreichische Landesbank AG
RCB Bank Ltd.
Erste&Steiermarkische Bank d.d.
Ceskoslovenska obchodna banka, a.s.
UniCredit Bank SA
Hypo Tirol Bank AG
AB SEB bankas
Rai(cid:11)eisen Bank SA
Swedbank, AB
HSBC Bank Malta Plc
Hellenic Bank Public Company Ltd.
MKB Bank Zrt.
Magyar Takarekszovetkezeti Bank Zrt.
Erste Bank Hungary Zrt.
Rai(cid:11)eisen Bank Zrt.
CEC Bank SA
DSK Bank EAD
AS SEB Pank

Yes
No
No
No
No
Yes
No
Yes
No
Yes
Yes
Yes
Yes
No
Yes
No
No
No
Yes
Yes
No
No
No
Yes
No
No
Yes
Yes
No
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
No
No
No
No
No
Yes

19987653
19727068
16980269
16419888
15622293
15392051
15105513
14885444
14873912
14825374
14100791
14037154
12709542
12094460
12039011
11984202
11443660
11429840
11373028
11014330
10867118
10424208
10266811
10233000
9148828
8861671
8756780
8699021
8680694
8543773
8284788
7632172
7517939
7371604
7324953
7305964
7037604
6804454
6776778
6627237
6457088
6204473
6050100
5775400

1
1
0
1
0
0
0
1
0
0
1
0
0
1
0
1
1
1
0
0
1
1
1
1
1
1
1
0
0
0
0
1
1
0
0
1
0
1
0
1
n/a
0
0
0

ECB Working Paper Series No 2449 / July 202070Table A-IV: Ctd. Banks with total assets lower than EUR 20 billions in total assets as of
2016Q4

Number Country GSI-OSI

Bank Name

Euro Area Total Assets

IRB

160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205

HU
LV
CZ
CZ
CY
SI
HR
BG
PL
SK
LT
LV
PL
SI
HR
LV
BG
BG
LV
LV
BG
BG
RO
SI
HR
BG
SI
HR
CY
SI
RO
MT
SI
LV
RO
HU
LT
SI
RO
CY
HR
BG
BG
RO
RO
HR

OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI
OSI

CIB Bank Zrt.
Swedbank AS
PPF banka a.s.
J&T Banka, a.s.
Eurobank Cyprus Ltd.
Nova Kreditna banka Maribor d.d.
Rai(cid:11)eisenbank Austria d.d.
First Investment Bank AD
Bank Polskiej Spoldzielczosci SA
Postova banka, a.s.
Luminor Bank AB
ABLV Bank, AS
SGB-Bank SA
Abanka d.d.
Splitska banka d.d.
AS SEB banka
United Bulgarian Bank AD
Eurobank Bulgaria AD
JSC "Rietumu Banka"
AS Citadele banka
Rai(cid:11)eisenbank (Bulgaria) EAD
Societe Generale Expressbank AD
Alpha Bank Romania SA
SKB banka d.d., Ljubljana
Addiko Bank d.d.
Central Cooperative Bank AD
UniCredit Banka Slovenija d.d.
Hrvatska postanska banka, d.d.
Alpha Bank Cyprus Ltd.
SID - Slovenska izvozna in razvojna banka, d.d., Ljubljana
SC Bancpost SA
MeDirect Group Ltd.
Banka Intesa Sanpaolo d.d.
Luminor Bank AS
Garanti Bank SA
FHB Jelzalogbank Nyrt.
siauliu bankas AB
Sberbank banka d. d.
OTP Bank Romania SA
Alfa Capital Holdings (Cyprus) Ltd.
OTP banka Hrvatska d.d.
CIBANK EAD
Piraeus Bank Bulgaria AD
Piraeus Bank Romania SA
Banca Romaneasca SA
Sberbank d.d.

