Bail-in expectations for European banks : actions speak louder than words. No 7, April 2016.
Date: 2016-08-26
Institutions: European Systemic Risk Board
Subjects: bank, banking, banking policy, financial institution, public policy

Working Paper Series 

No 7 / April 2016 

Bail-in expectations for 
European banks:  
Actions speak louder than words 

by 
Alexander Schäfer 
Isabel Schnabel 
Beatrice Weder di Mauro 

 
 
 
 
Abstract 

The  declared  intention  of  policy  makers  is  that  future  bank  restructuring  should  be  conducted 
through bail-in rather than bail-out. Over the past years there have been a few cases of European 
banks  being  restructured  where  creditors  were  bailed  in.  This  paper  exploits  these  events  to 
investigate  the  market  reactions  of  stock  prices  and  credit  default  swap  (CDS)  spreads  of 
European  banks  in  order  to  gauge  the  extent  to  which  it  is  expected  that  bail-in  will  indeed 
become  the  new  regime.  We  find  evidence  of  increased  CDS  spreads  and  falling  stock  prices 
most notably after the bail-in in Cyprus. However, bail-in expectations appear to depend on the 
sovereign’s  fiscal  strength,  i. e.,  reactions  are  stronger  for  banks  in  countries  with  limited  fiscal 
space  for  bail-out.  Moreover,  actual  bail-ins  lead  to  stronger  market  reactions  than  the  legal 
implementation of bank resolution regimes, supporting the saying that actions speak louder than 
words. 

JEL classifications: G21, G28. 

Keywords:  Bail-in;  bank  restructuring;  Single  Resolution  Mechanism;  creditor  participation; 
event study.  

 
 
 
 
 
 
 
 
1. Introduction 

“Never again” was the collective determination of governments after being forced to back-stop 

the financial system and provide guarantees and capital to avert systemic collapse. The financial 

and  political  capital  involved  continues  to  be  mind-boggling.  The  UK  put  up  40  percent  of  its 

GDP in guarantees; many other countries provided a multiple of that through assurances for the 

entire deposit base. It is true that the back-stops broadly worked, the guarantees were often not 

used, and bank capital injections by the state in some cases even turned out to be profitable for 

the state. However,  bail-outs  are  hugely unpopular  and  are  likely  to  cause  severe  moral  hazard. 

Therefore, the regulatory community has worked hard to establish a new regime in which bail-

out is to be replaced by bail-in.  

In  Europe,  several  countries  quickly  adopted  special  resolution  regimes,  which  provided 

mechanisms for dealing with failing banks while protecting both the financial system as well as 

the taxpayer. Large banks have to submit recovery and resolution plans (living wills), which have 

to spell out the actions that would be taken in case of deteriorating capital ratios. Their purpose is 

to make banks more resolvable and to ensure that shareholders and creditors will be carrying the 

losses rather than taxpayers. The legislative culmination so far has been the adoption of the Bank 

Recovery and Resolution Directive (BRRD) in the EU and the agreement on a Single Resolution 

Mechanism (SRM) in the euro area, which both subscribe to the bailing-in philosophy. 

At  the  same  time,  sometimes  cautiously  sometimes  audaciously,  policy  makers  have  already 

embarked  on  the  route  towards  bailing  in.  Certainly,  the  most  famous  and  controversial 

experience was the bail-in of bank creditors in Cyprus. Before and after Cyprus there had been a 

few other cases. We propose to use these events to study the change in creditors’ expectations of 

bail-in across European banks and countries.  

In particular, we are interested in the reactions of credit default swap (CDS) spreads and stock 

returns to the announcement of a bail-in. If a bail-in event reduced bail-out expectations across 

European banks, risk premia for all banks should rise, which would then be reflected in a rise in 

CDS spreads and a drop in stock returns. We then ask: do these reactions differ across the types 

of bail-in events? We classify events according to their bail-in basis (junior, senior debt) and to 

the strength of their political spillover effects on other banks in Europe. Finally, we ask whether 

the effects differ across different types of banks, such as systemically important banks and banks 

from European crisis countries (GIIPS). The latter is of particular interest because it is possible 

that bail-in events have a stronger political spillover effect in countries that are already perceived 

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as  vulnerable  and  that  have  little  fiscal  capacity  for  bailing  out  their  banks.  Conversely,  the 

expectation of a bail-out may remain higher for banks in fiscally stronger countries. 

To answer these questions we analyse the reactions of CDS spreads and stock returns in response 

to  the  bank  bail-in  events  in  Denmark,  Spain,  Holland,  Cyprus,  Portugal,  as  well  as  to  the 

implementation of the Single Resolution Mechanism (SRM). We employ an event study analysis 

on a broad sample of stock returns and CDS spreads of banks in the European Union. There is a 

long tradition of employing event study analysis to evaluate the extent of bail-out guarantees in 

the  banking  sector  or  the  impact  of  regulatory  reforms  aiming  to  reduce  such  guarantees.  An 

early paper by O’Hara and Shaw (1990) investigates the announcement by the US Comptroller of 

the  Currency  that  certain  banks  were  too  big  to  fail  and  finds  positive  wealth  effects  for  these 

banks. Spiegel and Yamori (2003) assess banks’ stock price reactions in response to a tightened 

resolution regime in Japan and find that it negatively affected large banks’ market values relative 

to other banks. 

More  recent  papers  by  Bongini,  Nieri,  and  Pelgatti  (2015)  and  Moenninghoff,  Ongena,  and 

Wieandt (2015) analyse the effects of the designation and special regulation of global systemically 

important banks (G-SIBs), proposed by the Financial Stability Board. The authors generally find 

negative  stock  price  reactions,  albeit  some  of  the  events,  especially  the  official  designation  of 

banks as G-SIBs, lead to partly offsetting positive effects. Schäfer, Schnabel, and Weder di Mauro 

(2015) analyse the impact of major structural reform measures at national level in the aftermath 

of  the  subprime  crisis  on  CDS  spreads  and  stock  returns.  The  considered  reforms  include  the 

prohibition  of  certain  activities,  ring-fencing,  higher  capital  requirements,  and  early  national 

reforms  regarding  bank  resolution.  Many  of  the  national  reforms  led  to  an  increase  in  CDS 

spreads  and  a  drop  in  stock  returns,  consistent  with  a  reduction  of  bail-out  expectations.  The 

detected effects of resolution procedures were, however, relatively weak, which was explained by 

the  lack  of  cross-border  resolution  procedures.  In  a  similar  vein,  Acharya,  Anginer,  and 

Warburton (2014) find that the Dodd-Frank Act did not significantly reduce bail-out expectations 

for systemic banks in the US. In reaction to the crisis in the euro area, European policy makers 

agreed to introduce a supranational resolution regime in the euro area, in the form of the Single 

Resolution  Mechanism.  Such  regimes  promise  to  be  much  more  effective  than  purely  national 

schemes. But the proof of the pudding is in the eating. Therefore, it is particularly instructive to 

not only consider regulatory reforms but also actual bail-in events. 

Our results show that creditor bail-in events indeed led to an increase in CDS spreads and hence 

to a reduction in bail-out expectations of European banks. We also find decreasing stock returns, 

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mirroring the results for CDS spreads. However, the reaction is not uniform across events. Early 

instances in Denmark and Spain did not trigger significant market reactions, either because the 

political spillover was small or because the event sent blurred signals because it combined a bail-

out with a comparatively small bail-in basis. In contrast, sharp market reactions are found around 

the most extreme event, the bail-in in Cyprus, which featured a comparatively large bail-in basis 

and  a  strong  political  spillover  effect.  The  second  event  with  significant  market  reactions,  the 

bail-in  at  the  Dutch  bank  SNS  Reaal,  also  took  place  in  the  context  of  the  Cyprian  crisis  and 

therefore  produced  significant  political  spillovers  although  the  bail-in  basis  was  narrower.  The 

Portuguese case took place much later (actually after the agreement on the SRM) and had lower 

political  spillover  and  a  narrow  bail-in  basis  and  could  hence  not  be  expected  to  show  strong 

effects. Finally, the legislative process of the SRM yields relatively small though significant market 

reactions. This may be related to the uncertainty regarding the actual creditor involvement in case 

of future bank distress. The bail-in rules agreed under the new framework allow for exceptions 

and  substantial  discretionary  leeway,  limiting  their  binding  powers.5  Moreover,  some  of  the 

effects may already have been captured by the earlier bail-in events, especially the Cyprian bail-in. 

We find heterogeneous effects for different groups of banks. Most importantly, banks in GIIPS 

countries  are  much  more  strongly  affected  than  banks  from  other  countries.  This  may  suggest 

that the  fiscal capacity  of host countries  is one important determinant of bail-out expectations. 

This is confirmed in supplementary cross-sectional regressions. These results suggest that bail-out 

expectations were reduced most effectively in fiscally weak countries but less so in countries with 

large fiscal capacity. Finally, we find evidence that the rise in CDS spreads is more pronounced 

for systemically important banks. 

Overall our results suggest that the Cyprian crisis constituted a watershed regarding the bail-in of 

bank creditors. The actual occurrence of a broad-based bail-in convinced investors that a regime 

change had occurred. Some doubts remain whether a bail-in could also occur in countries with 

high  fiscal  capacity.  Such  countries  may  be  inclined  to  bail  out  their  banks  in  spite  of  the  new 

SRM  regulation.  Hence,  the  strengthening  of  bail-in  rules  remains  an  important  regulatory 

challenge. 

Our paper adds to the huge literature on bail-out and the small literature on bail-in. Most existing 

papers focus either on the empirical identification and quantification of bail-out guarantees or on 

their effect on bank behaviour, especially risk-taking. The event studies described above belong 

to the first category. For example, Acharya et al. (2014) find an average funding cost advantage 

5 See German Council of Economic Experts (2014) for a discussion of the existing exemptions from bail-in under 
the SRM. 

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for systemic US banks of 30 basis points over the period 1992 until 2012, peaking at more than 

100 basis points in 2009. Based on a large bank sample from a large number of developed and 

developing  countries,  Ueda  and  Weder  di  Mauro  (2013)  estimate  a  funding  cost  advantage  for 

systemic banks of 60 basis points in 2007 and 80 basis points in 2009. The IMF (2014) finds that 

implicit  funding  cost  subsidies  rose  across  the  board  during  the  crisis.  Interestingly,  the  report 

shows  that  these  subsidies  have  declined  since  the  crisis  in  many  countries  but  remained 

comparatively elevated in the euro area. However, rating agencies in the meantime reacted to the 

new bail-in rules in Europe by substantially reducing ratings measuring public support, indicating 

that the new rules start having an effect.6 Furthermore, Barth and Schnabel (2014) show that the 

value of bail-out guarantees, i. e., the funding cost advantage from a given implicit guarantee (as 

measured by support ratings by Fitch), increased sharply in the crisis but decreased afterwards. 

Demirgüç-Kunt  and  Huizinga  (2010)  as  well  as  Völz  and  Wedow  (2011)  additionally  find 

evidence of banks becoming too big to be saved when their size exceeds certain thresholds. Barth 

and  Schnabel  (2013)  show  that  the  importance  of  too-big-to-fail  and  too-big-to-be-saved 

problems changed over different phases of the financial crisis. Finally, Hett and Schmidt (2015) 

provide evidence of an erosion of market discipline, especially after the Lehman collapse.  

The other part of the literature focusses on the effect of bail-out guarantees on banks’ risk taking. 

Most of the empirical literature finds a risk-increasing effect of bail-out guarantees, starting from 

an  early  contribution  by  Boyd  and  Gertler  (1994).  Schnabel  (2004,  2009)  confirms  higher  risk-

taking  of  too-big-to-fail  banks  for  German  banks  during  the  Great  Depression.  More  recently, 

Black  and  Hazelwood  (2013)  showed  that  large  banks  rescued  under  the  troubled  asset  relief 

program (TARP) were encouraged to increase their risk-taking. Based on a German bank sample, 

Dam and Koetter (2012) also find a positive relationship between bail-out expectations and risk-

taking.  Gropp,  Hakenes,  and  Schnabel  (2011)  show  that  bail-out  guarantees  may  also  lead  to  a 

rise in the risk-taking of competitor banks. A different strand of the literature argues that public 

bail-out  guarantees  may  not  always  lead  to  excessive  risk-taking  due  to  charter  value  effects 

(Cordella  and  Yeyati,  2003;  Hakenes  and  Schnabel,  2010).  Lower  funding  costs  raise  charter 

values  and  thereby  reduce  risk-shifting  incentives.  In  line  with  this  observation,  German 

Landesbanken and savings banks appear to have increased their risk-taking after the abolishment 

of  bail-out  guarantees  in  Germany  (Fischer,  Hainz,  Rocholl,  and  Steffen,  2011;  Körner  and 

Schnabel, 2013; see, however, conflicting evidence in Gropp, Gruendl, and Guettler, 2014). 

6 For example, in their latest rating review of banks located in the EU and Switzerland Standard and Poor’s 
concluded that the prospects of government support are now uncertain and eliminated any state support uplift in 
bank’s ratings. See Standard and Poors, RatingsDirect, December 2, 2015. 

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In  contrast  to  the  large  literature  on  bail-out,  there  hardly  exists  any  empirical  evidence  on  the 

effects of bail-in. This is not surprising as bail-out has been much more prominent than bail-in. 

But  there  is  by  now  a  large  policy-oriented  literature  discussing  how  bail-in  regimes  should  be 

designed (see, for example, Beck, 2011; Avgouleas, Goodhart, and Schoenmaker, 2013; Huertas, 

2013). There have also been some critical voices regarding the benefits of bail-in relative to bail-

out  (e. g.,  Dewatripont,  2014;  Goodhart  and  Avgouleas,  2014;  Allen,  Carletti,  Goldstein,  and 

Leonello, 2015). Detailed institutional information on a number of case studies of recent bail-in 

events  can  be  found  in  the  work  by  Dübel  (2013b).  This  paper  intensively  draws  on  this 

information in order to classify different bail-in events. 

The  paper  is  structured  as  follows.  The  next  chapter  outlines  the  considered  bail-in  cases  and 

describes expected market reactions. Chapter 3 explains the methodology. We first introduce our 

identification  procedure  for  the  event  selection. Then  we  describe  the  data  sample  and  present 

our  empirical  model.  The  fourth  chapter  contains  the  empirical  results  for  the  bail-in  events, 

putting particular emphasis on the events in Cyprus. Chapter 5 summarizes and concludes. 

2. Bail-in event classification and expected market reactions 

A bail-in procedure aims at letting investors participate in a bank’s losses at time of bankruptcy. 

The strength of market reactions at other banks in response to a bail-in depends on two aspects: 

first,  the  bail-in  basis  and  second,  the  strength  of  the  political  signal  the  event  sends  to  other 

countries.  The  size  of  the  bail-in  basis  plays  a  crucial  role  for  market  participants  because  it 

determines  which  investors  participate  in  the  losses.  If  the  announced  bail-in  comprises  hybrid 

capital and subordinated debt only, we classify its basis as junior debt. In this case, the bail-in 

basis is considered as relatively small. If, in addition, senior unsecured debt and parts of customer 

deposits are included, we define the basis as senior debt.7 In the latter case, the bail-in basis is 

wide and the bail-in affects a broader group of investors. 

