Bank balance sheet constraints and bond liquidity.
Date: 2021-09-30
Institutions: European Central Bank
Subjects: balance sheet, bank, bond, coronavirus disease, financial requirements, investment company, money-market liquidity

Johannes Breckenfelder, Victoria Ivashina 

Working Paper Series 

Bank balance sheet constraints and 
bond liquidity 

No 2589 / September 2021 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
We  explore  the  ties  between  bonds  and  individual  dealers  formed  through  home 
advantage  and  the  persistence  of  previous  underwriting  relationships.  Building  on 
these  connections,  we  show  that  the  introduction  of  the  leverage  ratio  for  the 
European banks had a large impact on exposed bonds’ liquidity. Moreover, based on 
these  ties,  we  show  that  bond  mutual  fund  panic  following  the  2020  pandemic 
outbreak affected substantially more mutual funds with the larger exposures to dealer 
banks’ balance sheet constraints.   

Keywords:  Bond  liquidity;  market-making;  capital  requirements;  leverage  ratio; 
mutual funds; COVID-19   

JEL Classification: G12, G18, G21 

ECB Working Paper Series No 2589 / September 20212Non-technical summary 

The  regulatory overhaul  in  the aftermath of the 2008 global financial crisis 

(GFC)  raised  banks’  minimum  capitalization  requirements  and  curtailed  their 

broader  risk  exposure.  One  of  the  unintended  consequences  of  these  new 

regulations  has  been  reduced  liquidity  in  the  bond  markets,  as  bank-affiliated 

dealers reduced balance sheet space available for market-making, and costs faced 

by  some  counterparties  have  increased.  Consistent  with  these  observations,  as  of 

2017, primary dealer inventories of corporate bonds appeared to be at an all-time 

low, relative to the market size.  

Practitioners and policy-makers have specifically cited the Basel III leverage ratio 

and the Volcker Rule in the U.S. as key drivers of reduced market liquidity. As part 

of  the  broker-dealer  activities,  banks  maintain  inventories  of  securities.  The 

aggregate  decline  in  the  size  of  the  inventories  is  the  concern.  The  capacity  and 

willingness of dealers to warehouse securities, especially for the bond market which 

relies heavily on principal-based market-making services, are essential for liquidity. 

Because of its non-risk-weighted nature, the leverage ratio—which requires banks to 

maintain a minimum equity capital as a fraction of its assets—makes it less profitable 

for banks to engage in low profit margin activities.  An illustration of this mechanism 

can be seen in March 2020, when the inflow of deposits inflated bank assets, making 

the  leverage  ratio  more  binding.  As  a  result,  banks  were  believed  to  have  been 

pushed to offload lower-yielding Treasury bonds, among other things.  Thus, we will 

use slack under the leverage constraint as our central explanatory variable.  

The contribution of our paper to the debate about the regulatory impact on the 

bank  market-making  activities  and,  ultimately,  bond  market  liquidity  is  two-fold. 

The structural shifts in the bank bond inventories are unmistakable. However, the 

connection  to  specific  bank  constraints  is  harder  to  establish  for  several  reasons, 

including other market shifts that might have influenced the broker-dealer role over 

the same period. On the other hand, in absence of bond-to-dealer “stickiness,” bonds 

would  be  transacted  through  the  least  constrained  dealer,  making  balance-sheet 

ECB Working Paper Series No 2589 / September 20213 
constraints of individual dealers (and cross-sectional analysis) irrelevant.  As our first 

contribution, we overcome this problem by looking at (i) the home bias among the 

dealers, and (ii) the persistence in dealing activities among bond underwriters as a 

source of plausibly exogenous assignment between dealers and bonds. Simply put, 

we should  see that the bulk of French  bonds are likely to be transacted by French 

dealer banks. Similarly, if Deutsche Bank was the underwriter of the Air France-KLM 

bond issue, it is very likely that it is also the key dealer for these bonds long after the 

initial placement. (To the best of our knowledge, the evidence supporting the latter 

fact is novel to the academic literature.)  

We  provide  two  sets  of  results.    First,  armed  with  the  plausibly  exogenous 

matching between bonds and dealers, we anchor a set of tests around December 31, 

2013,  the  date  when  as  a  result  of  2014  Comprehensive  Assessment  exercise  the 

leverage ratio became effectively binding for the euro area banks for the first time. 

We find that for countries where bank dealers are one percentage point closer to the 

regulatory requirement, the bid-ask spread is 8 basis points higher (about a quarter 

of the median bid-ask  spread in our sample).  In  addition,  we  look at  the leverage 

ratio  of  past  bond  underwriters,  which  provides  additional  support  for  our 

hypothesis.  We  find  that  if  the  bond  dealer  with  existing  underwriting  ties  is  one 

percentage  points  closer  to  the  regulatory  requirement,  the  bid-ask  spreads  of  the 

bond increases by 4 basis points (about 6.8 % of the mean). 

The first set of results identifies the impact of dealer banks’ regulatory constraints 

on  bond  liquidity.  In  the  second  set  of  results,  we  connect  the  2020  outflows  and 

selling behavior of fixed income mutual funds to illiquidity due to the dealer bank 

balance-sheet  constraints.  To  do  so,  once  again,  we  rely  on  the  importance  of  the 

home advantage, as well as past bond underwriting relationships. We show that fund 

outflows and bond selling pressure were significantly more severe for mutual funds 

exposed to dealer illiquidity through banks’ balance sheet constraints. 

ECB Working Paper Series No 2589 / September 20214 
1. 

Introduction 

The regulatory overhaul in the aftermath of the 2008  global financial  crisis 

(GFC)  raised  banks’  minimum  capitalization  requirements  and  curtailed  their 

broader  risk  exposure.  One  of  the  unintended  consequences  of  these  new 

regulations  has  been  reduced  liquidity  in  the  bond  markets,  as  bank-affiliated 

dealers reduced balance sheet space available for market-making, and costs faced 

by some counterparties have increased (e.g., Powell, 2015; Duffie, 2016; European 

Commission, 2017).1 Consistent with these observations, as of 2017, primary dealer 

inventories  of  corporate  bonds  appeared  to  be  at  an  all-time  low,  relative  to  the 

market size. Figure 1 provides statistics for the euro area banks’ bond holdings and 

the overall size of the euro area bond market.2 As can be seen in 2017, banks’ average 

holdings were approximately 6% of market size compared to approximately 30% in 

2009.  Over the same period, the euro area long-term bond market size increased by 

EUR 950 billion or roughly 80%.  

[FIGURE 1] 

Practitioners and policy-makers have specifically cited the Basel III leverage ratio 

and the Volcker Rule in the U.S. as key drivers of reduced market liquidity.3 (Our 

1 In his 2015 speech Jerome Powell stated that: “many point to post-crisis regulation as 
a key factor driving any recent decline  in liquidity (…) I would agree that it is one 
factor driving recent changes in market making.” The European Commission report 
concludes  that  “[b]anks  and  dealers  have  more  limited  balance  sheet  capacity  now 
than prior to the financial crisis. In general, it is more difficult for investors to trade in 
large sizes. […] Traders that require immediate executions in large size now pay more 
in price impact because the cost of liquidity has risen post-crisis.”  
2 Similar patterns were highlighted for the U.S. in Liberty Street Economics blog, August 
21,  2015,  “What’s  Driving  Dealer  Balance  Sheet  Stagnation?”  by  Tobias  Adrian, 
Michael Fleming, Daniel Stackman, and Erik Vogt, 
https://libertystreeteconomics.newyorkfed.org/2015/08/whats-driving-dealer-
balance-sheet-stagnation.html. 
3  For  example,  see  “The  Impact  of the  Basel  III  Leverage Ratio  on Risk-Taking  and 
Bank  Stability,”  Special  Feature  in  the  ECB  Financial  Stability  Review  (November 
2015). 
https://www.ecb.europa.eu/pub/pdf/fsr/art/ecb.fsrart201511_01.en.pdf?8dbb0ec

ECB Working Paper Series No 2589 / September 20215 
study focuses on the euro area banks, so the latter is not directly relevant to us.) As 

part  of  the  broker-dealer  activities,  banks  maintain  inventories  of  securities.  The 

aggregate decline in the size of the inventories is precisely the concern illustrated in 

Figure 1. The capacity and willingness of dealers to warehouse securities, especially 

for the bond market which relies heavily on principal-based market-making services, 

are  essential  for  liquidity.  Because  of  its  non-risk-weighted  nature,  the  leverage 

ratio—which requires banks to maintain a minimum equity capital as a fraction of its 

assets—makes it less profitable for banks to engage in low profit margin activities.  

An  illustration  of  this  mechanism  can  be  seen  in  March  2020,  when  the  inflow  of 

deposits inflated bank assets, making the leverage ratio more binding. As a result, 

banks were believed to have been pushed to offload lower-yielding Treasury bonds, 

among other things. This prompted the U.S. Federal Reserve to temporarily exempt 

Treasuries and other safe securities from leverage calculations.4  Thus, we will use 

slack  under  the  leverage  constraint  as  our  central  explanatory  variable.  However, 

leverage ratio was part of a broader regulatory package, and we will discuss farther 

the role of other constraints at the dealer level.     

The contribution of our paper to the debate about the regulatory impact on the 

bank market-making activities and, ultimately, bond market liquidity is two-fold. As 

Figure 1 illustrates, structural shifts in the bank bond inventories are unmistakable. 

However, the connection to specific bank constraints is harder to establish for several 

reasons, including other market shifts that might have influenced the broker-dealer 

role  over  the  same  period.  On  the  other  hand,  in  absence  of  bond-to-dealer 

“stickiness,” bonds would be transacted through the least constrained dealer, making 

balance-sheet  constraints  of  individual  dealers  (and  cross-sectional  analysis) 

irrelevant.  As our first contribution, we overcome this problem by looking at (i) the 

8072de08c70002fc0a68ebd81. 
4 See “Federal Reserve Board Announces Temporary Changes to Its Supplementary Leverage 
Ratio Rule to Ease Strains in the Treasury Market Resulting from the Coronavirus and Increase 
Banking  Organization’s  Ability  to  Provide  Credit  to  Households  and  Businesses”,  April  1, 
2020, https://www.federalreserve.gov/newsevents/pressreleases/bcreg20200401a.htm. 

ECB Working Paper Series No 2589 / September 20216home  bias  among  the  dealers,  and  (ii)  the  persistence  in  dealing  activities  among 

bond underwriters as a source of plausibly exogenous assignment between dealers 

and bonds. Simply put, we should see that the bulk of French bonds are likely to be 

transacted by French dealer banks. Similarly, if Deutsche Bank was the underwriter 

of the Air France-KLM bond issue, it is very likely that it is also the key dealer for 

these  bonds  long  after  the  initial  placement.  (To  the  best  of  our  knowledge,  the 

evidence  supporting  the  latter  fact  is  novel  to  the  academic  literature.)  As  we 

elaborate in Section 3, these mechanisms make dealers hard to replace. Ultimately, 

this  enables  us  to  use  cross-sectional  heterogeneity  in  bond  exposure  to  banks’ 

balance  sheet  constraints,  and  trace  its  impact  on  the  liquidity.  This  approach 

potentially  provides  an  alternative  to  measurement  of  bond  liquidity  in  future 

studies, as bonds are generally illiquid and lack consistent data. Our methodology 

provides an insight for measuring liquidity using dealer-level information. 

We  will  show  that  cross-sectional  differences  in  liquidity  due  to  dealer’s 

constraints are sizable. However, it is hard to argue that, in nearly a decade following 

the implementation of the leverage ratio, the aggregate impact on bond liquidity was 

a major impediment to bond market growth illustrated in Figure 1.5 (Same applies to 

the US market.) Indeed, the primary concern with the consequences of the reduced 

banks’ market-making capacity was not the immediate illiquidity costs, but instead 

the  build-up  of  financial  fragility.  This  is  consistent  with  Bao,  O’Hara,  and  Zhou 

(2018) and Dick-Nielsen and Rossi (2019) who emphasize that the liquidity matters 

the most during market stress or illiquidity events. The specific mechanism at play 

for  mutual  funds  was  articulated  by  Goldstein,  Jiang  and  Ng  (2017),  and  more 

recently by Ma, Xiao and Zeng (2020). The basic idea is that illiquidity and its uneven 

distribution  among  portfolio  assets  expose  mutual  funds  holding  such  assets  to 

5 Between 2013 and 2019 the euro area non-financial corporate bond market has nearly 
doubled  in  size,  growing  from  EUR  1.17  trillion  to  EUR  2.05  trillion  in  amounts 
outstanding, corresponding to roughly a 10% cumulative annual growth.  

ECB Working Paper Series No 2589 / September 20217significant fund outflows.  Until 2020, such fragility was largely hypothetical. But, in 

early 2020, mutual funds in the U.S. and Europe faced significant fund outflows, a 

dynamic that is believed to have contributed to the broader  bond market distress. 

