Rollover Risk and Bank Lending Behavior: Evidence from Unconventional Central Bank Liquidity
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A paper titled "Rollover Risk and Bank Lending Behavior: Evidence from Unconventional Central Bank Liquidity" by Martina Jasova, Caterina Mendicino and Dominik Supera, in a version dated January 17, 2018. The abstract asks how a sudden extension of bank debt maturity affects bank lending in times of crisis, using the 2011 European Central Bank's (ECB) very long-term refinancing operations (vLTRO) as a natural experiment. It says the analysis matches ECB monetary policy and market operations data with the firm credit registry and banks' security holdings in Portugal. The authors state that lengthening bank debt maturity in crisis times has a positive and economically sizable impact on bank lending to the real economy, with stronger effects on credit to smaller, younger, riskier firms.
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“Rollover Risk and Bank Lending Behavior: Evidence from Unconventional Central Bank Liquidity.”
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Rollover Risk and Bank Lending Behavior:
Evidence from Unconventional Central Bank Liquidity∗
Martina Jasova‡ Caterina Mendicino § Dominik Supera ¶
Princeton University European Central Bank The Wharton School
Charles University
This version: January 17, 2018
Abstract
How does a sudden extension of bank debt maturity affect bank lending in times of crisis?
We use the provision of long-term funding by the 2011 European Central Bank’s (ECB) very
long-term refinancing operations (vLTRO) as a natural experiment to address this question.
Our analysis employs a novel dataset that matches the ECB monetary policy and market
operations data with the firm credit registry and banks’ security holdings in Portugal. We
show that lengthening of bank debt maturity in crisis times has a positive and economically
sizable impact on bank lending to the real economy. The effects are stronger on the supply
of credit to smaller, younger, riskier firms and firms with shorter lending relationships. We
also find that loan-level results translate to real and credit effects at the firm level. Finally,
we discuss policy side-effects and show how the unrestricted liquidity provision provided
incentives to banks to purchase more securities and partially substituted away from lending
to the real economy.
JEL classification: E44, E52, E58, G21, G32
Keywords: Rollover Risk, Liquidity Risk, Bank Credit, Unconventional Monetary Policy
∗
Martina Jasova is extremely grateful to Atif Mian and Markus Brunnermeier for invaluable guidance and
support. We thank António Antunes, Francesca Barbiero, Giorgia Barboni, Diana Bonfim, Luisa Carpinelli,
Isabel Correia, Luı́sa Farinha, Thomas Fujiwara, Jordi Galı́, Erik Gilje, Itay Goldstein, João Gomes, Florian
Heider, Jakub Kastl, Benjamin Keys, Evžen Kočenda, Luc Laeven, Michele Lenza, Ernest Liu, Tong Liu, Stephan
Luck, Simone Manganelli, Jiřı́ Novák, Alexander Popov, Giorgio Primiceri, Alexander Rodnyansky, Stephanie
Schmitt-Grohe, Philipp Schnabl, Luke Taylor, Motohiro Yogo, Wei Xiong and seminar participants at Princeton
University, Young Scholars Initiative Edinburgh, Bank of Portugal, European Central Bank and the Wharton
School for helpful comments and suggestions. Jasova gratefully acknowledges support from the European Unions
Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No.681228.
We thank Bank of Portugal and European Central Bank for providing us with the data and hospitality during
our research visits. The opinions expressed herein are those of the authors and do not necessarily reflect those of
the ECB or the Eurosystem. All errors are our own.
‡
Email: martina.jasova@gmail.com, Phone:(267) 601-6172
§
Email: Caterina.Mendicino1@ecb.int
¶
Email: superad@wharton.upenn.edu
Introduction
The global financial crisis raised attention to the crucial amplification role of banks’ short-term
debt, maturity mismatch and rollover risk (Brunnermeier (2009) and Krishnamurthy (2010)).
Positive and negative aspects of short-term debt issued by banks has long been discussed in the
banking literature. On the one hand, short-term debt plays a disciplining role for bank managers
and alleviates moral hazard problem (Calomiris and Kahn (1991), Diamond (1991), Diamond
and Rajan (2001)). On the other hand, more recently, Brunnermeier and Oehmke (2013), Farhi
and Tirole (2012), He and Xiong (2012), Segura and Suarez (2017) highlight that market forces
lead to the issuance of too much short-term debt, excessive maturity mismatch, unnecessary
rollover risk and higher credit risk.
The maturity structure of debt has an important impact on bank investment. Diamond and
He (2014) argue that in good times short-term debt is better at inducing banks to invest due to
a lower debt overhang.1 In bad times, however, when bank assets are more volatile, short-term
debt generates high rollover risk and hurts banks incentives to undertake profitable investment.
The theory in Stein (2012) suggests that cheaper debt of longer maturity can decrease bank
funding costs, reduce fire-sale externalities and increase investment. Both papers imply that
policies of lengthening debt maturity in the times of crisis can therefore decrease the rollover
risk and have positive effects on bank lending and real activity. Empirical evidence on the
impact of debt maturity extension and the reduction in rollover risk is however scarce as it
faces a number of challenges. Most importantly, the banks’ debt structure is endogenous to the
composition of assets, investors’ preferences and the cost of financing.
This paper contributes to the literature by providing causal evidence on how lengthening
of bank debt structure in times of crisis reduces rollover risk and affects bank lending. To
this end, we address the empirical challenges by using 2011 European Central Bank’s (ECB)
very long-term refinancing operations (vLTRO) as a natural experiment. Banks participating
in the vLTRO could receive unlimited funding of long-term (three-year) maturity at the same
financing conditions as in case of the short-term borrowing (the same interest rate, pool of
eligible securities and haircut policy). At the time of the ECB policy decision, euro area banks
were relying on short-term debt and were largely exposed to rollover risk. By ensuring stable
funding for a horizon of three years, the vLTRO substantially lengthened the maturity of bank
liabilities and sizably reduced uncertainty about the financing conditions over a longer period.
As a result, these institutional features are particularly suitable to regard the vLTRO as a large
shock to a bank debt structure that lengthened debt maturity and reduced rollover risk.2
1
This argument for the lower debt overhang associated with short-term debt was first formalized by Myers
(1977). Debt overhang problem arises when the burden of existing debt is so large that it reduces firm’s incentives
to invest in new profitable projects. As short-term debt is less sensitive to firm value, a larger part of the return
on new investment is captured by the equity holder.
2
As a reflection of severe funding needs, more than 800 euro area banks took a total amount of liquidity of
1
The main hypothesis that we test in this paper is that during a period of bank funding
uncertainty, banks more exposed to lengthening of the debt maturity provide more credit to the
real economy than less exposed banks. We test this hypothesis by using a novel dataset that
perfectly matches the ECB monetary policy and market operations data with the firm credit
registry and banks’ security holdings in Portugal.
The intuition behind the effect of elongating debt maturity on bank lending can be illustrated
as follows. Banks simultaneously choose the composition of their assets and liabilities. Due to
creditors’ preferences for short-term debt, higher cost of long-term financing and lower debt
overhang, banks use short-term debt to finance long-term projects. The maturity mismatch,
intrinsic to the maturity transformation function of banks, exposes them to rollover risk. As a
reaction to the vLTRO, Portuguese banks converted most of their short-term secured funding
into long-term debt. Specifically, they swapped a substantial amount of their existing short-
term borrowing granted by the ECB into the newly available three-year loans (Figure 1). The
vLTRO induced an increase in the overall maturity of banks’ liabilities and, hence, reduced their
maturity mismatch (Figure 2). Following the vLTRO, banks increased the maturity of assets
against their new maturity structure of liabilities (Figure 3). This suggests that the vLTRO
induced banks to perform adjustments on their asset side.
In our empirical analysis, we use a difference-in-differences framework and exploit the varia-
tion in banks’ exposure to the lengthening of debt maturity prior to the policy announcement.
That is, we compare the evolution of lending to firms by banks that were more exposed to the
policy relative to the lending by banks with less exposure. We construct two exposure measures:
(i) liability-side and (ii) asset-side measure. On the liability side we use total ECB secured
short-term bank borrowing that could be costlessly swapped into the three-year funding. The
asset-side measure captures total banks holdings of securities that are eligible as a collateral for
ECB funding. Banks with larger holdings of these securities are eligible to subscribe to more
vLTRO funding. In line with Khwaja and Mian (2008), we incorporate firm-time fixed effects to
isolate the causal effect of the policy by comparing lending outcomes of the same firm borrowing
from at least two differently exposed banks. We also include bank-firm fixed effects to absorb
any time-invariant bank, firm and bank-firm variation.
This paper provides several insightful results. First, we show that a reduction in rollover risk
through lengthening of bank debt maturity has positive and economically sizeable impact on
bank lending. In terms of sensitivities, we find that a one standard deviation increase in bank
exposure to the reduction in rollover risk is associated with a 5.3 percent increase both on the
existing and new lending. We also find that more exposed banks are less prone to terminate
existing lending and are more probable to make a loan to a new client after the vLTRO.
about EUR 1 trillion in the vLTRO. Thus the policy intervention represents an unprecedented provision of central
bank liquidity.
2
Second, we examine the impact of a reduction in rollover risk on bank lending to different
type of firms. We find stronger bank-lending channel for smaller, younger, riskier firms and firms
with shorter lending relationship. Further, we also investigate whether lengthening of bank debt
maturity transmitted into firm-level credit. We find that the policy had significant effect not
only on loan-level credit but more importantly also on the firm-level, i.e. firms did not merely
substituted borrowing from less to more exposed banks but they effectively increased their total
borrowing. We estimate that for every EUR 100 of debt swapped from short-term to long-term,
firms received EUR 2.5 in new lending. Third, we document that the policy contributed to
higher investment and for small firms also to an increase in employment.
Finally, we highlight how the unrestricted liquidity provision provided incentives to banks
to purchase more securities and substitute lending to the real economy (the so-called collateral
trade). We document that after the policy announcement some banks purchased new eligible
securities and immediately pledged these securities with the central bank to take up more vLTRO
funding. Banks used the newly obtained ECB funding to decrease their dependence on other
non-core deposits (in particular loans from other financial institutions). We show that as banks
did not expand the balance sheet with the new vLTRO funding, this behavior led to a partial
substitution away from lending in favor of security holdings. We find that the magnitudes of the
strategic purchases depend on the bank business model. As a result, we find that the positive
effects of a lengthening of debt maturity on lending was partially downsized by the strategic
security purchases.
Literature Review
This paper relates to several streams of literature. First, our empirical evidence contributes to
the theoretical literature on rollover risk (Brunnermeier and Oehmke (2013), Diamond (1991),
Diamond and Rajan (2001), Farhi and Tirole (2012), Flannery (1986) and He and Xiong (2012)).
Our results on the positive effect of a reduction in rollover risk on bank lending support the
theoretical work by Diamond and He (2014), Stein (2012) and Segura and Suarez (2017). In the
model of Diamond and He (2014) short-term debt may lead to stronger debt overhang compared
to the long-term debt. A combination of high asset volatility and short-term debt deters equity
holders from investment and induces them to default earlier. Instead, long-term debt provides
incentives to undertake investment in bad times. In Stein (2012), an excessive amount of short-
term debt and its refinancing during the crisis forces banks to sell some of their assets at fire-sale
prices, which contributes to the tightening of their financial constraints. Cheaper debt of longer
maturity (like in case of vLTRO) would be socially beneficial by reducing this externality and
increasing lending to firms in general. Similarly, Segura and Suarez (2017) find that social
surplus can be increased by lengthening the maturity of bank debt thanks to reduced costs
of crisis financing and pecuniary externalities produced by banks refinancing needs during the
3
crisis. In terms of existing empirical evidence, Almeida et al. (2011) find real effects (decrease
in investment) among firms for which long-term debt matured during the 2007–2009 financial
crisis. To the best of our knowledge, however, ours is the first paper that links debt structure of
banks with the impact on borrowing to firms and firms’ real outcomes through the bank-lending
channel.
