(AGENPARL) – mer 08 gennaio 2025 Mercati, infrastrutture, sistemi di pagamento
(Markets, Infrastructures, Payment Systems)
A general framework to assess
the smooth implementation of monetary policy:
an application to the introduction of the digital euro
Number
January 2025
by Annalisa De Nicola and Michelina Lo Russo
Mercati, infrastrutture, sistemi di pagamento
(Markets, Infrastructures, Payment Systems)
A general framework to assess
the smooth implementation of monetary policy:
an application to the introduction of the digital euro
by Annalisa De Nicola and Michelina Lo Russo
Number 56 – January 2025
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A general framework to assess the smooth
implementation of monetary policy:
an application to the introduction of the digital euro
by Annalisa De Nicola* and Michelina Lo Russo*
Abstract
This paper proposes a methodological framework for estimating the maximum amount of digital euro
(D€) that is consistent with a smooth monetary policy implementation (MPI) in the euro area (EA).
To this end, we consider that monetary policy is implemented smoothly following the introduction
of the D€ when i) the remaining aggregate liquidity in the EA is sufficient to anchor short-term
rates to the deposit facility rate and ii) EA national banking sectors can largely meet D€ demand
with excess reserves and additional central bank credit. We estimate that, for a smooth MPI, the
maximum amount of D€ should not exceed EUR 1.7 tn under an approach that takes into account
the heterogeneity across EA countries and banks and prevents any EA national banking sector from
facing a too severe liquidity distress following the introduction of the D€. Our analysis suggests
the importance of refinancing operations with a broad collateral framework in the Eurosystem
operational framework, due to their key role in allowing the central bank to elastically withstand
additional reserve demand stemming from the introduction of the D€.
JEL Classification: E41, E52, E58, G21.
Keywords: central bank digital currency, ECB, Eurosystem, central bank reserves, monetary policy
implementation.
Sintesi
Il lavoro propone una metodologia per stimare la quantità massima di euro digitale (D€) che risulti
coerente con una ordinata attuazione della politica monetaria nell’area dell’euro (AE). A tal fine,
definiamo l’attuazione della politica monetaria come ordinata se dopo l’introduzione del D€: i) la
liquidità aggregata rimanente nell’AE è sufficiente per ancorare i tassi a breve termine al tasso sulla
deposit facility e ii) i settori bancari nazionali dell’AE sono in grado di soddisfare in larga misura
la domanda di D€ con l’utilizzo delle riserve in eccesso e un maggiore ricorso al credito di banca
centrale. Stimiamo che, per una ordinata attuazione della politica monetaria, la quantità massima
di D€ non dovrebbe superare 1.700 miliardi di euro. Tale risultato tiene conto dell’esistente
eterogeneità tra i paesi e le banche nell’AE e della necessità di garantire che nessun sistema bancario
nazionale si trovi ad affrontare una crisi di liquidità a seguito dell’introduzione del D€. L’analisi
suggerisce l’importanza, nell’assetto operativo dell’Eurosistema, di operazioni di rifinanziamento
a fronte di una gamma estesa di garanzie, dato il loro ruolo cruciale nel consentire alla banca
centrale di assorbire elasticamente il fabbisogno aggiuntivo di riserve derivante dall’introduzione
del D€.
Banca d’Italia, Directorate General for Markets and Monetary Policy Operations.
CONTENTS
1. Introduction 7
2. Conceptual framework 9
3. The data 11
4. The model 13
5. The results 15
5.1. The maximum amount of D€ for a smooth MPI: an illustrative scenario
5.2. Sensitivity analysis of the local condition z
6. Preliminary considerations on the EA money market and the Eurosystem footprint 21
7. Conclusions 22
Acknowledgments 24
References 25
1. Introduction
This paper proposes a methodological framework for the estimation of the maximum amount
of a retail central bank digital currency (CBDC) that is consistent with a smooth implementation of
monetary policy and reflects, accordingly, the central bank’s response to the resulting liquidity drain.
If a CBDC is issued, end users might substitute banknotes (central bank money) and/or bank
deposits (commercial bank money) with the digital currency, with implications on both the banking
sector and the central bank’s balance sheets (Auer et al. 2024; Caccia et al. 2024). In the case of a
full substitution of banknotes, the CBDC would change the central bank’s balance sheet composition
on the liability side while leaving unchanged the balance sheet size of both the central bank and
commercial banks. In the case of a full substitution of bank deposits, the CBDC might instead lead
to a structural shift in banks’ funding conditions. Credit institutions would lose a stable and cheap
source of funding and might need additional central bank reserves to accommodate the CBDC
demand. Brunnermeier and Niepelt (2019) argue that if the central bank has a strong commitment to
act as lender of last resort, it can theoretically buffer all the effects of outflows from bank deposits
into the CBDC by substituting deposit funding with central bank funding. However, this “neutrality
theorem” runs into the physical limit of the amount of liquidity that can be effectively injected by the
central bank. This limit relates to the availability of eligible collateral that can be pledged by
individual banks to obtain central bank credit or securities that can be purchased by the central bank
under a purchase programme. Even if the central bank does not want to fully compensate for the
outflows from commercial bank deposits into the CBDC with additional reserves provision, it still
needs to ensure that the amount of reserves in the banking sector is consistent with a smooth monetary
policy implementation (MPI).