We  also  consider  the  likely  strength  of  the  signal  for  future  regime  change  emanating  from  a 

particular  event  (called  “political  spillover”  in  the  following).  We  suggest  that  a  bail-in  event  is 

likely to emit a stronger signal for a future bail-in regime if (i) it happened within the period when 

the European SRM was being designed, or (ii) if there was Eurogroup involvement. The decision 

to  involve  bank  creditors  then  depends  on  the  stance  of  the  Eurogroup  leaders  rather  than 

7 For a further discussion of the bail-in basis, see Dübel (2013a) and Dübel (2013b). 

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national policy makers. We define the period in which the SRM was being negotiated from June 

2012 until April 2014, the time when the European Parliament adopted the SRM.8 

Outline of bail-in cases 

In the following, we outline five European cases of creditor bail-in and the implementation of the 

European Single Resolution Mechanism.9 Figure 1 displays the timeline of the five bail-in cases. It 

shows  that  the  negotiation  about  the  SRM  stretched  over  an  extended  period,  encompassing 

other bail-in events. 

Figure 1: Timeline of bail-in events 

Our first case is the creditor bail-in of the Danish bank Amagerbanken. The small retail bank – 

with total assets of only 4.5 billion euros – was wound up in early 2011 under the Danish national 

resolution procedure “Bank Package III”.10 The Danish resolution procedure aimed at protecting 

taxpayers  from  bank  losses  and  included  a  bail-in  of  senior  debt.  Hence,  depositors  and  other 

unsecured  creditors  of  this  distressed  bank  could  not  be  sure  to  receive  full  coverage  of  their 

claims.11 On Sunday, 6 February 2011, the bank announced the transfer of its assets to a state-

owned bank. CreditSights estimated that holders of senior debt and unsecured deposits would face 

a haircut of 41 percent.12 This case is of particular interest since it was the first European bank in 

our  sample  whose  bail-in  basis  included  senior  unsecured  debt  as  well  as  larger  deposits.  It  is 

noteworthy that the authorities in Denmark, which is not part of the eurozone, decided to bail in 

bank creditors long before the decision for a European banking union and the creation of a SRM.  

8 See Financial Times of 12 June 2012 “Barroso pushes Banking Union”. 
9 The European Banking Union consists of three pillars: the Single Supervisory Mechanism (SSM), the Single 
Resolution Mechanism (SSM), and the Harmonized Deposit Insurance (DGS), of which the latter has not yet been 
finalized. We do not investigate events attached to the construction and the implementation of SSM since it does not 
contain any specific rules concerning bail-in. 
10 See Dübel (2013b). 
11 See Denmark’s Nationalbank (2013). 
12 See Financial Times, 8 February 2011, p. 27. 

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Spain  applied  for  ESM  assistance  in  bank  restructuring  and  recapitalization  in  June  2012.13  At 

this time recapitalization needs of Spanish banks were estimated at 100 billion euros. The largest 

bank in distress was Bankia with a balance sheet of about 300 billion euros. At the insistence of 

euro  area  finance  ministers,  the  Memorandum  of  Understanding  (MoU)  included  the 

participation of junior creditors in the losses of the Spanish institutes as a necessary condition for 

granting  bank  aid.  Subordinated  Liability  Exercises  (SLEs)  included  hybrid  capital  and 

subordinated debt and were either voluntary or – where necessary – mandatory.14 In the second 

half of the year 2012 the Spanish  government implemented  a  national law on the  restructuring 

and resolution of their credit entities.15  

Case number three is the creditor bail-in of the Dutch bank SNS Reaal, which had total assets 

of about 80 billion euros. After the bank had suffered from substantial write-downs on its real 

estate  portfolio  during  the  year  2012,  the  Dutch  government  nationalized  SNS  Reaal  on  1 

February 2013. In the context of nationalization, the state injected 2.2 billion euros, shareholders 

and  junior  creditors  were  both  wiped  out.  One  billion  of  subordinated  debt  was  expropriated 

with zero compensation under a new Dutch law.16 This case happened during the negotiation of 

the SRM. Its political spillover effect was probably further magnified for an additional reason: the 

responsible Dutch finance minister had just been appointed as the president of the Eurogroup. 

Hence,  his  involvement  in  the  decision  to  bail  in  creditors  in  the  Netherlands  was  a  strong 

indication  for  the  future  stance  of  the  Eurogroup,  including  in  their  negotiations  with  the 

incipient case, Cyprus.17 

Cyprus  is  the  key  bail-in  event  because  it  clearly  transported  the  signal  that  the  euro  area  was 

going for a bail-in of creditors in bank restructurings and moreover that the bail-in basis could be 

very wide, including senior unsecured debt and even large deposits. Apart from the early Danish 

case,  retail  investors  had  not  yet  faced  haircuts.  The  different  bail-in  options  became  public  in 

February  2013.  On  18  March  2013,  the  government  of  Cyprus  and  the  eurozone  Finance 

Ministers  announced  that  all  deposits,  including  those  below  100  000  euros  (the  legal  deposit 

guarantee limit  of the EU),  would  be  facing  losses. Following an uproar and a week of further 

frantic negotiations, the deal finally announced on 25 March 2013 bailed in senior unsecured debt 

and large deposits but not retail deposits below 100 000 euros.  

13 See Financial Times – European Edition on 25 June 2012. This event date is not examined since a creditor bail-in 
was not discussed at this early stage. 
14 See Memorandum of Understanding (2012). 
15 See Ashurst (2012). 
16 See Dübel (2013b), p. 40. 
17 See Financial Times – European edition „Dutch moralist sends stern message“, on 26 March 2013, p. 2. 

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The last country case focuses on the creditor bail-in of the Portuguese bank Banco Espírito 

Santo, which had total assets of about 85 billion euros. For a few days, this event dominated the 

news and raised the spectre of renewed turbulence in the euro area. On 10 July 2014, fears over 

this bank briefly  triggered a stock sell-off across European financial markets. Portugal’s PSI 20 

share  index  dropped  by  4.3%,  the  biggest  drop  in  more  than  a  year.18  In  September  2014  the 

bank posted record losses for the first half of the year. On 4 August 2014, the bank was split up 

into  a  “good  bank”  and  a  “bad  bank”  after  a  frenzied  weekend  of  negotiations  between 

Portuguese  and  European  Union  officials.  The  good  bank,  Novo  Banco,  received  all  sound 

assets, deposits and senior debt plus a capital injection of 4.9 billion euros. The bad assets were 

transferred to the bad bank and its losses had to be borne by junior creditors. 19 

Finally  we  analyse  the  market  reactions  in  response  to  the  implementation  of  the  European 

Single Resolution Mechanism (SRM), fixing bail-in rules within the European Banking Union. 

The  purpose  of  the  SRM  is  to  ensure  that  potential  future  bank  failures  in  the  euro  area  are 

managed efficiently, such that taxpayers and the real economy are burdened with minimal costs. 

One  of  its  key  elements  is  the  centralized  competence  to  wind  down  distressed  banks.  It  is 

therefore endowed with more comprehensive and effective arrangements to tackle cross-border 

banking failures than national supervisory authorities.20 The SRM has access to a European Single 

Resolution  Fund,  which  is  supposed  to  be  sourced  from  the  banking  sector  instead  of  the 

taxpayer. Regarding the design of creditor bail-in, the SRM applies the rules established under the 

Bank  Recovery  and  Resolution  Directive  (BRRD).  According  to  the  BRRD,  creditors  are 

primarily  supposed  to  bear  the  costs  of  the  bank  failure  in  order  to  minimise  the  burden  for 

taxpayers.  The  possible  bail-in  basis  under  the  BRRD  ranges  from  junior  to  senior  unsecured 

debt  and  can  involve  uninsured  customer  deposits.  Deposits  from  small  and  medium-sized 

enterprises as well as from natural persons, including in excess of hundred thousand euros, will 

be preferred over senior creditors.21 The EU Finance ministers agreed upon the BRRD in June 

2013,  thereby  preparing  the  bail-in  rules  for  the  SRM.  A  milestone  was  passed  in  March  2014 

when  the  European  Parliament  and  the  Council  reached  a  provisional  agreement  on  the 

proposed SRM. In April 2014 the SRM was finally adopted by the European Parliament. 

18 See Financial Times, online: “Fear over Banco Espirito Santo trigger stock sell-off”, 10 July 2014, 6:48pm. 
http://www.ft.com/intl/cms/s/0/4b0ce5ce-0815-11e4-9afc-00144feab7de.html#axzz3HcLjBF7Y 
19 See also Reuters (2014). 
20 See European Commission (2014a). 
21 See European Commission (2014b) for more information about the liability cascade in case of a bail-in. 

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Expected market reactions 

The overarching hypothesis in this paper is that the occurrence of a bail-in event at some bank 

leads to a rise in CDS spreads and a drop in stock returns at other European banks if it reduces 

bail-out expectations. This captures the idea that the announcement of future bail-in remains 

incredible unless such bail-ins are observed in reality. CDS spreads are affected directly because a 

lower  bail-out  probability  raises  the  probability  of  default.  Stock  prices  are  affected  indirectly 

since,  everything  else  equal,  lower  bail-out  expectations  increase  banks’  costs  of  financing  and 

thereby  reduce  profits.  However,  banks  have  other  margins  of  adjustment  (e. g.,  an  increase  in 

loan rates), therefore the effect of a bail-in event on stock prices is likely to be less pronounced 

than that on CDS spreads.22 

The strength of market price reactions is likely to depend on two aspects: first, on the extent of 

the observed bail-in (bail-in basis), and second, on the relevance that the event has for future bail-

in decisions (political spillover). 

As  pointed  out  above,  only  two  country  cases  incorporate  a  bail-in  of  senior  debt,  namely 

Denmark and Cyprus. In all other cases, senior unsecured debt holders as well as depositors were 

spared. Since a bigger bail-in basis makes it more likely that senior creditors are also going to be 

bailed  in  in  future  crises,  we  would  expect  stronger  market  reactions  for  country  cases  with  a 

bigger bail-in basis, especially given that our CDS spreads refer to senior unsecured debt. 

Moreover, not all country cases should have affected bail-in expectations to the same extent. If a 

bail-in  was  considered  as  a  purely  national  event,  spillovers  would  be  more  limited  than  if  an 

event  could  be  seen  as  a  model  case  for  the  future  implementation  of  bank  bail-ins  in  the 

European  Union.  A  high  political  spillover  effect  is  likely  if  the  event  happened  amidst  the 

creation  of  the  SRM.  Such  instances  could  serve  as  showcases  how  the  future  bail-in  regime 

would function. Furthermore, the involvement of European (rather than national) leaders in the 

decision process would make it more likely that future cases would be handled in a similar way. 

Three country cases happened during the implementation process of the SRM, namely those in 

Spain,  the  Netherlands  and  Cyprus.  These  cases  are  likely  to  have  induced  political  spillovers. 

Denmark’s bail-in happened much earlier and under a pure national scheme. Therefore, political 

spillovers are expected to be small. Finally, the Portuguese case took place after the agreement on 

the SRM. Hence, expectations had already adjusted due to previous bail-in events and the news 

content would be minor. Hence, it should lead to less pronounced market reactions. 

22 Schäfer et al. (2015) also consider regulatory events that directly affect banks’ profitability, which is then reflected 
in a drop in stock returns.  

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Figure 2: Expected strength of market reactions 

As shown by Figure 2, only the Cyprian case combines a large bail-in basis and a strong political 

spillover; hence, it is expected to lead to the strongest market reactions, i. e., to the strongest rise 

in  CDS  spreads  and  the  most  severe  drop  in  stock  returns.  The  Spanish,  the  Dutch  and  the 

Danish cases only fulfil one of the postulated conditions for a strong expected change in market 

prices.  Their  market  reactions  are  therefore  likely  to  be  less  pronounced  than  those  in  Cyprus. 

The  Portuguese  case  happened  after  the  completion  SRM  process  and  its  bail-in  basis  is 

comparatively small. Hence, in this case we expect the smallest effects in market prices. 

The  SRM  provides  for  a  large  bail-in  basis  and  affects  banks  in  all  countries,  even  beyond  the 

eurozone countries. However, the identification of events is less clear-cut than for the country-

specific  cases  because  the  process  of  negotiation  stretches  over  an  extended  period  and  the 

informational content of the identified events may be limited as a lot of information has already 

been incorporated in prices. In fact, the “actions” (actual bail-in events) may be expected to have 

a much stronger impact on prices than the “words”, i. e., the rules underlying the new resolution 

regime. 

11 

 
 
We  furthermore  expect  different  market  reactions  for  different  types  of  banks.  Systemically 

important institutions are expected to experience a larger reduction in bail-out expectations when 

it  comes  to  a  bail-in  decision  because  they  are  the  ones  who  had  benefited  most  from  implicit 

public bail-out guarantees under the old regime.23 

Finally, the strength of market reactions may also depend on a bank’s host country. In particular, 

we expect stronger reactions in market prices for banks from Greece, Ireland, Italy, Portugal, and 

Spain  (GIIPS),  the  countries  most  strongly  affected  by  the  European  sovereign  debt  crises.  As 

those countries were facing already high public debt levels, their ability to finance national bank 

bail-outs on their own was limited, which made a bail-in more likely. For those countries, a bail-in 

in  another  country  could  also  provide  information  on  the  probability  of  banks  experiencing 

another crisis (and, hence, a bail-in). However, in most cases, the information on the poor state 

of banks was published well before the actual bail-in event. Therefore, this interpretation is less 

plausible than the previous one. 

3. Methodology 

In the following, we outline the methodology employed to investigate the effects of bail-in events 

on market prices. The first subsection describes the identification of the events. Then we provide 

information on the data sample before we introduce the empirical models. 

3.1 Identifying events 

An  event  study  analysis  relies  on  the  idea  that  news  should  be  incorporated  directly  in  market 

prices and that – unless the event date contains other relevant news – the observed reaction can 

indeed be attributed to the event in a causal way. Hence, we have to identify the exact point in 

time when bail-in relevant news reaches the markets. This is of particular importance as we are 

analysing daily market price data. Negotiations regarding the treatment of distressed banks as well 

as  the  political  process  to  form  the  European  SRM  typically  extend  over  a  longer  time  period. 

For this reason it would not be sufficient to rely only on press releases about the final outcomes, 

as  important  interim  results  from  longer  lasting  negotiations  may  have  been  spread  out  to  the 

markets before. Following O’Hara and Shaw (1990), we classify an incident as a relevant event if 

it was published on the front page of an internationally reputable newspaper. The objective of a 

financial newspaper’s editor is to report about the news driving the markets. This is exactly the 

kind  of  event  we  are  looking  for.  For  this  study  we  rely  on  the  Financial  Times  –  European 

23 See Ueda and Weder di Mauro (2010) and Ueda and Weder di Mauro (2013) for an analysis of bail-out 
expectations for systemically important banks.  

