Heterogeneity in corporate debt structures and the transmission of monetary policy.
Date: 2020-06-26
Institutions: European Central Bank
Subjects: bond, business administration, euro area, financial market, indebtedness, loan, monetary policy

Fédéric Holm-Hadulla, Claire Thürwächter 

Working Paper Series 

Heterogeneity in corporate debt 
structures and the transmission  
of monetary policy 

No 2402 / May 2020 

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

 
 
 
Abstract

We study how differences in the aggregate structure of corporate debt ﬁnancing affect the transmis-

sion of monetary policy. Using high-frequency ﬁnancial market data to identify monetary policy shocks

in a panel of euro area countries, we ﬁnd that: bond ﬁnance dampens the overall response of ﬁrm credit

to monetary policy shocks in economies with a high initial share of bond- relative to bank-based ﬁnance;

this effect weakens, and may even reverse, in economies with a low share of bond ﬁnancing; and the

dampening effect of a larger bond ﬁnancing share also attenuates the ultimate impact of monetary policy

on economic activity. These ﬁndings point to corporate bond markets acting as a “spare tire” in situations

when bank lending contracts.

JEL Classiﬁcation: E44, E52, G21, G23.

Keywords: Firm Financing Structure, Bank Lending, Corporate Bonds, High-Frequency Identiﬁcation, Lo-

cal Projections.

ECB Working Paper Series No 2402 / May 20201Non-technical summary

We study how the relative role of corporate bonds versus bank loans in the debt structure of ﬁrms affects the

transmission of monetary policy shocks to the economy. To this end, we extend a standard empirical macro

model with variables measuring the volume and cost of credit to ﬁrms. Estimating this model in a panel of

euro area countries, and using high-frequency methods to identify monetary policy shocks, we ﬁnd that the

corporate debt ﬁnancing structure is highly consequential for monetary policy transmission. In particular,

a greater role of bond ﬁnancing goes along with a weaker response of ﬁrm credit and economic activity to

monetary policy shocks. Accordingly, bond ﬁnance acts as a substitute source of credit when bank lending

contracts and, vice versa, it is crowded out when bank lending expands. At the same time, this substitution

function requires the initial bond ﬁnancing ratio in the economy to be relatively high: in economies with a

low bond ﬁnancing ratio, the adjustment patterns weaken and may even reverse so that bond ﬁnance instead

reinforces the overall response of ﬁrm credit to monetary policy shocks.

These patterns point to two countervailing mechanisms emphasised in the related theoretical literature.

The substitution pattern at high bond ﬁnance ratios is consistent with the bank lending view of transmission,

according to which monetary policy triggers shifts in loan supply that go beyond its impact on broader

ﬁnancing conditions in the economy. The ampliﬁcation pattern at low bond ﬁnance ratios is consistent

with state-contingent ﬁrm preferences for different ﬁnancing instruments, as the ﬂexibility afforded by bank

lending (e.g. in renegotiating credit contracts in response to weakening business prospects) becomes more

valuable in tight and less valuable in loose monetary conditions. The reversal in the impact of monetary

policy on the bond ﬁnance ratio depending on the initial ﬁnancing structure suggests that a sufﬁciently large

initial reliance on bond ﬁnance is necessary for the substitution effect to dominate.

Our ﬁndings imply that differences in ﬁrm ﬁnancing structures constitute an important source of geo-

graphical heterogeneity in the transmission of monetary policy across the euro area. Such heterogeneity

may complicate efforts to ensure a broadly uniform monetary policy stance throughout the economy. And it

highlights the need for convergence not only in the economic but also the ﬁnancial structures across different

parts of the currency union, including via a broadening of ﬁrm access to direct market ﬁnance as envisaged

in the context of the EU Capital Markets Union.

ECB Working Paper Series No 2402 / May 202021

Introduction

The theory and practice of corporate ﬁnance draw a sharp distinction between bank loans and corporate

bonds as sources of ﬁrm credit. Among the distinctive features, bank loans are typically easier to renego-

tiate or restructure in case a borrower falls on hard times, whereas corporate bonds tend to be less exposed

to macroeconomic disturbances that induce shifts in loan supply. These differences in the underlying eco-

nomics of different debt-ﬁnancing instruments in turn may impact their behaviour in response to monetary

policy shocks. But the direction of the impact is ambiguous and recent empirical evidence on the interaction

between the structure of corporate debt and the transmission of monetary policy is scarce. At the same time,

the issue has acquired renewed relevance as many advanced economies have experienced a marked shift in

the ﬁnancing mix of ﬁrms, leading to an increasingly relevant role of corporate bonds relative to bank loans

over the past decade (see, e.g., Adrian et al. (2013) and De Fiore and Uhlig (2015)).

Against this background, the current paper studies whether the ﬁnancing structure of ﬁrms matters for the

transmission of monetary policy to the economy. To this end, we subdivide the question into three distinct

but integral aspects and set up an empirical model that allows us to analyse them simultaneously. The ﬁrst

aspect is whether the two debt ﬁnancing instruments differ in their response to monetary policy shocks. The

second aspect is whether any such differences become more or less accentuated depending on the ﬁnancing

structure prevailing prior to the shock. And the third aspect is whether these differences in the responsiveness

and their dependence on the initial ﬁnancing structure alter the ultimate impact of monetary policy on key

macroeconomic aggregates. Our empirical approach consists of a local projections setup for a panel of euro

area countries, using high-frequency surprises to identify monetary policy shocks and allowing the impact

of these shocks on ﬁrm credit and economic activity to differ with the bond-to-loan ﬁnance ratio prevailing

prior to the shock.

Besides their obvious policy relevance, our research question plays into a rich, but highly contested,

literature on the interaction between corporate ﬁnance and monetary policy transmission. As to the ﬁrst

aspect, a prominent strand of this literature has argued that bank balance sheet frictions may render loan

supply more responsive to monetary policy shocks than other sources of debt ﬁnance (Kashyap et al. (1993)).

As a consequence, bond issuance may mitigate or even counteract the impact of monetary-policy induced

shifts in loan supply on the overall amount of credit available to ﬁrms. However, the subsequent literature

has also highlighted alternative mechanisms pointing in the opposite direction. In particular, the inherent

ECB Working Paper Series No 2402 / May 20203advantages of bank lending – in terms of the screening and monitoring of borrowers (Diamond (1984)), as

well as the ﬂexibility to renegotiate the terms of a credit contract in case of need (Berlin and Mester (1992))

– may become more valuable when monetary conditions tighten and, consequently, collateral values decline

and business prospects deteriorate (Bolton and Freixas (2006)).1 Hence, monetary policy tightening shocks

may shift the preferred debt ﬁnancing structure of ﬁrms towards bank loans and, via this channel, trigger a

stronger contraction in corporate bonds than in loan volumes.2

The resultant theoretical ambiguity also extends to the second aspect of whether the relative responsive-

ness of different ﬁnancing instruments depends on the existing ﬁnancing structure of the respective economy.

For instance, changes in ﬁrm ﬁnancing structures entail ﬁxed costs – e.g. for an entirely bank-dependent ﬁrm,

the process of building a presence in global capital markets is resource- and time-consuming. This mecha-

nism, in turn, would weaken the capacity of bond markets to act as a substitute ﬁnancing option when loans

contract and it would become more relevant the higher is the reliance on bank relative to bond ﬁnance in an

economy.3 However, also here, countervailing effects are conceivable: for instance, investors in international

bond markets may strive for some geographic diversiﬁcation, which may provide the opportunity for ﬁrms

in countries with small bond markets to issue at relatively favourable rates due to the limited supply from

other, competing issuers (Coeurdacier and Martin (2009)).