In this paper, we examine the substitution of commercial bank money with the CBDC, while
excluding the impact of CBDC issuance on banknote demand. The CBDC we refer to is the so called
digital euro (D€) explored by the ECB. While the model focuses on the case of a D€, it is sufficiently
general to be applicable to other instances where liquidity shocks may impair MPI.
Our methodological framework builds on a definition of “smooth MPI” that accounts for the
heterogeneity among euro area (EA) member countries. We acknowledge that EA credit institutions
might not be equally equipped to react to the (potential) outflows in retail deposits induced by the D€
issuance as they differ in reserve holdings, funding models and size. For instance, institutions with
large funding from retail depositors may suffer more than institutions relying on wholesale funding
sources. Furthermore, the extent to which central bank liquidity provision can substitute deposit
outflows must be operationally verified both at an aggregate and individual bank level on the basis
of available eligible collateral. In our model, we assume that the Eurosystem accommodates banks’
demand for reserves after the D€ issuance via credit operations only.
By using bank level data, we first calculate the amount of reserves and additional central bank
credit that banks can use to accommodate the D€ demand and then we investigate the impact of the
D€ demand on MPI. To this end, we consider that monetary policy is smoothly implemented if i) the
remaining EA aggregate liquidity is sufficient to anchor short-term rates in a floor system and ii) each
countries’ national banking sector is able to accommodate to a large extent the D€ demand with
reserves and additional central bank credit.
By running the model based on data as of September 2021,1 we find that the maximum amount
of D€ that does not interfere with a smooth implementation of monetary policy should not exceed
EUR 1.7 tn under the proposed approach. This figure is not intended to be used to infer the individual
holding limits (i.e. by dividing the aggregate figure by the number of potential D€ holders). Rather,
it represents the largest possible amount of D€ in circulation that – based on available reserves and
additional central bank credit – would preserve a smooth MPI. The estimated figure should thus be
interpreted with caution for two main reasons. First, the framework for the estimation of the maximum
amount focuses solely on the implications for MPI and may contribute only from this perspective to
the broader assessment on the methodology for the calibration of individual holding limits. This
calibration requires a more comprehensive monetary and economic evaluation, including
considerations on the potential impact of digital payment solutions on banknote demand, as well as
the effects of the D€ on the monetary policy transmission and financial stability. Second, the figure
is sensitive to the choice of key parameters, which are designed to illustrate an extreme scenario
where, for instance, we assume that banks utilize all their excess liquidity and unencumbered eligible
collateral to meet D€ demand.2
Our findings bring about a relevant policy implication: taking into account heterogeneity
across credit institutions and jurisdictions in the EA is crucial when calibrating the Eurosystem
response to a liquidity outflow like the one that would occur if the D€ was issued (Assenmacher and
Smets 2024). From an MPI perspective, the most suitable instrument for addressing such
heterogeneity are properly designed demand-driven reserve providing operations. Also the 2024
Authors’ last available data.
The figure of our extreme scenario is consistent with the preliminary analysis made by the ECB and reported by F. Panetta at the
European Parliament in 2022. Accordingly, the D€ in circulation should not exceed EUR 1.5 tn to avoid negative effects for the
financial system and monetary policy (Panetta 2022).
review of the Eurosystem operational framework (ECB 2024; Schnabel 2024) confirmed the key role
of elastic reserve provision through credit operations with a broad collateral framework.
Related literature. Our contribution to the existing literature is twofold. First, we add to the
relatively less explored literature on the implications of CBDC from the point of view of the
implementation of the monetary policy. As also shown in Infante et al. (2022), most academic
literature has focused on the macroeconomic impacts of a CBDC introduction; few studies have
examined the interaction between the CBDC and monetary policy implementation. Among these,
many papers investigated, from different angles, the mechanics of bank deposit conversion to CBDC
on the central bank and the banking sector’s balance sheets (Adalid et al. 2022, Auer et al. 2024,
Malloy et al. 2022, Caccia et al. 2024). Abad et al. (2023) further investigated the macroeconomic
effects of reserve regime switches associated to increasing levels of CBDC demand. To the best of
our knowledge, our paper represents the first attempt to inform the calibration of D€ holding limits
based on a possible definition of smooth MPI. Second, we contribute to the strand of literature that
proposes quantitative models for the calibration of D€ holding limit. We align with Meller and Soons
(2023) in basing our analysis on granular data of individual banks, but we adopt a different
perspective. Meller and Soons (2023) simulates how banks’ funding structure might respond to
different retail deposit outflows based on a constrained optimisation model aimed at maximizing
banks’ profit. Differently, our approach is rooted in the central bank’s perspective and quantifies the
maximum amount of D€ that minimizes the risks for a smooth implementation of monetary policy,
duly accounting for the heterogeneity across EA countries and banks.
Structure of the paper. Section 2 introduces the conceptual framework. Section 3 describes
the underlying data. Section 4 illustrates the model. Section 5 presents the results of the model’s
application for an illustrative scenario and a sensitivity analysis. Section 6 provides preliminary
qualitative considerations on the impact of D€ issuance on the money market. Finally, Section 7
concludes the paper.
2. Conceptual framework
Since its inception, the operational framework of the Eurosystem has been grounded on a broad
counterparty framework and a broad collateral framework3 designed to ensure uniform access to
The counterparty framework establishes criteria that allow a broad range of credit institutions, mainly banks, to participate in
Eurosystem monetary policy operations, while safeguarding the Eurosystem from the risk of a counterparty defaulting. Complementing
this, the Eurosystem collateral framework regulates the collateralisation of Eurosystem credit operations, providing an additional layer
of protection against counterparty default (ECB 2024b).
central bank reserves through open market operations to banks operating in different jurisdictions
(Bindseil et al. 2017, Cœuré 2016). In the EA bank-based economy, these elements were fundamental
for the smooth implementation of monetary policy, while also safeguarding the proper functioning of
the money market.