12 

 
                                                            
Edition (FT) for our event search, which is a reputable newspaper that covers all parts of Europe 

with a profound reporting on financial markets. We scanned all FT front pages from September 

2010  until  October  2014,  containing  a  total  amount  of  approximately  one  thousand  and  two 

hundred front pages. If we detect a bail-in relevant article with respect to the above cases on a 

front page, we check as well the subsequent articles on the following pages to determine the exact 

timing of the event.24 This is of particular importance as we need to know whether the respective 

event, e. g., the consensus of the Eurogroup meeting, was reached before or after markets closed. 

Table  1  summarizes  the  identified  events  according  to  their  dates  of  occurrence,  as  well  as  the 

respective headlines of the FT article. Typically the relevant event happened one day prior to its 

publication. However, there are also cases in which the relevant news was produced on a Sunday 

(e. g., related to Cyprus) or late at night after markets closed (e. g., related to the SRM) such that 

the event day coincides with its publication date. Hence, the event date is determined by the day 

on  which  markets  opened  the  first  time  after  its  release.  As  can  be  seen  from  Table  1,  the 

timeframe of the different cases varies quite substantially. While the Cyprian, the Spanish and the 

SRM  cases  include  several  event  dates,  Netherlands’,  Denmark’s  and  Portugal’s  cases  are 

restricted to just one event date. 

3.2 The data 

The  analysis  is  based  on  daily  stock  returns  and  CDS  spreads  for  all  available  banks  in 

Datastream  (Thomson  Reuters)  from  the  European  Union,  adding  banks  from  Norway, 

Switzerland  and  Liechtenstein  due  to  their  closeness  to  the  EU.  We  downloaded  all  available 

banks  and  removed  banks  that  were  inactive  and  not  continuously  traded  within  the  sample 

period. After these adjustments we obtained a sample of 64 banks for CDS spreads and 85 listed 

banks for stock returns. For the analysis of CDS spreads we use the day-to-day mid-prices (first 

differences) of 5-year senior tranches on an end-of-day basis. With regard to the equity analysis 

we  use  the  daily  returns  of  stocks  based  on  their  closing  auctions  and  listed  at  their  domestic 

stock exchanges. Tables 2a and 2b show the summary statistics for the data sample. All bank data 

is  from  Datastream  (Thomson  Reuters).  The  number  of  observations  for  each  bank  is 

determined by the estimation window of 80 trading days plus the event window of 3 trading days 

for  each  event  (excluding  overlapping  observations).  Since  we  estimate  all  events  for  all  banks 

simultaneously  rather  than  for  each  country-specific  subsample  separately,  the  number  of 

observations  remains  constant  for  all  banks  across  countries.  When  testing  for  heterogeneity 

effects,  we  split  our  sample  in  systemically  important  banks  (G-SIBs)  and  banks  that  are  not 

24 We complemented the newspaper search by a comprehensive internet research. 

13 

 
                                                            
considered as systemically important (non-G-SIBs). The selection is based on the list of twenty-

eight  globally  systemically  important  financial  institutions,  published  by  the  Financial  Stability 

Board  on  1  November  2012.  Matching  our  sample  with  this  list  provides  us  with  sixteen 

European  G-SIBs.  Furthermore  we  construct  a  subsample  of  banks  that  are  based  in  those 

countries that have been predominantly affected by the European sovereign debt crisis. Hence, 

we  define  banks  from  Greece,  Ireland,  Italy,  Portugal,  and  Spain  as  GIIPS  banks  whereas  the 

remaining ones are labelled as non-GIIPS banks. For CDS spreads, we end up with a subsample 

of  22  banks  from  GIIPS  countries  while  the  stock  price  sample  comprises  a  subsample  of  27 

banks from GIIPS countries. Tables 2a and 2b incorporate our classifications of banks according 

to the two criteria for each sample bank. We furthermore conduct a number of cross-sectional 

regressions in order to assess the drivers for the rise in CDS spreads in response to the bail-in in 

Cyprus. For this purpose we use the country-specific debt-to-GDP ratios (public debt divided by 

the gross domestic product) as of 2012, taken from the World Economic Outlook Database by 

the IMF. Moreover, we construct the following bank-specific control variables: the average equity 

ratio  is  defined  as  average  common  equity  in  2012  divided  by  average  assets  in  2012.  The  net 

income  ratio  is  calculated  as  banks  net  income  in  2012  scaled  by  average  assets  in  2012  and 

ln(Assets)  stands  for  the  natural  logarithm  of  average  assets  in  2012.  All  bank-specific  controls 

variables  are  retrieved  from  Bankscope  (Bureau  van  Dijk).  Table  2c  displays  the  summary 

statistics for the above variables while correlations are given in Table 2d. 

3.3 Empirical model 

Our goal is to analyse abnormal differences in CDS spreads and stock returns on the identified 

event dates. The empirical approach is similar to the paper by Schäfer et al. (2015). Our empirical 

model  of  differences  in  CDS  spreads  is  based  on  the  constant  return  model.  We  estimate  a 

system  of  equations  in  which  the  first  differences  in  CDS  spreads  are  regressed  on  a  bank-

specific  constant  and  a  set  of  dummy  variables.  Instead  of  the  originally  proposed  two-step 

procedure  for  event  studies  as  described  by  Campbell,  Lo,  and  MacKinlay  (1996),  we  include 

dummy  variables  in  the  equations,  equal  to  one  at  an  event  date  and  zero  otherwise.  Hence, 

event-  and  bank-specific  abnormal  first  differences  in  CDS  spreads  are  captured  by  the 

coefficients  of  the  respective  dummies.25  In  order  to  estimate  the  equations  simultaneously,  we 

are applying the seemingly unrelated regression technique by Zellner (1962).  

The system of equations then looks as follows:  

25 See Binder (1985) and Karafiath (1988) for details. 

14 

 
                                                            


CDS
1
t




1



T

1



1
n
1


Tn

D
1
nt




1
t

… 



CDS




j



jt

T

1



jn
1


Tn

… 



CDS

Jt




J



T

1



Jn
1


Tn

D

jnt




jt

D

Jnt




Jt



CDS

jt

denotes the first difference of CDS spreads, 

j  stands for the mean of first differences 

of bank j within the estimation window, whereas

jntD indicates the vector of dummy variables for 

all events. For each of our identified events in Table 1 we define three dummy variables: one for 

the event date itself, T, one for the following date, T+1, and one for the day prior to the event, T-

1. All of our regressions are calculated on the basis of an estimation window of 80 trading days. If 

an  event  occurs  in  an  estimation  window,  it  is  “dummied  out”  by  incorporating  the  respective 

dummy variables into the regression. The estimation window length is enlarged accordingly, such 

that we conduct every regression with exactly 80 trading days. 

We model normal returns of banks’ stock prices using the market model.26 In order to proxy the 

market return with a broad based benchmark, we use a widely diversified and globally structured 

index, namely the Stoxx Global 1800, denominated in euro. 27 In doing so, we avoid the distortion 

of  effects  due  to  the  interdependency  of  financial  and  non-financial  firms  within  our  sample.28 

The difference compared to the model above consists of the inclusion of the market return RM: 

R
1
t





1

1

R

Mt



…

R

jt





j
j

R

Mt



T

1



1
n
1


Tn

T

1



jn
1


Tn

D
1
nt




1
t

D

jnt




jt

26 See Campbell et al. (1996). 
27 We reran the regressions for stock returns on the basis the Stoxx Europe 50 index, which, in contrast to the 
former, is a purely European index. The results obtained by using the Stoxx Europe 50 Benchmark index are 
qualitatively very similar to the results below. Using a broader country index leads, however, to cleaner identification. 
28 See for example Ongena, Smith, and Michalsen (2003). 

15 

 
 
 
 
 
 
 
                                                            
…

R
Jt





J

J

R

Mt



T

1



Jn
1


Tn

D

Jnt




Jt

Coefficients 

j and 

j

denote  the  bank-specific  intercept  and  the  beta  factor  attached  to  the 

market  return,  respectively.  Apart  from  those  differences,  the  regressions  are  conducted  in  the 

same way as before. 

On the basis of our regressions we run a number of tests. We start by evaluating the impact on 

the  full  sample  of  European  banks.  This  coefficient  is  obtained  by  calculating  the  average 

abnormal difference in CDS spreads (stock returns) of all banks in our sample. Furthermore we 

display  the  coefficients  for  G-SIBs,  for  non-G-SIBs,  as  well  as  for  the  difference  in  abnormal 

return between those two subgroups. Finally we repeat this procedure for banks from the GIIPS 

countries  and  the  remaining  banks.  The  enlarged  event  window  [0+1]  shows  the  cumulated 

coefficients of the event date and the following date. We also check for anticipatory effects on 

the day prior to the announcement and display them only when they are statistically significant. 

We  display  for  each  coefficient  the  respective  p-value.  Tables  3  to  16  contain  our  regression 

results for CDS spreads and stock returns.  

4. Results 

We  start  with  the  discussion  of  results  for  country-specific  events.  As  we  will  see,  early  bail-in 

events  hardly  produced  any  market  reactions  at  banks  in  other  countries.  The  regime  shift 

appears to have occurred at the time of the Cyprian bail-in, for which we find sharp reactions in 

CDS  spreads  and  stock  prices.  The  Portuguese  case  again  did  not  induce  any  notable  market 

reactions. Finally, we will discuss the effects of the negotiation of the BRRD and the SRM, which 

forms  the  legal  basis  for  bank  bail-ins  in  Europe.  Market  reactions  are  found  to  be  relatively 

small.  This  seems  to  suggest  that  the  actual  occurrence  of  a  bail-in  as  in  Cyprus  impressed 

markets  much  more  than  the  implementation  of  the  legal  framework.  Actions  appear  to  speak 

louder than words. 

4.1 Denmark – A largely national event 

In Denmark, the bail-in procedure of the Danish retail bank Amagerbanken is characterized by a 

comparatively  large  bail-in  basis  but  a  small  political  spillover  effect  on  the  European  banking 

16 

 
 
 
 
sector. The newspaper search provides us with one event date only, the day of the announcement 

that the bank was to be wound up. 

As shown by Tables 3 and 4, this event hardly caused any market reactions in CDS spreads or 

stock  prices,  respectively.  The  only  statistically  significant  result  is  a  relative  increase  in  CDS 

spreads of about six basis points for systemic banks compared to the remaining banks, shown by 

difference  of  G-SIBs  versus  non-G-SIBs.  No  other  coefficient  is  statistically  significant.  These 

results can be attributed to the fact that the early Danish resolution was conducted under a purely 

national scheme and was therefore not considered as a signal for the future way of conducting 

bank bail-ins in Europe, especially given that Denmark was not even part of the eurozone. Also, 

Amagerbanken was a small bank, with total assets of only 4.5 billion euros29 and a market share 

of  only  one  percent  even  in  Denmark.30  This  explains  why  financial  markets  did  not  seriously 

consider Amagerbanken as a model for bail-ins at larger institutes in other European countries. 

4.2 Spain – A European event with a small bail-in basis 

The Spanish case happened during the creation of the SRM and is therefore expected to exhibit 

rather  high  political  spillover  effects  especially  because  Spain  is  one  of  the  big  eurozone 

countries.  However,  the  Spanish  bail-in  procedure  affected  junior  debt  only,  while  senior 

unsecured debt holders and depositors were spared from the banks’ losses. Hence, the event sent 

blurred  signals  because  it  combined  a  bail-out  of  senior  creditors  with  a  bail-in  of  junior  debt 

holders.  

Our procedure identifies three dates on which bail-in relevant news were published on page one 

of the FT. The first event on 10 July 2012 is given by a proposal for a Spanish bank rescue plan, 

implying  junior  creditor  participation  according  to  a  memorandum  of  understanding  (MoU)  by 

the  eurozone.  On  the  second  event  date,  19  July  2012,  the  German  government  backed  the 

European bank rescue plan and implicitly the involvement of junior bank creditors. On the last 

event on 23 August 2012 of Spanish authorities express their intention to implement a national 

bank resolution law. This  effort could be  considered  as  the  response to  the  terms of the MoU 

declared before. 

Tables  5  and  6  display  the  results  for  abnormal  changes  in  CDS  spreads  and  stock  returns, 

respectively.  As  before,  we  find  only  one  significant  reaction,  namely  for  CDS  spreads  in 

response  to  the  third  event,  again  regarding  the  difference  of  G-SIBs  vs.  non-G-SIBs.  The 

29 See Dübel (2013b). 
30 See Denmark’s Nationalbank (2013).  

17 

 
                                                            
increase amounts to roughly 15 basis points and is highly significant. Stock markets, as before, do 

not exhibit any significant reaction. Similar to the previous case, overall bail-out expectations do 

not seem to have adjusted downwards in reaction to the bail-in at Spanish banks because the bail-

in basis was so small. 

4.3 Netherlands – Foreshadowing the Cyprian bail-in 

The bail-in of the Dutch bank SNS Reaal also comprised only junior debt. However, it took place 

in the run-up to the Cyprian bail-in, which meant that investors were watching the Dutch case 

closely in order to learn about the future stance of policy makers towards bail-in. In addition, the 

SRM was already in the making at that time. This suggests a high political spillover effect on the 

European banking sector. Our front page filtering methodology provides us with a single event: 

The  announcement  to  nationalize  the  SNS  Reaal  on  1  February  2013  including  a  loss 

participation of junior creditors. 

The Dutch bail-in is the first event where we observe large and highly significant effects on other 

European  banks,  as  can  be  seen  in  Tables  7  and  8.  The  bail-in  led  to  a  sharp  and  highly 

significant abnormal increase in CDS spreads for the full sample (Table 7). The effect amounts to 

almost  12  basis  points  in  the  enlarged  event  window.  Interestingly,  we  find  the  largest  rise  for 

banks  from  the  GIIPS  countries,  which  have  been  predominantly  affected  by  the  European 

sovereign debt crisis. The rise for banks in GIIPS countries amounts to roughly 26 basis points 

and  is  significantly  different  from  the  rise  in  other  countries,  which  amounts  to  only  5  basis 

points and is statistically insignificant. The results for stock returns (Table 8) point in the same 

direction. Abnormal returns in the full sample (in the enlarged event window) are – 2.5 percent 

and statistically significant. Similar to the results for CDS spreads, banks in the GIIPS subsample 

experience the strongest reaction, amounting to – 4.4 percent (compared to just – 1.6 percent for 

non-GIIPS  countries  although  the  difference  is  insignificant  here).  No  statistically  significant 

differences  are  observed  between  G-SIBs  and  non-G-SIBs,  neither  for  CDS  spreads  nor  for 

stock returns. 

Taken together, the results suggest a notable reduction in bail-out expectations in response to the 

Dutch  bail-in,  especially  for  banks  from  GIIPS  countries.  Hence,  investors  appear  to  have 

expected a higher probability of a future bail-in particularly in those countries, a result that will 

reappear  in  the  Cyprian  crisis.  Figures  3a  and  3b  illustrate  the  gradual  widening  between  CDS 

spreads  and  stock  prices,  respectively,  of  banks  from  GIIPS  and  non-GIIPS  countries  in 

18 

 
response  to  the  Dutch  bail-in  case.31  Figures  4a  to  4d  illustrate  the  events  in  a  longer  time 

window, also displaying the key events in Cyprus. 