Taken together, the theoretical predictions on whether loans or bonds are more responsive to monetary

policy shocks and how this relative responsiveness would change with the relative prevalence of different

ﬁnancing instruments are therefore not clear-cut. And, since these two aspects constitute integral elements

of the transmission process, this theoretical ambiguity carries over to the third aspect, consisting in the overall

implications of the corporate ﬁnancing structure for the effects of monetary policy on the economy.

In view of these ambiguous theoretical predictions, the current paper provides an empirical analysis of

1For further, closely related, analysis on the speciﬁc role of banks in screening and monitoring borrowers, see also Holmstrom
and Tirole (1997); Diamond (1991); Rajan (1992); and on the differences in bank- versus direct market-based ﬁnance, see Dewa-
tripont and Maskin (1995); Berlin and Mester (1992); Boot et al. (1993). While useful to highlight key conceptual differences, the
distinction between different types of credit is approximate: in practice, bank lending relationships often involve several contract-
ing parties, e.g. via syndicated loans; loans may also be sold, either directly or in securitized form; bond markets also perform
monitoring functions etc..

2This mechanism may be reinforced by composition effects: to “qualify” for bond market access, ﬁrms have to meet certain
credit quality standards and monetary policy may shift the dividing line between qualifying and non-qualifying ﬁrms in a procyclical
manner (for a related argument, see De Fiore and Uhlig (2011, 2015) and Crouzet (2017)).

3There are further mechanisms that may inhibit changes in ﬁrm ﬁnancing structures. For instance, existing banking relationships
may lead to some lock-in as borrowers and lenders try to capitalize on the informational monopoly established over time (Sharpe
(1990); Rajan (1992); Kashyap and Stein (1994); Berger and Udell (1995, 2002)).

ECB Working Paper Series No 2402 / May 20204how the structure of corporate debt ﬁnancing affects the transmission of monetary policy. As the speciﬁc

context of the analysis, we consider a panel of euro area countries which, for two reasons, provides a particu-

larly suitable setting to study this question. First, the shift in the relative importance of bond- vs. bank-based

ﬁnance has been particularly pronounced (ECB (2016)). In particular, the ratio of corporate bonds to bank

loans in the overall stock of euro area ﬁrm debt has almost doubled since its trough in 2007. Second, the euro

area exhibits a high degree of heterogeneity in the relative role of these different ﬁnancing instruments across

countries (Rodriguez-Palenzuela et al. (2013)). For instance, since the start of the euro, the average ratio of

bond- over bank-based ﬁnance has ranged from less than 0.1 in Greece and Spain to 0.5 in France. This

variation in both, the time-series and cross-sectional dimension, offers ample scope for empirical analysis.

Moreover, given these countries belong to one currency union, it allows us to clearly identify a common mon-

etary policy stance, without requiring assumptions about the comparability of measures taken by different

central banks.

Our econometric model starts from the standard set of variables typically considered in the monetary

economics literature, including indicators for real activity, inﬂation and policy-controlled short-term interest

rates. It then adds a set of variables measuring corporate ﬁnancing volumes, broken down by bank loans and

debt securities, as well as a set of controls that may inﬂuence the relative role of these ﬁnancing instruments

across countries and over time. In terms of estimation, we follow Jordà (2005)’s local projections method,

which offers a ﬂexible approach to model how the interaction of monetary policy with the ﬁnancing structure

of ﬁrms alters its effects on key macroeconomic aggregates. For the identiﬁcation of monetary policy shocks,

we resort to high-frequency surprises in key ﬁnancial market prices around ECB policy events based on a

comprehensive dataset, recently published by Altavilla, Brugnolini, Gürkaynak, Motto and Ragusa (2019).

Our ﬁndings show that the corporate debt ﬁnancing structure is highly consequential for monetary policy

transmission. In particular, we ﬁnd that a greater role of bond ﬁnancing goes along with a weaker overall

response of ﬁrm credit to monetary policy shocks and this dampening effect also attenuates the impact of

these shocks on economic activity. The underlying mechanism is that bond ﬁnance acts as a substitute source

of credit when bank lending contracts (and, vice versa, it is crowded out when bank lending expands). But

this substitution function requires the initial bond ﬁnancing ratio in the economy to be relatively high. In

fact, in economies with a low bond ﬁnancing ratio, the adjustment patterns reverse so that bond ﬁnance

ampliﬁes the contraction (expansion) in bank lending in response to a policy tightening (loosening). As a

ECB Working Paper Series No 2402 / May 20205consequence, larger bond markets act as a buffer that dampens the response of debt ﬁnancing to ﬁrms in

response to policy shocks and this buffering function is also reﬂected in a weaker contractionary effect of the

monetary tightening on output. Overall, the ﬁndings thus are consistent with the interpretation of corporate

bond markets as acting as a “spare tire” in situations when banks cut back on lending.

Related literature. These ﬁndings add to a rapidly growing empirical literature that has sprung from

the shift in ﬁrm ﬁnancing structures observed since the crisis and touches upon some of the aspects we

address in the current paper. Regarding the ﬁrst aspect, how the response to monetary policy differs across

bank versus bond-ﬁnance, Becker and Ivashina (2014) also ﬁnd evidence for the substitution function of

bond ﬁnance in ﬁrm level data, thus conﬁrming earlier ﬁndings by Kashyap et al. (1993) and Kashyap et al.

(1996), as well as the pattern we observe for economies with a high bond ﬁnancing ratio. Further, several

recent studies, e.g. including Lhuissier and Szczerbowicz (2018), Arce et al. (2017) and Grosse-Rueschkamp

et al. (2019), distinguish between conventional and unconventional monetary policy (or zoom in on speciﬁc

unconventional measures such as the ECB’s corporate sector purchase program) and ﬁnd differences in the

response patterns across ﬁnancing instruments. Finally, Crouzet (2019) presents a structural model that

incorporates the countervailing mechanisms by which monetary policy shocks may affect ﬁrm ﬁnancing

structures and tests these mechanisms in a panel of US ﬁrms.

Regarding the second aspect, whether differences of monetary policy transmission depend on the ﬁnanc-

ing structure prevailing prior to the shock, the literature is much scarcer. In a methodologically similar setup

to ours, Grjebine et al. (2018) study how corporate debt structures affect macroeconomic outcomes; but their

focus is on the pace of recovery after recessions, rather than the transmission of monetary policy. A poten-

tially interesting, related strand of work is the paper by Darmouni et al. (2019), which shows that, in the euro

area, the responsiveness of ﬁrm share prices to monetary policy shocks intensiﬁes the higher is their reliance

on bond ﬁnance, whereas this relationship is absent in the US. The empirical designs of the two papers, fo-

cused on stock price reactions of individual ﬁrms in Darmouni et al. (2019) as opposed to the overall macro

transmission across different economies in ours, is too far apart to try and square these different ﬁndings.

However, efforts to bridge the more micro corporate-ﬁnance based perspectives with the analysis of macro

transmission appear as a very interesting avenue for further research.

To the best of our knowledge, our paper is the ﬁrst to simultaneously explore how the response to mon-

etary policy differs across corporate debt instruments, how these responses are shaped by the debt ﬁnancing

ECB Working Paper Series No 2402 / May 20206structure prevailing prior to the shock, and how these differences ultimately affect the macroeconomic im-

plications of a given monetary policy shock.

The remainder of the paper proceeds as follows. Section 2 introduces the model and identiﬁcation

strategy. Section 3 describes the data and key stylized facts. Section 4 presents our main results followed by

a battery of robustness checks in Section 5. Section 6 concludes.

