When margins call : liquidity preparedness of non-bank financial institutions.
Date: 2025-07-04
Institutions: Cappiello, L, European Central Bank, Giuzio, M, Ianiro, A, Lillo, F, Macchiati, V
Subjects: financial derivative, financial institution, financial market, financial risk, financial stability, insurance company, investment company, money-market liquidity, pension fund, report

ECB-PUBLIC 

Working Paper Series 

V. Macchiati, L. Cappiello,
M. Giuzio, A. Ianiro,
F. Lillo

When margins call: liquidity 
preparedness of non-bank financial 
institutions 

No 3074 

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

Abstract

We propose a novel framework to assess systemic risk stemming from the inadequate

liquidity preparedness of non-bank financial institutions (NBFIs) to derivative margin calls.

Unlike banks, NBFIs may struggle to source liquidity and meet margin calls during periods

of significant asset price fluctuations, potentially triggering asset fire sales and amplifying

market volatility. We develop a set of indicators and statistical methods to assess liquidity

preparedness and examine risk transmission through common asset holdings and counterparty

exposures. Applying our framework to euro area NBFIs during the Covid-19 turmoil and

the 2022–2023 monetary tightening, we observe an increase in distressed entities, which, in

turn, seem to exhibit more liquidity-driven selling behaviours than their non-distressed peers.

Network analysis suggests that certain counterparties of distressed entities appear particularly

vulnerable to margin call-induced liquidity shocks. Our framework offers policymakers

valuable tools to enhance the monitoring and resilience of the NBFI sector.

JEL codes: C02, E52, G01, G11, G23

Keywords: Non-bank Financial Institutions, Derivative margin calls, Liquidity risk, Financial

stability, Network analysis

ECB Working Paper Series No 30741Non-technical summary

Recent financial market disruptions, such as the turmoil in March 2020, the collapse of Archegos

in 2021, and the volatility in commodities and UK pension funds in 2022, have highlighted a

critical challenge for financial stability: the ability of non-bank financial institutions (NBFIs) to

meet liquidity demands during periods of market stress. This paper proposes a novel framework

to monitor the risks associated with margin calls in the derivatives market, focusing on NBFIs,

and to assess their broader implications for financial stability.

Margin calls require financial institutions to provide cash or eligible assets to cover potential

losses on derivative positions. These requirements were strengthened after the global financial

crisis to reduce counterparty credit risk. At the same time, they have also introduced a potential

pro-cyclical source of vulnerability, particularly for NBFIs, which often do not have the same

liquidity buffers as banks during market distress. When market volatility increases sharply,

margin calls rise, creating sudden and significant liquidity needs. If NBFIs struggle to meet these

calls, they may be forced to sell assets quickly, potentially worsening market instability. Also, if

they fail to meet their margin obligations, this stress can spillover to banks and other financial

entities that act as clearing members or trading counterparties. Institutions like the Financial

Stability Board (FSB), the European Central Bank (ECB), and the Bank of England (BoE) have

emphasised the need for improved liquidity preparedness.

First, we develop a set of entity-level indicators and analytical techniques to identify NBFIs

facing liquidity stress due to large margin calls relative to their liquid asset holdings. Once

distressed entities are identified, we assess the broader systemic implications of liquidity shortfalls

using two complementary approaches. The first examines portfolio overlap between distressed and

non-distressed NBFIs to determine which asset classes are most susceptible to margin call-driven

fire sales. The second employs network analysis to estimate the spillover effects of these fire sales

on banks and other counterparties.

In this context, we developed a new analytical framework for assessing the liquidity pre-

paredness of NBFIs, mapping the transmission channels, and identifying vulnerable entities.

First, we develop a set of entity-level indicators and statistical methods to identify NBFIs facing

liquidity stress due to large margin calls relative to their liquid asset holdings. Once distressed

entities are identified, we assess the broader systemic implications of liquidity shortfalls using

two complementary approaches. The first examines portfolio overlap between distressed and

ECB Working Paper Series No 30742non-distressed NBFIs to identify which asset classes are most likely to be liquidated in margin

call-driven fire sales. The second employs network analysis to estimate the spillover effects of

NBFI fire sales on banks and other counterparties.