4.4 Cyprus – Triggering a regime shift 

The  Cyprian  country  case  is  of  particular  importance  for  the  analysis  because  it  combines  two 

important  features  that  we  assume  to  be  essential.  First,  the  bail-in  basis  is  wide  due  to  the 

involvement of senior debt and even large customer deposits. Second, the decision to expand the 

bail-in basis to senior debt and retail depositors  was backed by the Eurogroup after  protracted 

negotiations. This was a watershed in the way of dealing with distressed banks.  

We  identify  three  events  for  the  Cyprian  case:  First,  the  outcome  of  the  eurozone  finance 

minister  meeting  on  11  February  2013.  At  this  early  stage,  the  bail-in  of  senior  debt  was 

considered as one of three possible alternatives of the meeting. The second event on 18 March 

2013 is characterized by the Cyprian proposal to introduce a levy on all depositors, even if their 

claims  were  below  the  insured  amount  of  one  hundred  thousand  euros.32  While  this  is  not  an 

explicit  bail-in  procedure  its  economic  effect  on  private  investors  would  be  very  similar,  as  it 

transfers the rescue costs from taxpayers to investors. The last event on 25 March 2013 marks 

the actual bail-in in Cyprus. 

The  results  referring  to  the  Cyprian  bail-in  are  displayed  in  Tables  9  and  10.  The  results  are 

striking for both CDS spreads and stock returns. First looking at the full sample, we see a sharp 

increase  in  CDS  spreads  for  the  second  and  third  event,  amounting  to  12  and  15  basis  points, 

respectively,  when  considering  the  enlarged  event  windows  (Table  9).  Systemic  banks  show 

sharper increases for both events, with the difference being statistically significant for the third 

event. The most striking result is the boost in CDS spreads of banks from the GIIPS states. The 

effect  amounts  to  22  and  31  basis  points  for  events  number  two  and  three,  respectively. 

Moreover, the difference to banks from the non-crisis countries is at 15 and 24 basis points and 

is highly statistically significant. The results for stock returns, displayed in Table 10, mirror our 

results on CDS spreads. The full sample coefficients show sharp significant reactions. Again, G-

SIBs are affected more strongly than non-G-SIBs in case of the third event. Finally, the rise in 

CDS spreads for banks from European crisis countries is also reflected in sharp drops in stock 

returns. Stock returns from banks of the GIIPS countries are – 6.7% and – 5.1%, respectively, 

31 Note that charts are prepared on the basis of actual market data and not on the basis of our regression results. For 
this reason the change in market prices within the graphical illustration might differ from the results given in the 
tables. 
32 The proposal at this time provided for a 6.75% levy on all deposits under 100.000 euros and a 9.9% levy above this 
threshold.  

19 

 
                                                            
and again the difference with respect to their non-GIIPS counterparts is significant. Figures 5a to 

5d illustrate the market reactions in response to the proposition of the deposit tax whereas the 

effects of the actual bail-in in Cyprus are depicted in Figures 6a to 6d. As can be seen from those 

graphs, the heterogeneity of effects is particularly large for CDS spreads. 

The strong evidence for a CDS spread increase in response to the bail-in events in Cyprus can be 

explained by the fact that a comparatively large bail-in basis was combined with a high political 

spillover effect. However, the particularly strong reaction for banks from the GIIPS-countries is 

worth to be investigated in greater detail. In the following, we focus on the key event in Cyprus, 

namely the decision to bail in senior debt on 25 March 2013. 

The role of fiscal capacity 

One  plausible  explanation  for  the  particularly  strong  reaction  of  banks  in  GIIPS  countries  is 

these countries’ much stronger indebtedness as measured by the ratio of government debt over 

GDP. This is supported by Figure 7, which shows a scatter plot of the average country-specific 

abnormal rises in CDS spreads on 25 March 2013 against each country’s debt-to-GDP ratio. The 

figure suggests a positive relationship between the size of public debt (relative to GDP) and the 

CDS spread increase. Interestingly, banks located in the severely indebted country Greece do not 

seem to have been impressed much by the decision to bail in senior debt in Cyprus. This could 

be due to the fact that Greece banks were already in intensive care at the time, such that reactions 

were subdued due to the already strong interventions in the Greek banking sector. In contrast, 

Switzerland and the United Kingdom show a notable increase in CDS spreads even though their 

public debt levels stand at comparatively low levels. Especially for Switzerland, this could be due 

to too-big-to-be-saved considerations. 

The strong relationship between the abnormal change in CDS spreads and the debt-to-GDP ratio 

can  be  explained  by  the  importance  of  fiscal  capacity  for  bail-out  expectations.  Only  a  country 

with sufficient fiscal space would be able to bail out banks. In contrast, an already highly indebted 

country would very likely be forced into a bail-in in case of disturbances in the banking sector.  

In order to further investigate this issue we run cross-sectional regressions, explaining the bank-

specific abnormal change in CDS spreads on 25 March 2013 by a number of country- and bank-

specific  variables  (Tables  11  and  12).  Besides  the  debt-to-GDP  ratio  (as  of  2012),  we  include 

banks’ risk characteristics, which could be correlated with a sovereign’s indebtedness and affect 

abnormal changes in CDS spreads. As a broad measure of bank risk, we include the bank’s CDS 

spreads as of 31 December 2012. In additional specifications, we include the interaction term of 

20 

 
those  two  variables  to  capture  the  idea  that  CDS  spreads  of  weaker  banks  may  react  more 

strongly  if  these  banks  are  located  in  fiscally  weak  countries.  The  debt-to-GDP  ratio  and  CDS 

levels  are  demeaned  in  order  to  facilitate  the  interpretation  when  using  interaction  terms.  We 

furthermore control for other bank-specific variables: the natural logarithm of average assets as a 

measure of bank size, a dummy variable indicating whether a bank is a G-SIB, the average equity 

ratio  as  a  measure  of  a  bank’s  leverage,  and  net  income  relative  to  total  assets  to  measure 

profitability. Finally, we include a dummy variable for supersized banking sectors if the ratio of 

bank assets over GDP is larger than 400 percent in order to capture the effect that some banking 

sectors may be too large to be rescued, which would also affect the probability of a bail-in.33 

Tables 11 and 12 show the results from the cross-sectional OLS regressions for the full sample 

and for a sample excluding banks from countries, which were already under an ESM (European 

Stability Mechanism) program, namely Greece, Spain, Ireland, and Portugal. 

In  both  tables  we  observe  that  the  debt-to-GDP  ratio  is  significant  in  all  model  specifications. 

The  coefficient  is  also  economically  significant:  Increasing  the  debt-to-GDP  ratio  by  one 

standard deviation (i. e., by 28 percentage points) raises the abnormal change in CDS spreads by 

3  basis  points  in  the  baseline  specification  in  column  1  (compared  to  a  mean  effect  of  7  basis 

points and a standard deviation of 10, see Table 2c). The effect increases to 4 basis points when 

program countries are omitted. The level of banks’ CDS spreads is not significant in all but one 

specification. The results on the interaction effect are mixed. The full sample yields the somewhat 

puzzling  result  that  the  interaction  term  is  negative  and  sometimes  statistically  significant  (see 

Table 11), although quite small. In contrast, the coefficient becomes positive and significant, once 

we drop the program countries. Bank size has a strong positive and significant effect, no matter 

whether it is measured by total average assets or the G-SIB dummy. Hence, results are consistent 

with the previous findings from the event-study analysis. Omitting program countries (see Table 

12)  improves  the  explanatory  power  of  the  model  without  changing  the  overall  structure  of 

results.  The  increase  in  the  adjusted  R  squared  is  mostly  driven  by  the  fact  that  Greek  banks 

show  a  comparatively  low  increase  in  CDS  spreads  combined  with  a  huge  public  debt  level. 

Furthermore  the  dummy  for  supersized  banking  sectors  gains  significance  in  this  alternative 

specification.  The  result  is  mainly  driven  by  the  substantial  CDS  increase  of  banks  from 

Switzerland and the United Kingdom. Both countries host huge domestic banking sectors whose 

33 As at 2012, the United Kingdom, Switzerland, and Ireland were the only countries within this sample where the 
dummy turns 1. See data for bank assets scaled by GDP at the HelgiLibrari: 
http://www.helgilibrary.com/indicators/index/bank-assets-as-of-gdp. 

21 

 
                                                            
banks  exhibit  a  substantial  rise  in  CDS  spreads  while  Ireland,  where  CDS  reactions  are  rather 

mild, is excluded as a program country (see Figure 7). 

The most important result from these regressions is that the abnormal change in CDS spreads in 

response  to  the  bail-in  event  in  Cyprus  appears  to  be  driven  to  a  large  extent  by  the  host 

country’s fiscal capacity, which limits a country’s ability to bail out the domestic banking sector 

and therefore makes a bail-in more credible. 

Overall  it  seems  that  that  the  Cyprian  crisis  constituted  a  watershed  regarding  the  bail-out  of 

bank creditors. The bail-in of senior creditors and even large depositors showed investors quite 

plainly  that  bail-outs  would  be  much  less  likely  in  the  future.  However,  not  all  banks  were 

affected  equally.  The  effects  were  strongest  in  countries  with  low  fiscal  capacities.  In  contrast, 

countries with high fiscal capacity may still be inclined to bail out their banks. 

4.5 Portugal – Late event with little impact 

The  last  country  event  is  the  bail-in  of  the  Portuguese  bank  Banco  Espírito  Santo.  Political 

spillovers are likely to be small because the event occurred at a time when the SRM had already 

been agreed upon. Moreover, the bail-in basis was only junior debt, hence senior creditors were 

spared  another  time.  We  identified  one  bail-in  relevant  event  only,  which  is  given  by  the 

announcement to split up the bank into a good and a bad bank. 

Table 13 reports the results for CDS spreads. Interestingly, the event itself does not lead to any 

significant reactions whereas the Friday prior to the weekend of negotiations exhibits a significant 

coefficient for the full sample. As can be seen from the last column, this is largely driven by the 

GIIPS  banks.  Given  that  CDS  spreads  are  bouncing  back  on  the  actual  day  (albeit  not 

significantly), it seems that markets had anticipated a less favourable outcome than the one finally 

realized. Conceivably, markets might have expected parts of senior debt to be bailed in addition 

to junior debt. Equity markets do not seem to have been impressed much by the Portuguese case 

(Table  14).  Only  the  difference  of  GIIPS  versus  their  counterparts  turns  out  to  be  marginally 

significant within the enlarged event window.  

4.6 The Single Resolution Mechanism (SRM) 

Finally,  we  consider  the  implementation  of  the  new  European  framework  for  bank  resolution, 

namely  the  Single  Resolution  Mechanism  (SRM).  We  identified  five  bail-in  relevant  events 

according to our article search. The first event occurred when the EU finance ministers agreed 

22 

 
upon the rules for the Bank Recovery and Resolution Directive (BRRD) on 28 June 2013. The 

second  event  is  the  proposal  of  the  SRM  by  the  European  Commission  on  9  July  2013. 

According  to  this  proposal  the  power  to  wind  down  failing  banks  was  to  be  centralized  in 

Brussels.34  Event  number  three  on  18  December  2013  combines  two  relevant  messages  for 

financial markets. While the Council of the European Union agreed upon the general approach 

towards  the  SRM,  including  the  applicable  rules  of  the  BRRD35,  the  European  Central  Bank 

(ECB)  cast  some  fundamental  doubts.  The  key  concern  according  to  the  ECB  was  the 

unresolved question about the financing of the resolution fund, required to conduct a centralized 

wind-down  procedure  in  a  credible  way.  The  fourth  event  describes  the  provisional  agreement 

between the Council and the European Parliament on the construction of the SRM, reached on 

20  March  2014.36  An  important  result  of  this  compromise  included  the  financing  of  the 

resolution fund. Hence, this event could be regarded the crucial step in reaching a conclusion to 

the  long-lasting  negotiations.  Finally,  the  fifth  event  on  15  April  2014  refers  to  the  formal 

agreement of the European Parliament to back the Commission’s proposals on completing the 

SRM. 

Given  the  lengthy  negotiation  process  typical  for  this  type  of  legal  reform  an  evaluation  using 

event study methods is complicated by the permanent revisions of expectations in the context of 

the  gradual  negotiation  process.  Nevertheless,  our  analysis  is  able  to  detect  some  noteworthy 

reactions to the SRM implementation process. 

This  concerns  especially  the  fourth  event,  which  can  be  considered  the  key  step  towards  the 

introduction of the Single Resolution Mechanism because a provisional agreement was reached. 

We find a significant abnormal increase in CDS spreads by 3 basis points in the full sample (see 

Table  15).  The  reaction  of  G-SIBs  is  significantly  stronger  (by  3  basis  points),  as  would  be 

expected. Interestingly, we do not find any significant differences between banks from GIIPS and 

non-GIIPS  countries.  This  may  reflect  the  introduction  of  common  rules  for  all  eurozone 

countries.  The  other  events  show  hardly  any  reactions,  apart  from  a  significant  difference  of 

systemic vs. non-systemic banks at the time of the first event. Interestingly, the second and third 

event display negative though mostly not significant coefficients, which may reflect disappointed 

expectations. 

34 Note that the proposal was going to be presented on the following day. But the key points had been published by 
the Financial Times – European Edition one day before. 
35 For further details, see Council of the European Union (2013). 
36 European Parlament (2014). 

23 

 
                                                            
Stock prices, given in Table 16, largely mirror CDS reactions for the fourth event (though only in 

the  enlarged  event  window),  with  stock  returns  dropping  by  1.6  percentage  points  in  the  full 

sample. There is no significant difference between G-SIBs and non-G-SIBs here. In addition, the 

fifth  event  generates  widespread  negative  effects  on  stock  returns,  with  a  slightly  stronger 

negative  effect  on  G-SIBs  and  banks  from  GIIPS  countries  (although  the  difference  is  not 

significant).  This  seems  to  be  driven  less  by  changes  in  bail-out  expectations,  given  the  lack  of 

significance in the CDS regressions, but rather by a perceived decrease in bank profitability, e. g., 

through  the  required  contributions  to  the  Single  Resolution  fund.  A  significant  difference 

between banks from GIIPS and non-GIIPS countries is found only for the second event; there 

the difference is, however, substantial. 

Overall  it  appears  that  the  effects  are  quantitatively  much  less  important  than  those  from  the 

Cyprus event, in spite of the significance of the SRM for the occurrence of future bail-outs. This 

strengthens the view that bail-out expectations are determined more by the actual occurrence of 

bail-ins than by the implementation of rules. It may also reflect the general scepticism of markets 

towards the commitment to these rules in crisis times. 

5. Conclusion 

In this paper we have investigated whether the European policy makers’ intentions to move from 

bail-out to bail-in has led to market reactions indicating reduced bail-out expectations. We have 

analysed  the  reactions  of  CDS  spreads  and  stock  returns  in  response  to  five  bail-in  cases 

occurring  between  2011  and  2014  as  well  as  to  the  implementation  of  the  European  Single 

Resolution Mechanism. We employ an event study analysis on a broad sample of European CDS 

spreads and stock returns. 