Accordingly, in our methodological framework we introduce three conditions for monetary
policy to be smoothly implemented. First, the aggregate amount of reserves should be consistent with
the so called Floor Required Excess Liquidity (FREL), that is the minimum amount of liquidity that
keeps money market rates anchored to the deposit facility rate4 under an ample reserve regime
(aggregate condition).5 Second, the provision of central bank reserves should limit the share of banks
in liquidity distress in all EA national banking sectors (local condition). Third, money markets
distribute reserves throughout the banking system without impairments (market condition). In this
paper, we propose a model for the quantitative assessment of the maximum amount of D€ that is
consistent with the aggregate and local conditions; as for the consistency of the D€ amount with the
market condition, we provide only qualitative considerations.
From a liquidity perspective, the issuance of one unit of D€ has the same implications as the
issuance of one additional unit of banknotes, as they are both autonomous factors. Thus, in case the
D€ demand replaces banknote demand, no changes are expected in aggregate and individual liquidity
conditions. By contrast, if the D€ demand replaces commercial bank money, reserves are drained
from the banking sector.6 Specifically, credit institutions would observe, as a first round effect, a
reduction of their deposit base. For the purpose of this work, we define the deposit base as the amount
of sight commercial bank deposits held by households and non-financial corporations. The choice of
considering commercial banks’ euro-denominated sight deposits as the variable at risk in the proposed
framework is justified by the need to focus on a form of money that could potentially be converted,
any time by end users on demand, for central bank money. Furthermore, sight deposits are in principle
used as a means of payment – representing the closest substitute for a D€ designed for this purpose –
in contrast to deposits with pre-set maturity, which might be intended as store of value instruments.
In a subsequent step, banks have to provide their customers with the requested amount of D€ and –as
is the case with banknote demand – this results in a reduction of reserves in circulation. Indeed, to
The deposit facility rate is the rate paid by the Eurosystem to reserves held overnight by banks at the deposit facility. The Governing
Council of the ECB decided in March 2024 to continue to steer the monetary policy stance in the vicinity of this rate.
5 A floor system is an operating framework in which the control of short-term interest rates is ensured by supplying ample reserves and
paying interest on those reserves at a policy rate that, in the Eurosystem, is represented by the deposit facility rate. The concept of
FREL refers to the optimal level of reserves needed to implement a floor system and for the EA was first introduced by P. Aberg et al.
(2021); it is typically expressed as a percentage of the banking sector’s total assets.
6 Namely, we assume that one euro of D€ substitutes with one euro of commercial banks sight deposits.
accommodate the D€ demand, banks have to exchange reserves with the central bank. We consider
that this might occur through two channels. Firstly, by using their excess liquidity (EL) that is the
amount of reserves held in excess of the minimum reserve requirement. Secondly, by using their
additional funding capacity (AFC), that is, the amount of additional central bank reserves they can
borrow from the central bank, provided that they have eligible unencumbered collateral. We assume
that the central bank is willing to satisfy any additional demand for reserves due to the D€ introduction
through credit operations, while we exclude the possibility that banks can obtain reserves to react to
the D€ via monetary policy asset purchases.7 If banks accommodate the D€ demand entirely with EL,
we would observe a reduction in the size of their balance sheet (by the same amount as the decrease
in the deposit base);8 if banks accommodate the D€ demand with AFC, then a re-composition in
banks’ liabilities would occur (i.e. the central bank funding increases to compensate for the decrease
in the deposit base).9
In the end, the use of EL and AFC to accommodate the D€ demand leads to changes in the
aggregate and individual liquidity positions, with potential consequences for MPI. Hence we assess
what is the maximum amount of D€ that ensures that both the aggregate and local conditions for a
smooth MPI are met.10
3. The data
This analysis uses bank level information gathered from multiple Eurosystem proprietary
databases and related to banks’ balance sheets, banks’ liquidity position, banks’ holdings of eligible
unencumbered marketable and non-marketable assets.11 The sample consists of 1,207 EA credit
institutions. The reference date for the input variables is 30 September 2021, except for banks’
liquidity position which is expressed as an average value over September 2021.
While we recognize that, at an aggregate level, outright purchases do have a relevant role in keeping the amount of reserves close to
the level considered coherent with a floor system and, to a certain extent, may allow to counteract the decline of reserves determined
by the issuance of a D€, it cannot be ensured that reserves provided in this way directly and immediately reach the credit institutions
most affected by the D€ shock (Schnabel 2024). For this reason, we exclude this possibility from the methodology proposed in this
paper.
We assume that banks adapt their balance sheet on the asset side via a reduction in EL; other deleveraging measures are not considered.
We also neglect the marginal reduction in the minimum reserve requirements associated with the deposit outflow.
We do not envisage the possibility that banks increase their market funding to react to the deposit base decline. Also, intragroup flows
are excluded.
This proposed framework focuses only on the instant reactions of the Eurosystem and the banking system to the D€ issuance under
an ample reserve environment. Thus, we do not assess the case of a gradual adoption of the D€ and the potential adjustments that banks
could put in place in a world with a D€ (e.g. if they adapt their funding model).