Second, we contribute to a growing field of empirical literature that investigates the lending
and real outcomes of unconventional monetary policies (see, among others, Ferrando et al.
(2015), Chakraborty et al. (2017), Luck and Zimmermann (2017), Rodnyansky and Darmouni
(2017)). More generally, we also relate to the literature on the bank lending channel of monetary
policy transmission (Bernanke and Blinder (1992), Kashyap et al. (1994)) and in particular to
the latest work that relies on loan-level data (Jimenez et al. (2012), Jimenez et al. (2014b))
extending the Khwaja and Mian (2008) methodology. Our research also relates to the literature
studying the effect of liquidity shocks or financial crisis on bank lending channel and the real
economy (Chodorow-Reich (2014), Ivashina and Scharfstein (2010), Iyer et al. (2014), Schnabl
(2012)).
In addition, we relate to the existing work that analyzes different aspects of the ECB vLTRO
using credit registry data. Carpinelli and Crosignani (2017) investigate how the extension of
a pool of eligible collateral by the Italian government for the vLTRO restored bank credit
supply after the previous wholesale funding dry-up. van Bekkum et al. (forthcoming) study the
impact of the lower rating requirement for residential mortgage-backed securities announced in
the context of the vLTRO on bank lending in the Netherlands. Different from these previous
studies, we identify the direct effects of the reduction in rollover risk induced by the ECB policy
action and offer original evidence on the effects of the vLTRO on bank lending in Portugal.
Finally, our work contributes to the literature on the perils of the unrestricted liquidity
provision and changes in risk behavior of banks. Acharya and Steffen (2015) document that the
vLTRO incentivized banks to engage in strategic security trade and in particular to purchase
government debt securities. This also closely relates to the model of sovereign-bank diabolic loop
as banks increased their sovereign exposures (Brunnermeier et al. (2016a)). Crosignani et al.
(2017) provide evidence of a collateral carry trade for government debt securities in Portugal.
Our paper contributes to existing literature by providing new evidence on how security purchases
impacted the propagation of the policy stimulus to bank-lending and real outcomes.
The remainder of the paper is organized as follows. Section 1 presents institutional back-
ground. Section 2 discusses the data and Section 3 presents the empirical strategy. Section 4
presents the results on loan-level effects, Section 5 discusses the heterogeneous results by firm
characteristics and Section 6 presents firm-level credit and real outcomes. Section 7 address the
impact of policy side-effects. Finally, Section 8 concludes.
4
1 Background
1.1 ECB’s non-standard monetary policy measures
The ECB provides liquidity through open market operations in the form of repurchase agree-
ments against eligible collateral. The amount of liquidity a bank can receive depends on a
riskiness of an underlying asset expressed through the haircut policy, i.e. the riskier the asset,
the higher the haircut, and the lower is the funding a bank can receive. Until early 2008, the
ECB provided liquidity to banks through auctions with a maximum maturity of three months3
at variable rate (American) auction.4 As a reaction to the financial crisis, the ECB implemented
two main changes in its liquidity provision policy. First, it replaced the existing tender proce-
dure with a fixed-rate full allotment policy. This change allowed for unlimited funding at a fixed
interest rate as long as banks could pledge sufficient collateral. Second, the ECB introduced a
series of longer-term refinancing operations (LTRO) with maturity of 6 and 12 months.
In December 2011, the ECB unexpectedly announced a new Very Long-Term Refinancing
Operation (vLTRO). The crucial features of the policy were the following: (i) unlimited amount
of funding against eligible collateral and unconditional on its use, (ii) extraordinary three-year
long maturity, and (iii) the same interest rate as the existing short-term (weekly) repo operations.
Financing conditions were fixed for the first year, during which the loan could not be repaid.
In 2011, European debt markets were very fragile. Banks mostly borrowed in short-term
maturities and they were expected to roll over e700 bn for each year in the coming period
2012–2014 (Brunnermeier et al. (2016b)). vLTRO provided the banking sector with stable and
predictable funding. Banks could reduce funding liquidity risk associated with the debt rollover
and avoid a potential liquidity dry-up on interbank market and central bank policy changes
in the medium term. vLTRO was announced on December 8, 2011 and administered in two
operations on December 21, 2011 and February 29, 2012. In total, more than 800 European
banks subscribed to approximately e1 trillion. To date, vLTRO is the largest liquidity provision
in the history of modern central banking.
1.2 Portuguese banking sector
We choose Portugal as a laboratory for our analysis for a number of reasons. First, Portuguese
banks represent the second-largest intake of vLTRO with respect to the size of banking sector
with an individual bank vLTRO uptake on average 11% of total assets. Second, the degree of
dependence of Portuguese banks on the ECB was very sticky and did not increase before the
implementation of the vLTRO, thus, indicating that the policy intervention had been unexpected
3
Weekly Main Refinancing Operations (MRO) and 1- and 3-month Longer-Term Refinancing Operations.
4
See Cassola et al. (2013) for the analysis of banks’ bidding behavior under the multiple rate auction during
the 2007 subprime market crisis.
5
by the Portuguese banking system. Third, the focus on Portugal also overcomes main data
limitations and subsequent identification challenges. In particular, we are able to exploit the
loan-level credit registry data to isolate the movement in the credit supply. Also, we can match
the banks’ security holdings portfolios (and their evolution over time) with the securities pledged
as a collateral with the ECB. This allows us to provide a new evidence on strategic timing of
security purchases by banks after the vLTRO announcement. Finally, Portugal provides a very
rich coverage for small and medium enterprises (SMEs) which are generally underrepresented
in a number of existing datasets. This large variation in firms size, allows us to investigate
if the lending and real outcomes significantly vary by firm size, an analysis of limited scope
in existing samples that disproportionately favor large firms. Finally, in Portugal the vLTRO
was not accompanied by any country-specific additional institutional changes in regulation that
would confound the results.
In May 2010, Portuguese banks lost access to the international wholesale market and in-
creased their dependence on the ECB liquidity operations (Figure 1).5 Prior to the introduction
of vLTRO, banks borrowed from the ECB in shorter maturities (from 1 week to 6 months)6 .
All Portuguese banks that had been previously using ECB funding subscribed to the vLTRO.
In total, they borrowed e20.2bn in December and an additional e26.8 bn in February. Individ-
ual bank vLTRO uptake constituted on average 15.3% of total assets. Banks shifted most of
their shorter funding needs into vLTRO. Moreover, an additional vLTRO uptake was obtained
against newly pledged securities. In particular, since the policy announcement Portuguese banks
purchased e14 bn eligible securities that they immediately pledged with the ECB in order to
obtain additional liquidity through vLTRO.
2 Data
For the purpose of our analysis, we create a novel dataset that matches the data of the European
Central Bank and the Bank of Portugal. This dataset allows us to observe a full transmission
of the unconventional monetary policy into bank lending and real economy. Below we describe
the data top-down:
(Un)conventional monetary policy: We start with the ECB monetary policy and market
operations data. The dataset collects the information on all ECB liquidity operations split by
categories (i.e., weekly main refinancing operations (MRO); longer-term refinancing operations
(LTROs) for 1, 3, 6, 12, and 36 months, respectively). The data are reported for all banks on a
daily basis. In this paper, we analyze the effect of unconventional 36-month liquidity operations
that were organized in two allotments, on December 21, 2011 and February 29, 2012.
5
Alves et al. (2016) find that the banks did not freeze lending to the real economy as banks effectively substi-
tuted their source of funding with the central bank liquidity operations.
6
Two months before the vLTRO, ECB also introduced a one-year refinancing operations.
6
Second, we use the Eurosystem Collateral Data to extract the information on the securities
pledged with the ECB and used as collateral to obtain ECB funding. The ECB maintains a list of
eligible assets, most of which are marketable securities. We observe the following characteristics
of the pledged assets at bank-security-month level: ISIN-code, nominal value, haircut adjusted
value, haircut category, quantity, issuance date, and maturity date.
Bank-level data: We use several sources maintained by the Bank of Portugal. Most no-
tably, Securities Statistics Integrated System (SIET) contains information on the pool of all
marketable securities held by banks such as quantity, book value, and market value available
at bank-security-month level. We match the SIET database with the Eurosystem Collateral
database to clearly disentangle the securities that were held by banks before the policy an-
nouncement (December 2011) from securities that were both purchased and pledged after the
policy announcement.
Next, we use a set of bank balance sheet and prudential databases to construct controls for
observable bank characteristics such as size, equity ratio, capital ratio, liquidity ratio, loan-to-
assets ratio, and equity-to-assets ratio. These datasets are reported on the bank-month level.
We restrict our analysis to domestic banks and domestic subsidiaries of foreign banks.7 We drop
mutual agricultural banks, whose business model differs from the rest of the banking sector.
Credit register: Central de Responsabilidades de Credito (CRC) provides monthly loan-
level information on the universe of outstanding loans to Portuguese firms above the reporting
threshold of e50. CRC includes data on loan amounts and key loan characteristics (maturity,
currency, type of the loan, and guarantee/collateral used to secure the loan, if any). CRC
allows us to observe both drawn and potential credit (unused credit lines, credit cards, etc.) We
focus on all outstanding loans granted by banks to non-financial firms residing in Portugal and
borrowing in euro currency between June 2011 and June 2012. In the core part of the analysis
we focus on private non-financial firms with multiple bank lending relationships, this accounts
for almost 1.5 million observations and 135,751 bank-firm relationships.
Financial institutions are legally required to report data to the CRC. In return, they can
access information on their current borrowers (such as their total debt and overdue or default
loans from all institutions). Banks consult the CRC as part of a borrower screening for a new
loan. If a bank does not currently have a relationship with a potential borrower, it can request
information from the CRC at a negligible cost. All the new loan consultations are recorded
and stored in the consultation database. It includes a time stamp when each bank accessed a
CRC record of a potential borrower who is currently not its client. We match the consultation
database with the CRC to construct the measure of a credit approval rate for the extensive
margin analysis.
7
We do not include in our analysis branches of foreign banks because those banks are not subject to prudential
regulation by the Central Bank of Portugal.
7
Firm-level data: Lastly, we match the dataset with firm-level annual census data which
contain balance sheet and financial reports as well as regional and sectoral classification of the
firms. We use this information to control for firm characteristics (total assets, employment,
age, industry, and district) as a substitute to firm fixed effects. Additionally, we focus on
investment outcomes when we examine real effects of the policy. We also use firm establishment
level employee-employer database Quadros de Pessoal and following Chodorow-Reich (2014) we
construct a measure of firm employment.
3 Empirical strategy
We use the difference-in-differences framework to compare lending before and after the policy
intervention by exploiting the variation in the cross-section of banks’ exposure to lengthening
of bank debt maturity triggered by the vLTRO policy. We examine the time series evolution of
bank credit supply following the baseline specification:
log(crediti,j,t ) = αjt + αij + β(XiRR × P ostt ) + i,j,t (1)
Our outcome variable log(crediti,j,t ) is log amount of all credit that firm j obtains from bank
i at time t. In the main analysis we focus on drawn credit. We define drawn credit as a sum
of regular, renegotiated and under 90 days overdue loans. In the robustness, we also present
results on total credit which is measured as a sum of drawn and potential credit.
We define XiRR as an exposure to the elongating of debt maturity triggered by the vLTRO
policy. XiRR captures the total borrowing capacity of a bank measured three months before the
policy announcement (September 2011).