Indeed,  in  both  sovereign  and  corporate  bond  markets,  the  central  banks  had  to 

intervene in a sizable and unprecedented way to stabilize the bond markets. A run 

on mutual funds in early 2020, therefore, constitutes a key economic setting to study 

the role of dealer banks’ balance sheet constraints in propagating financial fragility 

by amplifying the run dynamic. Our second contribution, therefore, is to show that 

fixed  income  funds  that  were  exposed  to  dealer  balance-sheet  constraints  in  their 

portfolio faced bigger selling pressure. 

In line with these two main contributions, we provide two sets of results.  First, 

armed  with  the  plausibly  exogenous  matching  between  bonds  and  dealers,  we 

anchor  a  set of  tests  around  December  31,  2013,  the  date  when  as  a  result of  2014 

Comprehensive  Assessment  exercise  the  leverage  ratio  became  effectively  binding 

for the euro area banks for the first time. Our results include bond fixed effects; that 

is, we compare the shift in liquidity for the same bond two years before and two years 

after the leverage  ratio is reported to bank supervisors.  We find that for  countries 

where bank dealers  are one percentage  point closer  to  the regulatory requirement 

(about one standard deviation), the bid-ask spread is 8 basis points higher (about a 

quarter of the median bid-ask spread in our sample). This result is robust to several 

ways  of  identifying  a  broker-dealer  bank  in  the  data,  and  to  constructing  bank 

balance-sheet  constraint  at  the  bond  level.  We  also  show  that  the  effect  comes 

through  the  domestic  dealer  banks,  but  not  through  other  domestic  banks.  We 

control for a range of contemporaneous macro variables including bond’s domestic 

country GDP growth, equity market and banking sector growth, volatility index and 

sovereign  spreads.  This  helps  us  to  make  sure  that  bond  liquidity  and  the  banks’ 

constraints are not trivially correlated in the country-level analysis. In addition, we 

look  at  the  leverage  ratio  of  past  bond  underwriters,  which  provides  additional 

support  for  our  hypothesis.  We  find  that  if  the  bond  dealer  with  existing 

ECB Working Paper Series No 2589 / September 20218underwriting ties is one percentage points closer to the regulatory requirement, the 

bid-ask spreads of the bond increases by 4 basis points (about 6.8 % of the mean). 

The first set of results identifies the impact of dealer banks’ regulatory constraints 

on  bond  liquidity.  In  the  second  set  of  results,  we  connect  the  2020  outflows  and 

selling behavior of fixed income mutual funds to illiquidity due to the dealer bank 

balance-sheet  constraints.  To  do  so,  once  again,  we  rely  on  the  importance  of  the 

home advantage, as well as past bond underwriting relationships. We show that fund 

outflows and bond selling pressure were significantly more severe for mutual funds 

exposed to dealer illiquidity through banks’ balance sheet constraints. 

Overall, our paper contributes to and interconnects two strands of research: 

(i) studies  focused  on  the  impact  of  Basel  III  regulatory  constraints  on  bond

liquidity, and (ii) fragility of bond mutual funds due to illiquidity.

Using  US  corporate  bond  data,  Schultz  (2017)  shows  that  after  the  Volcker 

Rule was finalized, dealers were more reluctant to take bonds into their inventory 

and unwound inventory positions more quickly. Adrian, Boyarchenko and Shachar 

(2017)  is  the  closest  study  to  our  first  insight.  Their  paper  links  changes  in  the 

liquidity of  individual  US  corporate bonds to financial institutions’ balance sheet 

constraints and find that bonds traded by more levered institutions are less liquid, 

especially  after  the  financial  crisis.  Their  paper  takes  the  assignment  of  bonds  to 

dealers as given. Our contribution is to micro-found bond-dealer matching, which 

is  not  trivial.  As  explained  earlier,  it  is  not  fully  clear  why  individual  bank 

constraints  matter  for  bond  intermediation.  Indeed,  Bessembinder,  Jacobsen, 

Maxwell  and  Venkataraman  (2018)  show  that  while  the  bank‐affiliated  dealers 

decreased  their  “capital  commitment”  in  US  corporate  bond  markets,  nonbank 

dealers  (unaffected  by  regulations)  have  increased  their  market  commitments. 

Aggregate impact on market liquidity remains debated. According to Bao, O'Hara 

and Zhou (2018), the net effect in the aftermath of the bank regulatory adjustment 

has been negative and, overall, corporate bonds in the US have become less liquid 

during times of stress. But Trebbi and Xiao (2019), Adrian, Fleming, Shachar and 

ECB Working Paper Series No 2589 / September 20219Vogt (2017) find only limited evidence of a deterioration in market liquidity. Choi 

and Hu (2019) point out that customers, rather than dealers, increasingly provide 

liquidity to other customers. However, for those trades in which dealers do provide 

liquidity using their inventory capacity, they document an increase in transaction 

costs after the financial crisis. (Bank market-making is not constrained to bonds, and 

derivative markets offer additional settings to analyze regulatory impact.6) 

In our study we directly explore the assignment of bonds to specific dealers, 

and  identify  cross-sectional  variation  in  the  regulatory  impact  on  bond  liquidity. 

Furthermore, we specifically focus on the compliance with the minimum leverage 

ratio. Ultimately, our specific interest lies in understanding consequences of bond 

illiquidity for stability of mutual fund investing. Bao et al. (2018) and Dick, Nielsen 

and Rossi (2019) stress that the liquidity matters most during specific market stress 

or liquidity events. Mutual funds worldwide had faced a significant shock with the 

COVID-19 related economic lockdown. This has been studied by Falato, Goldstein, 

and Hortaçsu (2020) in the US context. Our specific focus is on the role of the banks’ 

constrains in bond market making and its connection to specific pressures faced by 

the mutual funds. In this sense our work is closest to O’Hara and Zhou (2020) who 

also seek to understand the role of frictions among the market makers in driving the 

bond market turmoil in March of 2020. 

The rest of the paper is organized in six sections. Section 2 discusses the data 

sources used for this study. Section 3 provides more background on the leverage 

ratio requirement and builds the case for the “stickiness” of the dealers at the bond 

level. Section 4 presents the set of results that measure the impact of bank leverage 

ratio  requirement  on  bond  liquidity.    Section  5  looks  into  how  bank  leverage 

constraints differentially affected the liquidity of bond mutual fund holdings during 

6 For example, Acosta Smith, Ferrara and Rodriguez-Tous (2018) show that leverage-
constrained  banks  are  less  willing  to  clear  derivatives  on  behalf  of  their  clients. 
Cendese, Della Corte and Wang (2018) show that a tighter dealers’ leverage ratio is 
associated with  a deterioration in FX trading activity and an increase in short-term 
CIP deviations. 

ECB Working Paper Series No 2589 / September 202110the COVID-19 pandemic shock.  Section 6 concludes. 

2.

Data

We employ a range of datasets in this study: 

To identify broker-dealers we use the Eurosystem Asset Purchase Database, which 

is proprietary Eurosystem data on all executed trades under the European Central 

Bank’s  (ECB)  and  the  euro  area  national  central  banks’  Asset  Purchase  Program 

(APP). The purchases are conducted with eligible counterparties which we define as 

broker-dealers. We use two alternative definitions. First, we use broker-dealer banks 

that  engage  in  trading  of  corporate  bonds  under  the  Corporate  Sector  Purchase 

Program  (CSPP),  i.e.,  these  are  market  makers  in  the  corporate  bond  market.  The 

second definition uses the sample of broker-dealer banks that engage in trading of 

sovereign bonds under the Public Sector Purchase Program (PSPP). Note that both 

programs take place after the introduction of the leverage ratio, so, we are implicitly 

assuming that being a large bank dealer is a persistent characteristic. This is in line 

with Di Maggio, Kermani and Song (2017) who find that the bond dealer market in 

the U.S. is highly concentrated, with a few core dealers intermediating most of the 

transactions.  

Based  on  this  criterion,  we  identify  14  broker  dealers  using  corporate  bond 

purchase program, and 41 using the sovereign bond purchase program. Overall, the 

sample  contains  116  (dealer  and  non-dealer)  banks.  Our  methodology  likely  only 

picks up the largest broker-dealer banks, however, as we will show later, it picks up 

a substantial share of bond holdings (which is again in line with the “core-periphery” 

structure of the dealer segment documented in the previous literature). 

We rely on DataScope to collect daily corporate bond bid-ask spreads for the euro 

area.  For  information  on  bond  characteristics,  we  use  the  Centralized  Securities 

Database (CSDB), a security-by-security level Eurosystem database that contains data 

on instruments  and  issuers including maturity and  issuance date,  bond  type  (e.g., 

zero coupon), currency, ratings, and issuer information (location, issuer organization 

ECB Working Paper Series No 2589 / September 202111number, name). 

To obtain regulatory leverage ratios for euro area banks, we use the confidential 

SSM Supervisory Statistics, as well as leverage ratios gathered during the 2014 stress 

tests and asset quality review of euro area banks. These data are used to measure 

slack  under  the  leverage  ratio  constraint.  How  we  aggregate  the  leverage  ratio 

across  dealers  to  the  bond  level,  as  well  as  construction  of  mutual  fund  level 

exposure to dealer’s balance sheet constraints, is important and we will discuss it in 

the results section.  

The granular fund performance and fund flows data for mutual funds is from 

the Thomson Reuters Lipper Database.  

Our  macroeconomic  variables  and  underwriter  information  come  from 

Bloomberg.  Country-specific  time-varying  variables  are:  local  GDP,  local  equity 

indexes,  local  bank  indexes,  and  1-,  3-,  5-  and  10-  year  local  government  bond 

spreads.  

Security  Holdings  Statistics  (SHS)  is  a  security-level  database  that  provides 

information  on  holdings  by  groups  of  euro  area  investors.  The  data  have  been 

collected quarterly since 2013:Q4. The data are broken down by instrument type, 

and issuer country and allows us to track security holdings of banks over time as 

well as in relation to other investors.  

3.

Institutional details

3.1. Leverage ratio 

In the wake of the 2008 Global Financial Crisis the Basel Committee of Banking 

Supervision  (BCBS)  undertook  a  significant  program  of  reform  for  banking 

regulation known as Basel III. The reform introduced new international regulatory 

standards  for  both  capitalization  and  liquidity  risk  management.  One  of  the  key 

regulatory reforms  was  the  introduction  of  the  leverage ratio,  which  is  our  main 

focus.  The  leverage  ratio  aims  to  insure  minimal  bank  equity  capitalization. 

ECB Working Paper Series No 2589 / September 202112However, as opposed to the risk-based capital requirement, the leverage ratio seeks 

to recognize limitations in one’s ability to measure risk (i.e., the “model risk”), and 

therefore it is not risk-weighted. Instead, it is a simple ratio of Tier 1 capital to book 

value  of  total  assets  (including  both  on-balance  sheet  exposures  and  some  off-

balance sheet items).7  The problem is that —due to the non-risk weighted nature—

the leverage ratio makes it more costly for banks to engage in low margin activities. 

Specifically,  it  has  been  argued  that  the  leverage  ratio  could  constrain  bond 

intermediation  as  the  margin  on  bond  dealing—and  especially  dealing  in  safe 

bonds—is low, yet, it expands banks’ balance sheets and therefore attracts a capital 

charge under the leverage ratio. This is the reason why in studying the regulatory 

impact on bond liquidity we focus on the leverage ratio as the relevant constraint at 

the bank level.  

Our  first  set  of  results  is  using  difference-in-differences  methodology.  In 

particular,  we look at the bond liquidity two  years before (2012:Q1-2013:Q4)  and 

two  years  after  (2014:Q1-2015:Q4)  December  31,  2013.  This  is  the  date  when 

European banks were required to report their leverage ratio to their supervisor for 

the  first  time  as  a  part  of  the  Comprehensive  Assessment  exercise,  the  first 

standardized euro area-wide assessment of the health of bank balance sheets.  

Ideally,  the  introduction  of  the  leverage  regulation  would  be  isolated  and 

unexpected. Alas, that is not the case, as the Basel Committee first indicated that it 

planned  to  introduce  a  leverage  ratio  in  a  consultation  document  in  2009  and 

proposed  a  3  percent  target  in  2010  (BCBS,  2009  and  2010).    Figure  2  gives  an 

overview  of  the  implementation  of  different  policy  and  regulatory  measures 

including the timeline of the regulatory leverage ratio across the European Union, 

the United Kingdom, and the U.S.  Overall, our results are unlikely to be impacted 

7 In corporate finance, a “leverage ratio” has debt in the denominator (in this context, 
Debt/Assets) thus, borrowers’ constraints on leverage are typically set as a maximum 
leverage ratio. Bank regulatory leverage ratio has equity in the denominator, which is 
effectively  (1-Debt/Assets).  So,  in  the  banking  context,  we  talk  about  a  minimum 
leverage ratio. 