11 The datasets are collected by the ECB and are accessible to the NCBs upon request.
The first dataset consists of the individual bank balance sheet items (IBSI) statistics. In our
framework we define the deposit base as the variable at risk, that is the amount of banks’ eurodenominated sight deposits held by households and non-financial corporations; the deposit base is
equal to EUR 6.4 tn in our sample. We also rely on IBSI statistics to allocate banks into dimensional
and funding model clusters. By using a parametric approach, banks are labelled as “large” if their
total assets are above EUR 30 bn, “small” if their total assets are below EUR 5 bn and “medium”
otherwise. In our sample the majority of banks are small (731, 60% of the sample), followed by
medium-sized (346, 29%) and large (130, 11%). By applying a k-means clustering algorithm on a set
of four balance sheet variables,12 we also group banks in three funding model clusters. First, the retailfunded banks are characterised on average by a high reliance (around 70% of their total main
liabilities) on deposits from households and non-financial corporations and a low share of wholesale
deposits (5%), interbank funding (13%) and debt securities issuance (1%). This is the largest group
in our sample (74%), with 895 banks. Second, interbank-funded banks have a higher share of
interbank borrowing (66% of total main liabilities) and a lower share of retail and wholesale deposits
(9% each) and debt issuance (6%); 127 banks fall within this cluster (11% of the sample). Third,
banks with mixed-funding are characterized by a well-diversified funding structure: retail deposits,
wholesale deposits, interbank funding and market funding represent around 20% of total main
liabilities each. This group includes 185 banks (15% of the sample).13
The second dataset consists of banks’ liquidity position vis-à-vis the Eurosystem, based on the
ECB’s Market Operations Database. Total excess liquidity amounts to EUR 3.3 tn in our sample and
is heterogeneously distributed across countries. In absolute terms, it is concentrated in the largest EA
jurisdictions; in relative terms, i.e. when taking into account the size of the respective banking
systems, smaller countries hold a higher share of EL. Banks with a mixed funding structure tend to
hold more EL due to the Eurosystem asset purchase programmes, as they often hold the deposits of
wholesale clients who are the ultimate sellers of assets. By contrast, interbank and retail funded
institutions have negligible shares of wholesale deposits in their liabilities and tend to hold lower
shares of EL.
The third dataset relates to banks’ holdings of unencumbered eligible marketable assets (UEMA),
estimated from the Securities Holdings Statistics Groups (SHS-G) database. For banks reporting in
We use the following balance sheet characteristics as input variables for the statistical clustering: i) the ratio of household and nonfinancial corporation deposits to total main liabilities; ii) the ratio of total debt securities issued to total main liabilities; iii) the ratio of
non-monetary financial institutions deposits to total main liabilities; iv) the ratio of monetary financial institutions deposits to total
main liabilities. Balance sheet items not included in the clustering are the amount of deposits with central government, the amount of
deposit not denominated in euro and capital and reserves.
The detailed allocation of banks among size groups and funding models is shown in Annex.
the SHS-G, we consider their holdings of securities eligible for monetary policy operations and select
those reported either as not encumbered in market transactions or pledged at the Eurosystem. We then
compute the collateral value after haircuts of these holdings, by reducing their market value by the
valuation haircuts in place at the time of the model estimation, increased by a fixed factor of 20%, to
neutralise the effect of the temporary increase in the Eurosystem risk tolerance level related to the
pandemic crisis.14 We also account for additional haircuts in the case of own-used securities. For
those banks that do not report in SHS-G,15 we proxy their UEMA variable by applying the same share
over total assets held by their peers. In our sample, we estimate that banks hold an aggregate amount
of UEMA equal to EUR 1.1 tn, that would potentially allow them to increase their collateral pools by
The fourth dataset consists of banks’ unencumbered eligible non-marketable assets (UENMA),
estimated from the Anacredit database. We consider only credit claims eligible under the ordinary
Eurosystem collateral framework and apply conservative average haircuts calculated at the
bank/country level where all necessary information about the assets is not available. We net the
amount of UENMA by the value of credit claims already pledged at the Eurosystem. The total amount
of UENMA for the whole sample is equal to EUR 0.7 tn.
4. The model
Based on the described conceptual framework we build a novel quantitative approach for
estimating the maximum amount of D€ that does not impair a smooth MPI.
Let ? ? ? be the generic EA bank belonging to our sample. For each ? ? ?, the D€ introduction
determines a percentage reduction ?? of its deposit base ???? such that
D€ = ?
???
?? ???? .
In order to react to the deposit base decline induced by the introduction of the D€, each bank ? ?
? may use first a share ? of its excess liquidity ??? (if any), and then (if still needed) a share ? of its
additional funding capacity ????, i.e. the amount of additional reserves the bank ? ? ? can borrow
from the central bank based on its eligible unencumbered collateral. If the sum of reserves resulting
from these two contributions is not sufficient to accommodate the D€ demand, the bank is in a
See the ECB press releases of 7 April 2020 and 22 April 2020 for the package of collateral easing measures.
They represent 24% of the sample by total assets and 19% by excess liquidity.
situation of liquidity distress. With the aim to identify such banks, for each bank ? ? ? and any share
?? of the deposit base, we define the Attention Index ??? (?? ) as in
??? (?? ) = {
???? + ?????
?? ????
otherwise
The Attention Index is 1 for banks resulting in liquidity distress after the D€ introduction and it is 0
for all the other cases.