2 Econometric methodology

2.1 Model

We estimate the dynamic response of key macroeconomic aggregates and ﬁrm ﬁnancing variables to a mon-

etary policy shock via local projections (Jordà (2005)). These consist in a set of separate panel regressions

for each of the dependent variables and each horizon. The baseline model to estimate the impulse response

functions (IRFs) is:

Yi,t+h = αi,h + (cid:0)β0,h + βh(B/L)i,t−1

(cid:1) shockIR

t + γh

2
∑
p=1

Xi,t−p + θh

2
∑
p=1

¯Xt−p + εi,t+h

(1)

where subscripts i, t, and h denote the country, month, and IRF horizon, respectively, and subscript p denotes

the number of lags included in the sets of control variables Xi,t−p and ¯Xt−p.

The dependent variables Yi,t+h comprise real GDP and the GDP deﬂator, as well as bank loans to non-

ﬁnancial corporations and the ratio of corporate bonds over bank loans (henceforth referred to as the bond

ﬁnance ratio). The former two variables serve as measures of economic activity and the general price level

and the latter two as measures of the volume and structure of corporate debt ﬁnancing in each country and

month. The dependent variables also include a policy-controlled short-term interest rate, which is the same

across countries and which helps assess the plausibility of our proxy for monetary policy shocks.4 All

variables enter in log-levels, except the bond ﬁnance ratio, which is expressed as 100 times the notional

stocks of bonds over loans, as well as the shock, which is expressed in percentage points, and the policy rate,

4The underlying logic is that changes in policy-controlled short-term interest rates constitute the ﬁrst step in the transmission
of standard monetary policy shocks. So, a signiﬁcant and relevant response in these variables to the shock-variable are a minimum
criterion for the latter to be a plausible proxy for exogenous changes in the monetary policy stance; see, e.g., Gertler and Karadi
(2015).

ECB Working Paper Series No 2402 / May 20207which is expressed in percent.5

The key explanatory variables of interest are the monetary policy shock (shockIR

t ) and its interaction with

the bond ﬁnance ratio (B/L)i,t. As Section 2.2 explains in detail, the shock series is extracted from high-

frequency changes in key money market interest rates around ECB monetary policy events. The coefﬁcient

β0,h captures the response in each of the dependent variables in period t + h to an exogenous monetary policy

tightening in period t. The coefﬁcient βh shows whether and how this impact changes depending on the

ﬁnancing structure prevailing in the period prior to the shock. Taken together these coefﬁcients summarize

the combined impact of the monetary policy shock at horizon h conditional on the prevailing bond ﬁnance

ratio as:

∂Yi,t+h
∂shockIR
t

= β0,h + βh(B/L)i,t−1.

(2)

Further, the model includes a set of control variables to capture common and country-speciﬁc inﬂuences

on the economy over time, denoted by ¯Xt−p and Xi,t−p, respectively. The common inﬂuences consist of euro

area real GDP and the GDP deﬂator, as well as the EUR-USD exchange rate and global commodity prices

to capture the external environment. Moreover, ¯Xt−p includes the “loan supply indicator” (LSI) constructed

by Altavilla, Darracq Pariès and Nicoletti (2019) and the “composite indicator of systemic stress” (CISS)

by Kremer et al. (2012). Both variables are motivated by the speciﬁc timing of the observed shifts in ﬁrm

ﬁnancing structures in the euro area: the trend towards a greater relative role of bond-based ﬁnance started

from the dislocations in the banking system and the protracted period of ﬁnancial stress that followed the

Global Financial Crisis (Adrian et al. (2013); De Fiore and Uhlig (2015); Crouzet (2017)). Over the same

period, also the monetary policy stance underwent marked changes, as major central banks sought to contain

the fallout from the crisis. Accordingly, the speciﬁc conditions prevailing over the post-crisis period may be

correlated with both, monetary policy and the relevant outcome variables in Yi,t+h. By including the CISS

and LSI in the speciﬁcations, with the former capturing ﬁnancial market stress and the latter aggregate loan

supply conditions in the euro area, we control for this possibility and thereby further sharpen the identiﬁ-

cation. We also include lags of the euro area wide policy interest rate and the shock in ¯Xt−p. In particular,

the inclusion of the lagged shock is important to purge the high-frequency surprises from serial correla-

tion, which further underpins the identifying assumption that the shock represents unanticipated changes to

5By including both, bank loans Li,t in logs and the bond ﬁnance ratio (B/L)i,t we ensure that the overall debt ﬁnancing volume

(Vi,t = Li,t + Bi,t ) from these two sources is reﬂected in the regressions, as approximated by logVi,t ≈ logLi,t + (B/L)i,t .

ECB Working Paper Series No 2402 / May 20208monetary policy (Ramey (2016)).

Besides lags of the variables included in Yi,t+h, the country-speciﬁc controls Xi,t−p also comprise two

composite ﬁnancing cost measures, one on bank borrowing and one on bond ﬁnancing of ﬁrms, to capture

the relative attractiveness of these different instruments; and they include the lagged interactions of the shock

with the bond ﬁnance ratio. Last, we saturate the model with a set of country-ﬁxed effects αi,h.6

Our estimations use Driscoll and Kraay (1998) standard errors that allow for both serial correlation,

which is an inherent to local projection estimates, and spatial dependence across countries. Our baseline

regressions include p = 2 lags of the variables comprised in Xi,t−p and ¯Xt−p.7

Taken together, this baseline speciﬁcation closely corresponds to the standard model set-up used in much

of the related literature to study the impact of monetary policy and augments it with additional variables to

capture corporate ﬁnancing volumes. Besides including the key variables of interest from a macroeconomic

perspective, this close correspondence has the additional beneﬁt of allowing us to benchmark our ﬁndings

against other studies. Due to its ﬂexible functional form, the local projections method is as a suitable mod-

elling framework to assess how the corporate debt structure affects the transmission of monetary policy.8

2.2

Identiﬁcation strategy and estimation

High-frequency identiﬁcation. Our identiﬁcation strategy follows an increasingly active strand of the mon-

etary economics literature using high-frequency changes in ﬁnancial market variables around events of cen-

tral bank communication as a measure of exogenous changes in the monetary policy stance (see Ramey

(2016) for a review of this literature). The basic rationale of this high-frequency identiﬁcation strategy (HFI)

is that, provided the time window around these events is sufﬁciently narrow, it is plausible to attribute the

observed changes in interest rates to monetary policy – and to exclude that they reﬂect any other confound-

ing factors that may be simultaneously correlated with the policy stance and the macroeconomic or ﬁnancial

outcome variables.

Following this logic, which essentially treats the policy events as a controlled experiment, HFI has been

applied in a host of papers studying the impact of monetary policy on the US economy and ﬁnancial mar-

6We also tested speciﬁcations including a deterministic time trend, which left our main results unaffected however.
7We experimented with different lag numbers, but also found this to be inconsequential for our estimates.
8For a similar modelling strategy, see Auer et al. (2019), who study the link between corporate leverage and monetary policy

transmission.

ECB Working Paper Series No 2402 / May 20209kets (Kuttner (2001); Cochrane and Piazzesi (2002); Gürkaynak et al. (2005); Piazzesi and Swanson (2008);

Barakchian and Crowe (2013); Gertler and Karadi (2015); Nakamura and Steinsson (2018)). More recently,

a nascent literature has adopted this approach to similar applications in the euro area context (Lhuissier and

Szczerbowicz (2018); Andrade and Ferroni (2018); Jarocinski and Karadi (forthcoming); Altavilla, Brugno-

lini, Gürkaynak, Motto and Ragusa (2019); Auer et al. (2019)).