ECB Working Paper Series No 2589 / September 202113by the ECB purchasing programs; as Figure 2 illustrates, these programs took place 

at  the  later  stage:  the  sovereign  bonds  purchase  program  (PSPP)  starts  in  March 

2015, over one year and three months after the leverage ratio becomes binding, and 

the corporate bonds purchase program (CSPP) starts in mid-2016 and is outside of 

the analysis window. However, we cannot rule out that regulatory measures other 

than  the  leverage  ratio  were  at  play.  The  EU  introduced  a  package  of  capital 

requirements  directives  in  July  2013.8  This  was  the  third  set  of  amendments  to  the 

original banking directive which transposed Basel III recommendations into EU law, 

which set out the rules for calculating capital requirements and reporting and general 

obligations  for  liquidity  requirements.  Where  possible,  we  will  explore  differential 

predictions that arise due to risk-weighting. In addition, in the last set of results, we 

will look at deposits inflows in the context of 2020 economic uncertainty that put 

pressure on the leverage ratio compliance due to a sudden increase in the size of the 

balance sheet.  

[FIGURE 2] 

There  are  also  reasons  to  believe  that  over  the  period  of  our  analysis  the 

leverage  was  the  binding  constraint.  Following  the  Comprehensive  Assessment, 

several banks with low leverage ratio were asked to develop a plan to improve their 

slack.  The  capital  plans  provided  by  banks  fed  into  the  Supervisory  Review  and 

Evaluation Process for purposes of calculating their capital requirements.9 In line 

with  this  observation,  Figure  3  shows  that,  as  of  2013,  three  of  the  eight  largest 

broker-dealers  had  a  leverage  ratio  below  3.5%.  Table  1  shows  that  the  average 

statistics  in  the  broader  sample  of  broker-dealers  are  even  more  binding.    For  14 

corporate bond dealers that participated  in the CSPP,  the  average distance to the 

regulatory requirement is 0.87 percentage points. This is about half of the distance 

for non-dealer banks. Ultimately, the numbers were disclosed to the public, with the 

8 This package known as CRD IV included Directive 2013/36/EU and Regulation (EU) N° 
575/2013. 
9  For more details on the ECB’s Comprehensive Assessment see Breckenfelder and 
Schwaab (2019.)   

ECB Working Paper Series No 2589 / September 202114message that the steps toward compliance are on the way, but still faced substantial 

public scrutiny (e.g., Acharya and Steffen, 2014.) In contrast, the phasing-in of the 

liquidity  coverage  ratio  (LCR)  was  gradual:  banks  started  reporting  the  ratio  to 

supervisors  in  2014,  but  this  number  was  not  made  public.    LCR  became  60% 

binding as of October 2015 and phased in to 100% by 2018. 

It is also relevant to recall that at the end of 2013 European banks were under 

substantial stress.10 In fact, the Comprehensive Assessment for which the regulatory 

leverage ratio had to be submitted was a one-off exercise of unprecedented scope 

and granularity, aimed at achieving the goals of establishing transparency on the 

condition of bank balance sheets and restoring confidence in the European banking 

sector.11  Thus,  banks  did  not  have  much  capacity  to  prepare  for  the  regulatory 

compliance.  

3.2. Dealer-bond ties  

[FIGURE 3 & TABLE 1] 

An important challenge of cross-sectional identification is to establish a source 

of  quasi-exogenous  assignment  of  bonds  to  broker-dealers:  for  individual  banks’ 

constraints to have impact on bond liquidity, there has to be something that ties the 

bond to an individual dealer, making it difficult to switch. In the absence of such ties, 

all  bonds  would  be  intermediated  by  unconstrained  dealers  since  they  charge  the 

lowest  price  for  the  intermediation.  The  existing  research  offers  multiple 

explanations  for  how  broker-dealers  facilitate  trade.  Specifically,  we  build  on  the 

importance of dealers in reducing search costs (e.g., Rubinstein and Wolinsky, 1987, 

Duffie, Garleanu, and Pedersen, 2005) and information asymmetry (e.g., Kyle, 1985; 

Glosten and Milgrom, 1985; and Biglaiser, 1993) in bilateral trades. Keep in mind that 

10 Mario Draghi’s famous “whatever it takes” speech which sets the recovery phase 
for the euro area dates back to July 26, 2012, and bond purchase programs were not 
implemented until 2015. 
11 The results of the Comprehensive Assessment and the capital plans provided by 
banks fed into the Supervisory Review and Evaluation Process (SREP) for purposes of 
calculating capital requirement.  

ECB Working Paper Series No 2589 / September 202115bond  market  is  a  quote-based,  over-the-counter  (OTC)  market,  that  is,  it  is  non-

centralized and non-standardized. This amplifies market frictions that underpin the 

economic role of dealers. Building on these observations, we propose two ways of 

filtering bond-dealer pairings that are a result of persistent connections and are likely 

to be exogenous to the impact of regulatory changes on individual dealers. Both of 

these explanations rely solely on dealer’s information extraction from order flow. To 

the  best  of  our  knowledge,  persistence  of  the  bond-dealer  connections  is  a  novel 

insight of our paper. 

First, in view of the long literature building on the local and country bias, we 

look at whether domestic dealers are the most relevant for bond intermediation. A 

home bias for dealer activities could be tied to search cost, or to the importance of 

connections to a network of institutional investors. The role of these connections is 

central at the underwriting stage (Benveniste and Spindt, 1989) but, as emphasized 

below, they are also likely to carry over to the secondary market. Table 2 shows a 

striking pattern: on average, as of the end of 2013, the large domestic dealers held 

about 46.5% of the bond, whereas the largest holding of a foreign dealer was about 

2.1%. The economic size of the difference between domestic and foreign dealers is 

large and consistent throughout the sample. (It is worth emphasizing that because 

we  look  within  the  euro  area,  such  difference  cannot  be  explained  by  currency 

denomination.)  

[TABLE 2] 

Second, through the interviews with several experienced market participants 

we have learned that bond underwriters continue to play an important role in the 

secondary market. This is best understood if one puts themselves in the shoes of a 

bond investor.  To start, any sizable institutional investor has a representative at each 

underwriter/dealer. Although some of these relations might be more important than 

others, investors’ access to any dealer in today’ market does not appear to be an issue. 

So, if an investor wants to buy bonds at issuance which is being underwritten and 

placed by JPMorgan, it would be natural for them to reach out to JPMorgan directly. 

ECB Working Paper Series No 2589 / September 202116Now, imagine instead that the bond was already placed, and the investor is trying to 

purchase it in the secondary market. The choice of the dealer to call is informed by 

two factors: the first is the quoted price, and the second is the probability of order 

fulfillment at that price within a given time window.  This is because quotes are not 

binding and, most likely, only a fraction of the order will be filled at the quoted price. 

To the degree that the investor values certainty and speed of the execution on the full 

order, it would want to reach out to the dealer that has the best understanding of 

“where the bodies are buried,” and this is where the underwriter (JPMorgan) comes 

into focus again. (Note that the identity of the underwriter is on display on the same 

screen  together  with  quotes  throughout  the  life  of  the  bond.)  In  other  words,  the 

information about the initial demand and placement of the bond in this decentralized 

OTC market gives the underwriter a private insight that is relevant to the investors 

in  the  secondary  market.  This  is  likely  to  diminish  over time,  but anecdotally  this 

appears to persist for a few years.   

We  can  provide  some  evidence  for  this  account  by  looking  at  the  dealers’ 

activity  in  the  ECB’s  corporate  bond  purchase  program,  CSPP.  The  data  is 

proprietary  ECB’s  trade-by-trade  data  containing  all  trades  executed  by  the 

Eurosystem  from  June  2016  to  March  2017.  (The  buyer  is  the  Eurosystem.)  The 

starting sample contains 637 bonds; conditional on having an issue date we end up 

with 569 ISINs. For each bond, we construct a variable that measures the share of the 

total CSPP bond volume that was intermediated by a given dealer.   

The analysis is presented in Table 3. In the first two columns, the explanatory 

variable of interest is an indicator of whether the dealer is also the bond underwriter.  

The hypothesis is that—even though we are looking at the secondary market—the 

transaction  volume  is  tilted  toward  bonds  underwritten  by  the  dealer.  Consistent 

with this hypothesis, we find that, on average, dealer banks have about 25% higher 

transaction  volume  in bonds where  the dealer was also  an underwriter relative to 

transaction volumes in bonds underwritten by other banks. In column (3), we include 

interaction terms indicating whether the bond was outstanding for one year, or two 

ECB Working Paper Series No 2589 / September 202117to three years since issuance. Although the special role of the underwriter dissipates 

over time, even for bonds that have been outstanding between two and three years 

the  differential  effect  is  substantial  and  statistically  significant.  For  bonds 

outstanding less than a year, we see that underwriters intermediate an astonishing 

78% (=56%+(100%-56%)/2) of the CSPP volume (statistically significant at 1% level). 

[TABLE 3] 

The fact that the underwriter plays a special role as a dealer in the secondary 

market  helps  us  separate  the  choice  of  the  dealer  from  bank-level  constraints. 

Moreover, the underwriting relationships tend to be “sticky”. For example, Drucker 

and Puri (2005) results indicate that in 45% of the follow-up equity offerings, issuers 

keep the same underwriter (57% for deals with previous concurrent lending, which 

is the focus of their study).  

Figure  4  presents  the  probability  that  an  issuer  picks  the  same  lead  bond 

underwriter as the one it used in the past. In any given year (T) we take firms that 

issue  bonds,  and  we  consider  firms  that  also  issued  a  bond  one  year  ago  (T-1). 

Looking at the firms that issued a bond at T and T-1, we create a variable that takes 

on the value of 1 if the firm used the same lead underwriter, and 0 otherwise. We 

then take an average across all issuers.  We repeat this exercise for up to 10 years in 

the past, that is, from T-1 to T-10.  The overall sample covers 2001-2017 period. Figure 

4 displays the time-series average, and the 95% confidence bounds for bonds issued 

between 2008 and 2017. The result indicates that in about 46-50% of the cases firms 

use the same lead underwriter as they used 1-3 years ago. The relevant benchmark is 

a random choice from a potential pool of underwriters that could be at least as large 

as  9  (the  number  of  large  broker  dealers  in  our  sample.)  As  Figure  4  shows,  the 

stickiness of the choice of underwriters decays over time, but even 10 years out it is 

about 30%, and for issues 5 years out it is 40%.   

[FIGURE 4] 

The stickiness in bond underwriting could be a result of search costs, although 

it  is  unlikely  that  search  costs  are  high  in  this  setting.  It  could  also  be  a  result  of 

ECB Working Paper Series No 2589 / September 202118proprietary information production. Indeed, certification is one of the fundamental 

roles performed by the underwriters. Relatedly, Drucker and Puri (2005), and Gande, 

Puri, Saunders, and Walter (2015) emphasize the information production synergies 

between underwriting and lending. To be clear, it is less relevant to us what exactly 

leads to issuer-underwriter stickiness, as long as there is a switching cost. All in all, 

the idea is that we can rely on past underwriting relationships to filter bond-dealer 

pairings which were unlikely to be influenced by the bank slack under the leverage 

ratio as a source of quasi-exogenous assignment. This approach also helps us distance 

from country-level factors that might impact liquidity through channels other than 

balance sheet cost.     

4. 