We apply our tools to two case studies using data from the euro area during the Covid-19

market turmoil in 2020 and the monetary policy tightening in 2022-2023. Our findings indicate

that liquidity stress among NBFIs spiked significantly in both periods, and that the selling

strategies of distressed NBFIs were less proportional to their portfolio composition than those of

other, non-distressed entities. During the Covid-19 turmoil, both distressed and non-distressed

funds primarily sold equities, although they differed in terms of issuer country and industry

sector. In contrast, during the monetary policy tightening period, government bonds were more

frequently among the top five asset classes sold by liquidity distressed funds. Furthermore,

while most counterparties had limited exposure to distressed NBFIs, a few exhibited higher

vulnerability, raising concerns about potential liquidity shortages and systemic risks.

Our study contributes to the growing research on monitoring liquidity risk in NBFIs. While

traditional stress-testing frameworks have expanded beyond banks to include investment funds and

insurance firms, they still lack comprehensive integration of NBFIs. As NBFIs have significantly

increased their role in financing the real economy, incorporating their liquidity risks into stress-

testing methodologies is crucial. Our framework provides a practical tool to identify liquidity

stress transmission channels and assess potential contagion effects, complementing existing

supervisory approaches such as system-wide stress tests conducted by prudential authorities.

Building on recent research on liquidity preparedness in NBFIs, our study employs network

analysis to examine how liquidity stress spreads in the derivatives market, particularly from

investment funds to other financial institutions. Given the interconnectedness of NBFIs within

the broader financial system, understanding these transmission mechanisms is essential for

designing effective macroprudential policies. Our framework can also be used to evaluate policy

measures aimed at enhancing NBFI resilience, such as mandating higher liquidity buffers and

diversifying funding sources. By systematically analysing these policy tools, our findings offer

valuable insights for regulators seeking to mitigate systemic risks associated with NBFI liquidity

shocks.

ECB Working Paper Series No 307431 Introduction

In recent years, several episodes of liquidity stress in financial markets – such as the March 2020

market turmoil, the Archegos failure in March 2021, the commodities markets turmoil in 2022,

and the September 2022 issues experienced by many pooled liability-driven investment funds in

the UK – have highlighted the need to monitor the liquidity preparedness of non-bank financial

institutions (NBFIs) to meet margin calls in the derivatives market [1, 2, 3, 4]. During these

episodes, financial market volatility sharply increased, triggering large spikes in margin calls on

derivative exposures and imposing significant liquidity pressures on market participants that

have to exchange daily variation and initial margins related to their derivatives positions.

The requirement to exchange margins in the form of cash or eligible assets on a daily basis

was implemented as part of the over-the-counter (OTC) derivative reform following the global

financial crisis, aiming to mitigate counterparty credit risk. However, it has also increased market

participants’ vulnerability to liquidity stress, due to the pro-cyclical nature of margins in response

to sharp price fluctuations. In [5], the authors show that, while margin requirements are designed

to reduce risk, they can increase market volatility, make margin constrained investors better

off, and lead to greater return reversals. While both banks and non-bank financial institutions

can face liquidity shortfalls under stress - bank runs and fire sales being classic examples in the

banking sector [6] - the measures available to them to manage such stress differ. Banks have more

stable access to central bank liquidity facilities and regulatory liquidity buffers, which can help

them absorb temporary funding shocks. By contrast, NBFIs lack comparable backstops and face

greater difficulty in securing cash or high-quality liquid assets (HQLA) in times of market turmoil.

This has important financial stability implications. A “dash for cash”1 driven by widespread

margin calls can lead to asset fire sales or a rapid unwinding of derivative exposures, which might

further fuel already high price volatility and lead to disorderly market functioning. In the event

of extraordinarily large market movements, the failure of NBFIs to meet margin obligations

can spread to other market participants, such as banks acting as clearing members of central

counterparties or dealers in the bilaterally cleared OTC space, potentially transforming liquidity

stress at the individual entity level into a system-wide issue. It can also increase step-in risk,

1The term “dash for cash” refers to a sudden widespread demand by financial institutions and investors of
liquid assets, typically cash or cash-equivalents, in response to severe market stress that increases liquidity needs,
e.g., during the Covid-19 market turmoil.