In order to assess the consequences on the MPI of the issuance of the D€, we define the aggregate
condition and the local condition.
According to the aggregate condition, whatever the impact of ?? across banks is, the amount of
EL, following the liquidity drain, must be equal at least to the FREL, as defined below
???
?? ?{??? ? ?? ???? ; 0} ? ????.
aggregate
condition
To account for the local dimension for a smooth MPI, we group EA banks into categories
characterized by common features. For instance, the same funding model, size or jurisdiction. Let ?
denote the set of all categories and let ? ? ? be a particular category; e.g. if banks are grouped on
the basis of EA jurisdictions, then ? = {??, ??, ??, …, ??, ??}. According to the local condition
and for each category ? ? ?, the sum of total assets of banks in liquidity distress (those with Attention
Index equal to 1) should represent no more than a given share ? of the total assets of all banks in that
category. More in detail
???? ??? ???? (?? )
???? ???
? ?.
local condition for
each ? ? ?
The choice of ? is exogenous and it depends on the extent to which the central bank deems
acceptable, from an MPI perspective, that banks belonging to a given category cannot accommodate
the D€ demand via EL and AFC. For instance, ? = 7% means that, according to the central bank’s
assessment, banks representing no more than 7% of total assets in a category (e.g. in a jurisdiction)
can fall short of EL and AFC in response to the D€ introduction, without impairing a smooth MPI.16
In the reminder of the paper we first present the results of an illustrative scenario for a selected value of ? and then we run a sensitivity
analysis for different values of ?.
To determine the maximum amount of D€, that can be issued by the Eurosystem under both the
local and the aggregate conditions, for each category ? ? ? and any share ?, we define the critical
threshold ??? (?) as the maximum deposit decline, share ? of deposit base, that satisfies the local
condition
??? (?) = ??? {? such that
???? ??? ? ??? (?)
? ?} .
???? ???
Then we compute the minimum threshold among all the categories
??(?) = ?????? ??? (?).
Calibrating the critical threshold to the minimum value observed across all EA countries (i.e.
at the level of the most exposed jurisdiction) is intended to minimize negative implications for a
smooth MPI.
Assuming that the deposit base decline affects uniformly all EA banks (and thus all categories),
the maximum amount of D€ for each cluster that is consistent with the smooth MPI is
????€(?) = ?? (?) ?
???
????
where the choice of ? is also constrained by the aggregate condition (3).
The maximum amount of D€ computed with Equation (7) is prudently calibrated based on the
category facing the most severe difficulties in addressing the liquidity drain from the D€
introduction.17 Such an amount reflects the central bank maximum response to the D€ shock in terms
of existing reserves and additional reserves provision via credit operations.
5. The results
5.1. The maximum amount of D€ for a smooth MPI: an illustrative scenario
This section presents the results of the model in an illustrative scenario where the key parameters
are defined as follows: (a) for the Attention Index, we consider that banks use their EL and AFC up
A more realistic case would consider that the D€ shock is distributed proportionally among banks within each category but not across
the categories, reflecting differentiated D€ demand across them. When the category under consideration is the jurisdiction, this case
aligns with the liquidity management process of national central banks for banknote forecasts (European Parliament, 2017). In such a
case, the maximum amount of D€, consistent with the smooth MPI conditions, would be calculated by applying each country’s specific
critical threshold in Equation (5) to the sight deposit aggregate of the national banking sector and then summing up across all
jurisdictions.
to the maximum possible level (? = 1, ? = 1); (b) for the aggregate condition, we assume that the
FREL is equal to EUR 1.5 tn, which corresponds to EUR 1.1 tn in our sample; (c) for the local
condition, we assume that the set of categories ? is represented by EA jurisdictions and that (d) the
share (in terms of total assets) of banks in liquidity distress (i.e. with Attention Index equal to 1) is
no more than ? = 7% in the jurisdiction most negatively affected by the D€ introduction. Further
insights on the rationale behind the choice of the key parameters are provided in Section 5.2.
To compute the maximum amount of D€ consistent with a smooth MPI, we estimate the share of
deposit base reduction following the issuance of the D€ that is coherent with the aggregate and local
conditions.
Figure 1: Relationship between the deposit base, EL and AFC by funding model (a) and size (b)
Sources: Eurosystem databases, own calculations. Data as of September 2021.
Note: The charts show for the sampled banks (points) the amount of EL and AFC over total assets (y-axis) in relation to the amount
of deposit base over total assets (x-axis).
Starting with the aggregate condition, we find that for a deposit outflow equal to ?? = 65% for
each bank ? in the EA, the EL that remains available to banks in the sample is equal to the FREL
(Equation 3), which implies that the central bank should be able to steer short-term rates at the
intended policy rate. Nevertheless, if we investigate the individual bank responses, we find that as
many as two thirds of banks – 816 over 1,207, representing 30% of the EA total assets – show an
Attention Index equal to 1 (Equation 2), i.e. these banks would not be able to accommodate the D€
demand with central bank reserves (EL and AFC). Among them, small retail banks are the majority,
given their higher exposure to sight deposits and lower share of EL in their balance sheet (Figure 1).