Leverage ratio and bond liquidity 

Our  first  set  of  results  examines  the  impact  of  bank  compliance  with  the 

minimum leverage ratio on bond liquidity. To trace the causal connection we focus 

on  cross-bank  variation,  that  is,  we  effectively  sort  banks  into  those  for  which 

leverage ratio is more and less binding and then compare changes in bond liquidity 

following  the  regulatory  change.  More  concretely,  we  estimate  the  following 

regression:  

(1) 

 𝐵𝐵𝐵𝐵𝐵𝐵-𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝐵𝐵𝑖𝑖,𝑡𝑡   =   𝛼𝛼1𝐵𝐵𝑎𝑎𝐵𝐵𝑎𝑎 𝑐𝑐𝑐𝑐𝐵𝐵𝑎𝑎𝑐𝑐𝑠𝑠𝑎𝑎𝐵𝐵𝐵𝐵𝑐𝑐𝑖𝑖 ∗ 𝑃𝑃𝑐𝑐𝑎𝑎𝑐𝑐𝑡𝑡 +   𝛿𝛿𝑖𝑖 +   𝑋𝑋𝐵𝐵, 𝑐𝑐 + ⋯ + 𝜖𝜖𝑖𝑖,𝑡𝑡

where i identifies the bond and t the date. The

 is the daily bid-ask 

spread, which is our measure of illiquidity. It is commonly used as a central measure 

 𝐵𝐵𝐵𝐵𝐵𝐵-𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝐵𝐵𝑖𝑖,𝑡𝑡

of  bond 

illiquidity 

(e.g.,  Falato,  Goldstein,  and  Hortaçsu,  2020.)  The 

 is the dealers’ constraint measured as distances to their required 

leverage  ratios  as  of  the  end  of  2013.  In  our  hypothesis,  smaller  slack  under  the 
𝐵𝐵𝑎𝑎𝐵𝐵𝑎𝑎 𝑐𝑐𝑐𝑐𝐵𝐵𝑎𝑎𝑐𝑐𝑠𝑠𝑎𝑎𝐵𝐵𝐵𝐵𝑐𝑐𝑖𝑖

leverage ratio should lead to lower liquidity, which means higher bid-ask spread; 

i.e., the predicted sign (after the constraint comes into effect) is negative.  

 is a 

dummy  variable  equal  to  1  for  the  period  following  December  31,  2013  (the  first 

𝑃𝑃𝑐𝑐𝑎𝑎𝑐𝑐𝑡𝑡

time  banks  calculated  and  reported  their  regulatory  leverage  ratio  to  their 

ECB Working Paper Series No 2589 / September 202119 
 
supervisor), and 0 otherwise. The overall sample period for the first set of tests is 

2012:Q1 through 2015:Q4 – a two year window before and after the leverage ratio 

becomes  binding.    The  regression  also  includes  bond  fixed  effects  (

),  and  bond 

time-varying  characteristics (

), namely its  remaining maturity. Depending on 

𝛿𝛿𝑖𝑖

the measurement of bank constraints we will use additional controls that we will 

𝑋𝑋𝐵𝐵, 𝑐𝑐

explain as we present the results. Standard errors are clustered at bond level. For 

our  benchmark  results  we  include  all  bonds  regardless  of  whether  they  are 

outstanding for the full period. (In the appendix we show that the analysis is robust 

if we zoom in on surviving bonds only.)  

4.1.  Domestic dealers 

Our main explanatory variable is Bank Constraint. To capture bank constraints 

at the country level, we use within-country weighted averages of dealer distances to 

the 3% leverage ratio (the regulatory requirement).  The results are reported in the 

lower panel of Table 1. Lines (i) through (iv) correspond to alternative ways weights 

are  used  to  construct  averages;  namely,  for  dealers  participating  in  the  corporate 

bond purchasing program the averages are: (i) weighted by total assets; (ii) weighted 

by trading volume under the CSPP; (iii) correspond to the top-1 dealer in a country 

by share of trading volume in the CSPP; (iv) correspond to the top-2 dealers in the 

country weighted by the trading volume in the CSPP.  

Table  4  shows  the  regression  result  of  the  impact  of  the  leverage  ratio 

regulation  on  corporate  bond  market  liquidity  by  focusing  on  the  domestic  banks 

that were dealers in the corporate bond purchasing program. Overall, we have nine 

countries. The average distance to regulatory requirement ranges from 1.07 to 1.14 

percentage points. In columns (1) to (3) we gradually introduce bond-level and time 

fixed effects. Not surprisingly, the regression explanatory power jumps to 80% when 

we include bond fixed effects.  Column (4) controls for bond remaining maturity. As 

mentioned earlier, one concern with focusing on domestic banks is that changes in 

bond  liquidity  might  be  reflecting  country-level  factors,  albeit  such  factors  would 

ECB Working Paper Series No 2589 / September 202120have  to  be  contemporaneous  to  the  leverage  ratio  implementation  schedule.  To 

moderate  this  concern,  in  column  (5)  we  introduce  a  range  of  country-level  time 

varying  measures.  Performance  of  local  equity  markets,  volatility  index,  and  the 

yield curve are statistically significant. However, the coefficient measuring the shift 

in bond liquidity is robust to these controls. The estimate of -0.08 indicates that for 

countries where banks are one percentage point closer to the regulatory requirement 

(about once standard deviation, according to Table 1), the bid-ask spread is 8 basis 

points higher. The average bid-ask spread in our sample is 59 basis points and the 

median is 37 basis points. 

[TABLE 4] 

Figure  5  shows  point  estimates  for  individual  quarters,  that  is,  it  displays 

coefficients on interaction terms from estimating the following regression:  

 𝐵𝐵𝐵𝐵𝐵𝐵-𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝐵𝐵𝑖𝑖,𝑡𝑡  

2015:Q 4

=  �

𝑡𝑡=2012:Q1

𝛼𝛼1,𝑡𝑡

𝐵𝐵𝑎𝑎𝐵𝐵𝑎𝑎𝐵𝐵𝑐𝑐𝐵𝐵𝑎𝑎𝑐𝑐𝑠𝑠𝑎𝑎𝐵𝐵𝐵𝐵𝑐𝑐𝐼𝐼 ∗ 𝑄𝑄𝑄𝑄𝑎𝑎𝑠𝑠𝑐𝑐𝑠𝑠𝑠𝑠𝑡𝑡 +   𝛿𝛿𝑖𝑖 + ⋯ + 𝜖𝜖𝑖𝑖,𝑡𝑡

where  Quarter  is  a  time  fixed  effect.  The  solid  line  in  Figure  5  depicts  the  point 

estimates, and the dashed lines depict the corresponding 95% confidence band.  This 

figure illustrates a significant and permanent shift in bond liquidity based on how 

(2) 

binding the leverage constraint for domestic dealers is.  

[FIGURE 5] 

While Table 4 includes a comprehensive set of country time-varying controls, 

another way to distance ourselves from potential country-level effects is to look at 

domestic  non-dealer  banks.  To  the  degree  that  bank  constraints  are  picking  up 

something that is specific to the banking sector overall, this would show up for all 

banks. “Placebo” bank constraints are based on the regulatory leverage ratio by non-

dealer banks, weighted by total  assets. But, as Table 5 shows,  the  impact on bond 

liquidity is coming through the balance sheet of the dealer banks. The economic and 

statistical significance of the coefficients disappears once we look at large domestic 

non-dealer banks.  

ECB Working Paper Series No 2589 / September 202121 
[TABLE 5] 

Table  6  presents  robustness  of  the  results  to  alternative  dealer  bank 

classifications  presented  in  Table  1.    As  one  can  see,  the  estimate  is  very  stable 

regardless of the weights used to aggregate banks constraints. The results are also 

economically and statistically similar if we use dealer banks that participated in the 

corporate bond purchase program or a larger sample of dealers that participated in 

the sovereign bond purchase program.  

[TABLE 6] 

Table 7 re-examines results in Table 4 separately for non-investment grade and 

investment grade bonds.  This test is aimed at reinforcing that we are measuring the 

effect  of  leverage  ratio.  The  idea  is  that  if  banks  are  less  likely  to  engage  in  low 

margin  activities  as  a  result  of  compliance  with  the  minimum  leverage  ratio,  it 

should  have  a  higher  impact  on  less  risky  bonds.12  Risk-weighted  capital 

requirements, or  demand for liquidity  pushes banks to hold safer bonds  instead. 

Table  7  shows  that  the  liquidity  impact  is  primarily  concentrated  in  investment 

grade bonds. Figure 6 displays a more granular insight by showing coefficients for 

different ratings. To construct Figure 6 we estimate a regression similar to equation 

(2), but instead of quarter dummies we use rating dummies.  Consistent with the 

idea that the leverage ratio is the binding constraint, there is a ranking in the impact 

on different rating categories, with AAA rated bonds affected the most.  

[TABLE 7 & FIGURE 6] 

4.2. Bond underwriters 

Our  earlier  results  are  robust  to  the  inclusion  of  a  wide  range  of  macro-

variables,  but  they  could  still  be  sensitive  to  country-level  developments  that 

simultaneously  correlate  with banks’ capitalization or balance sheet size,  and  bond 

liquidity. To shift away from the country-level aggregation, we instead look at bond 

12 Acosta Smith, Grill and Grill et al. (2017) and Choi, Holcomb and Morgan (2018) 
document that the leverage ratio incentivizes banks to shift their portfolio to riskier 
assets, but it does not increase overall bank risk. 

ECB Working Paper Series No 2589 / September 202122underwriters. Using both methods, country of issuance and bond underwriter identity 

to  assign  the  leverage  constraint  of  the  dealer,  allows  us  to  overcome  different 

shortcomings. In particular, while using underwriting connection leads to a cleaner 

identification, this analysis applies to a narrower sample of bonds.  

As discussed earlier, underwriters play a special role not only on the primary 

but also on the secondary market. In addition, underwriting relationships are sticky. 

Thus,  we  look  at  the  constraints  of  lead  underwriters,  which  we  identify  in  the 

Bloomberg data as those assigned the role of “lead manager” or “book runner” during 

the  bond  issue.    We  match  issuers  (vs.  bonds)  and  banks  based  on  their  2010-2011 

underwriting  relationships.  The  idea  is  to  strike  a  balance  between  looking  at  a 

sufficiently close window when underwriter stickiness is particularly high, but at the 

same time avoid getting too close to the date when leverage ratio comes into force. 

(Table A.2 presents robustness of our results to using 2010-2012 window instead.)   

Out of the 116 banks in our dealer sample (see Table 1), we find nearly 90 with 

some  underwriter  role  (the  remaining  banks  are  small  or  specialized).  Because 

underwriters also act as dealers, we no longer need to rely on the purchase programs 

data to identify dealer banks. We use the constraint of the largest underwriter for a 

given firm based on the number of transaction. The results of the analysis using this 

alternative connection between bonds and banks are reported in Table 8.  

[TABLE 8] 

The  structure  of  this  table  is  exactly  as  Table  4;  the  change  is  the  mapping 

between  bonds  and  banks  and,  relatedly,  the  sample  of  the  bonds.  The  estimated 

coefficient is economically large, robust and statistically significant. If the bond dealer 

with  existing  underwriting  ties  is  one  percentage  points  closer  to  the  regulatory 

requirement, the bid-ask spreads of the bond increases by 4 basis points (about 6.8 % 

of  the  mean).  Underwriter  constraints  range  from  about  -0.3  to  about  6  percentage 

points with the mean of 3.57 and standard deviation of 1.81. An alternative way of 

thinking about economic magnitudes of the estimates is that one standard deviation 

change  in  the  underwriter  constraint  alters  the  bid-ask  spreads  by  about  7.2  basis 

ECB Working Paper Series No 2589 / September 202123points (about 12.4 % of the mean).  

5. 

Bond liquidity and 2020 mutual fund outflows 

Bank  regulatory  adjustments  that  followed  the  global  financial  crisis  raised 

concerns in the public debate about the reduced ability of banks to make markets in 

the  bond  space.  These  concerns  were  raised  against  the  background  of  the  rise  in 

mutual funds holding, documented in Figure 1. Specifically, the anticipated problem 

was if mutual funds face significant and sudden redemptions they would need to exit 

their  bond  holdings.  If  banks  are  limited  in  their  ability  to  temporarily  take  these 

bonds on their balance sheet (i.e., inflate their balance sheet which is restricted under 

the  leverage  ratio),  this  would  impact  bond  valuations,  potentially  precipitating 

farther withdrawals and sales.  

The onset of the COVID-19 pandemic represents a clear instance when mutual 

funds faced pressure of fund outflows. Importantly, there is plenty of evidence that—

at least in the initial stage of  the  pandemic crisis—this was  a liquidity  shock to the 

firms  (e.g.,  Li,  Strahan  and  Zhang,  2020).  In  the  US  market,  Falato,  Goldstein,  and 

Hortaçsu  (2020)  analyze  large  capital  outflows  from  mutual  funds  following  the 

outbreak of the pandemic, indicating that both the illiquidity of fund assets and the 

vulnerability  to  fire  sales  were  important  factors  in  explaining  redemptions  during 

this episode. Figure 7 shows that a similar panic took place in Europe.  In this figure, 

daily flows are calculated as: 

where 

𝐹𝐹𝐹𝐹𝑐𝑐𝐹𝐹𝑎𝑎𝑖𝑖,𝑡𝑡   =   (𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡 − �1 + 𝑠𝑠𝑖𝑖,𝑡𝑡� ∗ 𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡−1) / 𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡−1
 is total net assets of fund i at day t, and 

is the fund’s daily return. 

(3) 

Figure  7  also  shows  that—similar  to  the  U.S.  market—extraordinary  direct 

𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡

𝑠𝑠𝑖𝑖,𝑡𝑡 

government interventions in corporate-bond markets mark the reversal of the fund 

outflows. The earliest vertical dashed line depicts the date of the ECB announcement 

of the Pandemic Emergency Purchase Program (PEPP) on March 18, 2020. 