First, in the baseline, we construct a liability-side measure as a sum of all liquidity providing
operations from the ECB taken up by banks and normalized to their total assets. These resources
were readily available to banks to be swapped from existing short-term maturities to three-year
loans by keeping all other conditions unchanged (e.g. eligible securities, haircuts, interest rates).
In other words, banks were able to costlessly increase maturity of their liabilities with the
ECB and hence lower their rollover risk. We choose our treatment to be a continuous variable
to address a concern that the policy did not affect all treated banks equally. In fact, banks
swapped on average 86 % of their short-term ECB funding into the vLTRO.
RR
As a robustness, we also consider the asset-side measure Xi,eligSec that captures total banks
holdings of securities that are eligible as a collateral for ECB funding.8 Banks with larger
holdings of these securities are eligible to subscribe to more vLTRO funding.
In order to check how our measures of exposure correlate with the realized vLTRO uptake
8
This approach is also analogous to the exposure measure to the quantitative easing in the US by Rodnyansky
and Darmouni (2017).
8
(vLT ROi ), we run the following regression:
vLT ROi = α + βXiRR + i (2)
As evidenced in Table 1, our measures of exposure are strongly, positively correlated with the
actual 3-year ECB liquidity uptake.
To minimize endogeneity, we focus on cross-bank variation in September 2011 (three months
before the policy announcement).9 Nonetheless, we observe that banks were very rigid in chang-
ing their exposure to the ECB (Figure 5) and security holdings, and the policy announcement
on 8 December 2011 came to a large extent as an unexpected shock. In fact, we do not observe
any significant changes in the bank security holdings or pledging behavior in the period prior to
the policy announcement.
Next, given that the dependence on short-term ECB repo funding prior to the policy an-
nouncement is a bank’s choice, a potential concern is that our choice of treatment can be
correlated with bank’s observables. To address this concern, we run univariate regressions to
regress our baseline exposure measure XiRR against a wide set of bank’s characteristics:
XiRR = α + ζWiSep2011 + i (3)
We examine a wide range of observables such as banks’ size, capital ratio, loan composition,
deposits, profitability, liquidity, leverage and equity ratios and we report the estimates of uni-
variate regressions (ζ) in Table 2. The results suggest that banks’ dependence on ECB funding
in September 2011 is only correlated with banks’ dependence on market repo funding before the
financial crisis (we use the measure from 2005). This observation suggests that banks that were
more reliant on ECB secured repo funding in 2011 used to be generally more reliant on secured
market funding prior to the crisis. As a reaction to the financial crisis, banks swapped their
secured repo loans obtained in the market into the ECB as a safer form of financing. Drechsler
et al. (2016) also show that the ECB offered lower haircuts to securities issued in distressed
countries (such as Portugal) and hence banks could also receive more favorable conditions from
the ECB as when borrowing on the market.10
In our difference-in-difference regressions, we analyze a period of 13 months: June 2011–
June 2012 and we use the data of monthly frequencies. P OST is a dummy variable equal to
one in the post-treatment period after the second allotment (February–June 2012). We end our
sample period in June 2012 to avoid an overlap with the announcement of Outright Monetary
9
We do so also to avoid a potential threat of capturing the effect of 1Y-LTRO which was announced on 6
October 2011 and replaced by the vLTRO in December 2011.
10
Drechsler et al. (2016) show that at the end of 2010 the ECBs haircut on 10-year Portugal bonds was 4%,
while LCH Clearnet, a private repo exchange, applied a 10% haircut on these bonds.
9
Operations (OMT) – the “whatever it takes” speech of the president of the ECB Mario Draghi
in July 2012.
We also saturate our model with fixed effects to address some of the main empirical chal-
lenges. First, a potential bias in estimating the causal effects of the liquidity injection can stem
from the interaction of demand and supply for credit. In line with Khwaja and Mian (2008)
we incorporate firm fixed effects to isolate the causal effect of the policy by comparing lending
outcomes of the same firm borrowing from at least two differently exposed banks. In our time
series set-up, we rely on monthly frequency credit registry data and implement firm-time fixed
effects.11
Second, bank-firm matching is not exogenous as banks choose their borrowers (or vice-versa)
and firm borrowing relationships can also be of a different quality across different banks. In our
preferred specification, we address a potential bias related to i) the correlation of our exposure
measure with observable and unobservable bank characteristics and ii) non-exogenous bank-
firm matching by including bank-firm fixed effects that absorb any time-invariant bank, firm
and bank-firm variation.
To sum up, our empirical specification relies on a combination of firm-time and bank-firm
fixed effects (Equation 1). The main results are presented for domestic and foreign non-financial
firms and non-profit firms which we denote as Private NFCs. We also report results which
further include self-employed entrepreneurs and public companies denoted as All firms.
Finally, we take several steps to support our identification. First, we examine the existence
of parallel trends by comparing lending dynamics of exposed and non-exposed in the period
leading up to policy. Second, we use the 2007 unexpected freeze of European interbank market
as a placebo sample. Still, a potential threat to our identification strategy is the existence of
time-varying bank specific characteristics that would be correlated with our choice of exposure.
One thing that we can do to address this issue is to check for any concurrent policy actions.
At the time of vLTRO, four banks were undergoing the stress tests conducted by the European
Banking Authority. To examine if our results are robust to this potential confounding factor,
we exclude these banks from our sample as a part of the robustness exercise.
4 Results
4.1 Intensive margin
Table 3 summarizes the main result on the intensive margin following the Equation 1. We exploit
the variation in the cross-section of the ex ante bank exposure to the vLTRO. We construct the
measure of exposure XiRR as the amount of total secured short-term funding received from the
11
We also performed the same analysis on a collapsed model by comparing average outcomes of the pre- and
post- period.
10
central bank one quarter before the policy announcement. This represents a measure of existing
borrowing that can be costlessly swapped into the three-year repo loan.
Column (1) presents the results using the full sample of loans (bank-firm pairs). Here we
introduce bank fixed effects (to absorb any time invariant bank characteristics) and time fixed
effects. From Column (2) onward we restrict the loan sample to only firms that at each month
borrow from at least two banks. We denote this as “multiple bank relationship”. In Column (3)
we replace time fixed effects with firm-time fixed effects to absorb any variation from firm-level
(demand) changes in line with Khwaja and Mian (2008).
It is well plausible that a firm has a different relationship with different banks. To address
this challenge, we first introduce a set of bank-firm control variables (Column 4) and finally in
our preferred specification shown in Column (5) we also saturate the model with bank-firm fixed
effects to address any potential threat coming from non-exogenous matching between banks and
firms.
The coefficient estimate of β for all specification is positive and statistically significant sug-
gesting that lengthening of bank debt maturity induced by the vLTRO had positive impact on
bank lending to firms. In terms of economic significance, the coefficient estimate of 0.631 (Col-
umn 5) implies that a one standard deviation increase in bank exposure to vLTRO (8.4 percent
from Table 14) is associated with an 5.3 percent increase on the intensive margin.
In both cases, we can also observe that specifications which are not controlling for overall
firm’s credit demand overestimate the true effect of the policy exposure on lending. The coeffi-
cient of interest β decreases in magnitude when we introduce the firm-time fixed effects but it
remains stable, positive and statistically significant across all specifications.
The results suggest that more exposed banks could transform larger share of their funding
from the short-term into long-term debt which in times of the sovereign debt crisis in Portugal
contributed to the reduction in rollover risk and caused positive effect on bank lending to firms.
This empirical evidence is consistent with implications from theoretical models of He and Xiong
(2012) and Stein (2012). In the model of Stein (2012), excessive amount of short-term debt and
its refinancing during the crisis forces banks to sell some of their assets at fire-sale prices that
contributes to tightening of their financial constraints. Cheaper debt of longer maturity (like
in the case of vLTRO) would be socially beneficial by reducing this externality and increasing
lending to firms in general. Similarly, He and Xiong (2012) find that in bad times, when assets
prices are more volatile, long-term debt reduces rollover risk and incentivizes equity-holders to
undertake profitable investment.
In Table 4, we show that our findings are robust to the collapsed specification of difference-
in-differences which compares the average lending before and after the policy announcement.
While the effects are quantitatively similar, we prefer to use the time-series specification as a
baseline since it allows us to absorb any observable and unobservable time-invariant bank-firm
11
characteristics.
In Table 5 we show that our findings are also robust to sample changes and alternative
measures of the exposure to the vLTRO. Columns (2)–(6) present variations to the baseline.
RR
Column (2) uses an alternative exposure measure Xi,eligSec defined as a sum of all securities
eligible for the vLTRO and held by the banks prior to the vLTRO (as a share of total assets).
Column (3) shows results for the dependent variable measured as a log of total credit. We
define total credit as a sum of drawn credit and potential credit (i.e. unused credit lines which
are reported off-balance sheet). Our results are also robust to different definitions of firms.
While the baseline results are reported for private non-financial corporations, we also extend
the definition of firms to account for public firms and individual entrepreneurs which leads to an
increase of the sample by additional 900,000 observations (denoted as All firms in Column(4)).
A possible concern is that banks that did not participate in the ECB’s open market operations
could be significantly different from the exposed banks. Based on the balancing checks reported
in Table 2, we do not find any observable differences between more and less exposed banks.
Nonetheless, in Column (5) of Table 5 we only focus on the variation in the cross-section of
exposed banks and the results remain robust.
Yet, a potential threat to our identification strategy is the existence of time-varying bank
specific characteristics that would be correlated with our choice of exposures. One thing that
may allow us at least in part to look into potential threads is to check for any concurrent
policy events. In case of Portugal, four main banks were undergoing the stress test exercise of
the European Banking Authority (EBA) around the same time as the vLTRO As a result, in
Column (6) we drop these banks from our sample but the results remain robust to the baseline.
We report estimates using two-way clustered standard errors at bank-time and firm level.
We choose this method of clustering as it allows us to address a threat that firm-shocks can be
serially correlated and also bank-time shocks (our source of variation) can be correlated across
firms. Our results are robust to alternative clustering either at the bank and firm level; or only
at the bank level and we report these estimates in the Appendix Table 16.
4.2 Dynamic difference-in-differences version
We modify the empirical framework into a dynamic difference-in-differences version (leads-and-
lags around the treatment effect). This specification allows us to observe how quickly banks
reacted to the policy and whether the impact accelerates, stabilizes or reverts to the mean
(Autor 2003). One might also infer causality via the timing of policy and visually examine
pre-existing parallel trends. Our dynamic difference-in-differences is summarized as follows:
X X
log(crediti,j,t ) = αij + βk 1t=k + γk (XiRR × 1t=k ) + i,j,t (4)
k6=2011m9 k6=2011m9
12
We start with a simple binary set-up where we divide banks into two groups: exposed and
non-exposed. Here, we consider both measures of exposure: as in the baseline, we first set XiRR
to 1 if a bank i was borrowing from the central bank in repo operations prior to the vLTRO
announcement and 0 otherwise. Second, as in the robustness, we set the XiRR to 1 if a bank i
was holding securities eligible for the vLTRO prior to the policy announcement and 0 otherwise.
Figure 6 represents the sequence of coefficient γk estimates for each month t from Equation 4.
Panel (a) shows the results for short-term secured borrowing and panel (b) for eligible securities.
The key identifying assumption of the DID framework is the existence of parallel trends prior to
the policy announcement. Figure 6 indicates that there was no significant difference in lending by
exposed and non-exposed banks prior to the policy implementation. This serves as an evidence
of no pre-trend. However, after the vLTRO we can observe significant differences in lending
behavior between exposed and non-exposed banks.