The data underlying our HFI approach comes from the Euro Area Monetary Policy Event-Study Database

(EA-MPD), constructed by Altavilla, Brugnolini, Gürkaynak, Motto and Ragusa (2019) and published on

the ECB website.9 The EA-MPD computes intra-day changes in a broad set of ﬁnancial market variables

around the time the ECB’s Governing Council communicates and explains its monetary policy decisions.

These communication events follow a preset schedule and format in that: the Governing Council meets in

regular intervals and the meeting dates are publicly known well in advance;10 at 1.45 pm on the day of the

meeting, a press release is published on the ECB website announcing the Governing Council’s monetary pol-

icy decision (which may also consist in an announcement that the policy conﬁguration remains unchanged);

and at 2.30 pm a press conference takes place, at which the President ﬁrst reads out a written statement

explaining the rationale of the decision, followed by a question- and answer-session with journalists. The

EA-MPD calculates three sets of surprises, one for the publication of the press release, one over the period of

the press conference, and one for both events together. Taken together, the EA-MPD constitutes a very com-

prehensive, detailed, and publicly available database available for high-frequency identiﬁcation of monetary

policy shocks in the euro area.11

Deﬁnition of monetary policy shocks. In our baseline estimations, we use the change in a short-term

interest rate over the entire event window, i.e. from before the press release to after the press conference,

as the basis for calculating the monetary policy shocks. In doing so, we take into account an important

9https://www.ecb.europa.eu/pub/pdf/annex/Dataset_EA-MPD.xlsx
10According to the regular schedule, the Governing Council has a monetary policy meeting every six weeks. This regular meeting
schedule has changed twice since the introduction of the euro in 1999. Initially, the meetings took place twice per month, whereas
after November 2001 and until December 2014 the regular monetary policy meetings followed a four-week rhythm. Further, on a
few occasions the Governing Council deviated from the regular meeting schedule to respond to acute crisis events. A list of all the
events included in the EA-MPD and a discussion of irregularities in the scheduling and practice of the Governing Council meetings
is laid out in the Appendix to Altavilla, Brugnolini, Gürkaynak, Motto and Ragusa (2019).

11For further detail, see Altavilla, Brugnolini, Gürkaynak, Motto and Ragusa (2019). A similarly rich dataset with high-frequency
surprises around monetary policy events for the euro area has been provided by Jarocinski and Karadi (forthcoming) . The main
reason for us to rely on the EA-MPD is that the latter is regularly updated, provides a more detailed breakdown of the policy
surprises into those deriving from the press release and those deriving from the press conference, and includes a broader set of
ﬁnancial market variables.

ECB Working Paper Series No 2402 / May 202010insight into the anatomy of monetary policy surprises deriving from the recent literature: when central banks

talk about policy, market participants do not only receive a signal on whether and how the central bank is

going to adjust its policy instruments, but also on how the central bank assesses economic prospects. If

market participants in turn assume that the central bank commands over superior information on the state

and prospects of the economy, this may lead them to revise their own economic assessment. Thus, depending

on which type of signal dominates, investors may draw very different inferences on the current and future

stance of monetary policy resulting in different constellations of ﬁnancial market adjustments around the

event.

For instance, an unexpected interest rate increase may be accompanied by a decline in stock prices if

market participants perceive the central bank decision as a true monetary policy tightening, which engenders

an expected contraction of economic activity; in this case, the negative cross-asset correlation between in-

terest rates and stock prices would qualify the high-frequency surprise as a genuine monetary policy shock.

Alternatively, a positive interest rate surprise may also be accompanied by rising stock prices if the rate in-

crease is interpreted as a sign of the central bank’s superior information, suggesting that economic prospects

are more buoyant than previously thought; in this case, the surprise would constitute what the literature has

come to refer to as an information shock.

As visible from Figure 1, this distinction is of major practical relevance in the euro area context (and,

as shown by Jarocinski and Karadi (forthcoming), also in the US context). For instance, over the sample

considered in our empirical analysis, almost 40% of high-frequency surprises fall under the information

shock category (see upper right and lower left quadrant). Since our aim is to estimate the effects of genuine

monetary policy shocks, our baseline analysis instead only considers the shocks in the other two quadrants,

so as to sharpen the identiﬁcation approach.

Choice of short-term rate. The speciﬁc variable used in our baseline to extract the monetary policy

shocks is the 1-month OIS rate. As the EA-MPD contains surprises for a broad range of risk-free interest

rates rates, including a range of maturities from 1-week to 30-years for the OIS and in the absence of strong

conceptual reasons to favour one over the other point on this spectrum, we are left with several degrees of

freedom in choosing between them.

We thus discipline our choice by imposing two criteria. The ﬁrst is to ensure a relatively close mapping

from the shock variable to the policy variable we included in our dynamic model. Since, in line with much

ECB Working Paper Series No 2402 / May 202011of the related literature, we choose the policy variable to be the 3-month OIS rate, a natural choice would

be to also use a shock located on the same or a similar point on the term structure. The second criterion

is that, within a reasonably narrow segment around the 3-month maturity, we choose a shock that has the

highest statistical ﬁt for the 3-month OIS rate after controlling for all other variables in equation (1). This is

important to ensure that, also from a statistical perspective, there is a close mapping from the shock variable

to the policy variable.

Figure 1: Stock price and policy rate surprises

Figure 2: Response of policy rate on impact

Note: Surprises in the Eurostoxx 50 and the 1-month OIS rate in Figure 1 are in percentage points and basis points respectively. The
response in Figure 2 is scaled to a 100 basis point tightening shock in the respective high-frequency surprise at the average bond
ﬁnance ratio and is shown in percentage points. "PC" refers to the ﬁrst principal component of the 1-week to 2-year OIS rates. The
range shows the 95% conﬁdence interval.

Regressing the residuals of the 3-month OIS rate on different shocks from the EA-MPD, the 1-month OIS

surprise emerges as the most suitable among a range of candidates (see Figure 2). Its coefﬁcient is estimated

with a markedly higher precision than that of the 3-month OIS rate shock and the principal component (PC)

of different rates; and for the 6-month OIS rate shock, the estimated 95%-conﬁdence interval includes zero.

In summary, our baseline deﬁnition of shockIR
t

thus consists of the monetary policy shock component of

the surprises in the 1-month OIS rate. In Section 5.2, we test for the robustness of our key ﬁndings to this

choice.12

12A time-series plot of the high-frequency surprises that we consider in our baseline model and the robustness section can be

ECB Working Paper Series No 2402 / May 2020123 Data and stylized facts

We estimate the model on monthly data over the sample period from January 2003 to April 2019.13 At the

cross-sectional level, we include a panel of 10 euro area countries, which together account for 96% of euro

area GDP.14 As the monetary policy variable, we use the monthly averages of daily observations of the 3-

month OIS rate. GDP and the deﬂator are interpolated from quarterly to monthly frequency, so as to match

the frequency of the ﬁrm ﬁnancing variables.15 Loans and bond ﬁnance to non-ﬁnancial corporations are

the notional stocks, which correct for changes in the ﬁnancing volumes that arise from valuation changes so

that we capture only changes in the stock stemming from transactions. Moreover, the loan data is adjusted

for securitisation, cash pooling, and sales and we restrict the counterparty sector to residential non-ﬁnancial

corporations.