ECB Working Paper Series No 30744transferring the pressure on the banking sector2 and spreading contagion through the financial

system, given the interconnectedness between banks and NBFIs [7].

For this reason, strengthening the NBFI sector’s resilience to liquidity shocks by enhancing

and monitoring NBFI liquidity preparedness is a key priority of the international NBFI policy

agenda [8]. According to the Financial Stability Board (FSB) [9], one of the “key amplifiers”

of financial contagion is the “unexpectedly large margin calls for derivatives and securities

trades.” In the Financial Stability Review (May 2025) [10] the ECB states that “an adequate

policy response should focus on addressing key structural vulnerabilities in the NBFI sector,

including monitoring and tackling risks arising from non-bank leverage, enhancing the liquidity

preparedness of non-bank market participants to meet margin and collateral calls, and mitigating

liquidity mismatch in the investment fund sector.” Also, the Bank of England (BoE) in its

Financial Stability Review [11, 12] argued that vulnerabilities related to margin calls are among

the key vulnerabilities in market-based finance.

This paper introduces a novel analytical framework to assess systemic risk stemming from

the inadequate liquidity preparedness3 of NBFIs to derivative margin calls. We first propose

a set of entity-level indicators and statistical methods to identify NBFIs experiencing liquid-

ity stress due to unexpected or large margin calls relative to their liquid asset holdings. To

evaluate the broader systemic implications of such liquidity shortfalls, we then introduce two

additional statistical methods that capture potential contagion through common asset holdings

and counterparties’ exposures. The first tool examines portfolio overlap between distressed and

non-distressed NBFIs to identify which asset classes are most likely to be liquidated in margin

call-driven fire sales and whether selling strategies are proportional to portfolio composition. The

second employs network analysis to estimate counterparties’ exposure to distressed NBFIs, to

consider the potential risks of contagion and financial instability. Together, these components

provide a comprehensive liquidity risk framework, detecting distressed NBFIs, analysing asset-

selling behaviour by distinguishing between distressed and non-distressed entities, and identifying

2This risk transfer can happen if banks acting, e.g., as clearing members step-in proving liquidity to non-bank

clients.

3In this paper, the expression adequate liquidity preparedness refers to the ability of NBFIs to respond to sudden
margin and collateral calls under conditions of financial stress, and does not relate to the notion of efficiency in
risk-return optimisation, short-term profitability, or optimal resource allocation during periods of market stress.
The term preparedness is intentionally used to align with the terminology and framework adopted by the FSB’s
working group on liquidity preparedness for margin and collateral calls (see final report [8]).

ECB Working Paper Series No 30745counterparties most exposed to potential contagion arising from NBFIs.

We apply our liquidity framework to two case studies, using euro area data: the Covid-19

market turmoil and the 2022-2023 monetary policy tightening. The Covid-19 crisis serves as an

example of how an exogenous market shock can trigger extreme spikes in margin calls, straining

NBFI liquidity. In contrast, the monetary policy tightening driven by Central Banks’ rate hikes to

curb inflationary pressures illustrates how a more gradual and, to some extent, anticipated shift

in financing conditions can influence NBFI liquidity needs through their derivative exposures.

Our findings show that the fraction of funds experiencing liquidity stress significantly in-

creased from late February to late March 2020, at the height of the Covid-19 market turmoil,

and declined sharply only after the ECB’s Pandemic Emergency Purchase Programme (PEPP)

announcement. During the 2022-2023 monetary policy tightening, we measure statistically

significant increases in liquidity stress around certain key announcements and effective dates of

rate hikes. In addition, during the Covid-19 turmoil, both distressed and non-distressed funds

primarily sold equities, although they differed in terms of issuer country and industry sector. In

contrast, during the monetary policy tightening period, government bonds were more frequently

among the top five asset classes sold by liquidity distressed funds. In both periods, we observe

that the selling strategies of distressed NBFIs were less proportional to their portfolio composition

than those of non-distressed entities. Finally, we find that counterparties of distressed NBFIs

generally maintain a low exposure to them in terms of margin calls to be received. However, a

few counterparties appear more vulnerable to market shocks, posing potential risks of liquidity

shortages or broader financial instability.