Second, we replace the binary XiRR with the continuous measure which we present in Section
4.1. As in our main result, XiRR is defined as a sum of all liquidity providing operations from
the ECB taken up by banks and normalized to their total assets. These resources were readily
available to banks to be swapped from existing short-term maturities to three-year loans by
keeping all other conditions unchanged. Figure 7 illustrates the full dynamic impact of exposure
to the vLTRO. Again, in months leading up to the vLTRO we find no difference in lending
between more exposed and less exposed banks which is in support of parallel trends. Notably,
after the vLTRO the coefficients rise and remain persistently positive and statistically significant.
This further confirms the positive effect of lengthening maturity of bank debt on bank lending.
4.3 Placebo test: 2007 freeze of the European interbank market
What if banks more exposed to the 2011 vLTRO are generally more prone to react to bank-
specific shocks? This would mean an important threat to our identification strategy as our
measure of exposure would merely capture banks which are inherently stronger propagators of
shocks. To address this question, we use the finding of Iyer et al. (2014) who show the negative
credit supply effects of the 2007 unexpected liquidity crunch using Portuguese credit registry
data. They find that that banks that relied more on interbank borrowing decreased their credit
supply more following the sudden freeze of the European interbank market in August 2007. We
use the 2007 liquidity freeze as a placebo sample to investigate whether the banks more exposed
to the vLTRO in 2011 were also more sensitive to the 2007 liquidity dry-up.
We follow the dynamic set-up specification from Equation 4 and we replace the LHS lending
outcomes in 2011–2012 with the lending outcomes in 2007. Figure 8 depicts the sequence of
coefficient γk estimates for each month t in 2007. If the 2007 and 2011 exposures were spuriously
correlated, we would expect negative and statistically significant coefficients after August 2007.
Instead, we however find that the plotted estimates of γk are not statistically different from
13
zero throughout 2007. As a result, we have no evidence that the banks exposed to vLTRO are
generally more affected by liquidity shocks.
4.4 Extensive margin
Did vLTRO affect bank-lending behavior also on the extensive margin by enabling banks to not
stop lending to firms altogether in the period of the sovereign debt crisis or even helping them
to establish new lending relationships?
We start by looking into the probability of the “exit rate” of existing loans (bank-lending
relationships). Consider a simple collapsed version of difference-in-differences where we compare
the period after the vLTRO (2012m2–2012m6) with the period before the vLTRO (2011m6–
2011m10). We set the variable EXIT to 1 if the loan only appears in the pre-treatment period
but ceases to exist in the post-treatment period. In other words, the loan is not renewed and
the bank-lending relationship is terminated. In case the loan appears both in pre- and post-
period, EXIT is classified as 0. As before, we follow the Khwaja and Mian (2008) approach and
we absorb firm fixed effects to investigate whether the same firm borrowing from multiple banks
is less likely to exit the loan from a bank that was more exposed to the positive shock to the
debt maturity. This set up is summarized as follows:
EXIT i,j = αj + βXiRR + τ Bi + i,j (5)
Columns (1) and (2) of Table 6 summarize the results. Column (1) uses a combination of
bank controls (size, capital ratio, liquidity ratio, equity-to-assets and loan-to-assets ratios) as
well as firm controls (firm size, district × NACE-2 industry FE), while Column (2) replaces firm
controls with firm fixed effects as described in Equation 5 above. Both results are quantitatively
similar and suggest that a 1 standard deviation increase in the exposure to the vLTRO (8.4
percent) decreases the exit rate by 6.6 percent (8.4×0.782). This confirms the hypothesis that
in the period of a severe crisis, banks that are more exposed to the policy of elongating debt
maturity are less prone to terminate relationships with firms and hence also have a slower pace
of financial deleveraging.
Are more exposed banks also more prone to start lending to new clients? Here, we start
analyzing the “entry rate” by repeating the collapsed difference-in-differences framework. We
follow the set-up as described in Equation 5 and we replace the outcome variable with ENTRY.
We define ENTRY as a binary variable set to 1 if a bank-firm lending relationship appears for
the first time after the vLTRO, and 0 if the relationship is present in both pre- and post- periods.
Columns (3) and (4) of Table 6 show the results. In both cases the coefficients are positive but
not statistically significant.
14
While the collapsed difference-in-differences framework allows for a straightforward compari-
son of the extensive and intensive margin, the more recent stream of literature proposes a cleaner
identification of the “entry rate” by comparing new (approved) loans with rejected loans. While
in the previous analysis our counterfactual consists of loans that always appeared in the credit
registry, it might be more informative to compare these newly granted loans with loan appli-
cations which were made at the same period but were not successful. One way to address this
challenge is to match the firm credit registry data with the firm credit consultation data (see for
example the work on Spain by Jimenez et al. (2014b) and Jimenez et al. (2012)). Utilization of
consultation data allows us to directly observe which banks consulted the loan application and
whether this application successfully translated into new lending.
Consultation data: We exploit the matching of the Portuguese credit registry with the
loan consultation database. The consultation database has a time stamp when a bank reviewed
a credit record of a firm that is currently not its client (i.e., bank and firm do not have a
lending relationship). Banks have access to the credit records of existing borrowers without
any need to use the consultation database. To obtain records of new firms, banks can access
the consultation database upon firm’s consent at a low monetary cost. Banks tend to use the
consultation database as part of the screening in the loan-granting process.12
We analyze all loan consultations after the announcement of the vLTRO and we match
them with actual entries in the credit registry. We construct a dummy variable newLoani,j
which takes a value of 1 if a bank-firm consultation entry is matched with a new bank-firm
record in the credit registry and 0 otherwise.13 In the main analysis we focus on consultations
made between December-April and we match them with credit registry outcomes in the period
December–June. The majority of the loans are approved within two months. Roughly 10% of
loan consultations are successful and appear in the credit registry as new loans.14 We estimate
the extensive margin with the following specification:
newLoani,j = αj + βXiRR + τ Bi + i,j (6)
We focus on the effect of elongating bank debt triggered by the vLTRO policy on the probabil-
ity of making a new loan. We start by controlling for both bank and firm-specific characteristics
and later we replace firm controls with firm fixed effects. We cluster the errors on the bank
level. The main results are ran as linear probability models as it is the most standard approach
12
Consultation database offers only a subset of information the banks can see in the credit registry for their
existing clients. Hence it would be suboptimal for banks to use consultation database for their current clients
during the loan approval process.
13
Our construction of the extensive margin is similar to Jimenez et al. (2014b) and Jimenez et al. (2012) who
study loan approvals in Spain. Spanish CIR credit registry has a very similar data structure to the Portuguese
CRC.
14
We also perform robustness tests for changes in the consultation window and the results are not affected if
we change the consultation sample by +/- 1 month: December-March and December-May.
15
in the literature (see Khwaja and Mian (2008), Jimenez et al. (2012)) and it is comparable to
FE specification and the rest of the results in the paper. Logit and probit versions, however,
provide us with very similar results.
Firm fixed effects absorb any changes at the firm level, including credit demand. In practice,
this condition may seem restrictive – we require a firm to consult a potential lending condition
with at least two banks from which it is currently not borrowing. Although the FE sample size
drops by a half, we still record 41,000 loan consultations. Table 7 shows that the coefficient is
very stable with or without including firm FE. In terms of sensitivities, a 1 standard deviation
increase in the exposure to the reduction in rollover risk increases the probability of making a
loan to a new client by 5.3 percent (0.63×0.84) – surprisingly, this is identical to the magnitude
previously reported for the intensive margin.
4.5 Do changes in maturity of bank debt affect maturity of loans to firms?
In this section we explore if and how the longer maturity of liabilities affected maturity transfor-
mation practiced by banks. In an ideal experiment, we want to isolate the changes in maturity
from the changes in loan size within the credit supply and at the same time we want to control
for the firm demand side. As a result, we restrict the analysis on 10,863 bank-firm relationships
for which the total outstanding amount of all lending is constant over the entire period of June
2011–June 2012. Unfortunately, this restriction does not allow us to further implement pre-
ferred firm FE as the sample would drop to only 800 observations. Instead, we opt to focus on a
fixed loan size bank-firm sample while controlling for firm characteristics by using firm controls,
firm-bank controls, and firm industry-district fixed effects.
In the data, we observe two measures of maturity: original and residual. Original maturity
is a maturity at origination and unless a bank/firm renegotiated the maturity extension, it
remains unchanged until the loan is repaid. Residual maturity, on the other hand, decreases
as the borrower repays the loan. We estimate changes in the original maturity by running a
difference-in-difference estimation on the sample of bank-firm pairs with constant outstanding
loans.
∆M aturity i,j = α + βXiRR + δQij + τ Bi + µFj + i,j (7)
Equation 7 summarizes the main set-up.15 The outcome variable ∆M aturityi,j is a change
in the median original maturity between the pre- (June–October 2011) and post- (March–June
2012) treatment period. As before, XiR R refers to the measure of exposure to the ECB policy
in September 2011. Bi , Qij , Fj are sets of bank, bank-firm and firm controls respectively, which
we progressively introduce to the specification. Table 8 summarizes the results. The positive
15
This set-up is analogous to the collapsed version of the intensive margin in Online Appendix.
16
and statistically significant coefficients suggest that the reduction in rollover risk and extension
of maturity of liabilities might have a positive effect on the maturity of assets (loans) of banks –
in the post-treatment period banks more exposed to the vLTRO modify more of their existing
loans by extending their original maturities.
Our estimation strategy might provide a more conservative estimate of the actual effects on
a loan maturity change. It is very plausible that banks also extended maturities along with
changes in the outstanding amount as a part of the loan renegotiation process or that newly
issued loans were now granted with longer maturities. These hypotheses are, however, more
challenging to test in the absence of a valid counterfactual.
5 Firm characteristics
In this section we examine the impact of the liquidity provision by firm type. We exploit the
detailed matching of the credit registry data with firm annual census data and we analyze
heterogeneous treatment outcomes by main firm characteristics (Fj ): size, age, riskiness, and
the length of a bank-firm relationship. We start by estimating the most generalized specification
of intensive margin with both firm-time and bank-firm fixed effects and we introduce a triple
interaction term XiRR × P ostt × Fj to measure which firms were more affected by the vLTRO
funding. Equation 8 summarizes the set-up:
log(crediti,j,t ) = αjt + αij + β1 (XiRR × P ostt ) + β2 (XiRR × P ostt × Fj ) + i,j,t (8)
Table 9 presents the results. We find that positive effect of banks’ debt maturity extension
is stronger for smaller and younger firms (negative and statistically significant triple-interaction
coefficients in Column (2) and (3)). Furthermore, vLTRO induced stronger bank lending channel
to riskier firms (Column (4)). For firms’ riskiness we utilize the measure of Antunes et al. (2016)
who develop default probabilities for Portuguese firms. Last, we find that firms with shorter
relationship with banks are more affected by the positive credit supply shock.
Our findings on the heterogeneous effects of vLTRO varying by firm size are symmetric to
the existing work on the propagation of negative shocks to small firms (Gertler and Gilchrist
(1994), Kashyap et al. (1994), Khwaja and Mian (2008)). Our results also suggest that banks
pass through positive debt maturity shock more to risky firms. This observation is in line with
the findings on bank risk-taking behavior in response to changes in overnight interest rates
(Jimenez et al. (2014b)) or positive liquidity shock (Crosignani et al. (2015)).