No
Yes
No
No
Yes
Yes
No
No
No
Yes
Yes
Yes
No
Yes
No
Yes
No
No
Yes
Yes
No
No
No
Yes
No
No
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
Yes
No
Yes
No
No
No
No
No
No

5277329
5242209
5063556
4926761
4879262
4823450
4679339
4647865
4578087
4261460
3988565
3973323
3947797
3614833
3577384
3523911
3495997
3486344
3473590
3349515
3329009
3246381
3245660
2955262
2777215
2651696
2642950
2611695
2596415
2596076
2564081
2489506
2325663
2259247
1973878
1921279
1861278
1846119
1808437
1803745
1694090
1584441
1508035
1446053
1418917
1225733

0
0
0
0
n/a
0
0
1
0
0
n/a
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
0
n/a
0
0
0
0
0
0
n/a
1
1
n/a
1
1
1
n/a
n/a

ECB Working Paper Series No 2449 / July 202071A-II Online Appendix: Literature Review

Related Theoretical Literature

The theoretical literature approached the question of the relationship between higher capital

and risk-taking from di(cid:11)erent angles. Since the pioneering contribution of Modigliani and Miller

(1958), the literature expanded and relied on a variety of modelling techniques. Despite the

richness of existing contributions a consensus has not been reached. According to theoretical

literature, the e(cid:11)ect of capital requirements on risk-taking behavior is ambiguous and hence

the relationship is still an open empirical question.54

In the basic version of their model, Modigliani and Miller (1958) assume that (cid:12)nancial

markets are e(cid:14)cient and perfect, while taxes, agency and bankruptcy costs are absent. As a

result, the Modigliani and Miller (1958) two famous propositions state: i) the capital structure

does not a(cid:11)ect the value of the (cid:12)rm and, ii) more levered (cid:12)rms have higher expected returns on

equity than non-levered (cid:12)rms. The literature has shown that failure of the M&M assumptions

may lead to departures from the theorems’ propositions. For instance, in their later correction

paper, Modigliani and Miller (1963) show how tax advantages for debt instruments lead to non-

proportional after tax returns across (cid:12)rms. Keeping constant the balance sheet size, stricter

capital requirements imply that banks are less able to exploit favourable tax treatment of debt.

For banks, Miller (1995) argues that the deposit insurance can be regarded as a net tax subsidy,

enabling banks to obtain funds at less than an appropriately risk-adjusted cost, promoting in

the limit the minimization of the desired equity ratio.

One of the implications of the second M&M proposition is that a higher capital requirement

would reduce the expected return of the bank’s earning assets and thus curb the incentive for

risk-taking ensuring that banks always choose socially optimal risk levels. At the same time,

higher capital requirement add another layer of protection for the taxpayer, and Miller (1995)

points out that capital requirements are no panacea in this regard because the banks cannot be

trusted from o(cid:11)setting the added taxpayer protection resulting from higher capital requirements

by increasing the risk of their assets further. Furthermore, the presence of signi(cid:12)cant agency

54Theoretical contributions range from portfolio models maximizing a mean-variance utility function (Kahane,
1977; Koehn and Santomero, 1980; Kim and Santomero, 1988; Rochet, 1992), models using option pricing
methods to value the deposit insurance subsidy (Merton, 1977; Galai and Masulis, 1976; Furlong and Keeley,
1989), dynamic models of charter value and competition (Keeley, 1990; Blum, 1999; Hellmann et al., 2000), or
the principal-agent framework, (Saunders et al., 1990; Dewatripont and Tirole, 1994)

ECB Working Paper Series No 2449 / July 202072costs, which are generally higher in large (cid:12)rms due to the diluted ownership structure, see Ang

et al. (2000), can also lead to the failure of the M&M propositions. Bankruptcy costs also

play a role, as pointed out by the Basel Committee in their study on the costs and bene(cid:12)ts

of stronger capital regulation, the main bene(cid:12)ts of a stronger (cid:12)nancial system re(cid:13)ect a lower

probability of banking crises and their associated bankruptcy costs, BCBS (2010). As such the

predictions of the M&M theorem on the risk-taking behavior of banks after a hike in capital

requirements remain uncertain.