For the control variables, the GDP and deﬂator series at the euro area level are interpolated from quarterly

to monthly frequency in the same procedure as the corresponding country level data. The cost of loan

ﬁnance is included as a spread of the bank lending rate for loans to corporations on new business vis-à-

vis a risk-free rate. As risk-free rate we choose the 1-year OIS rate so as to broadly match the maturity

and average rate ﬁxation proﬁle of the bulk of loans in our sample.16 The monthly series for the costs

of bond ﬁnance in each country are based on De Santis (2018) and are constructed by aggregating up the

spreads on individual bonds purged of bond- and issuer-speciﬁc characteristics.17 The variables capturing the

external environment are the EUR-USD exchange rate, averaging daily observations over each month, and

an encompassing commodity price index, which is provided by the IMF. For the CISS we also average the

found in Figure 10 in Appendix A.

13We follow the recommendation of Altavilla, Brugnolini, Gürkaynak, Motto and Ragusa (2019) and exclude the years from 1999
to the end of 2001 from the sample due to noise and sparse quotes in the intraday OIS data during the ﬁrst years of the euro. A
further sample restriction is imposed by the LSI which is only available from 2003 onward. Estimating the model on monthly data
allows us to exploit the availability of loan and bond volumes at that frequency. The series are retrieved from the Statistical Data
Warehouse (SDW) of the ECB, unless noted otherwise.

14The countries are Austria, Belgium, Germany, Spain, Finland, France, Ireland, Italy, Netherlands, and Portugal.
15For interpolation we employ the Chow and Lin (1971) method. The monthly variables along which we interpolate are industrial
production (excluding construction) and HICP for real GDP and the GDP deﬂator respectively. Industrial production data for Ireland
are from Eurostat.

16Over our sample, around 60% of euro area loans to non-ﬁnancial corporations have a maturity of up to one year or an interest
rate reset within one year if the initial maturity is above one year. Our results are unaffected when instead computing the spread
against the 2-year OIS rate.

17Among these characteristics, De Santis (2018) also includes the expected default frequency (EDF) of the issuer, which allows
for the construction of an excess bond premium. By contrast, the cost of bond ﬁnance measure used in our analysis does not
include EDF in the list of controls since differences in credit risk exposures may be one of the features that distinguish bond- from
bank-based ﬁnance.

ECB Working Paper Series No 2402 / May 202013daily values per month and for the LSI we linearly interpolate the quarterly measure to monthly frequency.

The macroeconomic variables, ﬁnancing volumes, and the commodity index are seasonally adjusted.

In constructing the monetary policy shock, we directly convert the daily high-frequency surprises to

monthly frequency.18 As Ramey (2016) points out, any time-averaging of the high-frequency surprises

introduces serial correlation, which is undesirable given the shock, by deﬁnition, should be unpredictable.19

In Section 5.2, we however test for the robustness of our ﬁndings under an alternative frequency conversion

that weights the shocks according to the dating of the monetary policy decision within the month.

Table 1: Summary statistics

Monetary policy shock
1m OIS
Dependent variables
GDP
Deﬂator
Loans
Debt securities
Bond ﬁnance ratio
3m OIS
Control variables
GDP (EA)
GDP deﬂator (EA)
Loan spread
Cost of bond ﬁnance
Loan supply indicator
CISS
EUR-USD
Commodity price index

Observations

Mean

Std.dev.

Min

Max

0.06

2.58

-20.20

14.20

75,244
6.49
329,030
122,737
15.86
1.47

41,737
5.91
0.97
1.68
1.43
0.17
0.12
34.92

13,342
79.11
27,695
1,706
1.28
-0.37

773,412
82.83
0.19
-9.18
-4.02
0.03
1.05
61.44

271,457
106.59
1,079,412
643,114
62.06
4.33

943,743
104.64
6.35
18.10
4.08
0.77
1.58
198.08

81,509
94.37
382,405
91,603
26.65
1.12

854,472
94.62
1.94
-0.10
-0.03
0.19
1.27
127.97

1,877

Note: The shock is in bps; GDP, loans, and debt securities are in million EUR; the 3-month OIS rate, loan spread, and cost of bond
ﬁnance are in percent. GDP is expressed in real terms with base year 2015; the deﬂator and commodity prices are indexed to 2015.

18If there are two events in one month we sum up the observations. If there is no event our shock measure carries a zero-value.
19As discussed in Gertler and Karadi (2015), a rationale for using weighted monthly averages of surprises around policy events
in constructing the shock is that this accounts for the fact that an event taking place later in a month will have less scope to transmit
to the monthly variables than one taking place early in the month.

ECB Working Paper Series No 2402 / May 202014Table 1 presents summary statistics of the variables that enter our model. The data exhibit rich variation,

also reﬂecting the pronounced size differences across euro area countries, visible for instance in GDP and

loan volumes. Important for our analysis is that the relative size of bank- to bond-based ﬁnance also covers

a wide range. In particular, the minimum bond ﬁnance ratio of only 1.28 indicates that for some countries

loans represent the dominant source of debt to ﬁrms while at the other end of the distribution, a value of

62.06 implies that for some countries more than a third of ﬁrms’ debt ﬁnance is directly sourced from credit

markets.

Figure 3: Cross-country distribution of bond
ﬁnance ratio

Figure 4: Euro area corporate bond and loan
volumes

Note: The drop in the interquartile range at the end of 2009 in Figure 3 is due to a to a shift in the distribution when data for Irish
bond ﬁnance becomes available. The series in Figure 4 are indexed to October 2008, which corresponds to the trough in the euro
area bond ﬁnance ratio depicted in Figure 3.

Meanwhile, the relevance of bond ﬁnance has clearly increased over time, both in individual countries

and in the euro area as a whole. In the cross-country distribution presented in Figure 3, several observations

stand out. First, the wide range of the bond ﬁnance ratio across countries appears to be a persistent charac-

teristic of the euro area; and not only the min-max range covers a broad spectrum, but also the interquartile

range points to pronounced and persistent cross-country differences. Second, the prevalence of bond ﬁnance

has become increasingly important, especially since the global ﬁnancial crisis after which the euro area aver-

age, as well as the median and the interquartile and min-max ranges of the distribution have risen markedly.

ECB Working Paper Series No 2402 / May 202015As visible from Figure 4, the time-series patterns of the bond ﬁnance ratio are primarily driven by the steady

increase in outstanding bond volumes over the sample period. In particular, since the onset of the ﬁnancial

crisis bond ﬁnance has shown a marked ascent and has more than doubled, whereas the outstanding volume

of loans plateaued.

4 Baseline results

Our estimates show that the two corporate debt ﬁnancing instruments differ in their response to monetary

policy shocks and that these responses are shaped by the ﬁnancing structure prevailing prior to the shock.

In economies with high bond ﬁnance ratios, the contraction (expansion) in bank lending after a monetary

policy tightening (loosening) is buffered by an increase in bond relative to loan volumes. This buffering

function weakens, and may even reverse, for lower initial bond ﬁnance ratios. The resultant differences in

the responses of overall ﬁrm credit also translate into the macro transmission of the shock in that the impact

of monetary policy on activity is weaker in economies with higher bond ﬁnance ratios.

In the following, we describe these ﬁndings in greater detail, starting with the impulse response functions

(IRF) to the monetary policy shock at the average bond ﬁnance ratio in the sample; then moving to the

interaction terms that capture how the IRFs change when the bond ﬁnance ratio rises by one percentage

point; and, ﬁnally, presenting the combined slope and interaction effect as a function of the bond ﬁnance

ratio (as per equation (2)).