6 Firm-level credit and real outcomes
As a next step, we collapse credit registry loan-level data to the firm level. Following the
literature on the bank-lending channel, we examine whether the significant loan-level results
17
reported so far also translate to firm-level credit and real outcomes. In other words, we want to
investigate if firms “hedged” against changes in bank lending following the vLTRO. We estimate
the following equation:
∆log(yj ) = α + βXjRR + τ Bj + δQj + µFj + j (9)
We start by analyzing the impact on firm-level credit. We compute our outcome variable as
a change in sum of all bank lending on the firm level between the pre- (June–October 2011) and
post- (March–June 2012) treatment period. For each firm, we compute an indirect measure of
XjRR as a weighted average of the XiRR of all banks a firm j is borrowing from. The weights are
based on the firm’s credit in the pre-treatment period:
RR × credit
P
i (Xi i,j,t=pre )
XjRR =
creditj,t=pre
We repeat the same procedure for computing an indirect measure of bank and bank-firm
control variables (denoted as Bj and Qj ). We also include firm controls (log of firm’s total
assets and industry-district fixed effects). Table 10 presents the results. Firm-level effects on
credit (Columns (1) and (2)) are consistent with the results reported on the loan level. In
particular, the effect of the exposure to the reduction in rollover risk on aggregate firm-level
credit is positive and statistically significant and stronger for smaller firms. For column (1) we
further compute bias-corrected estimates as proposed by Jimenez et al. (2014a), the corrections
cause the coefficient to drop by a half but the final estimates still remains positive, suggesting
that the effect of vLTRO remains positive also on the firm level.16
Next, we use those results to compute aggregate effects of the vLTRO on credit to firms.
For this back-of-the-envelope calculation, we assume partial equilibrium setting. We plug the
estimates from Column (1) of Table 10 into Equation 9 in order to compare the predicted firm-
level credit with the policy (β = 0.378) and without it (β = 0). We estimate that the credit
to firms would have been by e0.87bn smaller had the banks not been offered the lengthening
of their debt maturity. We know that the total amount of short-term debt that was swapped
into long-term debt through the vLTRO policy amounts to approximately e37bn. As a result,
we estimate that the policy translated into e2.5 of credit to firms for every e100 of the vLTRO
liquidity provision.
Our analysis so far suggests strong effects of the unconventional liquidity provision on lending.
But are these changes substantial enough to have real effect? To examine this question, we follow
16
Following Jimenez et al. (2014a), we compute the bias-correct coefficient as:
var(XiRR ) 0.08442
βbj = β j,OLS − (βOLS − βF E ) × = 0.700 − (1.485 − 1.428) × = 0.378
b b b
var(XjRR ) 0.035492
18
the same procedure and estimate the impact on investment and employment (Equation 9).
We measure ∆log(investmentj ) as an annual log change of investment using firm-level census
data. Columns (3) and (4) of Table 10 summarize the regression results. Two important
observations stand out. First, the indirect measure of the reduction in rollover risk (XjRR ) is
consistent with our previous results. Second, these estimates suggest that smaller firms are
more exposed to the positive effects of the reduction in bank rollover risk propagated through
the bank lending channel.
Finally, we examine ∆employmentj , which we measure using establishment-level employ-
ment microdata following employment growth rate definition also used in Chodorow-Reich
(2014). While we find no average firm-level effect on employment (Column (5) Table 10),
when differentiating firms by their size, we report a positive effect on employment for small
firms (Column (6)). This is also consistent with our previous findings that highlight stronger
bank-lending channel responses for smaller firms in Portugal.
7 Policy side-effects and the impact on lending
Until this point, we have discussed the effects of a bank debt maturity extension induced by
vLTRO which allowed banks to shift maturity of central banks’ liquidity they held before the
vLTRO announcement towards longer maturities. In this section, we address the changes in the
bank risk behavior between the policy announcement and the allotment, and their implications
on lending.
After learning the details of the policy announcement, banks increased their pledging of
eligible securities with the central bank. More importantly, banks did so by purchasing additional
eligible securities. This phenomenon has been discussed in the literature as collateral trade
(Acharya and Steffen (2015), Crosignani et al. (2017)). The perils of this behavior included
strengthening of the diabolic loop (Brunnermeier et al. 2016a) between the sovereign and the
banking sector as banks increased their home bias by purchasing more domestic government
bonds.
One potential challenge is to quantify the magnitude of the security carry trade. We de-
sign a measure of purchased & pledged securities (P P ) that matches ISIN-level security holdings
database maintained by the Bank of Portugal with ISIN-level security pledging database of the
European Central Bank. This perfect matching allows us to precisely measure the pledging
uptake from eligible assets purchased only after the policy announcement. Hence, we do not
capture the delayed pledging of pre-existing holdings nor the security purchases that were not
used with the vLTRO. Construction of the P P thus allows us to precisely measure the pledging
uptake from securities purchased after the banks learned the details of vLTRO in the announce-
ment. To the best of our knowledge, this is the first paper that directly measures the magnitude
19
of the security carry trade of banks. We start by exploring possible hypotheses to explain the
bank behavior and in the next step we examine its potential impact on bank lending.
7.1 Motives behind purchasing eligible securities
Why did some banks purchase eligible securities in order to obtain vLTRO funding and others
not? In this section, we test three hypotheses that were previously emphasized in the literature
(see Acharya and Steffen (2015), Crosignani et al. (2017), Abbassi et al. (2016) for details).
First, undercapitalized banks could shift their exposure from private lending to government
and bank securities in order to strengthen their regulatory capital position without issuing
new equity. This motive arises due to the lower risk weights applied to bank and government
securities when calculating the capital ratios in the Basel II regime. Second, banks short on
liquid assets could substitute illiquid lending to firms with liquid securities. As a result, they
could respond better to negative funding liquidity shocks or dry-ups. Third, banks with different
business model (i.e., specializing in security trading) may react differently to the newly emerged
trading opportunities. They could purchase additional high-yield securities to profit from their
potential price increase without exposing themselves to large liquidity risk thanks to the vLTRO.
In order to test which of the motives was present between the announcement and the final vLTRO
allotment, we run the following OLS regression:
P P (bv)i = α + θBankChari + i (10)
where BankChari is a proxy for one of the three motives mentioned above. We proxy the
capital hypothesis with different measures of capital ratio (capital adequacy ratio, Tier 1 ratio
and Core Tier 1 ratio) in September 2011. To test for the liquidity motive, we use three measures
of liquidity: (i) cash only, (ii) cash and short-term MFI loans and (iii) cash, short-term MFI
loans and short-term securities, all measured in September 2011 and normalized to total assets.
According to the reasoning behind those motives, banks, that are undercapitalized or have a
shortfall of liquid assets (and have a lower capital or a lower liquidity ratio), are expected to
purchase more securities.
To proxy for a business model we use the average amount of security holding in a period of
one year prior to the policy shock. Additionally, we also use the variation in sovereign bonds
purchases in 2009. The first measure stems from the notion that banks active in the financial
markets generally hold more securities on their balance sheets. The rationale behind the second
one is connected with banks’ strategic behavior at the beginning of the sovereign debt crisis
in the Eurozone in 2009. As documented in a number of studies (see for example Acharya
and Steffen (2015), Drechsler et al. (2016), Becker and Ivashina (2014), Battistini et al. (2014)),
banks from peripheral countries purchased additional domestic government bonds after investors
20
started to perceive those securities as riskier and the yields on those securities increased. Figure
11 in the Appendix documents a strong uptake of government debt holdings for some Portuguese
banks in 2009.17 If the purchases of eligible securities are correlated with bank business model,
one would expect that banks that held more securities and purchased high-yield government
bonds during the sovereign debt crisis would have also purchased securities during the vLTRO
inter-allotment period.
Table 11 presents the results of Equation 10. We find that none of the estimates that
proxy for a capital or liquidity motive is statistically significant which gives us no evidence
to assume that capital or liquidity needs might explain strategic security purchases after the
policy announcement. In contrast, we observe positive and statistically significant coefficient
for both proxies for the bank business model hypothesis. This positive relationship is consistent
with the empirical evidence in Abbassi et al. (2016). Using bank security-level data and credit
register in Germany, the authors find that when fire sales occur, banks with higher trading
expertise withdraw funding from corporate lending and invest in securities after their price
drops significantly.
The trading strategy based on purchases & pledging of eligible securities can be justified
from the perspective of both associated risk and profitability. First, by offering very long-term
repo loans, the ECB implicitly increased the pool of eligible assets that mature within the repo
period and it hence lowers banks’ repayment risk, i.e. the risk that the price of the asset at
the end of the repo will be lower than the price of the loan to be repaid. 90% of purchased &
pledged securities had remaining maturity below 3 years and 44% of them had maturity between
6 months and 3 years. Second, in terms of returns, 99% of securities offered high yields that were
well above the vLTRO interest rate (which, following the ECB accommodative policy, gradually
decreased from 1% in 2012 to 0.05% in 2015). Figure 10 presents the histogram of yields to
maturity offered by the purchased securities. We also estimate that on average banks obtained
an annual return of 7% in capital gains from this strategy. Here, we assume that banks held
securities until they matured if the maturity date was before the final vLTRO repayment date
and they sold securities with remaining maturity above three years at the vLTRO repayment
date. This also confirms the evidence of a “divine loop,” as an opposite to a “diabolic loop”
(Brunnermeier et al. 2016b) . With a benefit of hindsight, we see that the securities appreciated
in value, bank realized capital gains and improved their balance sheet positions – a mechanism
also related to “stealth recapitalization” by Brunnermeier and Sannikov (2016). Brunnermeier
et al. (2016b) further point out that stronger balance sheets eased banks’ position in subsequent
stress tests.
17
We observe significant heterogeneity in banks’ government security purchases in 2009. As a result, this
exercise compares the correlation in the cross-section of bank security purchases in 2009 and in the vLTRO period
(2011–20012.)
21
7.2 Security carry trade and lending
Next, we extend our framework to incorporate the impact of strategic security purchases on
lending to firms. During the sovereign debt crisis, peripheral banks were financially constrained
and could not expand their balance sheets freely. To test this hypothesis, we first regress the
size of the balance sheet on the amount of purchased & pledged securities:
BSitemi,t
= αi + αt + ξP Pi × P ostt + i,t (11)
T otalAssetsi,Sep11
We start with the specification where BSitemi,t refers to the total size of banks’ assets. We
document that banks involved in the security trade did not use vLTRO funding to expand their
balance sheet (Column (1) of Table 12).
Next, we replace BSitemi,t with the total size of central bank funding and non-core lia-
bilities, respectively and we report the regression coefficients in Columns (2) and (3) of Table
12. We confirm that as expected banks that purchased & pledged more securities increased
their dependence on the central bank (positive and statistically significant coefficient estimate
in Column (2)). Subsequently, we also find that banks that purchased & pledged securities to
obtain additional vLTRO funding used the ECB liquidity to decrease their non-core liabilities.18
As a consequence, the purchase of eligible securities might come at the cost of balance sheet
re-balancing. In order to test whether banks substituted lending to firms with security purchases,
we exploit the variation within exposed banks. We run the baseline regression on intensive margin
with an additional explanatory variable purchased & pledged securities (P P ) normalized to total
assets in September 2011. Equation 12 summarizes the set-up:
log(crediti,j,t ) = αjt + αij + β(XiRR × P ostt ) + γ(P P i × P ostt ) + δQij + i,j,t (12)
In a frictionless world, banks can expand their balance sheets to accommodate higher security
holdings. The impact of the sovereign debt crisis however negatively affected balance sheets of
European banks, in particular in periphery countries. As we have shown in Table 12, banks in
fact did not expand their assets and, hence, the purchase of eligible securities might come at the
cost of substitution.
In Table 13, we examine whether banks substituted lending to firms for security holdings.
We take advantage of the variation within exposed banks and the fact that not all banks that
subscribed to the vLTRO purchased additional securities after the policy announcement. In
other words, we consider only banks that participated in the vLTRO and we exploit additional
variation in their purchase & pledge behavior (measured as P P in Equation 12). We report the
results both for P P measured in the book value (bv ) and as a value-adjusted haircut (vah). We
18
We measures non-core liabilities as a sum of unsecured deposits of financial institutions and issued debt
securities.
22
normalize both measures to total assets in September 2011.