Limited liability and deposit insurance models claim that depositors do not have any in-

centive to monitor banks’ behaviour, it follows that managers would have more opportunity

to increase asset riskiness and exploit moral hazard arising from the deposit insurance subsidy,

Green (1984). In these models, the moral hazard problem may be further exacerbated by the

presence of informational advantage for bank managers, Jensen and Meckling (1976). Similarly,

Kareken and Wallace (1978) (cid:12)nd that in a monopoly model of banking with complete contin-

gent claims and under an FDIC-type deposit insurance scheme, the banking industry maintains

a risky portfolio and capital requirements do not forestall bankruptcy.55

Portfolio choice models (cid:12)nd results consistent with both arguments, on one side Kahane

(1977), Koehn and Santomero (1980), Flannery (1989) conclude that capital requirements are

ine(cid:14)cient in constraining the risk shifting in the bank portfolio insulating them from market

discipline; on the other side, Furlong and Keeley (1989) show that for a value-maximizing

bank and the presence of option-value of deposit insurance, the incentives to increase asset

risk decline as bank capital increases. More recently, Kim and Santomero (1988) show that

the the use of simple capital ratios is ine(cid:11)ective to bound the insolvency risk of banks, and

propose theoretically corrected risk-weights as a solution to the risk-taking behaviour. Similarly,

Rochet (1992) argues that utility, as opposed to value, maximizing banks can reduce risk-taking

if capital ratios take into account their asset risk. Blum (1999) models a dynamic decision

problem of a bank to conclude that capital adequacy rules may increase bank riskiness.

Merton (1977) fostered the use of option-pricing models which consider deposit insurance

as an option-value, to reach the conclusion that more skin in the game, i.e. higher capital

requirements, can reduce incentives for increasing portfolio riskiness. Galai and Masulis (1976)

55Notice that, as convincingly showed by Diamond and Dybvig (1983) deposit insurance is at the same time a
fundamental policy tool to avoid bank runs, hence notwithstanding the moral hazard incentive, deposit insurance
is widely used to limit bank panics and bank runs in time of distress.

ECB Working Paper Series No 2449 / July 202073use a capital asset pricing model and an option pricing model to show how unanticipated

changes in (cid:12)rm capital can induce investments in portfolios with higher variance. Similarly,

Gennotte and Pyle (1991) show how deposit guarantees in combination with higher capital

requirements lead banks to increase asset risk.

A further strand of models uses the charter value of the bank, i.e. the di(cid:11)erence between

going concern and liquidation value, to support the skin in the game argument, Marcus (1984).

By the same token, Benston (1986) argues that bank shareholders have more incentives to

operate conservatively when the amount of their own funds is at risk. The prospect of loosing

charter value on managers’ career can remind managers of the consequences of excessive risk-

taking. Saunders et al. (1990) show how managers may have incentives to reduce the default risk

below the shareholders desired level in order to protect their own human capital. The question

is if the bank-managers have the same incentives of the shareholders. Dewatripont and Tirole

(1994) model the classic moral hazard problem with unobservable managers’ e(cid:11)ort to conclude

that banks with low leverage may have an incentive to increase risk since interference from

principal is lower, and viceversa. We test for this hypothesis in section 5.2. More recently, the

theoretical literature using the charter value argument was augmented by including competition

in the banking industry to conclude how the presence of more competitors may reduce the

charter value and increase default risk through asset risk, Keeley (1990), Hellmann et al. (2000).

Related Empirical Literature

Previous empirical research on the impact of higher capital requirements on bank risk-taking

is scant. Pioneering empirical contributions focused on the introduction of risk-weighted reg-

ulatory standards in the late 1980s and was rather fervent in the 1990s. It used descriptive

regression analysis and simultaneous equation models relying thus on endogenous components

of capital increase by bank managers, Shrieves and Dahl (1992); Jacques and Nigro (1997).

One of the earliest empirical contributions is provided by Shrieves and Dahl (1992). The

authors adopt a two-stage simultaneous equation estimation to analyze the relationship between

risk and capital. They estimate discretionary changes in asset portfolio risk induced by a

variation of capital taken endogenously by the bank. The authors (cid:12)nd a positive relationship

between increased capital levels and risk-taking as measured by average risk-weights. The

positive relation holds also for banks with capital in excess of the minimum requirements,

ECB Working Paper Series No 2449 / July 202074leading the authors to conclude that risk-taking behaviour is in(cid:13)uenced by bank owners’ and/or

managers’ private incentives.