In  this  study,  we  are  especially  interested  in  the  role  of  bank  balance  sheet 

ECB Working Paper Series No 2589 / September 202124 
 
constraints on the mutual funds sell-off pressure. To examine this point, we build on 

the cross-sectional variation in bond exposure to dealers’ constraints identified using 

home bias, and previous underwriting relationships. Note that, previously, we were 

looking at the bond level illiquidity. In this section, we look at the mutual funds which 

hold portfolios of bonds. So, as a first step, we assign a constraint to a bond exactly as 

we did in the previous analysis. When using domestic dealers as the relevant match 

we can easily assign a constraint measure for most of the bonds in the portfolio. In this 

analysis, the slack under the leverage ratio is measured as of December 2019. We then 

aggregate  this  measure  of  illiquidity  based  on  banks’  balance-sheet  capacity  at  the 

mutual fund level. To do so we use mutual fund portfolio weights from Lipper as of 

January 31, 2020, i.e., the weights before the COVID shock. This enables us to rank 

mutual funds based on their exposure to the lack of depth in liquidity due to dealers’ 

balance sheet constraints. Accordingly, we classify mutual funds with above median 

exposure values as funds with Illiquidity exposure. (It is worth reiterating that we are 

not measuring bond liquidity directly, but instead measure the dealers’ slack under 

the leverage ratio.)  

Asset illiquidity is at the heart of the fixed income mutual fund instability; this is 

the  focus  of  Falato,  Goldstein,  Hortacsu,  2020,  and  Ma,  Xiao,  Zeng,  2020.  We  are 

interested  in  the  dealer’s  balance  sheet  constraint  as  an  amplifier of  this  effect.    The 

result can be seen in Figure 7. The blue line depicts the average change in market value 

(Panel A) and change in fund flows (Panel B) of mutual funds that are relatively more 

exposed to constrained dealers and the red line gives these values for funds that are 

relatively  less  exposed  to  constrained  dealers.  Whereas  leading  to  March  2020,  all 

funds closely track each other in terms of valuations and fund flows, the COVID-19 

shock results in decoupling, with funds exposed to banks with lower balance sheet 

capacity emerging as particularly affected.   

[FIGURE 7] 

We  further  zoom  in  on  funds  selling  off  liquid  bonds,  where  liquidity  is 

measured as bonds issued by countries with the least constrained domestic dealers. 

ECB Working Paper Series No 2589 / September 202125Note that since liquidity in this first part of the analysis is indicated by the country of 

issuance  and  it  is  the  same  for  all  funds,  effectively  we  are  looking  at  the  selling 

behavior in very similar, if not the same bonds. This makes it less likely that our results 

could be explained by country-level factors. Our hypothesis is that—conditional on 

their  pre-crisis  cash  positions—mutual  funds  that  were  relatively  more  exposed  to 

dealers  with  lower  market  making  capacity  faced  higher  sell-off  pressures  in  their 

liquid  bonds.  In  other  words,  looking  beyond  cash  and  cash-like  securities,  if 

additional  liquidations  are  necessary  and  part  of  the  bond  portfolio  is  exposed  to 

illiquidity due to the dealers’ constraints, the fund will prioritize selling bonds that 

are less exposed to such constraints.   

In Table 9 we estimate the following regression:  

(4) 

 ∆ 𝐿𝐿𝐵𝐵𝐿𝐿𝑄𝑄𝐵𝐵𝐵𝐵 𝑏𝑏𝑐𝑐𝐵𝐵𝐵𝐵𝑎𝑎𝑘𝑘,𝑡𝑡   =   𝛼𝛼1𝐼𝐼𝐹𝐹𝐹𝐹𝐵𝐵𝐿𝐿𝑄𝑄𝐵𝐵𝐵𝐵𝐵𝐵𝑐𝑐𝐼𝐼 𝑠𝑠𝑒𝑒𝑠𝑠𝑐𝑐𝑎𝑎𝑄𝑄𝑠𝑠𝑠𝑠  𝑘𝑘 ∗ 𝐵𝐵𝐶𝐶𝐶𝐶𝐼𝐼𝐶𝐶 𝑎𝑎ℎ𝑐𝑐𝑐𝑐𝑎𝑎𝑡𝑡 + 𝛿𝛿𝑘𝑘   +   𝜖𝜖𝑘𝑘,𝑡𝑡
where 

  is  the  monthly  (t)  change  in  holdings  of  liquid  bonds  by 

mutual fund k. Liquid bonds are defined as bonds issued by companies with the least 

 ∆ 𝐿𝐿𝐵𝐵𝐿𝐿𝑄𝑄𝐵𝐵𝐵𝐵 𝑏𝑏𝑐𝑐𝐵𝐵𝐵𝐵𝑎𝑎𝑘𝑘,𝑡𝑡

(above median) constrained domestic dealers.  In this analysis, we look at the first 

three month of 2020; COVID shock is equal to 1 for March and to 0 otherwise.  

 are 

fund fixed effects.  

𝛿𝛿𝑘𝑘

[TABLE 9] 

The results in Table 9 are consistent with our hypothesis showing that funds 

that had a bigger exposure to illiquidity due to banks’ market making constraints had 

to sell their liquid positions more. Table 9, column (6) indicates that investment into 

liquid  bonds  drops  by  1.27  percentage  points  for  funds  facing  an  illiquidity 

constraint. The average fund holding of liquid bonds for these funds is 22.97%. This 

implies  that  liquid  bonds  holdings  decline  by  5.5%  (=1.27/0.2297)  more  for  funds 

with exposure to bank balance constraints.  

Finally, in Table 10, instead of assigning bank constraints based on the country 

of the bond issuer, we measure it based on the constraint of the bond underwriter. 

Naturally,  it  is  substantially  harder  to  assign  an  underwriter  to  a  bond  than  the 

country of issuance. On average, we have underwriter constraints for 31% of funds’ 

ECB Working Paper Series No 2589 / September 202126 
 
corporate bond portfolio. In case there is more than one constraint associated with a 

bond, we take the average of the underwriter constraints at the bond level.  Overall, 

we have 732 funds that hold 1,373 individual bonds with an assigned leverage ratio 

slack.  

The results are consistent with findings in Table 9 and with the hypothesis that 

corporate bonds that had less exposure to illiquidity due to banks’ market making 

constraints sold more liquid bonds. The unit of observation is fund-bond-month, and 

we look at log differences of the nominal allocation to individual liquid bonds. Table 

9, column (6) indicates funds reduce their holdings of “liquid” bonds by 8.3 percent. 

In the last two columns we try to overcome the partial coverage of the fund portfolio 

by looking at funds that have a portfolio where we could identify at least 50 bonds 

that are linked to the underwriter constraint measure. Column (8) suggests that, on 

average, these funds sold 4.5 percentage point more of their liquid bonds than of their 

relatively illiquid bonds. 

 [TABLE 10] 

In the analysis focused on the 2020 mutual funds run we relied on the surge in 

selling pressure, but we did not directly speak to whether the leverage constraint was 

binding at the dealer level, the way we did in our earlier analysis. As a result, there is 

a remaining concern that dealer’s slack under the leverage ratio might be correlated 

to the bonds’ characteristics, and so the selling behavior that we observe is not causally 

tied to the constraint.  In the last two columns of Table 10 we look directly at the bank 

balance  sheet.  As  it  is  well  known,  periods  of  macro-economic  uncertainty  trigger 

inflow of deposits into banks (due to the deposit insurance), a fact that was originally 

documented  by  Gatev  and  Strahan  (2006).  2020  was  no  exception,  for  example 

American Bankers Association pointed out that in 2020 bank deposits increased by a 

record 22.6%.13   Because leverage ratio requires capitalization as a proportion of bank 

balance  sheet  size,  this  deposit  inflow  reduces  the  slack  under  that  leverage  ratio. 

13 https://bankingjournal.aba.com/2021/02/aba-data-bank-deposits-rise-by-a-
record-22-6-in-2020/. 

ECB Working Paper Series No 2589 / September 202127Indeed, for the euro area banks in our sample, we see that 24 banks experienced an 

increase in deposits (as a fraction of assets) already by March 2020, with an average 

deposit  growth  of  14%  for  this  sample.    Given  that  the  macroeconomic  shock 

underpinning  this  inflow  was  not  anticipated,  and  the  fact  that  deposit  insurance 

applies to all banks, we can treat deposits inflows as a source of exogenous variation 

in  slack  under  the  leverage  constraint.    That  is,  conditional  on  the  pre-crisis  slack 

under the leverage constraint, banks experiencing higher deposit inflows would have 

a more binding leverage ratio and, therefore, would be more likely to scale down their 

market making activities.  

In line with this intuition, we find that the cross-sectional correlation between 

the  change  in  the  deposit  ratio  (including  household  deposits  and  non-financial 

corporate deposits) and the change in the leverage ratio in 2020:Q1 is -0.41.  In Table 

10, columns (9) and (10), the assignment of bonds to dealers is still done using past 

underwriting  relations.  However,  instead  of  the  slack  under  the  leverage  ratio 

measured as of the end of 2019, we use the change in the deposit inflow in 2020:Q1 as 

an instrument. Column (9) is a simple regression, and column (10) is weighted by bank 

asset size. Conditional on a dealer bank experiencing deposit inflows, funds sell 6.1% 

bonds linked to underwriters that experience below the median deposit inflows (i.e., 

more  liquid  bonds)  relative  to  bonds  that  are  linked  to  dealers  experiencing  above 

median deposit inflows. 

6. 

Conclusion 

Mutual  fund  runs  in  the  U.S.  and  Europe  in  2020  put  an  unprecedented 

pressure  on  the  bond  market  and  culminated  with  a  sweeping  policy  intervention 

designed to stabilize the markets on both sides of the Atlantic. Much of this dynamic 

is rooted in the expansion of retail investing in the bond market and illiquidity of the 

bond  asset  class.  In  that  sense,  some  of  what  we  have  seen  would  have  unfolded 

regardless  of  the  bank  regulatory  changes  that  followed  the  Great  Financial  Crisis. 

However, for over a decade leading to the 2020 episode there has been a concern that 

ECB Working Paper Series No 2589 / September 202128 
banks regulatory adjustments, and leverage ratio in particular, have impacted bond 

liquidity by raising the cost of expanding bank balance sheet. Hence, we are interested 

in understanding how much bank balance sheet constraints for market making had 

added to the mutual fund instability and sell-off pressure. 

We  shed  new  light  on  this  question  by  exploring  persistent  connections 

between bonds and individual dealers formed through the home bias and previous 

underwriting relationships. Building on these connections we are able to show that 

the  introduction  of  leverage  ratio  for  the  European  banks  had  a  large  impact  on 

exposed bonds liquidity. Using the same connections, we show that, during the 2020 

run  episode,  mutual  funds  with  larger  exposures  to  bank  balance  sheet  constraints 

faced bigger redemptions and sell-offs of liquid bonds.    

ECB Working Paper Series No 2589 / September 202129 
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ECB Working Paper Series No 2589 / September 202131 
 
FIGURE 1 – BANK HOLDINGS OF NON-FINANCIAL CORPORATE BONDS 

This figure gives average bank holdings of non-financial corporate bonds (share of total, 
Panel A) as well as holdings of non-financial corporate bonds by all investors, the banking 
sector and the investment fund sector (EUR bn, Panel B). Source: ECB Security Holdings 
Statistics (SHS).  

Panel A: Average bank holdings of corporate bonds (share of total)  

ECB Working Paper Series No 2589 / September 202132 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Panel B: Total, banking and investment fund sector holdings of corporate bonds (EUR bn) 

ECB Working Paper Series No 2589 / September 202133 
 
 
 
FIGURE 2 – LEVERAGE RATIO IMPLEMENTATION TIMELINE 

This figure gives an overview of the implementation timeline for the regulatory Leverage Ratio (LR) across the European Union, the United 
Kingdom, and the United States. The figure shows both the baseline requirement of 3 percent and additional LR requirements across regulatory 
jurisdictions. The grey bars depict the Eurosystem Asset Purchase Programs, the Public Sector Purchase Program (PSPP) and additionally the 
Corporate Sector Purchase Program (CSPP). (*) The UK leverage ratio requirements (quarterly reporting) were reported as monthly averages, 
but as of January 2018 as daily averages. (**) Fed's rule is for US top-tier bank holding company (SLR 5%) with more than 700 billion dollars in 
consolidated assets or more than 10 trillion in assets under custody and for IDI subsidiaries (SLR 6%) of covered BHCs. 