We find that the effect of the exposure to the lengthening of bank debt maturity remains
positive and statistically significant as when estimating the baseline Equation 1. By contrast,
the coefficient for the purchase & pledge behavior on lending to firms is negative and statistically
significant. The estimate is stable for both book and value-adjusted haircut valuations. This
result favors the existence of a balance-sheet substitution from lending towards security holdings
after the vLTRO announcement and suggests the existence of policy side-effects.
Our result is analogous to the crowding out effect of commercial and industrial lending (C&I)
induced by quantitative easing (QE) in the US documented by Chakraborty et al. (2017). They
find that banks decreased C&I lending and moved funds into mortgage origination and creation
of the mortgage-backed securities in response to announced purchases of these securities by the
Federal Reserve within the QE program. Both empirical findings can also be related to the
“winner-picking” practice of actively shifting resources from one project to another observed in
theoretical models featuring internal capital markets (see for example Stein (1997) and Scharf-
stein and Stein (2000)). From the theoretical perspective, there is also a growing literature
analyzing the impact of security trading by banks on credit supply during a crisis. Diamond
and Rajan (2011) find that banks load their portfolios with high-yield securities prone to fire
sales which lead to contraction in credit supply. Shleifer and Vishny (2010) show that banks
prefer to shift capital from new loan investment into buying underpriced securities when the
latter bear higher return.
This part of our analysis is also related to the work of Becker and Ivashina (2014) and
Crosignani et al. (2017). Becker and Ivashina (2014) find that increased holding of government
debt by the Eurozone banks generated a crowding out of corporate lending. While Becker
and Ivashina (2014) measure this effect using the notion of firm switching from loan to bond
financing as a proxy for higher cost of bank credit, we can directly observe changes in loan
quantities for firm borrowing from two banks, one of which purchased securities while the other
did not. Crosignani et al. (2017) show that banks in Portugal acquired eligible securities after
the vLTRO announcement and those purchases are correlated with the vLTRO uptake during
second allotment. While authors mainly focus on the role of short-maturity government bonds,
we exploit the detailed bank-security level data on both sides of the vLTRO contract to identify
which securities were used as collateral to obtain this funding. We find that banks purchased not
only government bonds but also bank bonds. Furthermore, we show how this strategic activity
transmitted into the bank-lending channel.
23
8 Conclusion
This paper contributes to the literature by providing new empirical evidence on how lengthening
of maturity of bank debt in times of crisis reduces the rollover risk and affects bank lending. To
this purpose, we exploit the policy change of the ECB’s unconventional liquidity provision as a
natural experiment. Using a novel dataset that perfectly matches the ECB monetary policy and
market operations data with the firm credit registry and banks’ security holdings in Portugal,
we find that banks more exposed to the lengthening of debt maturity deleveraged more slowly
than did the less exposed banks. Our findings are economically significant for both existing and
new credit. We show that a reduction in rollover risk has a positive and economically sizable
impact on both bank lending to the real economy, investment and employment of firms.
Furthermore, we highlight how unrestricted liquidity provision contributed to banks’ incen-
tives to hold more securities and substitute lending to the real economy for security holdings.
We find that the magnitude of these side effects are dependent on the business model of banks.
This finding might be helpful in thinking about a design of policy regulation in the future. In
fact, some of the subsequent central banks’ initiatives aimed to enforce conditionality behind
liquidity provisions to make sure banks pass through positive stimulus to the real economy –
examples include ECB’s Targeted Longer-Term Refinancing Operation (TLTROs) and Funding
for Lending Scheme by the Bank of England. Yet, there is still little evidence to how successful
stronger conditionality of policy stimulus would translate into lending and in particular real
outcomes that opens new avenues for future research.
24
References
Abbassi, P., R. Iyer, J.-L. Peydró, and F. R. Tous (2016): “Securities trading by banks and
credit supply: Micro-evidence from the crisis.” Journal of Financial Economics 121(3): pp.
569–594.
Acharya, V. V. and S. Steffen (2015): “The greatest carry trade ever? Understanding
Eurozone bank risks.” Journal of Financial Economics 115(2): pp. 215–236.
Almeida, H., M. Campello, B. Laranjeira, and S. Weisbenner (2011): “Corporate debt
maturity and the real effects of the 2007 credit crisis.” Critical Finance Review 1: pp. 3–58.
Alves, N., D. Bonfim, and C. Soares (2016): “Surviving the perfect storm: The role of the
lender of last resort.” Working Paper .
Antunes, A., H. Goncalves, and P. Prego (2016): “Firm default probabilities revisited.”
Working Paper .
Autor, D. (2003): “Outsourcing at will: The contribution of unjust dismissal doctrine to the
growth of employment outsourcing.” Journal of Labor Economics 21(1): pp. 1–42.
Battistini, N., M. Pagano, and S. Simonelli (2014): “Systemic risk, sovereign yields and
bank exposures in the euro crisis.” Economic Policy 29(78): pp. 203–251.
Becker, B. and V. Ivashina (2014): “Financial repression in the European sovereign debt
crisis.” Working Paper .
van Bekkum, S., M. Gabarro, and R. M. Irani (forthcoming): “Does a larger menu increase
appetite? collateral eligibility and bank risk-taking.” The Review of Financial Studies .
Bernanke, B. and A. Blinder (1992): “The federal funds rate and the channels of monetary
transmission.” American Economic Review 82(4): pp. 901–21.
Brunnermeier, M. K. (2009): “Deciphering the liquidity and credit crunch 2007-2008.” Jour-
nal of Economic Perspectives 23(1): pp. 77–100.
Brunnermeier, M. K., L. Garicano, P. R. Lane, M. Pagano, R. Reis, T. Santos, D. Thes-
mar, S. Van Nieuwerburgh, and D. Vayanos (2016a): “The sovereign-banking diabolic
loop and ESBies.” American Economic Review Papers and Proceedings 106(5): pp. 508–512.
Brunnermeier, M. K., H. James, and J.-P. Landau (2016b): The euro and the battle of ideas.
Princeton, NJ, USA: Princeton University Press.
Brunnermeier, M. K. and M. Oehmke (2013): “The maturity rat race.” The Journal of
Finance 68(2): pp. 483–521.
25
Brunnermeier, M. K. and Y. Sannikov (2016): “The I theory of money.” Working Paper .
Calomiris, C. and C. Kahn (1991): “The role of demandable debt in structuring optimal
banking arrangements.” American Economic Review 81(3): pp. 497–513.
Carpinelli, L. and M. Crosignani (2017): “The effect of central bank liquidity injections on
bank credit supply.” Working Paper .
Cassola, N., A. Hortasu, and J. Kastl (2013): “The 2007 subprime market crisis through
the lens ofEuropean Central Bank auctions for short-term funds.” Econometrica 81(4): pp.
1309–1345.
Chakraborty, I., I. Goldstein, and A. MacKinlay (2017): “Monetary stimulus and bank
lending.” Working Paper .
Chodorow-Reich, G. (2014): “The employment effects of credit market disruptions: Firm-
level evidence from the 2008-09 financial crisis.” Quarterly Journal of Economics 129(1): pp.
1–59.
Crosignani, M., M. Faria-e Castro, and L. Fonseca (2015): “The Portuguese banking
system during the sovereign debt crisis.” Banco de Portugal Economic Studies 1(2): pp.
43–80.
Crosignani, M., M. Faria-e Castro, and L. Fonseca (2017): “The (unintended?) conse-
quences of the largest liquidity injection ever.” Working Paper .
Diamond, D. and R. Rajan (2001): “Liquidity risk, liquidity creation, and financial fragility:
A theory of banking.” Journal of Political Economy 109(2): pp. 287–327.
Diamond, D. W. (1991): “Debt maturity structure and liquidity risk.” The Quarterly Journal
of Economics 106(3): pp. 709–737.
Diamond, D. W. and Z. He (2014): “A theory of debt maturity: the long and short of debt
overhang.” The Journal of Finance 69(2): pp. 719–762.
Diamond, D. W. and R. G. Rajan (2011): “Fear of fire sales, illiquidity seeking, and credit
freezes.” The Quarterly Journal of Economics 126(2): pp. 557–591.
Drechsler, I., T. Drechsel, D. Marquez-Ibanez, and P. Schnabl (2016): “Who borrows
from the lender of last resort?” The Journal of Finance 71(5): pp. 1933–1974.
Farhi, E. and J. Tirole (2012): “Collective moral hazard, maturity mismatch, and systemic
bailouts.” American Economic Review 102(1): pp. 60–93.
26
Ferrando, A., A. Popov, and G. Udell (2015): “Do SMEs benefit from unconventional
monetary policy and how? Microevidence from the Eurozone.” ECB Working Paper (1820).
Flannery, M. J. (1986): “Asymmetric information and risky debt maturity choice.” The
Journal of Finance 41(1): pp. 19–37.
Gertler, M. and S. Gilchrist (1994): “Monetary policy, business cycles, and the behavior of
small manufacturing firms.” The Quarterly Journal of Economics 109(2): pp. 309–340.
He, Z. and W. Xiong (2012): “Rollover risk and credit risk.” The Journal of Finance 67(2):
pp. 391–430.
Ivashina, V. and D. Scharfstein (2010): “Bank lending during the financial crisis of 2008.”
Journal of Financial Economics 97(3): pp. 319–338.
Iyer, R., J.-L. Peydro, S. da Rocha-Lopes, and A. Schoar (2014): “Interbank liquidity
crunch and the firm credit crunch: Evidence from the 2007–2009 crisis.” Review of Financial
Studies 27(1): pp. 347–372.
Jimenez, G., A. R. Mian, J.-L. Peydro, and S. J. Saurina (2014a): “The real effects of the
bank lending channel.” Working Paper .
Jimenez, G., S. Ongena, J.-L. Peydro, and J. Saurina (2012): “Credit supply and mone-
tary policy: Identifying the bank balance-sheet channel with loan applications.” American
Economic Review 102(5): pp. 2301–26.
Jimenez, G., S. Ongena, J.-L. Peydro, and J. Saurina (2014b): “Hazardous times for mone-
tary policy: What do twenty-three million bank loans say about the effects of monetary policy
on credit risk-taking?” Econometrica 82(2): pp. 463–505.
Kashyap, A., O. A. Lamont, and J. C. Stein (1994): “Credit conditions and the cyclical
behavior of inventories.” The Quarterly Journal of Economics 109(3): pp. 565–592.
Khwaja, A. I. and A. Mian (2008): “Tracing the impact of bank liquidity shocks: Evidence
from an emerging market.” American Economic Review 98(4): pp. 1413–42.
Krishnamurthy, A. (2010): “How debt markets have malfunctioned in the crisis.” Journal of
Economic Perspectives 24(1): pp. 3–28.
Luck, S. and T. Zimmermann (2017): “Employment effects of unconventional monetary policy:
Evidence from QE.” Working Paper .
Myers, S. C. (1977): “Determinants of corporate borrowing.” Journal of Financial Economics
5(2): pp. 147–175.
27
Rodnyansky, A. and O. Darmouni (2017): “The effects of quantitative easing on bank lending
behavior.” The Review of Financial Studies 30(11): pp. 3858–3887.
Scharfstein, D. S. and J. C. Stein (2000): “The dark side of internal capital markets: Divi-
sional rent-seeking and inefficient investment.” The Journal of Finance 55(6): pp. 2537–2564.
Schnabl, P. (2012): “The international transmission of bank liquidity shocks: Evidence from
an emerging market.” The Journal of Finance 67(3): pp. 897–932.
Segura, A. and J. Suarez (2017): “How excessive is banks maturity transformation?” The
Review of Financial Studies 30(10): pp. 3538–3580.
Shleifer, A. and R. W. Vishny (2010): “Unstable banking.” Journal of Financial Economics
97(3): pp. 306–318.