Our  results  suggest  that  the  occurrence  of  a  bail-in  indeed  led  to  a  reduction  in  bail-out 

expectations. By far the strongest rise in CDS spreads was found in the Cyprian case, which had 

the largest bail-in basis and a strong political spillover. Equity markets mostly mirrored the results 

for  CDS  spreads,  especially  in  the  cases  of  the  Netherlands  or  Cyprus.  This  suggests  that  the 

expectation of bail-in affects future bank returns indirectly through the effect on funding costs. 

These strong results do not imply that the process of restructuring Cyprus’ banks should be taken 

as  a  blueprint  for  bank  restructuring.  After  all,  the  process  of  negotiation  was  chaotic  and 

unnecessarily disruptive. However, bailing in senior creditors and even depositors sent a strong 

and credible signal to investors that the euro area was entering a new regime, moving from bail-

out to bail-in. 

24 

 
Effects  are  not  homogeneous  across  banks.  In  many  cases  systemic  banks  showed  larger 

reactions. This is exactly what would be expected since bail-out expectations for these banks were 

larger to begin with. Even more importantly, there appears to be a strong heterogeneity between 

banks from GIIPS and non-GIIPS countries. Especially for the bail-in cases in the Netherlands 

and Cyprus the impact on CDS spreads was much stronger for banks located in GIIPS than in 

non-GIIPS  countries.  This  indicates  that  a  bail-in  was  perceived  to  be  more  likely  in  countries 

with  lower  fiscal  capacity.  Given  the  increasing  unwillingness  of  the  eurozone  to  finance  bank 

restructuring in vulnerable countries, such countries would simply not be able to conduct bank 

bail-outs. From a policy perspective, this is a somewhat troubling result because it might indicate 

that bank bail-out is still likely in fiscally strong countries such as Germany, which could easily 

finance  a  bank  bail-out  without  financial  support  from  the  eurozone.  This  would  also  entail 

competitive distortions in the European banking sector. 

Market reactions in response to the implementation of the European SRM were less pronounced 

than  for  the  most  prominent  country-level  events.  This  suggests  that  markets  were  impressed 

much  more  by  the  actual  occurrence  of  bail-ins,  such  as  in  Cyprus,  (“actions”)  than  by  the 

implementation  of  rules  (“words”).  However,  this  would  not  do  full  justice  to  the  SRM 

regulation.  First,  the  SRM  had  not  been  fully  implemented  yet.  Second,  some  of  the  change  in 

expectations that we measure in the individual events has to be assigned to the expectations on 

the future regime, as implemented by the SRM.  

In  this  interpretation,  the  new  restructuring  regime  indeed  reduced  bail-out  expectations  in  a 

significant  way.  However,  there  is  still  a  long  way  to  go.  The  observed  heterogeneity  across 

country  groups  suggests  that  the  credibility  of  bail-in  depends  on  countries’  ability  to  conduct 

bail-outs.  Hence,  bail-in  rules  need  to  be  further  strengthened  to  increase  their  credibility  and 

foster  national  governments’  commitment  to  bail-in.  It  will  also  be  crucial  how  future  banking 

crises will be handled. The fact that senior creditors of Greek banks were spared from losses in 

2015  and  the  political  uproar  upon  the  recent  bail-ins  of  investors  in  Italy  and  Portugal  raises 

concerns that policy makers will continue to try to circumvent bail-in rules. Whether Europe will 

truly get to bail-in instead of bail-out will depend crucially on the handling of the next resolution 

cases. Our results suggest that actions speak louder than rules and good intentions.  

25 

 
 
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Figure 3a. Bail-in Netherlands | CDS Spreads 

Figure 3b. Bail-in Netherlands | Stock Prices   

The figures show the reaction of stock prices and CDS spreads in response to the given event. The graph for stock prices shows equally weighted 
indices, normalized to 100 at the day prior to the event. The graph for CDS spreads shows the average differences in CDS spreads with respect to 
the day prior to the event. Both graphs are starting five trading days before and ending five trading days after the event. 

 
 
 
 
 
 
Figure 4a. Key Events in Netherlands and Cyprus | CDS Spreads 

Figure 4b. Key Events in Netherlands and Cyprus | Stock Prices 

The figures show the reaction of stock prices and CDS spreads in response to the given events. The graph for stock prices shows equally weighted 
indices, normalized to 100 at the day prior to the first event. The graph for CDS spreads shows the average differences in CDS spreads with respect 
to the day prior to the first event. Both graphs are starting five trading days before and ending ten trading days after the event. 

 
 
 
 
Figure 4c. Key Events in Netherlands and Cyprus | CDS Spreads 

Figure 4d. Key Events in Netherlands and Cyprus | Stock Prices 

The figures show the reaction of stock prices and CDS spreads in response to the given events. The graph for stock prices shows equally weighted 
indices, normalized to 100 at the day prior to the first event. The graph for CDS spreads shows the average differences in CDS spreads with respect 
to the day prior to the first event. Both graphs are starting five trading days before and ending ten trading days after the event. 

 
 
 
 
 
 
Figure 5a. Proposal of a Deposit Tax in Cyprus | CDS Spreads 

Figure 5b. Proposal of a Deposit Tax in Cyprus | Stock Prices 

The figures show the reaction of stock prices and CDS spreads in response to the given event. The graph for stock prices shows equally weighted 
indices, normalized to 100 at the day prior to the event. The graph for CDS spreads shows the average differences in CDS spreads with respect to 
the day prior to the event. Both graphs are starting five trading days before and ending five trading days after the event. 

 
 
 
 
 
 
Figure 5c. Proposal of a Deposit Tax in Cyprus | CDS Spreads 

Figure 5d. Proposal of a Deposit Tax in Cyprus | Stock Prices 

The figures show the reaction of stock prices and CDS spreads in response to the given event. The graph for stock prices shows equally weighted 
indices, normalized to 100 at the day prior to the event. The graph for CDS spreads shows the average differences in CDS spreads with respect to 
the day prior to the event. Both graphs are starting five trading days before and ending five trading days after the event. 

 
 
 
 
 
 
Figure 6a. Bail-in in Cyprus | CDS Spreads 

Figure 6b. Bail-in in Cyprus | Stock Prices 

The figures show the reaction of stock prices and CDS spreads in response to the given event. The graph for stock prices shows equally weighted 
indices, normalized to 100 at the day prior to the event. The graph for CDS spreads shows the average differences in CDS spreads with respect to 
the day prior to the event. Both graphs are starting five trading days before and ending five trading days after the event. 

 
 
 
 
 
Figure 6c. Bail-in in Cyprus | CDS Spreads 

Figure 6d. Bail-in in Cyprus | Stock Prices 

The figures show the reaction of stock prices and CDS spreads in response to the given event. The graph for stock prices shows equally weighted 
indices, normalized to 100 at the day prior to the event. The graph for CDS spreads shows the average differences in CDS spreads with respect to 
the day prior to the event. Both graphs are starting five trading days before and ending five trading days after the event. 

 
 
 
 
Figure 7. Abnormal CDS Increase vs. Debt over GDP | Senior Bail-in Cyprus 

Figure  6  shows  the  country  specific  average  increase  of  CDS  spreads  in  response  to  the  bail-in  of  senior  debt  in  Cyprus  on  25th  March  2013. 
Countries are sorted in an ascending manner. Figure 6 depicts a scatter plot of the country specific average increase of CDS spreads on 25th March 
versus the debt over GDP level. Data for Debt over GDP is taken from the World Economic Outlook Database by the IMF as of April 2014. 

 
 
 
Table 1: Event Overview and Newspaper Articles

Country

Date

Event

Article Headline in the FT - Europe Edition

Denmark

06.02.2011 Amagerbanken: Bail-in of senior debt

Spain

10.07.2012 Spanish bank rescue plan implies bail-in

19.07.2012 German government backs rescue plan

A senior haircut precedent in Denmark, 
08.02.2011*

Savers face losses in Spain bank rescue
plan, 11.07.2012
Spain bailout-backed, 20.07.2012

23.08.2012 Spain pushes national bank resolution-law

Spain bank rules push, 24.08.2012

Netherlands

01.02.2013 Nationalization of the SNS Reaal

Torrid week for European banks, 02.02.2013

Cyprus

11.02.2013 Eurozone Finance ministers: Bail-in as an 

option

18.03.2013 Proposal in Cyprus to tax bank deposits

25.03.2013 Bail-in of senior debt 

Radical Cyprus rescue plan puts uninsured 
depositors in line of fire, 11.02.2013
Cyprus in crisis over tax on bank deposits, 
18.03.2013
Eurozone shifts burden of risk from taxpayers to 
investors, 26.03.2013

EU / SRM

28.06.2013 EU Finance Ministers agreed rules of BRRD
09.07.2013 Presentation of the SRM proposal
18.12.2013 EU-Council generally accepts SRM,  doubts by 

EU bank rules deal, 28.06.2013
Broad EU bank plan, 09.07.2013
ECB to blow bank union blueprint, 19.12.2013

the ECB

20.03.2014 Provisional agreement on the SRM

15.04.2014 EU Parliament backs commission's proposal 

on the SRM

Marathon talks seal EU bank union, 
21.03.2014
EU banking reforms mark the biggest shake-up 
for 20 years, 16.04.2014

Portugal

04.08.2014 Creditor bail-in | Banco Espirito Santo

BES knocked on bail-in, 05.08.2014

Notes: The table shows all country-specific events according to the lead article of the Financial Times - Europe Edition, published on
page one. Note that the event date and the date of its publication can differ from one day in between. This could be either due to a
weekend or due to the fact that the decision about the regulatory issue was reached after markets closed. *The event in Denmark is
the only case where the article was published on page 27 instead of the front-page of the Financial Times – Europe edition.

BANK

COUNTRY

OBS MEAN

STD

MIN

MAX

G-SIB GIIPS

BANK

COUNTRY

OBS MEAN

STD

MIN

MAX

G-SIB GIIPS

Table 2-a. Summary Statistics for CDS Spreads

BAWAG
ERSTE GROUP BANK
RAIFFEISEN ZENTRALBANK
KBC 
DEXIA
DANSKE BANK
SOCIETE GENERALE
BANQUE FEDERATIVE DE CREDIT MUTUEL
BNP PARIBAS
CREDIT AGRICOLE
CREDIT LYONNAIS
NATIXIS*
BAYERISCHE LANDESBANK
COMMERZBANK
DEUTSCHE BANK
HSH NORDBANK
IKB
LANDESBANK BADEN WUERTTEMBERG
LANDESBANK HESSEN THUERINGEN
NORD-LB
PORTIGON
UNICREDIT BANK (HVB)
NATIONAL BANK OF GREECE
ALPHA BANK
BANK OF IRELAND
BANCA ITALEASE
INTESA SANPAOLO
MEDIOBANCA
UNICREDIT
UBI BANCA
BANCA MONTE DEI PASCHI DI SIENA
BANCO POPOLARE

Total Observations CDS

Austria
Austria
Austria
Belgium
Belgium
Denmark
France
France
France
France
France
France
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Greece
Greece
Ireland
Italy
Italy
Italy
Italy
Italy
Italy
Italy

40960

4.145
3.203
3.785
4.432
8.356
3.753
5.851
5.287
4.698
6.096
6.595
3.362
3.671
6.747
3.789
4.832
4.472
3.475
4.520
3.930
5.412
3.044

-26.850
-20.976
-25.460
-24.830
-72.418
-37.710
-31.840
-44.690
-20.760
-28.960
-32.400
-19.200
-40.760
-41.190
-17.150
-39.330
-21.880
-19.690
-33.400
-30.880
-44.210
-17.919

26.930
640 -0.101
19.480
640 -0.072
27.860
640 -0.134
54.630
640 -0.100
58.770
640 -0.003
29.830
640 -0.107
30.760
640 -0.078
44.820
640 -0.168
26.090
640 -0.031
34.490
640 -0.046
33.490
640 -0.044
24.235
640 -0.058
29.250
0.027
640
100.520
640
0.027
17.600
640 -0.027
39.370
0.007
640
22.780
640 -0.205
39.720
640 -0.005
35.650
640 -0.078
22.260
640 -0.026
59.810
640
0.147
19.980
640 -0.187
640 -0.750 38.237 -312.450 374.720
640 -3.518 39.048 -476.650 283.050
640 -0.011 19.277 -102.260 212.450
83.890
640 -0.036
40.838
640
0.033
75.760
640 -0.039
42.710
0.043
640
53.444
640 -0.163
98.707
640
68.100
640

8.321
9.502
6.940
8.993
7.152
0.303 14.106
0.118 12.186

-67.490
-33.900
-41.270
-40.220
-67.890
-55.751
-63.191

BANCO POPOLARE DIE MILANO
BANCA NAZIONALE DEL LAVORO
ING BANK
SNS BANK
RABOBANK
RBS N.V.
VAN LANSCHOT
DNB BANK
BANCO COMERCIAL PORTUGUES
BANCO ESPIRITO SANTO
BCP FINANCE BANK
BANCO SABADELL
BANCO POPULAR ESPANOL
BANCO SANTANDER
BANKINTER
BANCO BILBAO VIZCAYA ARGENTARIA
CAIXA D'ESTALVIS I PENSIONS DE BARCELONA
CAJA DE AHORROS DEL MEDITERRANEO
SEB
SVENSKA HANDELSBANKEN
SKANDINAVIA ENSKILDE BANKEN
NORDEA
SWEDBANK
UBS
CREDIT SUISSE
ROYAL BANK OF SCOTLAND
BARCLAYS
THE CO-OPERATIVE BANK
HSBC
LLOYDS
SANTANDER UK
STANDARD CHARTERED

X

X
X

X

X

X

X
X
X
X
X
X
X
X
X
X

0.198 10.799
6.036
3.866
8.957
2.419
5.784
5.430
1.728

Italy
Italy
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Norway
Portugal
Portugal
Portugal
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Sweden
Sweden
Sweden
Sweden
Sweden
Switzerland
Switzerland

-87.742
640
-29.000
640 -0.013
-19.260
640 -0.090
-99.700
0.158
640
-12.040
640 -0.080
-35.790
640
0.011
-39.084
640 -0.078
640 -0.043
-14.610
640 -0.569 15.392 -125.880
-76.170
0.128 13.728
640
-75.100
640 -0.589 17.461
-73.105
9.652
0.194
640
-59.006
9.697
640
0.238
-51.300
9.285
640 -0.018
-54.240
9.611
640
0.274
-36.890
7.976
640 -0.077
640
-48.900
7.737
0.231
640 -0.219 14.720 -126.408
640 -0.100
640 -0.059
640 -0.211
640 -0.048
640 -0.081
640 -0.149
640 -0.080
United Kingdom 640 -0.006
United Kingdom 640 -0.006
United Kingdom 640
United Kingdom 640 -0.063
United Kingdom 640 -0.111
United Kingdom 640 -0.106
United Kingdom 640 -0.032

1.729
1.422
3.018
1.644
2.294
3.098
3.078
5.953
4.292
0.427 10.052
3.065
5.301
4.490
3.936

-9.880
-6.660
-17.000
-7.380
-14.320
-11.960
-12.590
-35.790
-27.090
-64.010
-12.120
-33.550
-29.950
-18.600

X
X

X
X
X
X
X
X
X
X
X
X

68.350
43.140
17.290
89.860
12.702
31.510
77.690
14.410
77.340
84.010
114.550
66.620
68.930
38.888
68.450
42.160
57.820
92.350
12.810
11.420
18.120
12.130
15.930
15.650
14.160
29.620
18.950
187.079
17.020
23.510
39.770
25.260

X

X

X

X

X
X
X
X

X

X

Notes: Data for CDS spreads is retrieved from Thomson Reuters (Datastream). The number of observations "OBS" for each bank is determined by the estimation window of 80 trading days plus the event window of 3 trading days for each event (excluding overlapping observations). "STD" stands for
the Standard Deviation, "MIN" indicates the lowest and "MAX" the highest observed value within the sample. The column "G-SIB" displays whether the respective bank is considered as systemically relevant. *Natixis is the only listed subsidiary of the non-listed Groupe Banque Populaire Casse
d’Epargne (Groupe BPCE); see also Bongini et al. (2015). The column “GIIPS” indicates whether a bank is located in Greece, Ireland, Italy, Portugal or Spain. See further methodological details in chapter 3.