This study contributes to the growing literature on monitoring indicators for liquidity risk

by proposing a novel framework to assess NBFI vulnerabilities to unexpected derivative margin

calls. Our liquidity framework complements existing supervisory tools, such as system-wide stress

tests regularly conducted by prudential authorities. While traditional stress-testing frameworks

have expanded beyond banks [13] to include investment funds [14, 15, 16] and insurance firms,

a comprehensive integration of NBFIs remains limited. Given that NBFIs have significantly

increased their role in financing the real economy in the euro area—e.g., investment funds’

assets more than doubled between 2009 and 2019, reaching e14 trillion by the end of the period

[14]—incorporating their liquidity risks into stress-testing methodologies is crucial. Our indicators

provide a practical tool to identify liquidity stress transmission channels and assess potential

ECB Working Paper Series No 30746contagion effects [15].

Methodologically, our study builds on recent research highlighting the importance of liquidity

preparedness in NBFIs, particularly concerning margin calls, leverage, and hedging strategies.

For instance, Alfaro et al. [17] introduce the concept of Liquidity After Solvency Hedging (LASH)

risk, showing how NBFI hedging activities can generate substantial liquidity demands in response

to interest rate fluctuations. Similarly, Bouveret et al. [18] develop a framework to assess the

vulnerabilities of leveraged investment funds under market stress, while Ianiro et al. [19] propose

a duration-based approach to measuring synthetic leverage in interest rate swaps. Analysing

Dutch regulatory data, Jansen et al. [20] find that liquidity risk is more pronounced for pension

funds with lower funding ratios, as they hold larger swap positions and lower cash holdings,

exposing them to the risk of substantial margin calls when interest rates rise. Expanding on

this literature [21, 22, 23, 24, 25], our study employs network analysis to examine how liquidity

contagion spreads in the derivative market, particularly from investment funds to other market

participants such as banks. Given the high interconnectedness of NBFIs within the broader

financial system, understanding these transmission mechanisms is crucial for designing effective

macroprudential policies.

This work contributes to the debate around policy measures aimed at enhancing NBFI

resilience to liquidity shocks, particularly in the context of international regulatory discussions [8].

Our methodological framework can be used to assess the effectiveness of new policy tools, such as

mandating adequate levels of liquid assets and diversifying liquidity and funding sources. Further-

more, it allows authorities to test the sufficiency of NBFI liquidity buffers under adverse scenarios

and evaluate the potential impact of policy interventions, including higher cash buffers and

broader holdings of high-quality liquid assets. By providing a systematic approach to analysing

these policy tools, our findings offer valuable insights for regulators designing macroprudential

measures to mitigate systemic risk in the sector.

The paper is organized as follows: Section 2 describes the data, Section 3 introduces the

liquidity indicators and methodological framework, Section 4 presents the case studies and

main findings, and Section 5 provides the final remarks and conclusions. The appendix reports

supplementary plots and tables, as well as a list of acronyms used in the paper.

ECB Working Paper Series No 307472 Data

This section presents the data required to build and apply our framework for assessing the

liquidity preparedness of NBFIs for derivative margin calls. For the sake of simplicity, in the

following, NBFIs will be referred to as funds.4 The framework combines information on daily

derivative margin calls, collected under the European Market Infrastructure Regulation (EMIR),

with data on funds’ holdings provided by LSEG Lipper IM Data. The securities held by funds are

classified as HQLA using the European System of Central Banks’ (ESCB) Centralised Securities

Database (CSDB). This classification reflects the standards established for banks under the Basel

Liquidity Coverage Ratio requirements [26] and follows the methodology proposed in [27]. EMIR

and Lipper data offer fund-level granularity, and are merged using the Legal Entity Identifier

(LEI) code of each fund [27]. CSDB data, instead, provides granularity at the security level,

where each security is identified by its International Securities Identification Number (ISIN).