Stein, J. C. (1997): “Internal capital markets and the competition for corporate resources.”
The Journal of Finance 52(1): pp. 111–133.
Stein, J. C. (2012): “Monetary policy as financial-stability regulation.” Quarterly Journal of
Economics 127(1): pp. 57–95.
28
9 Figures and Tables
Figure 1: Liquidity provisions received by Portuguese banks from the central bank
60
40
20
0
01jan2007 01jul2009 01jan2012 01jul2014 01jan2017
date
other vLTRO
Notes: In billion EUR. Split by maturity: MRO (Main refinancing operations) are regular operation with an
average maturity of 1 week; other LTROs denote all longer-term refinancing operations except for the analyzed
vLTRO; vLTRO includes the analyzed 3Y LTRO (2011-2014) and the subsequent targeted-LTRO (from 2014
onwards).
Figure 2: Mechanism: maturity mismatch
27
25
23
21
19
17
15
Jan-05 Jun-05 Nov-05 Apr-06 Sep-06 Feb-07 Jul-07 Dec-07 May-08 Oct-08 Mar-09 Aug-09 Jan-10 Jun-10 Nov-10 Apr-11 Sep-11 Feb-12 Jul-12 Dec-12 May-13 Oct-13 Mar-14 Aug-14
Notes: In months. Averages for Portuguese banking sector. Grey-shaded area represents the period between the
vLTRO announcement and final (second) allotment.
29
Figure 3: Mechanism: maturity of assets and liabilities
46 27
45 25
44 23
43 21
42 19
41 17
40 15
Jan-05 Jun-05 Nov-05 Apr-06 Sep-06 Feb-07 Jul-07 Dec-07 May-08 Oct-08 Mar-09 Aug-09 Jan-10 Jun-10 Nov-10 Apr-11 Sep-11 Feb-12 Jul-12 Dec-12 May-13 Oct-13 Mar-14 Aug-14
Average Maturity of Assets (months, left axis) Average Maturity of Liabilities (months, right axis)
Notes: In months. Averages for Portuguese banking sector. Grey-shaded area represents the period between the
vLTRO announcement and final (second) allotment.
Figure 4: Security pledging by Portuguese banks with the central bank
50
40
30
20
10
0
01jan2011 01jul2011 01jan2012 01jul2012 01jan2013
date
Government-issued securities Bank-issued securities
Non-marketable Other
Notes: In billion EUR. Split by type: Government-issued securities are all securities issues by general or regional
governments; Bank-issued securities are covered and uncovered securities issued by the banking sector; Non-
marketable represent additional credit claims (bank loans) which became eligible for ECB funding after the
vLTRO Other include assets-banked securities, corporate loans or securities issued by other entities
30
Figure 5: Dependence on the ECB is sticky prior to vLTRO announcement
60
40
20
0
01jul2011 01jan2012 01jul2012 01jan2013
date
short-term funding vLTRO
Notes: Liquidity provisions received by Portuguese banks during the period Jun 2011–Dec 2012. Reported in
billion EUR. Short-term funding includes all liquidity provision with a maturity of less that one year.
Figure 6: Leads and lags around the treatment effect: binary treatment
(a) ECB repo funding (b) Eligible securities
.4 .4
.2 .2
0 0
-.2 -.2
2011m1 2011m7 2012m1 2012m7 2013m1 2011m1 2011m7 2012m1 2012m7 2013m1
date date
Notes: This figure presents coefficient estimates of γk for each month t from Equation 4. We divide banks into
two groups: exposed and non-exposed. Panel (a) sets XiRR to 1 if a bank i was borrowing from the central bank
in repo operations prior to the vLTRO announcement and 0 otherwise. Panel (b) sets the XiRR to 1 if a bank i
was holding securities eligible for the vLTRO prior to the policy announcement and 0 otherwise. Vertical bands
represent +/- 1.96 times standard error of each point estimate. Vertical dotted lines separate the vLTRO period.
Standard errors are two-way clustered at the bank-time and firm level.
31
Figure 7: Leads and lags around the treatment effect: continuous treatment
3
2
1
0
-1
2011m1 2011m7 2012m1 2012m7 2013m1
date
Notes: This figure presents coefficient estimates of γk for each month t from Equation 4. Exposure measure XiRR
is defined as a sum of all secured borrowing from the central bank prior to the vLTRO announcement (as a share
of total assets). Vertical bands represent +/- 1.96 times standard error of each point estimate. Vertical dotted
lines separate the vLTRO period. Standard errors are two-way clustered at the bank-time and firm level.
Figure 8: Placebo test: freeze of the European interbank market in August 2007
2
1
0
-1
2007m1 2007m7 2008m1 2008m7
date
Notes: The figure uses an unexpected freeze of the European interbank market in August 2007 as a placebo test.
The negative effects of the interbank liquidity crunch on lending in Portugal were previously shown by Iyer et al.
(2014). This figure presents coefficient estimates of γk for each month t from Equation 4. There is no evidence
that banks more exposed to the vLTRO in 2011 were generally more sensitive to the liquidity dry-up in 2007
(the plotted estimates of γk are not significantly different from zero). Vertical bands represent +/- 1.96 times
standard error of each point estimate. Vertical dotted line shows the European interbank market in August 2007.
Standard errors are two-way clustered at the bank-time and firm level.
32
Figure 9: Remaining maturity of purchased & pledged securities
6,000
4,000
2,000
0
6m 6m-1y 1-2y 2-3y 3-4y 4-5y 5-6y 6-7y 7-8y 8-9y 9-10y>10y
Notes: As of February 2012. In million EUR.
Figure 10: Distribution of yield to maturity of purchased & pledged securities
0.12
0.1
0.08
Frequency
0.06
0.04
0.02
0
0 5 10 15 20 25 30
Yield to maturity of purchased and pledged securities (%)
Source: Bloomberg.
33
Table 1: Exposure to the reduction of rollover risk and the actual vLTRO uptake
vLT ROi
(1) (2)
XiRR 0.780***
(0.080)
RR
Xi,eligSec 0.415***
(0.046)
Observations 30 30
R2 0.78 0.75
Notes: This table presents coefficients from a univariate regression (Equation 2) that regresses the actual value
of vTLRO liquidity against our treatment variable: XiRR denotes the total secured borrowing before the policy
RR
announcement and Xi,eligSec denotes the total holdings of eligible securities. All variables are normalized to
total assets in September 2011. Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Table 2: Exposure to the reduction of rollover risk and other bank characteristics
Est S.E. R2
log(TA) 0.001 0.006 0.00
Capital ratio 0.081 0.080 0.04
Loans (% Assets) -0.083 0.050 0.09
Loans (% Deposits) -0.021 0.019 0.04
Deposits (% Assets) -0.030 0.084 0.00
ROA -0.506 0.498 0.04
Cash reserves (% Assets) 0.113 1.500 0.00
Liquidity ratio -0.067 0.059 0.04
Leverage ratio -0.001 0.001 0.01
Equity (% Assets) -0.041 0.084 0.01
Average REPO funding (’05) (% Assets) 0.753*** 0.150 0.50
Notes: This table presents coefficients from set of univariate regressions that regress our treatment variable
XiRR against a set of bank observable characteristics (see Equation 3). log(T A) is log of total assets of a bank
i, CapitalRatio is a capital adequacy ratio, Loans (% Assets) is a share of loans to private sector normalized
by total assets, Loans (% Deposits) is a share of loans to private sector normalized by total deposits, Deposits
(% Assets) is a share of deposits normalized by total assets, ROA is bank’s return on assets, Cash reserves (%
Assets) is a share of cash reserves normalized by total assets, Equity (% Assets) is a share of equity normalized
by total assets, Average REPO funding (’05) (% Assets) is a bank funding coming from repo market (average
in 2005) normalized by total assets. Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.
34
Table 3: Intensive margin: time-series version
log(credit drawni,j,t )
(1) (2) (3) (4) (5)
XiRR × P ostt 0.702*** 0.740*** 0.623*** 0.607*** 0.631***
(0.088) (0.071) (0.076) (0.090) (0.063)
Time FE Yes Yes
Bank FE Yes Yes Yes Yes
Firm-Time FE Yes Yes Yes
Bank-Firm controls Yes
Bank-Firm FE Yes
Observations 2,914,218 1,488,436 1,488,436 1,488,436 1,487,089
R2 0.0674 0.123 0.568 0.862 0.993
Loan Sample Full Multiple Multiple Multiple Multiple
bank rela- bank rela- bank rela- bank rela-
tionships tionships tionships tionships
Notes: This table presents coefficients from regressions related to loan-level intensive margin, as described in
Equation 1. The dependent variable is log credit granted to private non-financial corporations in Portugal.
Standard errors are two-way clustered at the bank-time and firm level in parentheses. *** p<0.01, ** p<0.05,
* p<0.1.
Table 4: Intensive margin: collapsed version
∆log(credit drawni,j )
(1) (2) (3) (4)
XiRR 1.464** 1.502** 1.485** 1.428***
(0.590) (0.577) (0.546) (0.418)
Bank Controls Yes Yes Yes Yes
Bank-Firm Controls Yes Yes Yes Yes
Firm Controls Yes Yes
Firm FE Yes
Observations 231,350 231,350 135,751 135,751
R2 0.021 0.034 0.047 0.442
Sample Full Full Multiple bank Multiple bank
relationships relationships
Notes: This table presents coefficients from regressions related to loan-level intensive margin described as
∆log(credit drawnij ) = αj + βXiRR + τ Bi + δQi,j + i,j . The dependent variable ∆log(credit drawn)ij is a
change in the log of average credit drawn in the period before (2011m6–2011m10) and after (2012m2–2012m6)
the vLTRO. Bank controls are log(T otalAssets), CapitalRatio, LiqRatio, Equity/T A and Loans/T A. Bank-
Firm controls are variables Length, Share and Secured. Firm controls are log of firm total assets and 2-digit
NACE industry × district fixed effects. Standard errors clustered at the bank level in parentheses. *** p<0.01,
** p<0.05, * p<0.1.
35
Table 5: Intensive margin: robustness
Baseline Alternative Total All firms Exposed EBA
exposure credit banks shock
(1) (2) (3) (4) (5) (6)
XiRR × P ostt 0.631*** 0.613*** 0.639*** 0.532*** 2.043***
(0.063) (0.059) (0.053) (0.078) (0.215)
RR
Xi,eligSec × P ostt 0.089**
(0.045)
Firm-Time FE Yes Yes Yes Yes Yes Yes
Bank-Firm FE Yes Yes Yes Yes Yes Yes
Observations 1,487,089 1,487,089 1,673,619 2,387,616 1,243,816 765,574
R2 0.993 0.993 0.995 0.994 0.993 0.951
Firm sample Private Private Private All firms Private Private
NFC NFC NFC NFC NFC
Bank sample All banks All banks All banks All banks Only Excl.
exposed EBA
banks
Credit Drawn Drawn Total Drawn Drawn Drawn
Notes: This table presents coefficients from regressions related to loan-level intensive margin, as described in
Equation 1. Column (1) represents the baseline specification as shown in the last column of Table 3. The
dependent variable is log credit granted to private non-financial corporations in Portugal. The exposure XiRR
is defined as a sum of all available borrowing from the ECB as a share of total assets prior to the vLTRO.