BANK

COUNTRY

OBS MEAN

STD

MIN

MAX

G-SIB GIIPS

BANK

COUNTRY

OBS

MEAN

STD

MIN

MAX

G-SIB GIIPS

Table 2-b. Summary Statistics for Stock Returns

ERSTE GROUP BANK
RAIFFEISEN BANK INTERNATIONAL
DEXIA
KBC
CENTRAL COOPERATIVE BANK
CB FIRST INVESTMENT BANK
ZAGREBACKA BANKA
KOMERCNI BANKA
DANSKE BANK
JYSKE BANK
RINGKJOBING LANDBOBANK
SYDBANK
AKTIA BANK
POHJOLA BANK
BNP PARIBAS
CIC
CREDIT AGRICOLE
NATIXIS
SOCIETE GENERALE
COMMERZBANK
DEUTSCHE BANK
IKB
QUIRIN BANK
UMWELTBANK
AAREAL BANK
ALPHA BANK
BANK OF PIRAEUS
EUROBANK ERGASIAS
NATIONAL BANK OF GREECE
OTP BANK
ALLIED IRISH BANKS
BANK OF IRELAND
BANCA CARIGE
BANCA MONTE DEI PASCHI DI SIENA
BANCA POPOLARE DI MILANO
BANCA POPOLARE DI SONDRIO
BANCA POPOLARE DELL' EMILIA ROMAGNA
BANCO POPOLARE
CREDITO EMILIANO
BANCA PICCOLO CREDITO VALTELLINESE
INTESA SANPAOLO
MEDIOBANCA
UBI BANCA

X

X
X
X

X

Austria
Austria
Belgium
Belgium
Bulgaria
Bulgaria
Croatia
Czech Republic
Denmark
Denmark
Denmark
Denmark
Finland
Finland
France
France
France
France
France
Germany
Germany
Germany
Germany
Germany
Germany
Greece
Greece
Greece
Greece
Hungary
Ireland
Ireland
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy

640
0.000 0.022 -0.164 0.067
640 -0.001 0.023 -0.096 0.097
0.004 0.130 -0.333 0.500
640
0.001 0.026 -0.093 0.107
640
0.001 0.027 -0.111 0.157
640
0.002 0.029 -0.241 0.269
640
0.000 0.018 -0.068 0.080
640
0.000 0.015 -0.058 0.074
640
0.001 0.016 -0.094 0.067
640
0.001 0.015 -0.045 0.105
640
0.001 0.008 -0.028 0.036
640
0.001 0.013 -0.046 0.056
640
0.001 0.012 -0.064 0.071
640
0.001 0.018 -0.059 0.181
640
0.000 0.020 -0.057 0.097
640
0.000 0.009 -0.074 0.047
640
0.001 0.024 -0.080 0.087
640
0.001 0.023 -0.122 0.225
640
640
0.001 0.023 -0.072 0.103
640 -0.001 0.024 -0.092 0.110
640 -0.001 0.018 -0.071 0.087
0.002 0.046 -0.128 0.336
640
0.000 0.023 -0.080 0.121
640
0.001 0.022 -0.239 0.217
640
0.002 0.022 -0.065 0.100
640
640
0.001 0.054 -0.200 0.294
640 -0.002 0.058 -0.295 0.287
640 -0.003 0.074 -0.302 0.294
640 -0.002 0.054 -0.268 0.272
0.000 0.018 -0.061 0.067
640
0.000 0.047 -0.242 0.309
640
640
0.000 0.036 -0.228 0.164
640 -0.002 0.027 -0.173 0.129
0.000 0.039 -0.201 0.213
640
640
0.001 0.031 -0.104 0.111
640 -0.001 0.022 -0.072 0.095
0.000 0.030 -0.107 0.134
640
0.000 0.032 -0.149 0.189
640
0.001 0.023 -0.103 0.084
640
640 -0.001 0.026 -0.103 0.168
0.001 0.025 -0.096 0.126
640
0.000 0.024 -0.094 0.089
640
0.001 0.027 -0.092 0.088
640

Total Observations Stock Returns

54400

X
X
X
X

X
X
X
X
X
X
X
X
X
X
X
X
X

UNICREDIT
LLB
VP BANK
DNB
SPAREBANK 1 SR BANK
ING GROEP
VAN LANSCHOT
BANK BPH
BANK POLSKA KASA OPIEKI
MBANK
BANK HANDLOWY
ING BANK SLASKI
PKO BANK POLSKI
BANCO PORTUGUES DE INVESTIMENTO
BANCO COMERCIAL PORTUGUES
BANCO ESPIRITO SANTO
BANCA COMERCIALA CARPATICA
BANCA TRANSILVANIA
BANCO DE SABADELL
BANCO POPULAR ESPANOL
BANCO SANTANDER
BANKINTER
BANCO BILBAO VIZCAYA ARGENTARIA
CAIXABANK
NORDEA
SEB
SVENSKA HANDELSBANKEN
SWEDBANK
BANQUE CANTONALE DE VADAOISE
VONTOBEL
CREDIT SUISSE GROUP
JULIUS BAER
LUZERNER KANTONALBANK
ST. GALLER KANTONALBANK
UBS
VALIANT
ZUGER KANTONALBANK
BARCLAYS
HSBC
LLOYDS
ROYAL BANK OF SCOTLAND
STANDARD CHARTERED

Italy
Liechtenstein
Liechtenstein
Norway
Norway
Netherlands
Netherlands
Poland
Poland
Poland
Poland
Poland
Poland
Portugal
Portugal
Portugal
Romania
Romania
Spain
Spain
Spain
Spain
Spain
Spain
Sweden
Sweden
Sweden
Sweden
Switzerland
Switzerland
Switzerland
Switzerland
Switzerland
Switzerland
Switzerland
Switzerland
Switzerland
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom

640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640
640

0.000 0.026 -0.088 0.143
0.000 0.018 -0.073 0.070
0.000 0.012 -0.052 0.049
0.001 0.016 -0.107 0.073
0.001 0.013 -0.053 0.059
0.000 0.021 -0.074 0.108
0.000 0.018 -0.090 0.125
0.000 0.019 -0.079 0.097
0.000 0.016 -0.071 0.068
0.001 0.018 -0.056 0.097
0.000 0.020 -0.084 0.067
0.000 0.017 -0.096 0.072
0.000 0.014 -0.054 0.048
0.001 0.024 -0.084 0.134
0.001 0.031 -0.130 0.269
-0.003 0.042 -0.421 0.197
0.001 0.021 -0.147 0.149
0.001 0.015 -0.068 0.064
0.000 0.022 -0.075 0.139
-0.001 0.027 -0.123 0.147
0.000 0.020 -0.073 0.107
0.001 0.026 -0.072 0.139
0.000 0.021 -0.078 0.107
0.001 0.022 -0.100 0.169
0.001 0.013 -0.058 0.056
0.001 0.014 -0.051 0.082
0.000 0.012 -0.077 0.043
0.001 0.015 -0.079 0.105
0.000 0.011 -0.070 0.074
0.000 0.015 -0.055 0.103
0.000 0.017 -0.105 0.054
0.000 0.015 -0.074 0.083
0.000 0.007 -0.031 0.042
0.000 0.011 -0.050 0.051
0.000 0.016 -0.077 0.073
-0.001 0.015 -0.104 0.060
0.000 0.008 -0.031 0.054
0.000 0.021 -0.155 0.087
0.000 0.011 -0.035 0.047
0.001 0.019 -0.061 0.084
0.000 0.023 -0.115 0.108
0.000 0.017 -0.164 0.071

X

X

X
X
X

X
X
X
X
X
X

X

X

X

X

X

X

X
X

X
X

Notes: Data for stock returns is retrieved from Thomson Reuters (Datastream). The number of observations "OBS" for each bank is determined by the estimation window of 80 trading days plus the event window of 3 trading days for each event (excluding overlapping observations).
"STD" stands for the Standard Deviation, "MIN" indicates the lowest and "MAX" the highest observed value within the sample. The column "G-SIB" displays whether the respective bank is considered as systemically relevant. *Natixis is the only listed subsidiary of the non-listed Groupe
Banque Populaire Casse d’Epargne (Groupe BPCE): see also Bongini et al. (2015). The column “GIIPS” indicates whether a bank is located in Greece, Ireland, Italy, Portugal or Spain. See further methodological details in chapter 3.

Table 2c. Summary Statistics for Cross Sectional Regressions 

CDS Increase   Debt over GDP 

Average Common Equity 

Average Assets  Net Income  CDS Level  Average Equity Ratio  Net Income Ratio 

Obs 
Mean 
Std 
Min 
Max 

64 
7.129 
10.192 
-11.881 
61.212 

64 
88.514 
28.290 
30.000 
157.180 

59 
28,866 
28,468 
667 
103,138 

60 
657,595 
706,844 
13,066 
2,825,319 

60 
283 
2,836 
-9,162 
9,649 

64 
261.038 
253.944 
68.007 
1,490 

59 
0.0485 
0.0206 
0.0035 
0.1355 

60 
-0.0015 
0.0068 
-0.0205 
0.0080 

Table 2d. Correlations | Data for Cross Sectional Regressions  

CDS Increase 

Debt/GDP 

CDS Level 

Average Equity Ratio 

Ln (Assets) 

Net Income Ratio 

CDS Increase 

Debt / GDP 

CDS Level 

1 

0.3171 

1 

0.196 

0.6236 

1 

Average Equity Ratio 

0.1122 

0.3077 

-0.0209 

1 

Ln (Assets) 

0.1338 

-0.2893 

-0.3755 

-0.3691 

1 

Net Income / Average Assets 

-0.1452 

-0.5037 

-0.6211 

-0.0251 

0.4641 

1 

Table 2c shows the summary statistics for variables used in cross sectional regressions. CDS Increase is taken from the event study results as of 25 March 2015 and stemming from Datastream (Thompson 
Reuters). All other variables are taken as of end of year 2012. Data for Debt/GDP is taken from the World Economic Outlook Database by the IMF. All other variables are retrieved from Bankscope. 

 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Table 3. Abnormal Bank CDS Spread Differences | Creditor Bail-in Denmark

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] Amagerbanken: Bail-in of senior debt

06-Feb-11

   - Enlarged event window [0+1]

-1.369
[0.759]

-2.437
[0.700]

0.064
[0.986]

2.270
[0.669]

-1.837
[0.703]

-3.974
[0.562]

1.901
[0.430]

6.244*
[0.068]

-5.311
[0.508]

-10.691
[0.348]

0.648
[0.823]

1.745
[0.664]

-5.958
[0.301]

-12.436
[0.132]

Notes: The table show the results from SUR regressions using the daily first differences of bank CDS spreads as dependent variable. Abnormal differences in CDS spreads, displayed in
basis points, are estimated on the basis of the constant return model, using an estimation window of 80 trading days. Each system of regressions includes 64 banks. The first number
column “Full Sample” refers to the average abnormal differences of all banks at the respective event day. The column "G-SIB" displays the abnormal differences in banks CDS spreads of
the banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st
November 2012. The column “Non-G-SIB” shows the abnormal CDS spread differences of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two
subgroups. The column “GIIPS” shows the abnormal differences in banks CDS spreads of the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column
“Non-GIIPS” shows the abnormal differences of the remaining banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond
to the tests whether the average abnormal differences and the difference in abnormal differences between the given bank groups are equal to zero. All regressions include pre-event
dummies in order to account for anticipation effects. The enlarged event window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day
t=1. *** significant at 1 percent, ** significant at 5 percent, * significant at 10 percent.

Table 4. Abnormal Bank Stock Returns | Creditor Bail-in Denmark | Stoxx Global 1800

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] Amagerbanken: Bail-in of senior debt

06-Feb-11

   - Enlarged event window [0+1]

0.497
[0.595]

1.412
[0.286]

0.628
[0.642]

2.029
[0.289]

0.468
[0.590]

1.272
[0.301]

0.160
[0.825]

0.757
[0.461]

1.090
[0.499]

1.299
[0.570]

0.216
[0.754]

1.465
[0.133]

0.874
[0.446]

-0.166
[0.919]

Notes: The table shows the results from SUR regressions using the daily returns of bank stock prices as dependent variable. Abnormal bank stock returns, displayed in fractions, are
estimated on the basis of the market model, using the Stoxx Global 1800 benchmark index and an estimation window of 80 trading days. Each system of regressions includes 85 banks.
The first number column “Full Sample” refers to the average abnormal return of all banks at the respective event day. The column "G-SIB" displays the abnormal banks stock returns of the
banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st
November 2012. The column “Non-G-SIB” shows the returns of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two subgroups. The column “GIIPS”
shows the differences in abnormal returns for the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column “Non-GIIPS” shows the returns of the remaining
banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond to the tests whether the average abnormal returns and the
difference in abnormal returns between the given bank groups are equal to zero. All regressions include pre-event dummies in order to account for anticipation effects. The enlarged event
window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day t=1. *** significant at 1 percent, ** significant at 5 percent, * significant at
10 percent.