Their limited reliance on wholesale and market funding suggests that small retail banks may not have
a wide market access and, thus, could find it difficult to adjust their funding mix via money and/or
bond markets. When the share of deposit outflow induced by the D€ introduction is equal to 65%,
also several large and medium-sized banks have the Attention Index equal to 1. These larger credit
institutions might be better equipped than small banks in adjusting their funding mix towards market
funding to replenish their lack of central bank reserves. However, the higher recourse to money
markets by larger players could lead to upward pressure on secured and unsecured rates, which might
be also exacerbated if a large amount of collateral has already been encumbered with the Eurosystem
(in response to the deposit outflows). Overall, even if the aggregate condition is met, the
implementation of monetary policy could still be impaired.
This first evidence supports our idea that controlling only for the FREL does not suffice to ensure
a smooth MPI in the EA.
Figure 2: Share of deposit base decline – critical Figure 3: Deposit base and available resources
thresholds ??? (?) – for which no more than ? = 7% over total assets, for selected EA countries
(by total assets) of banks in each selected EA
country (Ctry) fall short of available resources
Sources: Eurosystem databases, own calculations. Data as of September 2021.
Note: Figures exclude data for 4 EA jurisdictions due to the limited representativeness of these countries’ banking sectors (total assets
in the sample are below 70%), that would have led to biased critical thresholds.
Hence we control also for the local condition at country level and we compute the share of deposit
base decline – i.e. the critical thresholds ?? ? (?) as per Equation (5) – following the D€ introduction
for which no more than 7% of banks’ total assets in each jurisdiction fall short of EL and AFC. The
results are shown in Figure 2: we find high dispersion across countries’ critical thresholds, which
span from 100% to 22%. Specifically, in few countries where the size of the deposit base is lower
compared to the sum of EL and AFC, the critical threshold reaches high values (Ctry1 100%, Ctry2
83%, Ctry3 77%, Ctry4 70%). Ctry1 represents the extreme case in this regard: a critical threshold
equal to 100% indicates that even if 100% of sight deposits were drained following the D€ shock,
less than 7% of this country’s banks (by total assets) would be in liquidity distress; this is because
nearly 80% of these banks hold an amount of available resources higher than their deposits base. For
the rest of the countries, instead, the opposite is true and the level of the critical threshold depends on
how the difference between the available resources and amount of sight deposits is distributed among
banks (Figure 3). In those countries where this difference is relatively larger for medium-sized banks,
the critical thresholds stand at lower values.
Following Equation (6), we identify the minimum critical threshold among all countries, that
in our illustrative scenario corresponds with ??(?) = 22% of Ctry15. When this level of D€ shock is
applied, almost only banks with a retail funding model experience a shortage of resources to
counteract the decline in sight deposits. They are characterized by a lower amount of EL and AFC
(9% over total assets) compared to their peers (18% on average for the whole group) and by a higher
reliance on sight deposit funding (56% versus 47%). On aggregate, banks in liquidity distress account
for 2.3% of EA total assets. According to the proposed approach outlined in Equation (7), we apply
the identified minimum critical threshold to the deposit base of each bank in our sample and we
ultimately estimate that for the whole EA a maximum amount of D€ equal to EUR 1.7 tn would be
consistent with a smooth MPI. Of this amount, 82% would be financed with EL, 16% with AFC; the
remaining 2% represents the share of D€ that banks with AI=1 are unable to offer with available
resources.18
5.2. Sensitivity analysis of the local condition ?
The results of the illustrative scenario are sensitive to the selection of the key parameters for the
Attention Index (? and ?), the aggregate condition (the level of FREL) and the local condition (the
category and the level of ?). The decision to fully use EL (? = 1) and AFC (? = 1) to calculate the
Attention Index aims at presenting a scenario with the largest possible central bank accommodation
of the deposit base decline following the D€ introduction through existing and additional reserves
provision via credit operations.19 For the aggregate condition, several estimates exist in the literature;
we aligned with Altavilla et al. 2023, computing the FREL as 4% of the EA banking sector total
assets. The choice of the category and level of ? depends on what the Eurosystem considers coherent
18The
results of our illustrative scenario are sensitive to the level of available resources and sight deposits at September 2021.
Nevertheless, it is likely that the model would gain similar results in a more recent data point, for the following reasons: (i) even if
reserves are less abundant, the aggregate condition is unlikely to become binding compared to the local condition, as it is still met for
a share of deposit base decline far higher than the one chosen when accounting for the local condition (65% vs 22%); (ii) past experience
in the EA shows that reserves injected through asset purchases accumulated in few banks and jurisdictions, resulting the richest of EL:
the gradual quantitative tightening has not changed this picture substantially; (iii) the runoff of TLTROs has a nearly neutral net effect
on the total amount of available resources, as most repayments were made with EL while determining a parallel increase in AFC; (iv)
sight deposits held by households and non-financial corporations slightly declined since September 2021, implying a reduction in the
variable at risk.
19 While the assumption that the individual banks would use all EL to respond to D€ demand may still reflect a physiological situation,
the assumption that a bank uses its entire AFC to request additional reserves represents an extreme scenario as, under ordinary
conditions, it is unlikely that a bank operates without any available collateral, other than the assets encumbered at the central bank.
with a smooth MPI and an orderly monetary policy transmission. For the category, we selected the
EA jurisdictions to take into account that a smooth MPI requires that banks operating in different
jurisdictions should uniformly access central bank reserves when needed (Cœuré 2016). For the level
of ?, instead, no specific stream of research provides guidance for its calibration. Thus we run a
sensitivity analysis to verify which level of ? could align with a smooth MPI.
In our framework, increasing values of ? imply that the Eurosystem deems acceptable, from an
MPI perspective, that a greater share of EA banks can fall short of EL and AFC in response to the D€
shock without impairments for a smooth MPI.