LR/SLR Requirements

Basel III

recommended 3%

public disclosure

binding

EU

UK

reporting public disclosure

binding

rec. 

public disclosure 

binding *

US (SLR)

recommended

public disclosure

binding

ECB Asset purchase program

PSPP

CSPP

Additional Requirements

Basel III

EU

UK

additional buffer recommended

binding

plus 50% for large banks

binding

plus .25 bp for large banks

US (SLR)

plus 2% ** reporting 

binding

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

ECB Working Paper Series No 2589 / September 202134FIGURE 3 – LEVERAGE RATIO OF LARGEST DEALER BANKS OVER TIME 

This figure depicts the regulatory leverage ratio of the largest euro area dealer banks from 
2013 q4 to 2020 q1. The thick grey line is the regulatory target for large banks in Europe. 

ECB Working Paper Series No 2589 / September 202135 
FIGURE 4 – PERSISTENCE OF BOND UNDERWRITING RELATIONSHIPS  

This figure presents probability that a company picks the same lead bond underwriter as the 
one it used in the past. The overall sample corresponds to 2001-2017. As an example, in year 
2017, we look at firms that also issued a bond in 2016 (1 year back) and assign a value of 1 if 
the firm used the same lead underwriter, and 0 otherwise. We then take an average across 
all  issuers  for  2017.  We  repeat  this  exercise  for  up  to  10  years  in  the  past,  which  in  this 
example would mean for years 2016 (1 year back) through 2007 (10 years back). At the end 
we have these series for firms with bond issues between 2008 and 2017. The figure plots the 
average across the years and the corresponding 95% confidence intervals. 

0.60

0.50

0.40

0.30

0.20

0.10

0.00

1

2

3

4

5

6

7

8

9

10

Years before the last issue

ECB Working Paper Series No 2589 / September 202136 
 
 
FIGURE 5 – IMPACT OF THE LEVERAGE RATIO ON BOND MARKET LIQUIDITY 

The figure shows the main regression result of the impact of the leverage ratio regulation on 
corporate bond market liquidity. The graph gives the point estimates for quarterly distances 
around the first time banks calculated and reported their leverage ratio to their supervisor. 
The regression specification is as follows:  

, 

𝛼𝛼1,𝑡𝑡

 is bid-ask spreads; the 

𝐵𝐵𝑎𝑎𝐵𝐵𝑎𝑎𝐵𝐵𝑐𝑐𝐵𝐵𝑎𝑎𝑐𝑐𝑠𝑠𝑎𝑎𝐵𝐵𝐵𝐵𝑐𝑐𝐼𝐼 ∗ 𝑄𝑄𝑄𝑄𝑎𝑎𝑠𝑠𝑐𝑐𝑠𝑠𝑠𝑠𝑡𝑡 +  𝐵𝐵𝑐𝑐𝐵𝐵𝑐𝑐𝑠𝑠𝑐𝑐𝐹𝐹𝑎𝑎 + 𝜖𝜖𝑖𝑖,𝑡𝑡

2015:Q 4
 𝐵𝐵𝐵𝐵𝐵𝐵-𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝐵𝐵𝑖𝑖,𝑡𝑡   =   ∑
where i is bond and t is days. The
 is the 
𝑡𝑡=2012:Q1
dealers’ constraints in the issuer’s domestic country measured as distances to their required 
leverage ratios as of the end of 2013. The regression also includes firm fixed effects, country- 
and security-specific time-varying controls. The y-axis gives the spread change relative to the 
period prior to the event depending on the distance to the regulatory leverage constraint. The 
solid line depicts the point estimates and the dashed lines the corresponding 95% confidence 
band. Standard errors are clustered at bond level.  

𝐵𝐵𝑎𝑎𝐵𝐵𝑎𝑎 𝐵𝐵𝑐𝑐𝐵𝐵𝑎𝑎𝑐𝑐𝑠𝑠𝑎𝑎𝐵𝐵𝐵𝐵𝑐𝑐𝐼𝐼

 𝑆𝑆𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝐵𝐵𝑖𝑖,𝑡𝑡

ECB Working Paper Series No 2589 / September 202137 
 
 
 
 
 
 
 
FIGURE 6– DIFFERENTIAL IMPACT OF BOND RATINGS 

The  figure  shows  the  regression  result  of  the  impact  of  the  leverage  ratio  regulation  on 
corporate  bond  market  liquidity  for  different  bond  ratings.  That  is,  instead  of  investment 
grade, non-investment grade split used in Table 6, we re-run the same regression as in column 
(3) but using more granular rating groups. The solid diamonds depict the point estimates and 
the  dashed  lines  the  corresponding  95%  confidence  band.  Standard  errors  are  clustered  at 
bond level.  

ECB Working Paper Series No 2589 / September 202138 
 
FIGURE 7 – MUTUAL FUND VALUE AND FLOW  

This figure gives the evolution of corporate bond mutual funds value (Panel A) and flows 
(Panel B) before and after the COVID-19 shock. The novel result is the difference in fund value 
and outflows depending on their holdings of bonds exposed to illiquidity through dealers’ 
balance sheet. The blue line depicts average flows of corporate bond mutual funds that are 
relatively more exposed to “illiquid” bonds and the red line gives average flows of mutual 
funds that are relatively less exposed to “illiquid” bonds. Daily flows are calculated as: 

 is total net assets of fund i at day t and 

where 
is the fund’s daily return. The vertical 
𝐹𝐹𝐹𝐹𝑐𝑐𝐹𝐹𝑎𝑎𝑖𝑖,𝑡𝑡   =   (𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡 − �1 + 𝑠𝑠𝑖𝑖,𝑡𝑡� ∗ 𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡−1) / 𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡−1
dashe  lines  depict  the  announcement  and  beginning  of  the  ECB’s  Pandemic  Emergency 
Purchase Program (PEPP).  

𝑇𝑇𝑇𝑇𝑇𝑇i,t

𝑠𝑠𝑖𝑖,𝑡𝑡 

Panel A. Changes in corporate bond fund market value 

PEPP announcement March 18

PEPP purchases start March 26

)

%

(
e
g
n
a
h
c
e
u
a
v

l

t
e
k
r
a
m
d
n
u
f

2

0

2
-

4
-

6
-

8
-

Jan

Mar

May
2020

Jul

Sep

less liquid bonds

more liquid bonds

ECB Working Paper Series No 2589 / September 202139 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Panel B. Changes in corporate bond fund flows 

PEPP announcement March 18

PEPP purchases start March 26

)

%

(

e
g
n
a
h
c
w
o
l
f

d
n
u
f

2

0

2
-

4
-

6
-

8
-

0
1
-

Jan

Mar

May
2020

Jul

Sep

less liquid bonds

more liquid bonds

ECB Working Paper Series No 2589 / September 202140 
 
 
 
 
 
 
TABLE 1 – SUMMARY STATISTICS, AS OF 12/31/2013 

This  table  reports  baseline  summary  statistics  for  the  regulatory  leverage  ratio  and  bank 
constraints measured as the distance to the regulatory requirement. Panel A shows statistics 
for the overall sample and for dealer banks and non-dealer banks separately. Dealer banks 
and non-dealer banks are defined in two ways: Definition 1 categorizes dealer banks as banks 
that engage as dealers with the Eurosystem in the corporate bond market for the Corporate 
Sector Purchase Program (CSPP). Definition 2 categorizes dealer banks as banks that engage 
as dealers with the Eurosystem in the sovereign bond market for the Public Sector Purchase 
Program (PSPP). For country level bank constraints (Panel B), we consider four alternative 
ways of aggregating the data: (i), weighted by total assets, (ii), weighted by dealers’ trading 
volume under the CSPP, (iii), largest dealer bank by CSPP trading volume in a country, (iv), 
two largest dealer banks by CSPP trading volume in a country. “Placebo” bank constraints 
are based on regulatory leverage ratios of non-dealer banks weighted by total assets.  

Panel A. Overall sample  

Leverage ratio (2013:Q4) 

Distance to requirement 

Obs. 

116 

Mean 

5.80 

SD 

7.29 

5th % 

2.16 

95th % 

9.50 

   Dealer banks: Banks acting as dealers in the corporate bonds purchase program 

Full sample 

Dealer banks 
  Non-dealer banks 

92 

14 

78 

1.78 

0.87 

1.94 

2.52 

0.97 

2.68 

-0.85 

-0.62 

-1.03 

   Dealer banks: Banks acting as dealers in the sovereign bonds purchase program 

Full sample 

Dealer banks 
  Non-dealer banks 

116 

41 

75 

2.80 

1.67 

3.41 

7.29 

1.72 

8.95 

-0.84 

-0.30 

-0.85 

4.84 

3.03 

5.34 

6.50 

4.78 

8.62 

Panel B. Country-level bank constraints 

Dealer banks 

Non-dealer banks 
(“placebo”) 

Obs.   Mean 

SD 

Mean 

SD 

Dealer banks: Banks acting as dealers in the corporate bond purchase program 

(i) 

Weighted by assets 

(ii)   Weighted by trading volume 

(iii)  

(iv) 

Top-1 dealer 

Top-2 dealers 

9 

9 

9 

9 

1.08 

1.14 

1.07 

1.12 

0.81 

0.95 

1.14 

0.99 

1.99 

1.99 

1.99 

1.99 

(i) 

Weighted by assets 

(ii)   Weighted by trading volume 

Dealer banks: Banks acting as dealers in the sovereign bond purchase program 
  3.53 
  3.53 
  3.53 
  3.53 

Top-2 dealers 

Top-1 dealer 

(iii)  

1.59 

1.51 

1.55 

1.48 

1.76 

1.86 

1.96 

1.93 

(iv) 

15 

15 

15 

15 

1.71 

1.71 

1.71 

1.71 

3.70 

3.70 

3.70 

3.70 

ECB Working Paper Series No 2589 / September 202141 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLE 2 – DOMESTIC CONCENTRATION OF CORPORATE BOND HOLDINGS 

This  table  shows  summary  statistics  of  corporate  bond  holdings  over  time.  Domestic 
concentration and largest foreign country concentration (shares of total holdings) are reported 
as quarterly means and standard deviations. *** indicate significance at the 1% level.  

Home 

Foreign (largest) 

Date 

Mean 

SD 

  Mean 

SD 

2009q1 
2009q2 
2009q3 
2009q4 
2010q1 
2010q2 
2010q3 
2010q4 
2011q1 
2011q2 
2011q3 
2011q4 
2012q1 
2012q2 
2012q3 
2012q4 
2013q1 
2013q2 
2013q3 
2013q4 
2014q1 
2014q2 
2014q3 
2014q4 
2015q1 
2015q2 
2015q3 
2015q4 
2016q1 
2016q2 
2016q3 
2016q4 
2017q1 
2017q2 
2017q3 
2017q4 

0.612 
0.573 
0.650 
0.642 
0.639 
0.653 
0.647 
0.646 
0.648 
0.616 
0.591 
0.589 
0.586 
0.573 
0.522 
0.510 
0.493 
0.474 
0.486 
0.465 
0.476 
0.463 
0.457 
0.447 
0.460 
0.467 
0.476 
0.464 
0.486 
0.476 
0.471 
0.451 
0.468 
0.468 
0.463 
0.457 

0.009 
0.010 
0.009 
0.009 
0.009 
0.008 
0.008 
0.008 
0.008 
0.008 
0.008 
0.009 
0.008 
0.008 
0.009 
0.009 
0.008 
0.008 
0.008 
0.008 
0.008 
0.008 
0.007 
0.008 
0.008 
0.008 
0.008 
0.008 
0.008 
0.008 
0.008 
0.008 
0.007 
0.007 
0.007 
0.007 

0.043 
0.044 
0.034 
0.033 
0.031 
0.032 
0.032 
0.029 
0.031 
0.031 
0.034 
0.030 
0.028 
0.028 
0.030 
0.028 
0.018 
0.019 
0.020 
0.021 
0.021 
0.022 
0.022 
0.019 
0.018 
0.018 
0.017 
0.018 
0.017 
0.017 
0.016 
0.016 
0.016 
0.016 
0.016 
0.017 

0.003 
0.003 
0.002 
0.002 
0.002 
0.002 
0.002 
0.002 
0.002 
0.002 
0.002 
0.002 
0.002 
0.002 
0.002 
0.002 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 
0.001 

Diff 

0.569 
0.530 
0.616 
0.609 
0.608 
0.621 
0.615 
0.617 
0.617 
0.586 
0.557 
0.559 
0.558 
0.545 
0.492 
0.482 
0.476 
0.455 
0.466 
0.444 
0.455 
0.442 
0.435 
0.428 
0.442 
0.449 
0.459 
0.446 
0.469 
0.459 
0.455 
0.435 
0.453 
0.452 
0.447 
0.440 

Obs. 