2.1 EMIR derivatives data

EMIR dataset contains information on derivative contracts collected under the European Market

Infrastructure Regulation (EMIR)5 which came into force in 2012. EMIR is a European Union

(EU) regulation aimed at improving the transparency of OTC derivatives markets, mitigating

credit risk, and reducing operational risk. To achieve this, EMIR requires that OTC derivatives

meeting certain requirements5 are subject to the clearing obligation, and for all OTC derivatives

that are not centrally cleared, that risk mitigation techniques apply. In addition, all derivatives

transactions conducted by counterparties resident in the EU must be reported to Trade Reposito-

ries (TRs). The regulation applies to both financial intermediaries and non-financial companies,

provided their positions exceed the specified thresholds set by the regulation5. It operates on a

dual-reporting system, imposing reporting obligations on both counterparties. The European

Central Bank (ECB) has access to transaction data when at least one of the following conditions

holds: at least one counterparty is located in the euro area (EA), the reference entity of the

derivative contract is in the EA, or the reference obligation is sovereign debt from a EA country.

The EMIR data are pre-processed using the ECB’s data cleaning procedure [28] and the sector

4NBFIs comprise a range of entities, including but not limited to, investment funds, insurance companies, and

pension funds.

5Regulation (EU) No 648/2012 of the European Parliament and of the Council of 4 July 2012 on OTC
derivatives, central counterparties and trade repositories. OTC derivatives are subject to the clearing obligation
when they meet specific criteria, including certain thresholds for notional amounts and types of derivatives.

ECB Working Paper Series No 30748classification enrichment [29]. In addition, data deduplication is performed during pre-processing

to address issues of double reporting at the counterparty level.

EMIR data are reported at trade level and include information on the counterparties involved,

the type of derivative instruments, the underlying assets, and whether the transactions are

centrally cleared or not. In the centrally cleared markets, transactions are processed through

a central clearing counterparty (CCP), which serves as an intermediary between the parties of

the contract, ensuring the integrity and stability of trades. The CCP mitigates counterparty

risk by requiring collateral (margin) from both parties to cover potential losses. Usually, it

relies on clearing members, typically banks, that are responsible for managing their clients’

margin requirements and ensuring compliance with the CCP’s risk management standards. This

framework is designed to enhance system resilience and reduce systemic risk. Conversely, in the

non centrally cleared case, transactions are cleared bilaterally, directly between the counterparties.

Entities must post margins to open and maintain a derivative position. These margins are

collected in cash and eligible assets and ensure counterparties are covered against potential losses

to mitigate default risk. Under EMIR regulation, margin information is reported at the portfolio

level rather than the trade level. In this context, a portfolio refers to a set of trades between

two counterparties that share the same portfolio code. This implies that the margin, even when

reported in each trade of the portfolio, applies once to all of them.6 Moreover, margins must

be reported as stocks, reflecting the cumulative margin payments since the starting date of the

contract. There are two types of margins: initial and variation margins.

Initial margin (IM), as stated in [30], “is collected to cover potential changes in the value

of each participant’s position – the potential future exposure – over an appropriate closeout

period, in the event that a participant holding the position defaults.” It is divided into a “core”

component, which addresses market risk, and “add-ons” intended to mitigate other risks, such as

liquidity or concentration risk. Typically, initial margins can be posted in cash and non-cash

collateral, with non-cash collateral comprising highly liquid assets.

Variation margin (VM), instead, “represents funds that are collected to extinguish current

exposures resulting from changes in market prices” [30]. In derivatives markets, VM exchanges

commonly involve only cash payments. VM is computed by the change in market value of the

derivative position. This entails establishing a fair market price for each position, evaluating

6For example, assuming a portfolio has three trades and each trade reports a margin of 10, the margin of the

whole portfolio is 10.