Table 5. Abnormal Bank CDS Spread Differences | Creditor Bail-in Spain

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] Spanish bank rescue plan implies bail-in

10-Jul-12

   - Enlarged event window [0+1]

[2] German government backs rescue plan

19-Jul-12

   - Enlarged event window [0+1]

[3] Spain pushes national bank resolution-law

23-Aug-12

   - Enlarged event window [0+1]

-0.557
[0.924]

-1.059
[0.898]

-0.291
[0.959]

2.421
[0.762]

2.065
[0.710]

1.786
[0.821]

-3.224
[0.612]

-2.909
[0.747]

-0.280
[0.963]

4.309
[0.617]

4.289
[0.497]

12.974
[0.148]

0.315
[0.959]

-0.454
[0.958]

-0.294
[0.960]

1.804
[0.830]

1.339
[0.812]

-1.868
[0.815]

-3.539
[0.452]

-2.454
[0.713]

0.014
[0.998]

2.505
[0.696]

2.950
[0.452]

14.842***
[0.008]

0.385
[0.972]

-1.685
[0.913]

-0.998
[0.925]

2.763
[0.855]

1.266
[0.904]

-7.253
[0.627]

-1.038
[0.794]

-0.721
[0.896]

0.071
[0.985]

2.195
[0.675]

2.474
[0.495]

6.264
[0.213]

1.424
[0.868]

-0.963
[0.937]

-1.069
[0.899]

0.568
[0.962]

-1.208
[0.883]

-13.517
[0.249]

Notes: The table shows the results from SUR regressions using the daily first differences of bank CDS spreads as dependent variable. Abnormal differences in CDS spreads, displayed in
basis points, are estimated on the basis of the constant return model, using an estimation window of 80 trading days. Each system of regressions includes 64 banks. The first number
column “Full Sample” refers to the average abnormal differences of all banks at the respective event day. The column "G-SIB" displays the abnormal differences in banks CDS spreads of
the banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st
November 2012. The column “Non-G-SIB” shows the abnormal CDS spread differences of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two
subgroups. The column “GIIPS” shows the abnormal differences in banks CDS spreads of the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column
“Non-GIIPS” shows the abnormal differences of the remaining banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond
to the tests whether the average abnormal differences and the difference in abnormal differences between the given bank groups are equal to zero. All regressions include pre-event
dummies in order to account for anticipation effects. The enlarged event window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day
t=1. *** significant at 1 percent, ** significant at 5 percent, * significant at 10 percent.

Table 6. Abnormal Bank Stock Returns | Creditor Bail-in Spain | Stoxx Global 1800

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] Spanish bank rescue plan implies bail-in

10-Jul-12

   - Enlarged event window [0+1]

[2] German government backs rescue plan

19-Jul-12

   - Enlarged event window [0+1]

[3] Spain pushes national bank resolution-law

23-Aug-12

   - Enlarged event window [0+1]

0.234
[0.867]

0.107
[0.957]

-0.120
[0.929]

-2.138
[0.258]

0.540
[0.668]

0.077
[0.966]

0.480
[0.799]

1.321
[0.622]

-0.334
[0.851]

-3.568
[0.156]

0.943
[0.606]

-0.637
[0.805]

0.179
[0.893]

-0.167
[0.930]

-0.071
[0.956]

-1.816
[0.317]

0.449
[0.700]

0.238
[0.885]

0.301
[0.767]

1.488
[0.303]

-0.263
[0.786]

-1.752
[0.201]

0.493
[0.6059

-0.874
[0.516]

-0.820
[0.736]

-0.905
[0.793]

0.029
[0.990]

-4.058
[0.225]

0.338
[0.879]

0.448
[0.886]

0.735
[0.746]

0.587
[0.688]

-0.190
[0.846]

-1.227
[0.376]

0.636
[0.496]

-0.100
[0.940]

-1.555
[0.380]

-1.492
[0.553]

0.220
[0.901]

-2.830
[0.254]

-0.298
[0.856]

0.548
[0.813]

Notes: The table shows the results from SUR regressions using the daily returns of bank stock prices as dependent variable. Abnormal bank stock returns, displayed in fractions, are
estimated on the basis of the market model, using the Stoxx Global 1800 benchmark index and an estimation window of 80 trading days. Each system of regressions includes 85 banks.
The first number column “Full Sample” refers to the average abnormal return of all banks at the respective event day. The column "G-SIB" displays the abnormal banks stock returns of the
banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st
November 2012. The column “Non-G-SIB” shows the returns of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two subgroups. The column “GIIPS”
shows the differences in abnormal returns for the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column “Non-GIIPS” shows the returns of the remaining
banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond to the tests whether the average abnormal returns and the
difference in abnormal returns between the given bank groups are equal to zero. All regressions include pre-event dummies in order to account for anticipation effects. The enlarged event
window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day t=1. *** significant at 1 percent, ** significant at 5 percent, * significant at
10 percent.

Table 7. Abnormal Bank CDS Spread Differences | Creditor Bail-in Netherlands

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] Nationalization of the SNS Reaal

01-Feb-13

   - Enlarged event window [0+1]

3.137
[0.439]

11.897**
[0.039]

3.304
[0.382]

3.082
[0.479]

10.194*
[0.058]

12.453**
[0.044]

0.222
[0.935]

-2.259
[0.562]

7.603
[0.367]

25.964**
[0.030]

0.852
[0.683]

4.593
[0.113]

6.751
[0.325]

21.371**
[0.029]

Notes: The table shows the results from SUR regressions using the daily first differences of bank CDS spreads as dependent variable. Abnormal differences in CDS spreads, displayed in
basis points, are estimated on the basis of the constant return model, using an estimation window of 80 trading days. Each system of regressions includes 64 banks. The first number
column “Full Sample” refers to the average abnormal differences of all banks at the respective event day. The column "G-SIB" displays the abnormal differences in banks CDS spreads of
the banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st
November 2012. The column “Non-G-SIB” shows the abnormal CDS spread differences of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two
subgroups. The column “GIIPS” shows the abnormal differences in banks CDS spreads of the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column
“Non-GIIPS” shows the abnormal differences of the remaining banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond
to the tests whether the average abnormal differences and the difference in abnormal differences between the given bank groups are equal to zero. All regressions include pre-event
dummies in order to account for anticipation effects. The enlarged event window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day
t=1. *** significant at 1 percent, ** significant at 5 percent, * significant at 10 percent.

Table 8. Abnormal Bank Stock Returns | Creditor Bail-in Netherlands | Stoxx Global 1800

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] Nationalization of the SNS Reaal

01-Feb-13

   - Enlarged event window [0+1]

0.185
[0.849]

-2.533*
[0.066]

0.058
[0.966]

-3.909**
[0.046]

0.214
[0.817]

-2.223*
[0.091]

-0.155
[0.854]

-1.686
[0.161]

-0.554
[0.748]

-4.398*
[0.072]

0.536
[0.461]

-1.648
[0.111]

-1.090
[0.405]

-2.750
[0.140]

Notes: The table shows the results from SUR regressions using the daily returns of bank stock prices as dependent variable. Abnormal bank stock returns, displayed in fractions, are
estimated on the basis of the market model, using the Stoxx Global 1800 benchmark index and an estimation window of 80 trading days. Each system of regressions includes 85 banks.
The first number column “Full Sample” refers to the average abnormal return of all banks at the respective event day. The column "G-SIB" displays the abnormal banks stock returns of the
banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st
November 2012. The column “Non-G-SIB” shows the returns of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two subgroups. The column “GIIPS”
shows the differences in abnormal returns for the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column “Non-GIIPS” shows the returns of the remaining
banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond to the tests whether the average abnormal returns and the
difference in abnormal returns between the given bank groups are equal to zero. All regressions include pre-event dummies in order to account for anticipation effects. The enlarged event
window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day t=1. *** significant at 1 percent, ** significant at 5 percent, * significant at
10 percent.

Table 9. Abnormal Bank CDS Spread Differences | Creditor Bail-in Cyprus

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] Eurozone Finance ministers: Bail-in as an option

11-Feb-13

   - Enlarged event window [0+1]

[2] Proposal in Cyprus to tax bank deposits

18-Mar-13

   - Enlarged event window [0+1]

[3] Bail-in of senior Debt 

25-Mar-13

   - Enlarged event window [0+1]

0.348
[0.927]

-0.832
[0.877]

6.5***
[0.007]

-0.067
[0.984]

-3.673
[0.448]

0.483
[0.905]

0.096
[0.987]

7.019**
[0.012]

6.33***
[0.009]

11.967***
[0.000]

12.767***
[0.001]

11.705***
[0.001]

7.129***
[0.002]

15.075***
[0.000]

9.317***
[0.001]

6.415***
[0.006]

15.16***
[0.000]

15.047***
[0.000]

-0.550
[0.809]

-3.770
[0.244]

0.689
[0.686]

1.062
[0.661]

2.902*
[0.084]

0.114
[0.962]

2.448
[0.754]

2.504
[0.821]

-0.726
[0.712]

-2.481
[0.363]

3.174
[0.615]

4.985
[0.580]

12.853***
[0.005]

3.249**
[0.034]

9.604***
[0.008]

21.717***
[0.001]

6.819***
[0.001]

14.897***
[0.004]

12.533***
[0.005]

4.365***
[0.003]

8.168**
[0.019]

30.801***
[0.000]

6.869***
[0.001]

23.933***
[0.000]

Notes: The table shows the results from SUR regressions using the daily first differences of bank CDS spreads as dependent variable. Abnormal differences in CDS spreads, displayed in
basis points, are estimated on the basis of the constant return model, using an estimation window of 80 trading days. Each system of regressions includes 64 banks. The first number
column “Full Sample” refers to the average abnormal differences of all banks at the respective event day. The column "G-SIB" displays the abnormal differences in banks CDS spreads of the
banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st
November 2012. The column “Non-G-SIB” shows the abnormal CDS spread differences of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two
subgroups. The column “GIIPS” shows the abnormal differences in banks CDS spreads of the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column “Non-
GIIPS” shows the abnormal differences of the remaining banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond to the
tests whether the average abnormal differences and the difference in abnormal differences between the given bank groups are equal to zero. All regressions include pre-event dummies in
order to account for anticipation effects. The enlarged event window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day t=1. ***
significant at 1 percent, ** significant at 5 percent, * significant at 10 percent.

Table 10. Abnormal Bank Stock Returns | Creditor Bail-in Cyprus | Stoxx Global 1800

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] Eurozone Finance ministers: Bail-in as an option

11-Feb-13

   - Enlarged event window [0+1]

[2] Proposal in Cyprus to tax bank deposits

18-Mar-13

   - Enlarged event window [0+1]

[3] Bail-in of senior Debt 

25-Mar-13

   - Enlarged event window [0+1]

-0.069
[0.944]

0.586
[0.671]

-1.367
[0.138]

-0.081
[0.954]

2.165
[0.274]

-2.080
[0.120]

-0.066
[0.943]

0.230
[0.861]

-1.207
[0.170]

-4.051***
[0.002]

-5.355***
[0.005]

-3.758***
[0.003]

-1.496*
[0.098]

-2.547**
[0.047]

-2.792**
[0.033]

-3.259*
[0.080]

-1.204
[0.163]

-2.387*
[0.052]

-0.015
[0.986]

1.935
[0.115]

-0.873
[0.309]

-1.597
[0.188]

-1.59*
[0.063]

-0.873
[0.471]

-0.421
[0.806]

0.834
[0.732]

-1.403
[0.397]

0.099
[0.893]

0.469
[0.654]

-1.35**
[0.047]

-6.703***
[0.004]

-2.793***
[0.004]

-2.560
[0.116]

-5.137**
[0.026]

-0.992
[0.138]

-1.318
[0.165]

-0.520
[0.691]

0.365
[0.844]

-0.053
[0.966]

-3.91**
[0.028]

-1.569
[0.209]

-3.819**
[0.031]

Notes: The table shows the results from SUR regressions using the daily returns of bank stock prices as dependent variable. Abnormal bank stock returns, displayed in fractions, are
estimated on the basis of the market model, using the Stoxx Global 1800 benchmark index and an estimation window of 80 trading days. Each system of regressions includes 85 banks.
The first number column “Full Sample” refers to the average abnormal return of all banks at the respective event day. The column "G-SIB" displays the abnormal banks stock returns of the
banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st
November 2012. The column “Non-G-SIB” shows the returns of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two subgroups. The column “GIIPS”
shows the differences in abnormal returns for the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column “Non-GIIPS” shows the returns of the remaining
banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond to the tests whether the average abnormal returns and the
difference in abnormal returns between the given bank groups are equal to zero. All regressions include pre-event dummies in order to account for anticipation effects. The enlarged event
window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day t=1. *** significant at 1 percent, ** significant at 5 percent, * significant at
10 percent.

Table 11. Cross-Sectional Regressions | Bail-in Cyprus | All Banks

Dependent Variable: Abnormal CDS Increase as at 25th March 2013 

Debt over GDP | de-meaned

CDS Level | de-meaned

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.116**
(0.023)

0.141***
(0.000)

0.114***
(0.000)

0.0852**
(0.022)

0.103***
(0.001)

0.0822*
(0.082)

0.0834*
(0.058)

0.0834*
(0.066)

-0.00442
(0.485)

0.00600
(0.502)

0.0197
(0.138)

0.0112
(0.181)

0.0164
(0.150)

0.0172*
(0.100)

0.0171
(0.115)

(Debt over GDP) x (CDS Level) | de-meaned

-0.000176
(0.141)

-0.000328*
(0.068)

-0.000229*
(0.052)

-0.000250
(0.131)

-0.000255
(0.105)

-0.000255
(0.108)

Ln(Average Assets)

G-SIB

Average Equity Ratio

Net Income Ratio

Supersized Banking Sector

Constant

Observations
R-squared
Adjusted R-squared
SE clusters on Country Level

2.529**
(0.017)

4.477**
(0.014)

4.815**
(0.017)

4.761**
(0.018)

4.773**
(0.010)

11.60
(0.875)

6.187
(0.244)

59
0.155
0.075
15

10.75
(0.878)

34.51
(0.881)

6.300
(0.204)

59
0.155
0.058
15

10.73
(0.880)

33.80
(0.889)

-0.0430
(0.982)

6.303
(0.215)

59
0.155
0.039
15

7.222***
(0.000)

7.222***
(0.000)

8.036***
(0.000)

-23.62*
(0.051)

7.166***
(0.000)

64
0.103
0.089
15

64
0.110
0.081
15

64
0.125
0.081
15

60
0.185
0.126
15

64
0.158
0.101
15

Notes: The table shows the results from cross-sectional regressions using the abnormal CDS spread increases as of 25th March 2013 (response to the senior bail-in in Cyprus) as dependent
variable. Standard errors are clustered on country-level. P-values are displayed in brackets. Control variables are defined as follows: Debt over GDP stands for the country specific public debt
divided by the gross domestic product in 2012; Average Equity Ratio is defined as Average Common Equity divided by average assets in 2012. Net Income Ratio is calculated as banks net
income in 2012 scaled by average assets in 2012. Ln(Assets) stands for the natural logarithm of the average assets in 2012; CDS Level stands for the absolute values in CDS spreads as at
31st December 2012; G-SIB denotes a dummy equal to 1 if the respective bank is considered as sytemically important; Supersized Banking sectors indicates a dummy equal to 1 if the
respective bank belongs to the Ireland, Switzerland or the United Kingdom. Series of Debt over DGP and CDS Level are adjusted for their mean, i.e. "de-meaned" Stars are to be interpreted as
follows: * significant at 10 percent; ** significant at 5%, *** significant at 1%.