2,436 
2,176 
2,498 
2,587 
2,664 
2,852 
3,070 
3,275 
3,237 
3,071 
2,864 
2,810 
2,909 
2,937 
2,771 
2,839 
2,903 
2,897 
2,999 
3,120 
3,434 
3,484 
3,757 
3,156 
3,404 
3,508 
3,600 
3,349 
3,544 
3,560 
3,696 
3,564 
3,851 
3,813 
3,793 
3,660 

*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 
*** 

ECB Working Paper Series No 2589 / September 202142 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
TABLE 3 – BOND UNDERWRITERS AND SECONDARY MARKET BROKER-DEALER ACTIVITIES 

The goal of this table is to illustrate that the bond underwriters (primary dealers) continue to 
play a special role in the secondary market. The sample consists of trades executed by the 
Eurosystem from June 2016 to March 2017 as part of the corporate bond purchase program. 
Unit  of  observation  is  bond-dealer.  The  dependent  variable  is  the  fraction  of  the  total 
transaction volume that was intermediated by a given dealer.  In the first two columns, the 
explanatory variable of interest is an indicator of whether the dealer is also bond underwriter.  
In column (3), we include interaction terms indicating whether the bond was outstanding for 
one, or two to three years from issuance. Standard errors are clustered at dealer bank level. 
***, **, and * indicate statistical significance at 1%, 5% and 10%, respectively.  

 Dependent variable 

Share of transaction volume 

(1) 

(2) 

(3) 

Dealer bank is underwriter 

Underwriter * 1 year from issuance  

Underwriter * 1-3 years from issuance 

Log(amount outstanding) 

Fixed effect: Dealer/Years from issuance 

Obs. 

R-squared 

0.248*** 

(0.076) 

0.252*** 

(0.075) 

-0.093 

(0.120) 

0.557*** 

(0.203) 

0.234* 

(0.119) 

-0.091** 

(0.044) 

Yes/Yes 

-- 

-- 

-0.090** 

(0.043) 

Yes/Yes 

4,137 

0.0387 

4,137 

0.0428 

-- 

-- 

-0.088** 

(0.043) 

Yes/-- 

4,137 

0.0379 

ECB Working Paper Series No 2589 / September 202143 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
TABLE 4 – IMPACT OF THE LEVERAGE RATIO ON BOND MARKET LIQUIDITY 

This table shows the main regression result of the impact of the leverage ratio regulation on 
corporate bond market liquidity. The regression specification is as follows:  

where i is the bond, t is the date, the

 𝐵𝐵𝐵𝐵𝐵𝐵-𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝐵𝐵𝑖𝑖,𝑡𝑡   =   𝛼𝛼1𝐵𝐵𝑎𝑎𝐵𝐵𝑎𝑎 𝑐𝑐𝑐𝑐𝐵𝐵𝑎𝑎𝑐𝑐𝑠𝑠𝑎𝑎𝐵𝐵𝐵𝐵𝑐𝑐𝐼𝐼 ∗ 𝑃𝑃𝑐𝑐𝑎𝑎𝑐𝑐𝑡𝑡 +   𝛿𝛿𝑖𝑖 +   𝑋𝑋𝐵𝐵, 𝑐𝑐 + ⋯ + 𝜖𝜖𝑖𝑖,𝑡𝑡
 is the measure of bond liquidity, the 
 is the bank constraint of country I (calculated as the weighted averages of 
 𝐵𝐵𝐵𝐵𝐵𝐵-𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝐵𝐵𝑖𝑖,𝑡𝑡
broker dealer  distances to their required leverage ratios), 
 is a dummy variable (1 for 
𝐵𝐵𝑎𝑎𝐵𝐵𝑎𝑎 𝑐𝑐𝑐𝑐𝐵𝐵𝑎𝑎𝑐𝑐𝑠𝑠𝑎𝑎𝐵𝐵𝐵𝐵𝑐𝑐𝐼𝐼
indicating  the  period  after  the  first  time  banks  calculated  and  reported  their  regulatory 
 is bond 
leverage ratio to their supervisor and 0 otherwise). 
remaining maturity, i.e., its time-varying characteristic.  Standard errors are clustered at bond 
level. ***, **, and * indicate statistical significance at 1%, 5% and 10%, respectively.  

 are bond fixed effects and 

𝑃𝑃𝑐𝑐𝑎𝑎𝑐𝑐𝑡𝑡

𝑋𝑋𝐵𝐵, 𝑐𝑐

𝛿𝛿𝑖𝑖

(1) 

(2) 

Bid-ask spread 
(3) 

(4) 

(5) 

-0.055** 
(0.022) 
-- 

-0.055** 
(0.022) 
-- 

-0.080*** 
(0.019) 
-- 

-- 

-- 

Dependent variable 

Bank constraint x Post 

Post 

Bank constraint 

Residual bond maturity 

∆ Log(Local GDP)  

∆ Log(Local equity index) 

∆ Log(Local bank index) 

∆ Log(Local volatility index) 

∆ Log(Local government spread, 10Y) 

-- 

∆ Log(Local government spread, 5Y) 

∆ Log(Local government spread, 3Y) 

∆ Log(Local government spread, 1Y) 

Fixed effects: Bond  
Fixed effects: Day 

Obs. 
R-squared 

-- 

-- 

-- 

-- 
-- 

-0.099*** 
(0.032) 
0.034 
(0.032) 
0.041 
(0.035) 
-- 

-- 

-- 

-- 

-- 

-0.062*** 
(0.022) 
-0.073*** 
(0.018) 
-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

0.012 
(0.021) 
-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-0.004 
(0.023) 
0.201 
(0.159) 
0.586*** 
(0.203) 
-0.119* 
(0.064) 
1.472*** 
(0.419) 
-0.201*** 
(0.043) 
0.264*** 
(0.085) 
-0.065 
(0.043) 
-0.138*** 
(0.041) 
Yes 
Yes 

Yes 
-- 

Yes 
Yes 

Yes 
Yes 

1,368,161 
0.0017 

1,368,161 
0.8003 

1,368,161 
0.8050 

1,368,161 
0.8050 

1,033,192 
0.7486 

ECB Working Paper Series No 2589 / September 202144 
 
 
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLE 5 – PLACEBO BROKER DEALER CLASSIFICATION 

This table re-examines results in Table 4, but using leverage ratio constraint of the domestic 
non-dealer banks. ***, **, and * indicate statistical significance at 1%, 5% and 10%, respectively. 

Dependent variable 

Bank constraint x Post 

Post 

Bank constraint 

Residual bond maturity 

∆ Log(Local GDP)  

∆ Log(Local equity index) 

∆ Log(Local bank index) 

∆ Log(Local volatility index) 

(1) 

-0.016 
(0.013) 
-0.010 
(0.026) 
0.059*** 
(0.013) 

-- 

-- 

-- 

-- 

-- 

∆ Log(Local government spread, 10Y) 

-- 

∆ Log(Local government spread, 5Y) 

∆ Log(Local government spread, 3Y) 

∆ Log(Local government spread, 1Y) 

Fixed effects: Bond 
Fixed effects: Day 

Obs. 
R-squared 

-- 

-- 

-- 

-- 
-- 

Bid-ask spread 
(3) 
(2) 

-0.003 
(0.007) 
-0.116*** 
(0.016) 
-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-0.002 
(0.007) 
-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

Yes 
-- 

Yes 
Yes 

(4) 

-0.010 
(0.008) 
-- 

-- 

-0.005 
(0.023) 
0.289* 
(0.155) 
0.755*** 
(0.218) 
-0.146** 
(0.066) 
1.618*** 
(0.435) 
-0.177*** 
(0.044) 
0.259*** 
(0.086) 
-0.071 
(0.044) 
-0.124*** 
(0.041) 

Yes 
Yes 

1,368,161 
0.0057 

1,368,161 
0.8000 

1,368,161 
0.8048 

1,033,192 
0.7480 

ECB Working Paper Series No 2589 / September 202145 
 
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLE 6 – ALTERNATIVE BROKER DEALER CLASSIFICATIONS 

This  table  re-runs  Table  (3),  specification  (5)  using  alternative  constructions  of  the  Bank 
constraint.  We  report  the  coefficient  of  interest,  which  is  the  coefficient  estimate  on  the 
interaction  term between  Bank constraint  and  Post.  Each  number  corresponds  to  a  different 
regression. ***, **, and * indicate statistical significance at 1%, 5% and 10%, respectively. 

Dealer banks: Banks acting as dealers in the corporate bond purchase program 

Table 4, column (5) 

(i) 
(ii)  

(iii)  

(iv) 

Weighted by assets 
Weighted by trading volume 

Top-1 dealer 

Top-2 dealers 

-0.080*** 
-0.083*** 

(0.019) 
(0.024) 

-0.084*** 

(0.030) 

-0.081*** 

(0.022) 

Coeff.  

SE 

Dealer banks: Banks acting as dealers in the sovereign bond purchase program 

(i) 

(ii)  

(iii)  

(iv) 

Weighted by assets 

-0.074*** 

(0.019) 

Weighted by trading volume 

-0.083*** 

(0.024) 

Top-1 dealer 

Top-2 dealers 

-0.088*** 

(0.030) 

-0.086*** 

(0.025) 

ECB Working Paper Series No 2589 / September 202146 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLE 7 – DIFFERENTIAL IMPACT OF BOND RATINGS 

The focus of this table is on the differential impact of the leverage ratio constraint on liquidity 
of investment grade (IG) and non-investment grade (NIG) bonds. Columns (1), (2) and (3) 
show regression results for non-investment grade bonds, investment grade bonds and the 
full sample, respectively. The specification in columns (1) and (2) is the same as in Table 
4, column (5). In column (3), the variable of interest is IG bond which is a dummy equal to 1 
if  the  bond  credit  rating  is  investment  grade  and  0  otherwise.  The  focus  is  on  the  triple 
interaction term, which shows the estimate for the differential effect for investment grade 
bonds  (as  compared  to  non-investment  grade  bonds).  Standard  errors  are  clustered  at 
bond level. ***, **, and * indicate statistical significance at 1%, 5% and 10%, respectively.  

Dependent variable  

Sample 

Bid-ask spread 

NIG bonds 

IG bonds 

Bank constraint * IG bond * Post  

Bank constraint * Post  

Bank constraint * IG bond 

IG bond* Post 

IG bond 

(1) 

--  

-0.027 
(0.055) 
--  

--  

--  

(2) 

 --  

-0.060*** 
(0.018) 
--  

--  

--  

Macro controls (Table 4, column (5)) 
Fixed effect: Bond/Day 

Yes 
Yes/Yes 

Yes 
Yes/Yes 

Obs. 
R-squared 

46,901 
0.8812 

394,608 
0.7823 

All 

(3) 

-0.095** 
(0.043) 
0.046 
(0.048) 
0.039 
(0.109) 
0.357** 
(0.169) 
-0.367 
(0.448) 
Yes 
Yes/Yes 

441,509 
0.7923 

ECB Working Paper Series No 2589 / September 202147  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
TABLE 8 – IMPACT OF UNDERWRITERS’ LEVERAGE RATIO ON BOND MARKET LIQUIDITY 

In this table, instead of looking at the dealers’ leverage ratio constraint at the country level, 
we look at the constraint of the bond underwriter.  The regression specification is as follows:  

 𝐵𝐵𝐵𝐵𝐵𝐵-𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝐵𝐵𝑖𝑖,𝑡𝑡   =   𝛼𝛼1𝐵𝐵𝑎𝑎𝐵𝐵𝑎𝑎 𝑐𝑐𝑐𝑐𝐵𝐵𝑎𝑎𝑐𝑐𝑠𝑠𝑎𝑎𝐵𝐵𝐵𝐵𝑐𝑐𝑖𝑖 ∗ 𝑃𝑃𝑐𝑐𝑎𝑎𝑐𝑐𝑡𝑡 +   𝛿𝛿𝑖𝑖 +   𝑋𝑋𝐵𝐵, 𝑐𝑐 + ⋯ + 𝜖𝜖𝑖𝑖,𝑡𝑡

where i is the bond, t is the date, and the
 is the measure of bond liquidity. 
. Specifically, we 
The main change is the granularity and definition of the 
 𝐵𝐵𝐵𝐵𝐵𝐵-𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝐵𝐵𝑖𝑖,𝑡𝑡
look at the leverage ratio as of 12/31/2013 for the main underwriter/primary dealer bank 
identified using a two-year window (2010-2011). As before, 
 is a dummy variable (1 for 
indicating  the  period  after  the  first  time  banks  calculated  and  reported  their  regulatory 
 is bond 
leverage ratio to their supervisor and 0 otherwise). 
remaining maturity, i.e., its time-varying characteristic.  Standard errors are clustered at bond 
level. ***, **, and * indicate statistical significance at 1%, 5% and 10%, respectively.  