As illustrated by Auer et al. (2024) in their literature review, most of existing quantitative
exercises on the impact of CBDC on banks’ balance sheets reflects CBDC take-up scenarios for
values lower than 32% of aggregate banks’ sight deposits. In our model, this share is represented by
the critical threshold ??? (?) (Equation 6) and corresponds to ? = 26%. Thus, we run the sensitivity
analysis for values of ? ? 26% (Table 1).
Table 1: Sensitivity analysis of the local condition ?: associated critical thresholds, banks in
liquidity distress and maximum amount of D€
Local condition
share of banks in liquidity distress (by total
assets) in the most affected jurisdiction
Critical Threshold
?? ? ?
number
Banks in liquidity distress
??? ?? = 1
share of EA
total assets (%)
Amount of D€
????€(?)
EUR tn
Sources: Eurosystem databases, own calculations. Data as of September 2021.
Note: The table shows for each level of ? (representing the maximum share of banks in liquidity distress, by total assets, in each EA
jurisdiction): i) the critical threshold ??? (?) representing the share of deposit base decline that applies to each EA bank; ii) the
number of banks with AI=1 and their weight in terms of total assets over the whole EA banking sector; iii) the amount of D€ that
derives from the application of the critical threshold to the deposit base of each EA bank.
For ? = 26% nearly one third of banks (349 with Attention Index =1), representing 6.0% of EA
total assets, would lack sufficient resources to absorb the D€-induced deposit outflow. Banks in
liquidity distress belong to all three dimensional groups and are mainly retail-funded banks. With
declining values of ?, the number of banks that are not able to fully absorb the deposit outflow with
available resources declines substantially: for ? = 10% it halves to 162 and for ? = 5% it further
reduces to 65. Overall, the sensitivity analysis confirms that, independently from the ?, retail banks
are always the most exposed to the risks stemming from the D€ introduction while, for the other
funding models, only banks that exhibit a lower share of available resources and higher share of sight
deposits compared to their peers are hit. Moreover, when the D€ issuance leads to a sizable reduction
in the deposit base (i.e. more than 13%, corresponding to ? = 3%), retail institutions of larger size
start being under liquidity distress (Table 2).
Table 2: Sensitivity analysis of the local condition ?: banks with Attention Index = 1 by funding
model (a) and size (b)
Banks with Attention
Index = 1
Mixed
Interbank
Retail
Local condition ( z )
All banks
Number
Available resources over total assets
Sight deposits over total assets
Number
Available resources over total assets
Sight deposits over total assets
Number
Available resources over total assets
Sight deposits over total assets
All banks
Banks with Attention
Index = 1
Large
Local condition ( z )
Number
Available resources over total assets
Sight deposits over total assets
Number
Medium
Small
Available resources over total assets
Sight deposits over total assets
Number
Available resources over total assets
Sight deposits over total assets
Sources: Eurosystem databases, own calculations. Data as of September 2021.
Note: The tables show for each level of z the number of banks with Attention Index = 1 and their average share of available resources
(EL and AFC) and sight deposits over total assets, for (a) funding model and (b) size groups. On the right side of the tables, the
statistics of the three funding models and dimensional groups are reported for comparison.
To determine a non-arbitrary value of ? for our illustrative scenario (Section 5.1), we referenced
the March 2023 crisis of US regional banks. Despite this crisis was not caused by a structural change
akin the potential issuance of a D€, it exemplifies a situation in which a shock on banks’ sight deposits
triggered the central bank intervention with additional reserves provision. Specifically, in that crisis,
a massive and fast deposit outflow originated at Silicon Valley Bank (SVB) and, in the subsequent
days, it spread to Signature Bank and First Republic Bank. These three banks represented the 2.3%
of US banking sector’s total assets and their liquidity distress caused the Fed decision to launch the
Bank Term Funding Program (BTFP) “to make available additional funding to eligible depository
institutions in order to help assure banks have the ability to meet the needs of all their depositors”
(Fed, 2023).20
In this regard, we refer to the 2.3% share of the three US banks’ total assets in liquidity distress
over US banking sector’s total assets as a possible “aggregate” trigger point that could impair a
smooth MPI. To apply it to the EA context, we need to account for the EA multi-country nature and,
accordingly, identify the “country” trigger point – represented by the ? parameter in our framework
– that aligns with the chosen “aggregate” level. As shown in Table 1, the 2.3% “aggregate” trigger
point corresponds to a “country” trigger point ? equal to 7%. This means that banks in liquidity
distress across the entire EA represent 2.3% of total EA banking sector assets when, in each EA
country, banks in liquidity distress do not exceed 7% of the respective national banking sector’s total
assets.21 Based on this value of the ? parameter, the maximum amount of D€ consistent with a smooth
MPI should not be higher than EUR 1.7 tn, as shown in our illustrative scenario (Table 1).
6. Preliminary considerations on the EA money market and the Eurosystem footprint
In principle, individual banks could respond to the substitution of sight retail deposits with the
D€ in different ways, namely through i) the recourse to Eurosystem funding, ii) the recourse to market
funding, iii) the deleveraging of their balance sheet. Actions (i) and (ii) might have direct and indirect
effects on money markets, whose magnitude depends – among other things – on the substitution rate
between sight retail deposits and the D€, the cluster (in terms of size and funding model) of the
affected credit institutions and the monetary policy operational environment (aggregate liquidity
conditions, collateral and counterparty framework).