Estimates at the average bond ﬁnance ratio. As visible from the ﬁrst column of Figure 5, the IRFs at

the average bond ﬁnance ratio display the typical transmission patterns of monetary policy shocks. The short-

term policy rate responds contemporaneously to the tightening shock and builds up gradually reaching a peak

of 1.7 percentage points after four months; it then reverts and shows some temporary undershooting, which is

also a common feature in many macro models. Loans contract and the response becomes signiﬁcant around

two years after the tightening shock, while the response path of the bond ﬁnance ratio remains insigniﬁcant

throughout the horizons, implying that bonds fall in roughly equal proportions to loans. The decline in credit

is also accompanied by falls in real GDP and prices. GDP declines gradually to a trough of -6.4% 30 months

after the shock; and prices, measured by the GDP deﬂator, respond with a lag relative to activity, which also

in line with typical transmission regularities. The trough response of the deﬂator is -1.4% and is attained

ECB Working Paper Series No 2402 / May 202016after 39 months.

These estimates are broadly in line with the related literature, also from a quantitative perspective. For

instance, scaling the GDP response to a shock leading to a 100 basis point (bps) peak increase in the OIS

rate, we obtain a trough effect of -3.8%, which falls within the upper part of the range of estimates for

real activity in the US context, as reviewed by Ramey (2016). The corresponding trough in the deﬂator, of

-0.8%, points to a fairly muted transmission to prices, compared to the transmission to real activity. Besides

replicating familiar patterns from the related literature, it is also worth noting that the IRFs from our model

display a very smooth pattern, considering that local projections usually produce rather jagged responses due

the direct estimation of coefﬁcients at each horizon.

Interactions with bond ﬁnance ratio. As visible from the second column of Figure 5, which plots
coefﬁcients ˆβh on the interaction term in equation (1), the response path of these variables materially changes
depending on the prevailing bond ﬁnance ratio. As the initial level of the bond ﬁnance ratio increases, the

build up of the policy rate is slightly muted and the OIS peak response declines by 0.04 percentage points.

For changes in loan volumes as well as the GDP deﬂator the interaction term is not statistically signiﬁcant.

By contrast, the response of the debt ﬁnance ratio to the shock signiﬁcantly varies with the bond ﬁnance ratio

prevailing prior to the shock. In particular, higher initial bond ﬁnance ratios are associated with a signiﬁcant

increase in that ratio in response to the shock. Similarly, the interaction term for the GDP response reveals a

relative increase in the IRF at higher initial levels of the bond ﬁnance ratio.

These ﬁndings suggest that the debt ﬁnancing structure alters the transmission of monetary policy to ﬁrm

credit and the macroeconomy. As the prevalence of market-based ﬁnance increases, corporate bonds offer an

alternative source of credit that acts as a substitute for bank lending when monetary policy tightens; and, vice

versa, it is crowded out when bank lending expands in response to a monetary policy easing. The availability

of an alternative source of ﬁrm credit is also reﬂected in real activity as a shift towards greater bond ﬁnance

goes along with a weaker transmission of monetary policy shocks to GDP.

ECB Working Paper Series No 2402 / May 202017Figure 5: Impulse responses (baseline)

Note: The IRFs in the left column are normalised to a 100 bps impact response in the 3-month OIS rate. The responses are measured
in percent except for the 3-month OIS rate and the bond ﬁnance ratio where it is in percentage points. The grey area is the 90%
conﬁdence interval.

ECB Working Paper Series No 2402 / May 202018Combined effects. Combining the coefﬁcients for the slope and interaction term, we trace out the

response to the shock over the full spectrum of bond ﬁnance ratios for a ﬁxed projection horizon in Figure

6.20 As dependent variables, we focus on the bond ﬁnance ratio and real GDP since they exhibit relevant

and signiﬁcant differences in the IRFs of the interaction term. As the projection horizon, we choose h = 24,

as most of the transmission has materialised after two years. All values are normalised to a 100 bps impact

response of the policy rate.21

Figure 6: Impulse responses across bond ﬁnance ratios (baseline)

(a) Bond ﬁnance ratio

(b) GDP

Note: The IRFs are normalised to a 100 bps impact response in the 3-month OIS rate at each point of the spectrum of bond ﬁnance
ratios. The response horizon is h = 24. For the bond ﬁnance ratio the response is in percentage points and for GDP in percent. The
grey area is the 90% conﬁdence interval.

While the combined impact, at the average bond ﬁnance ratio of around 27, is close to zero and statis-

tically insigniﬁcant, a nuanced response pattern emerges when moving away from the average. In fact, at

very low bond ﬁnance ratios, bonds contract relative to bank loans, whereas the opposite response emerges

20The range of the bond ﬁnance ratios over which we evaluate the responses goes from 0 to 62.1, which is the maximum value in
our sample. The minimum ratio in the sample is 1.3, but for presentational reasons we extend the estimated range to a bond ﬁnance
ratio of zero.

21The impact response in the policy rate may vary at the respective bond ﬁnance ratio which leads to the slight non-linear shape

in the graphs.

ECB Working Paper Series No 2402 / May 202019for high bond ﬁnance ratios. Likewise, the GDP contraction gets smaller as the prevalence of bond ﬁnance

increases and at high levels of the bond ﬁnance ratio, the impact of the shock even becomes insigniﬁcant.

The combined impact estimates at the higher end of the bond ﬁnance spectrum thus favour the bank

lending view of monetary policy transmission, by which direct market ﬁnance mitigates the credit contraction

caused by the bank lending response to monetary policy shocks; and this buffering function of market ﬁnance

is also reﬂected in a weaker responsiveness of activity in economies with a high prevalence of bond ﬁnance.

At the same time, the impact estimates at the lower end of the bond ﬁnance spectrum suggest that this

buffering function requires bond ﬁnance to exceed a certain threshold. By contrast, below that threshold the

response pattern reverses. This in turn may reﬂect that, in economies dominated by bank-dependent ﬁrms,

the ﬁxed cost of building a presence in bond markets may act as an obstacle to a greater shift to market-based

ﬁnance, whereas bilateral lending relationships with banks become more valuable as monetary conditions

tighten. Overall, this pattern implies that the contraction in credit in response to monetary policy tightening

shocks is ampliﬁed in economies with low prevalence of bond ﬁnance (and vice versa for easing shocks).22

5 Robustness

To test the robustness of our estimates, we modify the baseline speciﬁcation in two directions. First, we

control for additional types of cross-country heterogeneity that may affect monetary policy transmission and

potentially be correlated with the ﬁnancing structure. Second, we vary the way in which monetary policy

is measured and the monetary policy shocks are constructed. As we describe in greater detail below, these

modiﬁcations leave our main ﬁndings intact.

5.1 Further sources of cross-country heterogeneity

Our results may be prone to omitted variable bias if countries with high or low bond ﬁnance ratios also differ

in other aspects that are relevant to monetary policy transmission. In particular, two aspects deserve attention

22The ﬁnding that monetary policy effects on output vanish at high bond ﬁnance ratios is striking, but it warrants two qualiﬁca-
tions. First, it is based on coefﬁcients that are uniform across observations, whereas heterogeneity in the estimated impact solely
results from applying these uniform coefﬁcients to different values of the bond ﬁnance ratio as the conditioning variable. Thus, it
is not possible to infer from Figure 6 that monetary policy shocks do not affect output in speciﬁc economies at the upper end of the
spectrum of bond ﬁnance ratios as the uniform coefﬁcients may not capture all aspects of heterogeneity across economies. Second,
while the upward sloping pattern in Figure 6 is a robust ﬁnding, some of the modiﬁcations to our baseline speciﬁcation presented in
Section 5 yield substantial negative combined effects also at high bond ﬁnance ratios.

ECB Working Paper Series No 2402 / May 202020in this regard.