Haubrich and Wachtel (1993) apply an analysis of variance (ANOVA) to study whether the

1998 risk-based capital accord (Basel I) led to the risk shifting of commercial banks’ portfolio

toward government securities and hence a lower average risk-weight. The authors conclude that

the implementation of Basel I fostered risk reduction, with poorly-capitalized banks shifting

their portfolios away from high-risk assets and towards low-risk assets.

Using a three-stage least squares simultaneous equation model, Jacques and Nigro (1997)

examine the impact of the risk-based capital standards on bank capital and portfolio risk in the

(cid:12)rst year the Basel risk-based standards were in e(cid:11)ect. As in Shrieves and Dahl (1992), they

use discretionary bank management adjustments to capital, and measure risk as the ratio of

risk-weighted assets to total assets. The paper concludes that risk-based capital standards were

e(cid:11)ective in increasing capital ratios and reducing portfolio risk for banks which already met

the new risk-based standards. Interestingly, Jacques and Nigro (1997) de(cid:12)ne also a supervisory

pressure variable assuming that banks may respond di(cid:11)erently depending on whether they

are in excess or in shortage of required capital. For capital-constrained banks the responses

showed little connection to the degree to which they fell short of the standards. Applying a

similar simultaneous equation framework to a sample of Swiss banks, Rime (2001) (cid:12)nds that

supervisory pressure induces banks to increase their capital, but does not a(cid:11)ect the level of

risk.

More recently, Gropp et al. (2018) exploit the 2011 European Banking Authority (EBA)

capital exercise56 and a di(cid:11)erence-indi(cid:11)erence matching estimator to (cid:12)nd that treated banks

increase capital ratios by reducing their credit supply. On the margin of their study, the authors

show that the EBA capital exercise did not have signi(cid:12)cant e(cid:11)ects on risk reduction as measured

by the risk-weighted asset to total asset ratio. Similarly, Calomiris and Jaremski (2019) exploit

a staggered implementation of deposit insurance laws in the U.S. and the fact that those laws

were applied only to some depository institutions within the states to corroborate the theoretical

literature on the moral-hazard consequences of deposit insurance.

56The EBA capital exercise required 61 banks to build-up additional capital bu(cid:11)ers to reach a level of 9%

core tier 1 ratio in 8 months, from 26 October 2011 until June 2012

ECB Working Paper Series No 2449 / July 202075Acknowledgements 

The views expressed in this paper are those of the author and do not necessarily represent those of the institution with which the author 
is affiliated. The author would like to thank for valuable comments and suggestions on earlier drafts: Andreas Barth, Jonathan Bridges, 
Adrian Bruhin, Stephen Cecchetti, Marco D'Errico, Antonio Di Cesare, Frank Dieric, Klaus Düllmann, Michal Dvořák, Elena Esposito, 
Harald Hau, Heleen Hofmans, Mario Jovanović, Jan Hannes Lang, Frédéric Lardo, Robert Lauter, Axel Loefler, Sergio Masciantonio, 
Francesco Mazzaferro, Steven Ongena, Jakob Orthacker, Tuomas Peltonen, Mara Pirovano, Antonio Sanchez, Glenn Schepens and 
Tibor Szendrei. Members of the ESRB Instrument Working Group, the Risk Analysis division and the Macroprudential Analysis and 
Policy division at the European Central Bank; the participants of the Bank of Russia conference “Macroprudential Policy Effectiveness: 
Theory and Practice”; participants of seminar at the SAFE Institute at Goethe University Frankfurt. 

Ernest Dautović 
European Central Bank, Frankfurt am Main, Germany; email: ernest.dautovic@ecb.europa.eu 

© European Central Bank, 2020 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

+49 69 1344 0 
www.ecb.europa.eu 

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ISBN 978-92-899-4367-3 

ISSN 1725-2806 

doi:10.2866/002634 

QB-AR-20-101-EN-N