Columns (2)–(6) present variations to the baseline. Column (2) uses an alternative exposure measure Xisec
defined as a sum of all securities eligible for the vLTRO and held by the banks prior to the vLTRO (as a share
of total assets). Column (3) shows results for the dependent variable measured as a log of total credit (a sum
of credit granted and potential credit). Column (4) extends the sample to all firms in Portugal, including not
only private NFC but also independent entrepreneurs and publicly-owned companies. Column (5) focuses only
on the variation across banks that were exposed to the ECB open market operations prior to the vLTRO (in
September 2011). Column (6) drops four largest banks which were subject to the stress test exercise conducted
by the European Banking Association (EBA) around the same time as the vLTRO. Standard errors are two-way
clustered at the bank-time and firm level in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
36
Table 6: Extensive margin: entry and exit
EXITi,j EN T RYi,j
(1) (2) (3) (4)
XiRR -0.840*** -0.782*** 0.113 0.114
(0.168) (0.137) (0.390) (0.427)
Bank controls Yes Yes Yes Yes
Firm controls Yes Yes
Firm FE Yes Yes
Observations 284,179 274,605 283,250 270,372
R2 0.242 0.526 0.056 0.417
Notes: This table presents coefficients from regressions related to loan-level extensive margin, as described
in Equation 5. Columns 1–2 look at exit by including all loans that were outstanding in the period prior to
the vLTRO (2011m6–2011m10). For a given loan, EXIT is classified as 1 if the loan is not renewed and the
bank-firm relationship ceases to exist. Columns 3–4 look at entry and include all loans given out in the period
after the vLTRO (2012m2–2012m6). For a given bank-firm pair, EN T RY is classified as 1 if the loan appears
for the first time only after the vLTRO, and 0 otherwise. All regressions include bank level controls: ln(T A),
CapitalRatio, LiqRatio, Equity/T A and Loans/T A. Columns (1) and (3) include log of total assets of a firm
j and industry-district fixed effects to control for firm characteristics. Columns (2) and (4) further limit the
sample to only firms that were borrowing from multiple banks and absorb firm fixed effects. Standard errors
clustered at the bank level in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Table 7: Extensive margin: entry using loan consultation data
newLoani,j
(1) (2)
XiRR 0.630* 0.633***
(0.312) (0.211)
Bank controls Yes Yes
Firm controls Yes
Firm FE Yes
Observations 92,115 40,938
R2 0.055 0.459
Notes: This table presents coefficients from regressions related to loan-level extensive margin, as described in
Equation 6. The dependent variable newLoan takes a value of 1 if a bank-firm consultation entry is matched
with a new bank-firm record in the credit registry and 0 otherwise. Bank controls include ln(T A), CapitalRatio,
LiqRatio, Equity/T A and Loans/T A. Firm controls include log of total assets of a firm j and industry-district
fixed effects. Standard errors clustered at the bank level in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
37
Table 8: Maturity of loans
∆M aturityi,j
(1) (2) (3)
XiRR 0.792*** 0.850*** 0.618***
(0.181) (0.218) (0.173)
Bank controls Yes Yes Yes
Bank-Firm controls Yes Yes
Firm Controls Yes
Observations 10,863 10,863 7,539
R2 0.007 0.011 0.099
Notes: This table presents coefficients from regressions related to loan-level regression, as described in Equation
7. The dependent variable ∆M aturity is a change in the median original maturity between the pre- (June–
October 2011) and post- (March–June 2012) treatment period. Bank controls include ln(T A), CapitalRatio,
LiqRatio, Equity/T A and Loans/T A. Bank-firm controls are Length, Share and Secured. Firm controls are
log of total assets of a firm j and industry-region fixed effects. Standard errors clustered at the bank level in
parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Table 9: Heterogeneous outcomes by firm type
log(credit drawni,j,t )
(1) (2) (3) (4)
XiRR × P ostt 1.722*** 0.814*** 0.628*** 1.050***
(0.182) (0.0704) (0.0326) (0.0341)
XiRR × P ostt × Sizej -0.077***
(0.013)
Xi,RR × P ostt × Agej -0.052*
(0.027)
XiRR × P ostt × Riskyj 0.948**
(0.466)
XiRR × P ostt × Lengthij -0.006***
(0.000)
Firm-Time FE Yes Yes Yes Yes
Bank-Firm FE Yes Yes Yes Yes
Observations 1,166,299 1,166,299 1,166,299 1,166,299
R2 0.96 0.96 0.96 0.96
Notes: This table presents coefficients from regressions related to loan-level intensive margin, as described in
Equation 8. The dependent variable log(credit Drawn) is log credit drawn of firms in Portugal. Sizej is log of
total assets of firm j, Age is firm’s age in years, Risky is a measure of riskiness based on default probabilities for
Portuguese firms (Antunes et al. 2016), Length is a length of bank-borrowing relationship in months. Standard
errors are two-way clustered at the bank-time and firm level in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
38
Table 10: Firm-level outcomes
∆log(credit drawnj ) ∆log(investmentj ) ∆employmentj
(1) (2) (3) (4) (5) (6)
XjRR 0.700*** 2.257*** 0.595** 6.030*** -0.016 55.538**
(0.058) (0.366) (0.287) (1.805) (6.134) (1.805)
XjRR × Sizej -0.118*** -0.411*** -4.182**
(0.027) (0.135) (0.135)
Bias corrected XjRR 0.378
Bank controls Yes Yes Yes Yes Yes Yes
Bank-Firm controls Yes Yes Yes Yes Yes Yes
Firm controls Yes Yes Yes Yes Yes Yes
Observations 51,978 51,978 27,029 27,029 37,368 37,368
R2 0.038 0.038 0.046 0.047 0.001 0.001
This table presents coefficients from regressions related to firm-level intensive margin, as described in Equation
9. The dependent variable ∆log(credit drawnj ) a log change in total bank lending on the firm level between the
pre- (June–October 2011) and post- (March–June 2012) treatment period or ∆log(investmentj ) as a annual
log change in investment (2012 vs. 2011) or ∆employmentj as a annual change in employment (2012 vs.
2011). Bank controls are indirect measures of ln(T A), CapitalRatio, LiqRatio, Equity/T A and Loans/T A.
Bank-Firm controls are indirect measures of Share, Length and Secured. Firm controls are log of total assets.
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Table 11: Motives behind purchasing eligible securities
θBankChari Estimate Std. error R2
1. Capital motive
Capital ratio 0.024 (0.024) 0.033
2. Liquidity motive
Liquidity ratio -0.014 (0.018) 0.022
Liquidity ratio (noSec) -0.017 (0.018) 0.032
Cash/TA 0.057 (0.454) 0.001
3. Business model
Securities/TA 0.033** (0.016) 0.139
∆ govSecurities2009/TA 0.378*** (0.095) 0.362
Notes: This table presents coefficients from bank-level regressions as described in Equation 10. Capital ratio is
a capital adequacy ratio, Liquidity ratio is a sum of cash, short term securities and short term MFI borrowing
normalized by total assets, Liquidity ratio (noSec) is a sum of cash and short term MFI borrowing normalized
by total assets. Cash/T A is a ratio of cash to total assets, Securities/T A is a ratio of average security holdings
one year prior to vLTRO to total assets, ∆govSecurities2009/T A is a sum of purchases of the government debt
securities in 2009. Number of observations is 30. ***p<0.01, **p<0.05, *p<0.1.
39
Table 12: Strategic security purchases: deeper into balance sheet
Total Assets Central bank funding Non-core liabilities
(1) (2) (3)
P Pi × P ostt 0.366 0.248** -0.587***
(0.474) (0.124) (0.174)
Observations 360 360 360
R2 0.017 0.043 0.021
Bank sample All banks All banks All banks
Notes: This table presents coefficients from regressions as described in Equation 11, where the outcome variable
BSitemi,t refers to the total assets, central bank funding and non-core liabilities, respectively. Standard errors
in parentheses. ***p<0.01 **p<0.05, *p<0.1
Table 13: Intensive margin: policy side effects and substitution
log(credit drawni,j,t )
(1) (2)
XiRR × P ostt 0.725*** 0.741***
(0.140) (0.144)
P P (vah)i × P ostt -1.524***
(0.566)
P P (bv)i × P ostt -1.191***
(0.458)
Bank-Firm FE Yes Yes
Firm-Time FE Yes Yes
Observations 1,695,671 1,695,671
R2 0.97 0.97
Bank sample Exposed banks Exposed banks
Notes: This table presents coefficients from regressions related to loan-level intensive margin, as described
in Equation 12. The dependent variable log(credit drawni,j,t ) is a log credit drawn of non-financial firms in
Portugal. Standard errors are two-way clustered at the bank-time and firm level in parentheses. *** p<0.01,
** p<0.05, * p<0.1.
40
A Appendix
Table 14: Bank characteristics
Mean S.D. p25 p50 p75
September 2011
Total assets bn EUR 16.9 32.7 0.6 1.9 11.1
Capital ratio Capital/RWA 11.2 16.3 8.8 10.3 14.4
ROA Profit/Assets 0.4 2.6 -0.2 0.0 0.4
Cash reserves % Assets 0.7 0.9 0.0 0.5 1.1
Security holdings % Assets 19.4 17.2 5.6 19.6 29.7
Eligible securities % Assets 12.9 15.6 1.1 11.1 20.1
Loans % Assets 66.3 20.5 25.5 43.0 69.2
Equity % Assets 13.8 15.8 6.4 8.4 13.2
All LTROs % Assets 4.6 7.0 0.0 0.0 7.2
Total ECB liquidity % Assets 5.9 8.4 0.0 1.1 9.2
December 2011
All LTROs % Assets 5.3 7.5 0.0 1.1 8.8
vLTRO % Assets 3.1 4.6 0.0 0.0 4.9
Total ECB liquidity % Assets 5.8 7.8 0.0 2.1 9.7
March 2012
All LTROs % Assets 7.1 9.0 0.0 4.7 10.8
vLTRO % Assets 6.2 7.5 0.0 4.7 10.5
Total ECB liquidity % Assets 7.5 9.9 0.0 4.7 11.2
Table 15: Loan characteristics
N Mean S.D. p25 p50 p75
Main sample: private NFCs
Credit drawn 4,150,391 284,501 2,968,372 6,417 23,283 90,635
Total credit 4,712,155 323,571 3,630,589 5,465 22,475 91,693
All firms
Credit drawn 4,910,118 252,791 2,967,164 6,302 24,890 89,344
Total credit 5,685,298 263,194 3,356,880 4,109 20,000 80,997
Notes: Reported in EUR. Minimum reporting threshold is EUR 50. Credit drawn represents regular, renego-
tiated and less than 60 days overdue credit, Total credit represents credit drawn and potential credit (unused
credit lines, credit cards etc.) Main sample consists of domestic and foreign non-financial firms and non-profits.
Full sample additionally includes public firms and individual entrepreneurs.
41
Table 16: Intensive margin: robustness to standard error clustering
log(credit drawni,j,t )
S.E. Clustering Bank-Time and Firm Bank and Firm Bank
Baseline
(1) (2) (3) (4) (5) (6)
XiRR × P ostt 0.607*** 0.631*** 0.607*** 0.631*** 0.607*** 0.631***
(0.090) (0.063) (0.167) (0.168) (0.024) (0.214)
Bank FE Yes Yes Yes
Bank-Firm Controls Yes Yes Yes
Bank-Firm FE Yes Yes Yes
Firm-Time FE Yes Yes Yes Yes Yes Yes
Observations 1,488,436 1,487,089 1,488,436 1,487,089 1,488,436 1,487,089
R2 0.862 0.993 0.862 0.993 0.862 0.993
Notes: This table presents coefficients from regressions relating to loan-level intensive margin, as described in
Equation 1. The dependent variable is log credit granted to private non-financial corporations in Portugal.
Clustered standard errors in parentheses. Level of standard error clustering is bank-time and firm, bank and
firm and firm, respectively. *** p<0.01, ** p<0.05, * p<0.1.
Figure 11: Government debt holding by Portuguese banks
40
30
govSec
20
10
0
2005m1 2010m1 2015m1
date
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