The ﬁrst relates to differences in ﬁrm-size structures. In some euro area countries, such as Germany

and France, the universe of ﬁrms ranges from small and medium-sized enterprises (SMEs) to very large

corporates. In others, especially in Southern Europe, the ﬁrm-size structure is instead dominated by SMEs.

As it is typically only large corporates that have access to bond markets, these differences are also mirrored

in the aggregate bond ﬁnance ratio of each country. At the same time, large and small ﬁrms also tend

to differ in their response to monetary policy shocks, in that the latter tend to be more strongly affected

(Oliner and Rudebusch (1996), Gertler and Gilchrist (1994) and Crouzet and Mehrotra (2018)). So the

differential transmission of monetary policy shown in Figure 6(a) and 6(b) may not just reﬂect differences in

the ﬁnancing structures, but also in ﬁrm-size structures.

The second aspect pertains to the maturity structure of bank lending, which may also affect the transmis-

sion of monetary policy (Gertler and Gilchrist (1994) and Christiano et al. (1996)). In some countries, again

including Germany and France, a fairly large share of bank loans to ﬁrms have a medium- to longer-term ma-

turity, whereas in others, such as Italy or Portugal, short-term loans make up a greater share of bank lending

to ﬁrms.23 Again, these structural cross-country differences may impinge on monetary policy transmission.

For instance, a higher share of short-term loans may allow ﬁrms to more ﬂexibly adjust their external bor-

rowing and hence accelerate the response to a shock compared to a situation in which a large part of the

outstanding stock of loans, at each point in time, is tied up in long-term contracts.24

23In our sample, the average share of loans to non-ﬁnancial corporations with an initial maturity of less than one year is around

20% for Germany and France and 39% and 32% for Italy and Portugal, respectively.

24Indeed, when subdividing the loan-variable in equation (1) into short- and long-term loans, we are able to conﬁrm this presump-
tion (see Figure 11 in Annex B, where short-term loans are deﬁned as loans with an initial maturity of up to one year): the response
of short-term loans is stronger than that of long-term loans and the interaction term shows that the responsiveness of short-term
loans increases further as the bond ﬁnance ratio rises, again conﬁrming the substitution function of bond markets. These ﬁndings
suggest that the maturity composition of bank loans may be relevant.

ECB Working Paper Series No 2402 / May 202021Figure 7: Impulse responses across bond ﬁnance ratios (robustness: cross-country heterogeneity)

(a) Share of large ﬁrms

(b) Share of large ﬁrms in value added

(c) Maturity of bank lending

Note: The IRFs are normalised to a 100 bps impact response in the 3-month OIS rate at each point of the bond ﬁnance spectrum. The
response horizon is h = 30 for panels (a) and (b) and h = 24 for panel (c). For the bond ﬁnance ratio the response is in percentage
points and for GDP in percent. The grey area is the 90% conﬁdence interval.

ECB Working Paper Series No 2402 / May 202022To account for these points, we thus allow the transmission of shocks to differ not only with the bond

ﬁnance ratio but also with the ﬁrm-size structure and the maturity structure of bank lending. To this end, we

select a set of ﬁrm-size and maturity proxies and, for each of them, create a dummy variable that equals one

if a country’s median exhibits a value that is higher than or equal to the sample median and zero otherwise.

We then interact the respective dummy with the monetary policy shock and add this interaction along with

the dummy variable to the regression equation (1). As ﬁrm-size proxies, we use the share of non-ﬁnancial

ﬁrms with more than 250 employees in the universe of non-ﬁnancial ﬁrms and, as an alternative, the share

of value added of these ﬁrms in total value added in the business economy of the respective country.25 As

maturity proxy we use the ratio of short-term over long-term loans, with the former consisting of all loans

with an initial maturity of up to one year and the latter of all other loans.26

Our main ﬁndings are robust to these model extensions (Figures 7(a) to 7(c)). In particular, in all three

speciﬁcations the response of the bond ﬁnance ratio to a monetary policy shock increases and the contrac-

tionary effect of the shock on GDP is attenuated, the higher is the bond ﬁnance ratio prevailing prior to the

shock.27 The estimates are somewhat less precise than in the baseline as, for instance, the slight contraction

in the bond ﬁnance ratio at the lower end of the spectrum loses signiﬁcance in some of the alternative speci-

ﬁcations. But the baseline patterns overall remain intact, thus suggesting that differences in the industry and

loan maturity structure across countries do not drive our results.

5.2 Alternative monetary policy indicators and shocks

As is standard in the related literature, we use a short-term interest rate as our indicator of the monetary

policy stance. While appropriate for the initial years of our sample, this indicator might be an overly narrow

representation of the stance over the latter part of the sample, when short-term interest rates approached

their lower bound and the ECB resorted to unconventional monetary policy measures, such as quantitative

25The data for the number of large ﬁrms is obtained from Eurostat and the share of value added of large ﬁrms is retrieved from
the OECD data base. The data is available annually from 2005 except for Finland where the ﬁrst observation on the share of value
added is 2006. The latest available data for the share of value added is 2016 except for Spain where it is 2017 and for the number of
large ﬁrms the latest available data is 2017 except for Belgium, Finland, Ireland, and Italy where it is 2016.

26We obtain data for loans to the domestic sector by maturity from an internal ECB data base as these series are not publicly
available. In contrast to total loans, these series are not adjusted for cash pooling, sales, and securitisation. However, when estimating
our baseline model using aggregate loans data that is not adjusted for these items our results are unchanged.

27For the ﬁrm size estimates the horizon at which the impulse response is evaluated is set to h = 30 as the through response in
GDP at the average bond ﬁnance ratio is realized slightly later than in the baseline model (see Figure 12 and 13 in Appendix B for
the full set of IRFs).

ECB Working Paper Series No 2402 / May 202023easing, to inject additional accommodation. We therefore test the model in alternative speciﬁcations that

accommodate this change in policy conduct – ﬁrst, by including a broader measure of the monetary policy

stance in the regressions and, second, by allowing the coefﬁcient on the policy shocks to differ across sample

sub-periods.

To implement the former approach, we combine the 3-month OIS interest rate with a “shadow rate”

which approximates the short-term interest rate that, based on broader yield curve constellations, would be

expected to prevail in the absence of a lower bound. Following Hartmann and Smets (2018), we deploy a

summary statistic of ﬁve shadow rate estimates, which is less sensitive to the speciﬁc model from which

each individual shadow rate is derived. Speciﬁcally, we extract the ﬁrst principle component of the euro

area shadow rates by Kortela (2016), Krippner (2015), Lemke and Vladu (2017) (using an adaptive and a

monotonic lower bound speciﬁcation), and Wu and Xia (2017).28 Up until December 2011, which we deﬁne

as the starting point of the period in which the ECB engaged in unconventional monetary policy, we use

the 3-month OIS rate and afterwards we add the cumulative change from the principal component to it.29

This combined measure replaces the policy rate in our baseline model. Our second approach is slightly

more agnostic in that it accounts for the presence of unconventional monetary policy via a dummy that is

zero through November 2011 and one afterwards. This dummy is included in the regressions along with its

interaction with the monetary policy shock, thus allowing for differences in shock transmission during the

two sample periods.

Finally, we modify the construction of monetary policy shocks based on high-frequency surprises, which

is essential to our identiﬁcation approach. As laid out in Section 2.2, we carefully discipline our choice of the

1-month OIS rate surprises that we use as a measure of monetary policy shocks in our baseline estimation.