In this paper, we illustrate the case of higher recourse to central bank funding to assess the impact
on a smooth MPI. When this happens, the central bank balance sheet size increases and so does the
Eurosystem footprint in financial markets, with potential implications on market functioning. First,
repo markets are expected to be largely affected via two channels (BIS 2015): (i) the scarcity channel,
as a large amount of assets would be encumbered in credit operations; (ii) the structural channel, that
reflects the central bank decisions on which assets are accepted in its operations. The magnitude of
such effects on EA collateral markets depends on the size of Eurosystem operations and on the level
of scarcity of high quality and liquid assets. Second, the high engagement of the Eurosystem that
On March 9 2023, SVB recorded deposits withdrawals equal to 40% of its total assets; on March 10, deposit outflows reached 20%
and 17% of their total assets, respectively, for Signature Bank and First Republic Bank (NY State Department of Financial Services
2023; OIG 2023); on March 12, the Federal Reserve announced the launch of the Bank Term Funding programme (Fed 2023).
21 Moreover, the Advisor Scientific Committees report (ESRB 2024) shows that a large part of the EU banking system would not be
able to cope with runs like those at Silicon Valley Bank or First Republic with available liquidity (a share of 10% of banks in terms of
total assets in the EU banking system would have been in liquidity distress).
stems from the introduction of a D€ automatically reduces leeway for MPI in upcoming shocks or
crises (in the extreme case where D€ only replaces deposits). It is key to have both in mind when
investigating the impacts of the D€ introduction and calibrating its amount in circulation as well as
the expected benefits and costs of an increased financial market footprint.
Individual banks might alternatively, or in addition, decide to increase their market funding via
secured or unsecured money markets. In such a case, the impact of introducing the D€ on the EA
money market – in terms of volumes and prices – would be highly uncertain and depend on multiple
factors. Among the most relevant ones, the amount of excess reserves in the system, the willingness
of banks to redistribute reserves in the market also considering the regulatory constraints, the role of
non-bank financial institutions as liquidity providers for the banking sector. If the ECB decides to
issue a D€, these aspects deserve to be assessed through an in-depth analysis.
7. Conclusions
We propose a methodological framework for the estimation of the maximum amount of D€ that
is consistent with a smooth MPI in the EA. We focus on the substitution of sight retail deposits with
the D€ and estimate the leeway that EA banks have on their balance sheets to finance the D€, via a
reduction of their EL and/or via a larger recourse to central bank funding. We consider that monetary
policy is implemented smoothly if two conditions are verified: i) the EA aggregate liquidity is at least
equal to the FREL and ii) a non-negligible share of banks in each country has enough reserves to
accommodate the D€ demand. We find that accounting for this second condition is particularly
binding in the EA banking sector, where heterogeneity is a relevant fact and liquidity is unevenly
distributed across banks and countries. Indeed, if the maximum amount of D€ is calibrated based
solely on the FREL, a large number of banks would lack the resources to finance the demand of D€
with EL and additional Eurosystem credit. By accounting for the cross-country dimension, we
estimate that the maximum amount of D€ should not exceed EUR 1.7 tn to prevent any EA national
banking sector from facing a too severe distress following the D€ introduction. This amount
represents the largest possible amount of D€ in circulation beyond which a smooth MPI might be
challenged and it is not intended to be used to infer the individual holding limits. Instead, our
methodological framework aims to contribute to the broader assessment on the methodology for the
calibration of individual holding limits, from the MPI perspective.
Our findings have one main policy implication. The heterogeneity across credit institutions and,
consequently, across countries is crucial to properly assess the Eurosystem response to a liquidity
drain like the one occurring if the D€ is issued and substitutes with banks’ sight deposits. In this
regard, the importance of refinancing operations with a broad collateral framework emerges, due to
their key role in allowing the Eurosystem to elastically withstand additional reserve demand
stemming from the introduction of the D€. These operational framework design features are
preconditions aimed at ensuring that no categories of banks or jurisdictions are left behind. Their role
in preserving adequate access to central bank reserves for all banks across the EA was confirmed by
the 2024 review of the Eurosystem’s operational framework (ECB 2024; Schnabel 2024).
An interesting avenue for future research would be to account for bank characteristics when
studying the implications of the D€ introduction. The assumption of the uniform distribution of the
D€-induced deposit outflow among banks could be relaxed and other inputs might be included in the
methodological framework (e.g. users’ payments attitudes, users’ age). Furthermore, the role of
interbank and intra-group liquidity and funding flows might also be included in the assessment of
banks’ leeway.
Acknowledgments
The views expressed in this paper are those of the authors and do not necessarily reflect those of
Banca d’Italia or the Eurosystem; they also do not relate with the ECB workstream on a calibration
methodology to define the holding limits neither with the legislative discussions with European colegislators on the D€. All remaining errors are ours.
The authors would like to thank members of the Eurosystem Market Operations Committee
Task Force on CBDC, Hendrik Becker, Bernhard Lange, Paola Antilici de martini, Marco
Luca, Madalena Borges, Eleni Argiri, Ifigeneia Skotida, Enea Caccia, Ivan Almer, Béatrice
Brueckner, Samuel Bibier, Ludovic Jequier, Laura Monks, Brian Molloy and Jens Tapking,
for their contributions to the work. The authors are also indebted to Gioia Cellai, Alberto
Locarno, Marco Nicolosi, Stefano Siviero and one anonymous referee for valuable comments and
suggestions.
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