 are bond fixed effects and 

𝐵𝐵𝑎𝑎𝐵𝐵𝑎𝑎 𝑐𝑐𝑐𝑐𝐵𝐵𝑎𝑎𝑐𝑐𝑠𝑠𝑎𝑎𝐵𝐵𝐵𝐵𝑐𝑐𝑖𝑖

𝑃𝑃𝑐𝑐𝑎𝑎𝑐𝑐𝑡𝑡

𝑋𝑋𝐵𝐵, 𝑐𝑐

𝛿𝛿𝑖𝑖

Dependent variable 

Bank constraint x Post 

Post 

Bank constraint 

Residual bond maturity 

∆ Log(Local GDP)  

∆ Log(Local equity index) 

∆ Log(Local bank index) 

∆ Log(Local volatility index) 

∆ Log(Local government spread, 10Y) 

-- 

∆ Log(Local government spread, 5Y) 

-- 

∆ Log(Local government spread, 3Y) 

∆ Log(Local government spread, 1Y) 

(1) 

(2) 

Bid-ask spread 
(3) 

(4) 

(5) 

-0.031** 
(0.014) 

-0.032** 
(0.014) 

-0.040** 
(0.017) 

-0.050 
(0.035) 
0.198 
(0.199) 
-0.005 
(0.019) 

-- 

-- 

-- 

-- 

-- 

-0.032** 
(0.014) 
0.033 
(0.075) 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

0.010 
(0.030) 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-0.039 
(0.071) 
0.387 
(0.503) 
-0.761* 
(0.423) 
0.361* 
(0.212) 
1.724 
(1.538) 
-0.005 
(0.103) 
0.415* 
(0.215) 
-0.300 
(0.184) 
-0.128* 
(0.075) 

Yes 
Yes 

Fixed effects: Bond  
Fixed effects: Day 

-- 
-- 

Yes 
-- 

Yes 
Yes 

Yes 
Yes 

Obs. 
R-squared 

141,417 
0.0058 

141,417 
0.8375 

141,417 
0.8423 

138,037 
0.8434 

138,037 
0.8460 

ECB Working Paper Series No 2589 / September 202148 
 
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLE 9 – IMPACT OF DEALER BANKS’ LEVERAGE RATIO CONSTRAINTS ON FUND SELLS, DOMESTIC DEALERS 

This table shows regression results examining how leverage-ratio constrained dealer banks impact fund sell-offs during the start of the COVID-
19 crisis. Analysis considers euro area corporate bond mutual funds.  The unit of observation is fund*month. The core sorting variable is Illiquidity 
exposure which sorts funds into those above the median (equal to 1) and below the median (equal to 0) exposure to constrained dealers. The 
dependent variable is how much these funds sell “liquid” bonds: 

where 
clustered at the fund level. ***, **, and * indicate statistical significance at 1%, 5% and 10%, respectively. 

  is  the  monthly  (t)  change  in  holdings  of  mutual  fund  k.  The  variable 

 ∆ 𝐿𝐿𝐵𝐵𝐿𝐿𝑄𝑄𝐵𝐵𝐵𝐵 𝑏𝑏𝑐𝑐𝐵𝐵𝐵𝐵𝑎𝑎𝑘𝑘,𝑡𝑡   =   𝛼𝛼1𝐼𝐼𝐹𝐹𝐹𝐹𝐵𝐵𝐿𝐿𝑄𝑄𝐵𝐵𝐵𝐵𝐵𝐵𝑐𝑐𝐼𝐼 𝑠𝑠𝑒𝑒𝑠𝑠𝑐𝑐𝑎𝑎𝑄𝑄𝑠𝑠𝑠𝑠𝑘𝑘 ∗ 𝐵𝐵𝐶𝐶𝐶𝐶𝐼𝐼𝐶𝐶 𝑆𝑆ℎ𝑐𝑐𝑐𝑐𝑎𝑎𝑡𝑡 + 𝛿𝛿𝑘𝑘   +   𝜖𝜖𝑘𝑘,𝑡𝑡

 ∆ 𝐿𝐿𝐵𝐵𝐿𝐿𝑄𝑄𝐵𝐵𝐵𝐵 𝑏𝑏𝑐𝑐𝐵𝐵𝐵𝐵𝑎𝑎𝑘𝑘,𝑡𝑡

𝛿𝛿𝑘𝑘

  are  fund  fixed  effects.  Standard  errors  are 

Dependent variable 

Sample (funds)  

Illiquidity exposure * COVID shock 

Change in liquid bonds holdings 

Exposed 

(1) 
-- 

Less 
exposed 
(2) 
-- 

All 

(3) 
-1.266*** 
(0.169) 

Exposed 

(4) 
-- 

Less 
exposed 
(5) 
-- 

All 

(6) 
-1.268*** 
(0.272) 

COVID shock (March 2020) 

-1.189*** 
(0.131) 

0.027 
(0.104) 

0.060 
(0.103) 

-1.157*** 
(0.197) 

0.009 
(0.189) 

0.030 
(0.195) 

Fund cash position  

Illiquidity exposure 

Fixed effects: Bond 

Obs. 
R-squared 

-0.093*** 
(0.013) 

-0.026*** 
(0.006) 

-0.060*** 
(0.007) 

-0.281*** 
(0.047) 

-0.053* 
(0.029) 

-0.135*** 
(0.033) 

-- 

-- 

-- 

-- 

0.432*** 
(0.099) 

-- 

-- 

-- 

-- 

Yes 

Yes 

Yes 

2,342 
0.0838 

2,264 
0.0068 

4,606 
0.0523 

2,342 
0.4500 

2,264 
0.3600 

4,606 
0.4100 

ECB Working Paper Series No 2589 / September 202149 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
TABLE 10 – IMPACT OF DEALER BANKS’ LEVERAGE RATIO CONSTRAINTS ON FUND SELLS, UNDERWRITERS  

Similar to Table 9, this table shows regression results examining how leverage-ratio constrained dealer banks impact fund sell-offs during the 
start of the COVID-19 crisis. The difference is that here we use underwriter level constraints. Analysis considers euro area corporate bond mutual 
funds.  The unit of observation is fund*month*security. Standard errors are clustered at the fund portfolio level. Otherwise, the specifications are 
the same as in Table 8. ***, **, and * indicate statistical significance at 1%, 5% and 10%, respectively.  

Dependent variable 

Sample (funds)  

Illiquidity exposure * COVID 
Shock 

Exposed 

Less 
exposed 

All 

   Exposed 

Less 
exposed 

(1) 

(2) 

(3) 

(4) 

(5) 

All 

(6) 

>50 critical 
holdings  

IV 

(7) 

(8) 

(9) 

(10) 

Change in bonds holdings 

-- 

-- 

-0.079** 

-- 

-- 

-0.083** 

-0.041** 

-0.045** 

COVID Shock (March 2020) 

-0.073* 

0.02 

Illiquidity exposure 

Fund cash position  

Fixed effect: Fund/Bond 

(0.040) 
-- 

(0.057) 
-- 

-0.04 

(0.026) 
--/Yes 

-0.069 

(0.118) 
--/Yes 

(0.035) 
-0.041 

(0.032) 

0.023 

(0.015) 
-0.019 

(0.040) 
--/Yes 

-0.073* 

0.021 

(0.040) 
-- 

(0.058) 
-- 

(0.043) 
0.016 

(0.056) 
-- 

(0.018) 
-0.078 

(0.060) 

   0.005 

(0.006) 

(0.020) 
-0.076 

(0.060) 
-- 

-0.034* 

(0.019) 

-0.004 

(0.044) 
-- 

-0.061** 

(0.029) 

0.014 

(0.047) 
-- 

-0.046* 

-0.013 

0.006 

   0.06 

0.064 

   0.002 

(0.027) 

(0.124) 
   Yes/Yes  Yes/Yes  Yes/Yes 

(0.048) 

(0.073) 
--/Yes 

(0.087) 
Yes/Yes 

(0.004) 
   Yes/Yes 

-0.013 

(0.018) 
Yes/Yes 

Obs. 

R-squared 

15,860 

0.1264 

18,618 

0.2917 

34,469 

0.2549 

15,860 

0.1496 

18,618 

0.3 

34,469 

0.2666 

   17,842 

   0.0601 

17,785 

0.1013 

   13,271 

   0.2292 

13,272 

0.2296 

ECB Working Paper Series No 2589 / September 202150  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
TABLE A.1 – IMPACT OF THE LEVERAGE RATIO ON BOND MARKET LIQUIDITY, SURVIVING BONDS 

APPENDIX 

This table re-examines results in Table 4, specifications (3) and (5) for a subset of bonds that are outstanding for a minimum of (i) two years before 
and two years after the event (December 31, 2013), i.e., full sample; (ii) one year before and one year after the event; and (iii) six months before 
and six months after the event.  

Sample (bonds) 

2 years 

Minimum bond survival around event 
1 year 

6 months 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

Bank constraint x Post 

Residual bond maturity 

-0.065** 
(0.027) 
-- 

-0.076*** 
(0.023) 
0.025 
(0.036) 
Yes 

-0.091*** 
(0.022) 
-- 

-0.094*** 
(0.019) 
0.010 
(0.032) 
Yes 

-0.060*** 
(0.021) 
-- 

-0.084*** 
(0.019) 
0.002 
(0.027) 
Yes 

Macro controls (Table 4, column (5)) 
Fixed effects: Bond /Day 

Yes 
Yes/Yes  Yes/Yes 

   Yes 
   Yes/Yes  Yes/Yes 

   Yes 
   Yes/Yes  Yes/Yes 

Obs. 
R-squared 

460,391 
0.7309 

382,005 
0.6995 

   809,886 
   0.8605 

614,309 
0.7059 

   982,446 
   0.8489 

751,676 
0.7232 

ECB Working Paper Series No 2589 / September 202151  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
 
TABLE A.2 – IMPACT OF UNDERWRITERS’ LEVERAGE RATIO ON BOND MARKET LIQUIDITY 

This  table  shows  robustness  of  results  in  Table  8  to  the  use  of  an  alternative  window  for 
detecting core underwriting ties. In Table 8, we use a two-year window (2010-2011) to look at 
other bond issues. Here, we look at a three-year window (2010-2012) instead. Everything else 
is  the  same.  Standard  errors  are  clustered  at  bond  level.  ***,  **,  and  *  indicate  statistical 
significance at 1%, 5% and 10%, respectively. 

Dependent variable 

Bank constraint x Post 

Post 

Bank constraint 

Residual bond maturity 

∆ Log(Local GDP)  

∆ Log(Local equity index) 

∆ Log(Local bank index) 

∆ Log(Local volatility index) 

∆ Log(Local government spread, 10Y) 

-- 

∆ Log(Local government spread, 5Y) 

∆ Log(Local government spread, 3Y) 

∆ Log(Local government spread, 1Y) 

Fixed effects: Bond  
Fixed effects: Day 

Obs. 
R-squared 

-- 

-- 

-- 

-- 
-- 

(1) 

(2) 

Bid-ask spread 
(3) 

(4) 

(5) 

-0.054** 
(0.026) 

-0.054** 
(0.026) 

-0.053** 
(0.026) 

-0.056** 
(0.026) 
0.099 
(0.108) 
0.044 
(0.036) 

-- 

-- 

-- 

-- 

-- 

-0.054** 
(0.026) 
0.091 
(0.108) 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

0.013 
(0.032) 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

0.022 
(0.035) 
0.415 
(0.255) 
0.195 
(0.268) 
-0.135 
(0.135) 
0.355 
(0.454) 
-0.119* 
(0.065) 
0.157 
(0.253) 
-0.108 
(0.238) 
-0.025 
(0.047) 

Yes 
Yes 

Yes 
-- 

Yes 
Yes 

Yes 
Yes 

181,921 
0.0193 

181,921 
0.6684 

181,921 
0.6865 

180,877 
0.6880 

145,718 
0.7086 

ECB Working Paper Series No 2589 / September 202152 
 
 
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgements 

We thank seminar participants at the Federal Reserve Bank of Boston, the Indiana University Kelly School of Business, the University of 
British Columbia, the Bank of England, the Federal Reserve Board, and participants at the ECB 2021 Macro-Finance Conference, for 
helpful comments. We are grateful to Nina Boyarchenko (discussant), Alexander Duering, Ryan Garino, Marie Hoerova, Marco 
Macchiavelli, Kristin Mugford, Torsti Silvonen and Alex Zhou for especially detailed and insightful comments. The views expressed here 
are those of the authors and do not necessarily reflect those of the European Central Bank or the Eurosystem. We thank Baker Library 
Research Services, Kathleen Rayan and James Zeitler for data support on this project. 

Johannes Breckenfelder 
European Central Bank, Frankfurt am Main, Germany; email: johannes.breckenfelder@ecb.europa.eu 

Victoria Ivashina 
Harvard Business School, NBER and CEPR, Boston, United States; email: vivashina@hbs.edu 

© European Central Bank, 2021 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

+49 69 1344 0 
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All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

PDF 

ISBN 978-92-899-4812-8 

ISSN 1725-2806 

doi: 10.2866/948014 

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