Table 12. Cross-Sectional Regressions | Bail-in Cyprus | Excluding Banks from Programm Countries

Debt over GDP | de-meaned

CDS Level | de-meaned

Dependent Variable: Abnormal CDS Increase as at 25th March 2013 

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.149***
(0.003)

0.0946*
(0.059)

0.118**
(0.024)

0.115**
(0.042)

0.0219
(0.401)

-0.00289
(0.828)

0.0128
(0.584)

0.102**
(0.016)

0.00639
(0.630)

0.150**
(0.031)

0.147**
(0.045)

0.148**
(0.040)

-0.000247
(0.990)

-0.00680
(0.795)

-0.00534
(0.838)

(Debt over GDP) x (CDS Level) | de-meaned

0.00133***
(0.004)

0.00121***
(0.008)

0.00127***
(0.003)

0.00151***
(0.008)

0.00156**
(0.012)

0.00159**
(0.010)

Ln(Average Assets)

G-SIB

Average Equity Ratio

Net Income Ratio

Supersized Banking Sector

Constant

Observations
R-squared
Adjusted R-squared
SE clusters on Country Level

3.164***
(0.007)

5.381***
(0.000)

6.434***
(0.001)

6.434***
(0.000)

4.104**
(0.017)

-36.46**
(0.015)

2.723**
(0.043)

51
0.155
0.137
11

51
0.194
0.160
11

51
0.323
0.280
11

49
0.444
0.394
11

51
0.374
0.320
11

4.560*
(0.057)

-90.89
(0.392)

6.839
(0.219)

49
0.401
0.331
11

4.664*
(0.061)

-90.50
(0.458)

-212.7
(0.340)

6.624
(0.280)

49
0.409
0.325
11

3.711*
(0.098)

-93.90
(0.418)

-176.6
(0.445)

2.839*
(0.076)

6.489
(0.268)

49
0.418
0.319
11

Notes: The table shows the results from cross-sectional regressions using the abnormal CDS spread increases as of 25th March 2013 (response to the senior bail-in in Cyprus) as dependent
variable. Standard errors are clustered on country-level. P-values are displayed in brackets. Control variables are defined as follows: Debt over GDP stands for the country specific public debt
divided by the gross domestic product in 2012; Average Equity Ratio is defined as Average Common Equity divided by average assets in 2012. Net Income Ratio is calculated as banks net
income in 2012 scaled by average assets in 2012. Ln(Assets) stands for the natural logarithm of the average assets in 2012; CDS Level stands for the absolute values in CDS spreads as at
31st December 2012; G-SIB denotes a dummy equal to 1 if the respective bank is considered as sytemically important; Supersized Banking sectors indicates a dummy equal to 1 if the
respective bank belongs to the Ireland, Switzerland or the United Kingdom. Series of Debt over DGP and CDS Level are adjusted for their mean, i.e. "de-meaned" Stars are to be interpreted as
follows: * significant at 10 percent; ** significant at 5%, *** significant at 1%.

Table 13. Abnormal Bank CDS Spread Differences | Creditor Bail-in Portugal

Event

Anticipatory effect [-1]

[1] Creditor bail-in | Banco Espirito Santo

   - Enlarged event window [0+1]

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

01-Aug-14

04-Aug-14

3.485*
[0.081]

-0.646
[0.747]

-2.257
[0.426]

3.268
[0.154]

-1.384
[0.546]

-4.457
[0.170]

3.556*
[0.077]

-0.405
[0.840]

-1.538
[0.589]

-0.288
[0.830]

-0.979
[0.467]

-2.919
[0.126]

8.103**
[0.031]

-1.236
[0.742]

-3.575
[0.501]

1.122
[0.045]

-0.344
[0.799]

-1.546
[0.407]

6.98**
[0.018]

-0.893
[0.763]

-2.029
[0.630]

Notes: The table shows the results from SUR regressions using the daily first differences of bank CDS spreads as dependent variable. Abnormal differences in CDS spreads, displayed in basis
points, are estimated on the basis of the constant return model, using an estimation window of 80 trading days. Each system of regressions includes 64 banks. The first number column “Full
Sample” refers to the average abnormal differences of all banks at the respective event day. The column "G-SIB" displays the abnormal differences in banks CDS spreads of the banks in the
sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st November 2012. The
column “Non-G-SIB” shows the abnormal CDS spread differences of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two subgroups. The column “GIIPS”
shows the abnormal differences in banks CDS spreads of the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column “Non-GIIPS” shows the abnormal
differences of the remaining banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond to the tests whether the average
abnormal differences and the difference in abnormal differences between the given bank groups are equal to zero. All regressions include pre-event dummies in order to account for
anticipation effects. The enlarged event window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day t=1. *** significant at 1 percent, **
significant at 5 percent, * significant at 10 percent.

Table 14. Abnormal Bank Stock Returns | Creditor Bail-in Portugal | Stoxx Global 1800

Event

Anticipatory effect [-1]

[1] Creditor bail-in | Banco Espirito Santo

   - Enlarged event window [0+1]

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

01-Aug-14

04-Aug-14

-0.115
[0.867]

-0.189
[0.780]

-0.648
[0.499]

0.540
[0.417]

0.128
[0.845]

0.004
[0.997]

-0.262
[0.716]

-0.260
[0.715]

-0.795
[0.431]

0.802
[0.103]

0.388
[0.424]

0.799
[0.247]

-1.140
[0.385]

-0.234
[0.857]

-2.521
[0.171]

0.372
[0.464]

-0.167
[0.740]

0.241
[0.736]

-1.512
[0.168]

-0.068
[0.950]

-2.762*
[0.073]

Notes: The table show the results from SUR regressions using the daily returns of bank stock prices as dependent variable. Abnormal bank stock returns, displayed in fractions, are
estimated on the basis of the market model, using the Stoxx Global 1800 benchmark index and an estimation window of 80 trading days. Each system of regressions includes 85 banks.
The first number column “Full Sample” refers to the average abnormal return of all banks at the respective event day. The column "G-SIB" displays the abnormal banks stock returns of the
banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st
November 2012. The column “Non-G-SIB” shows the returns of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two subgroups. The column “GIIPS”
shows the differences in abnormal returns for the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column “Non-GIIPS” shows the returns of the remaining
banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond to the tests whether the average abnormal returns and the
difference in abnormal returns between the given bank groups are equal to zero. All regressions include pre-event dummies in order to account for anticipation effects. The enlarged event
window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day t=1. *** significant at 1 percent, ** significant at 5 percent, * significant at
10 percent.

Table 15. Abnormal Bank CDS Spread Differences | Single Resolution Mechanism

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] EU Finance Ministers agreed rules of BRRD

28-Jun-13

   - Enlarged event window [0+1]

[2] Presentation of the SRM proposal

09-Jul-13

   - Enlarged event window [0+1]

[3] EU-Council generally accepts SRM,  doubts by the ECB

18-Dec-13

   - Enlarged event window [0+1]

[4] Provisional agreement on the SRM

20-Mar-14

   - Enlarged event window [0+1]

[5] EU Parliament backs commission's proposal on the SRM

15-Apr-14

   - Enlarged event window [0+1]

2.358
[0.458]

3.170
[0.482]

-3.188
[0.315]

-2.701
[0.548]

-1.035
[0.719]

-3.123
[0.444]

3.241*
[0.092]

2.866
[0.293]

0.436
[0.826]

-0.009
[0.997]

5.229
[0.178]

5.112
[0.354]

-5.640
[0.144]

-2.160
[0.694]

-1.402
[0.701]

-4.729
[0.361]

5.494**
[0.021]

4.002
[0.236]

1.220
[0.605]

0.706
[0.833]

1.420
[0.644]

2.536
[0.561]

-2.387
[0.437]

-2.878
[0.509]

-0.915
[0.734]

-2.599
[0.497]

2.505
[0.179]

2.495
[0.345]

0.180
[0.926]

-0.242
[0.930]

3.809*
[0.058]

2.576
[0.366]

-3.254
[0.102]

0.718
[0.799]

-0.487
[0.764]

-2.130
[0.356]

2.989**
[0.022]

1.506
[0.417]

1.040
[0.419]

0.949
[0.604]

4.184
[0.454]

4.092
[0.606]

-1.842
[0.743]

-3.259
[0.682]

-1.537
[0.763]

-5.532
[0.445]

3.805
[0.281]

4.187
[0.403]

0.306
[0.935]

1.070
[0.839]

1.423
[0.509]

2.636
[0.378]

-3.876*
[0.070]

-2.361
[0.425]

-0.779
[0.680]

-1.848
[0.480]

2.952**
[0.023]

2.141
[0.236]

0.503
[0.696]

-0.548
[0.759]

2.761
[0.488]

1.456
[0.798]

2.034
[0.612]

-0.897
[0.876]

-0.759
[0.833]

-3.684
[0.475]

0.853
[0.753]

2.046
[0.596]

-0.197
[0.945]

1.619
[0.693]

Notes: The table shows the results from SUR regressions using the daily first differences of bank CDS spreads as dependent variable. Abnormal differences in CDS spreads, displayed in basis
points, are estimated on the basis of the constant return model, using an estimation window of 80 trading days. Each system of regressions includes 64 banks. The first number column “Full
Sample” refers to the average abnormal differences of all banks at the respective event day. The column "G-SIB" displays the abnormal differences in banks CDS spreads of the banks in the
sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability Board on 1st November 2012. The
column “Non-G-SIB” shows the abnormal CDS spread differences of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two subgroups. The column “GIIPS”
shows the abnormal differences in banks CDS spreads of the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column “Non-GIIPS” shows the abnormal
differences of the remaining banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond to the tests whether the average
abnormal differences and the difference in abnormal differences between the given bank groups are equal to zero. All regressions include pre-event dummies in order to account for
anticipation effects. The enlarged event window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day t=1. *** significant at 1 percent, **
significant at 5 percent, * significant at 10 percent.

Table 16. Abnormal Bank Stock Returns | Single Resolution Mechanism | Stoxx Global 1800

Event

Date

Full Sample

G-SIB

Non-G-SIB

G-SIB vs. Non-
GSIB

GIIPS

Non-GIIPS

GIIPS vs. Non-
GIIPS

[1] EU Finance Ministers agreed rules of BRRD

28-Jun-13

   - Enlarged event window [0+1]

[2] Presentation of the SRM proposal

09-Jul-13

   - Enlarged event window [0+1]

[3] EU-Council generally accepts SRM,  doubts by the ECB

18-Dec-13

   - Enlarged event window [0+1]

[4] Provisional agreement on the SRM

20-Mar-14

   - Enlarged event window [0+1]

[5] EU Parliament backs commission's proposal on the SRM

15-Apr-14

   - Enlarged event window [0+1]

-0.743
[0.405]

0.126
[0.920]

-1.004
[0.271]

-0.496
[0.699]

-0.189
[0.798]

-0.318
[0.619]

-0.683
[0.304]

-1.614*
[0.086]

-1.510
[0.191]

-0.101
[0.951]

-0.905
[0.563]

-1.072
[0.527]

-0.445
[0.619]

-0.229
[0.859]

-0.517
[0.503]

-1.51
[0.167]

-1.378**
[0.034]

-1.724**
[0.019]

-1.023
[0.271]

-1.865*
[0.076]

-0.570
[0.521]

0.178
[0.888]

-1.027
[0.255]

-0.366
[0.773]

-0.132
[0.862]

-0.338
[0.756]

-0.720
[0.285]

-1.637*
[0.085]

-1.3**
[0.050]

-0.833
[0.380]

-0.940
[0.233]

-0.279
[0.803]

0.122
[0.881]

-0.707
[0.583]

-0.313
[0.640]

0.109
[0.909]

0.203
[0.690]

0.127
[0.860]

-0.424
[0.381]

-1.032
[0.136]

-0.762
[0.658]

-0.063
[0.979]

-2.305
[0.185]

-3.228
[0.187]

-0.562
[0.668]

-0.174
[0.926]

-0.816
[0.472]

-2.035
[0.205]

-1.957*
[0.089]

-0.433
[0.792]

-0.734
[0.265]

0.216
[0.817]

-0.387
[0.568]

0.801
[0.400]

-0.012
[0.985]

-0.386
[0.671]

-0.619
[0.289]

-1.414*
[0.087]

-1.104**
[0.046]

-1.303
[0.998]

-0.028
[0.984]

-0.279
[0.891]

-1.918
[0.183]

-4.029**
[0.047]

-0.550
[0.626]

0.212
[0.896]

-0.197
[0.893]

-0.620
[0.652]

-0.853
[0.386]

0.869
[0.537]

Notes: The table shows the results from SUR regressions using the daily returns of bank stock prices as dependent variable. Abnormal bank stock returns, displayed in fractions, are
estimated on the basis of the market model, using the Stoxx Global 1800 benchmark index and an estimation window of 80 trading days. Each system of regressions includes 85
banks. The first number column “Full Sample” refers to the average abnormal return of all banks at the respective event day. The column "G-SIB" displays the abnormal banks stock
returns of the banks in the sample, considered as systemically important. The selection is based on the list of 28 systemically relevant institutions, published by the Financial Stability
Board on 1st November 2012. The column “Non-G-SIB” shows the returns of the remaining banks, whereas “G-SIB vs. Non-GSIB” stands for the difference of these two subgroups. The
column “GIIPS” shows the differences in abnormal returns for the banks, belonging to the countries Greece, Ireland, Italy, Portugal and Spain. The column “Non-GIIPS” shows the returns
of the remaining banks, whereas “GIIPS vs. Non-GIIPS” stands for the difference of these two subgroups. The p-values in brackets correspond to the tests whether the average abnormal
returns and the difference in abnormal returns between the given bank groups are equal to zero. All regressions include pre-event dummies in order to account for anticipation effects.
The enlarged event window [0+1] shows the average cumulated abnormal differences of the event date t=0 and the following day t=1. *** significant at 1 percent, ** significant at 5
percent, * significant at 10 percent.

Acknowledgements 

We thank conference participants at the 4th Research Workshop in Financial Economics in Mainz and at the 
Annual Meeting of the German Economic Association (Verein für Socialpolitik) in Münster as well as seminar 
participants at Johannes Gutenberg University Mainz for helpful comments and suggestions. Ami Dalloul and 
Manuel Pelzer provided excellent research assistance. An earlier version of this paper carried the title “Getting 
to Bail-in: Effects of Creditor Participation in European Bank Restructuring” (Schäfer, Schnabel and Weder di 
Mauro, German Council of Economic Experts Working Paper No. 08/2014). Financial support from DFG Special 
Priority Program SPP 1578 is gratefully acknowledged. 

Alexander Schäfer 
German Council of Economic Experts c/o Federal Statistical Office, 65180 Wiesbaden, Germany; email: 
alexander.schaefer@svr-wirtschaft.de. 

Isabel Schnabel 
Institute for Finance & Statistics, University of Bonn, 53012 Bonn, Germany; email: isabel.schnabel@uni-
bonn.de 

Beatrice Weder di Mauro 
Gutenberg School of Management and Economics, Johannes Gutenberg University Mainz, 55099 Mainz, 
Germany; email: beatrice.weder@uni-mainz.de 

© European Systemic Risk Board, 2016 

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are affiliated. 

ISSN 
ISBN 
DOI 
EU catalogue No 

2467-0677 (online) 
978-92-95081-34-5 (online) 
10.2849/768515 (online) 
DT-AD-16-007-EN-N (online)