However, to assure that this choice does not impact our ﬁndings we also test alternative measures. First, we

vary the maturity of the shock, by instead: (i) using surprises in the 3-month OIS, which directly corresponds

to the policy indicator in the model; and (ii) in line with Nakamura and Steinsson (2018), constructing a

composite measure based on the ﬁrst principle component of the surprises in the 1-week, 1-month, 3-month,

6-month, 1-year, and 2-year OIS rates. Second, we change the way we aggregate daily surprises to monthly

frequency: in the baseline, the shock in each month consists of the unweighted sum of surprises taking

28Since the measure by Wu and Xia (2017) starts only in September 2004 we extend it with the EONIA rate prior to that.
29In December 2011, the ECB’s Governing Council decided on the ﬁrst round of longer-term reﬁnancing operations to banks

which, given the very long (three-year) maturity, constituted a break with prior practice (ECB (2011)).

ECB Working Paper Series No 2402 / May 202024place over this period; but a monetary policy event that takes place at the beginning of the month has more

time to propagate to other variables than a decision that is dated at the end of a month; to ensure that our

baseline aggregation approach does not disregard relevant information, we thus construct weighted shocks,

as proposed by Gertler and Karadi (2015), that reﬂect the timing of the monetary policy event. In particular,

for each month, we construct the weighted average of the surprise in the 1-month OIS rate from the current

and the previous month with the weights reﬂecting the number of days that have elapsed within each month

that have elapsed up until the policy event relative to the total number of days in the month.30

Again, the robustness checks conﬁrm our main ﬁndings. In the speciﬁcations with alternative policy

indicators, the results point to a signiﬁcant reduction of bonds relative to loans in response to the shock when

bond ﬁnance is low and an expansion when the prevalence of bond ﬁnance rises. The contraction in GDP

again becomes smaller for higher levels of the initial bond ﬁnance ratio and loses signiﬁcance at the upper

end of the spectrum (see Figures 8(a) and 8(b); the IRFs for the entire model are available in Appendix B).

Likewise, the alternative approaches to construct monetary policy shocks yield very similar point estimates,

while in some cases exhibiting slightly lower precision (see Figure 9). Taken together, we can therefore also

rule out that our particular measure of the monetary policy stance or the approach to construct policy shocks

determines our ﬁndings.

30As a concrete example: policy events took place on 12 September 2019 and on 24 October 2019. So, the value for the weighted
policy shock for October 2019 is computed as 24/31 times the plain shock recorded in September plus 7/31 times the plain shock
recorded in October. Further, by including lagged values of the weighted shock in the estimations, we purge them from the serial
auto-correlation that this weighting scheme introduces; see also Ramey (2016) for a discussion of this issue.

ECB Working Paper Series No 2402 / May 202025Figure 8: Impulse responses across bond ﬁnance ratios (robustness: alternative monetary policy indi-
cators)

(a) Shadow rate

(b) Unconventional policy period

Note: The IRFs are normalised to a 100 bps impact response in the 3-month OIS rate at each point of the bond ﬁnance spectrum.
The response horizon is h = 24. For the bond ﬁnance ratio the response is in percentage points and for GDP in percent. The grey
area is the 90% conﬁdence interval.

ECB Working Paper Series No 2402 / May 202026Figure 9: Impulse responses across bond ﬁnance ratios (robustness: alternative shocks)

(a) 3-month OIS

(b) Principal component

(c) Weighted surprises

Note: The IRFs are normalised to a 100 bps impact response in the 3-month OIS rate at each point of the bond ﬁnance spectrum.
For the panels (a) and (b) the response horizon is h = 24 and h = 30 for panel (c). For the bond ﬁnance ratio the response is in
percentage points and for GDP in percent. The grey area is the 90% conﬁdence interval.

ECB Working Paper Series No 2402 / May 2020276 Conclusion

In this paper, we have studied how the relative role of corporate bonds versus bank loans in the debt structure

of ﬁrms affects the transmission of monetary policy shocks to the economy. To this end, we have extended a

standard empirical macro model with variables measuring the volume and cost of credit to ﬁrms. Estimating

this model in a panel of euro area countries, and using high-frequency methods to identify monetary policy

shocks, we ﬁnd that the corporate debt ﬁnancing structure is highly consequential for monetary policy trans-

mission. In particular, a greater role of bond ﬁnancing goes along with a weaker response of ﬁrm credit and

economic activity to monetary policy shocks. Accordingly, bond ﬁnance acts as a substitute source of credit

when bank lending contracts and, vice versa, it is crowded out when bank lending expands. At the same

time, this substitution function requires the initial bond ﬁnancing ratio in the economy to be relatively high.

By contrast, in economies with a low bond ﬁnancing ratio, the adjustment patterns weaken and may even

reverse so that bond ﬁnance reinforces the overall response of ﬁrm credit to monetary policy shocks.

These patterns point to two countervailing mechanisms emphasised in the related theoretical literature.

The substitution pattern at high bond ﬁnance ratios is consistent with the bank lending view of transmission,

according to which monetary policy triggers shifts in loan supply that go beyond its impact on broader

ﬁnancing conditions in the economy. The ampliﬁcation pattern at low bond ﬁnance ratios is consistent

with state-contingent ﬁrm preferences for different ﬁnancing instruments, as the ﬂexibility afforded by bank

lending (e.g. in renegotiating credit contracts in response to weakening business prospects) becomes more

valuable in tight and less valuable in loose monetary conditions. The reversal in the impact of monetary

policy on the bond ﬁnance ratio depending on the initial ﬁnancing structure suggests that a sufﬁciently large

initial reliance on bond ﬁnance is necessary for the substitution effect to dominate.

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ECB Working Paper Series No 2402 / May 202033Appendices

A High-frequency surprises

Figure 10: Time series of shocks

Note: All shocks except for "1m OIS - all" have been identiﬁed through the cross-asset correlation of the surprise between the
respective interest rate and the stock price. The series with the x-marker represents the shock from our baseline model. The shocks
are in bps.

ECB Working Paper Series No 2402 / May 202034B Robustness

Figure 11: Impulse responses (maturity breakdown of loans)

Note: The IRFs in the left column are normalised to a 100 bps impact response in the 3-month OIS rate. The responses are measured
in percent except for the 3-month OIS rate and the bond ﬁnance ratio where it is in percentage points. The grey area is the 90%
conﬁdence interval.

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ECB Working Paper Series No 2402 / May 202039Figure 16: Impulse responses (robustness: alternative shocks – 2)

(c) Weighted surprises

Note: The IRFs in the left column are normalised to a 100 bps impact response in the 3-month OIS rate. The responses are measured
in percent except for the 3-month OIS rate and the bond ﬁnance ratio where it is in percentage points. The grey area is the 90%
conﬁdence interval.

ECB Working Paper Series No 2402 / May 202040Acknowledgements 

We are grateful for comments by Carlo Altavilla, Lorenzo Burlon, Peter Karadi, Per Krusell, Massimo Rostagno, Mika Tujula, Natacha 
Valla, Thomas Vlassopoulos, and seminar participants at the European Central Bank and the Institute for International Economic 
Studies. We thank Carlo Altavilla, Matthieu Darracq-Pariès, and Giulio Nicoletti; Roberto De Santis; and Wolfgang Lemke and Andreea 
Vladu for providing us with data on the loan supply indicator, cost of bond finance, and shadow rates, respectively. We also thank Filippo 
Claps for valuable research assistance. The views expressed here are those of the authors and do not necessarily reflect those of the 
European Central Bank. 

Fédéric Holm-Hadulla 
European Central Bank, Frankfurt am Main, Germany; email: federic.holm-hadulla@ecb.europa.eu 

Claire Thürwächter 
Institute for International Economic Studies, Stockholm University, Stockholm, Sweden; email: claire.thurwachter@iies.su.se 

© European Central Bank, 2020 

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