ECB Working Paper Series No 30749whether a position incurred a loss or gained a profit, and subsequently posting or receiving VM

amounts to or from the counterparty. While VM payments typically occur at least once daily,

more frequent intra-day settlements are also possible.

Net margin flow

To assess the liquidity preparedness of non-bank financial institutions, we compute the daily net

margin flow that entities must post in cash or eligible assets. Given that margins are reported as

stock, we first determine the daily net stock between posted and received margins. We assume

that an entity can net the daily posted and received variation margins at the counterparty

level, but not the initial margin calls, since VMs are posted in cash. The daily net stock from

counterparty i to j is given by:

netstockij(t) = IMpstdij(t) + VMnetstockij(t)

(1)

where IMpstdij(t) is the stock of posted initial margin and VMnetstockij(t) = VMpstdij(t) −

VMrcvdij(t) is the net variation margin stock, i.e. the difference between the posted and received

variation margin stocks from entity i to j at the day t.

To obtain the margin that an entity i has to post each day to j, if any, we compute the daily

net flow7, i.e., the difference between the net stock position of two consecutive business days.

netflowij(t) = netstockij(t) − netstockij(t − 1)

Then, the total flow that entity i has to post during day t is given by:

Flowi(t) =

(cid:88)

j

netflowij(t)

(2)

(3)

Furthermore, we can categorise portfolios based on their underlying asset type α ∈ [INTR, EQUI,

CURR, CRDT, COMM, MIXT], i.e.

interest rate, equity, currency, credit, commodity, and

mixed assets, and whether they are centrally cleared, γ ∈ [CC, N CC].

i.e., centrally cleared

portfolio or not. Our focus is on pure portfolios, which are composed exclusively of derivative

7Net margin flows may, by design, experience distortions around contract closure, when the stock of related

variation margin posted is reset to zero, without a corresponding exchange of cash or HQLA. [31]

ECB Working Paper Series No 307410trades on the same asset type α. We thus have:

netstockα,γ

netflowα,γ

ij (t) = IMpstdα,γ
ij (t) = netstockα,γ
(cid:88)

Flowα,γ

i

(t) =

netflowα,γ

ij (t)

ij (t) + VMnetstockα,γ
ij (t) − netstockα,γ

ij (t − 1)

ij (t)

(4)

When we do not distinguish between cleared, not cleared portfolios, we refer to net stock and

j

flow margins as follows: netstockα
γ Flowα,γ

i (t) = (cid:80)

Flowα

(t).

i

ij(t) = (cid:80)

γ netstockα,γ

ij (t), netflowα

ij(t) = (cid:80)

γ netflowα,γ

ij (t) and

2.2 NBFIs portfolio holdings

We obtain the funds’ portfolio holdings data from LSEG Lipper IM.8 This is a commercial data

provider that aggregates data from different sources, including fund companies, regulatory filings,

and financial data providers. It also allows fund management firms to disclose data to ensure

that their holdings and performance are fully covered. Holdings are available for individual LEIs

associated with primary funds at the ISIN level and with a monthly frequency. Relevant variables

include the market value of holdings, the security type, funds’ attributes, such as the domicile

country (c), the asset universe (au), and asset type (at).9

We merge holdings’ data with securities characteristics using the ESCB Centralised Securi-

ties Database. This source includes information on individual securities, such as debt securities,

equities and mutual fund shares, that are either issued by EU residents, or held and transacted

by EU residents, or denominated in euros, irrespective of location of the issuer or the holder.

CSDB uses commercial data providers and other existing sources to select the most reliable value

for each attribute and enhance data quality, addressing possible data gaps.

We use CSDB to classify securities as HQLA.10 HQLA holdings are classified in three levels:

Level1 assets include qualifying government bonds; Level2A and Level2B include less liquid

assets such as qualifying covered bonds, corporate bonds, asset-backed securities, and stocks.

8https://www.lseg.com/en/data-analytics/financial-data/fund-data/lipper-fund-data
9Please note that α indicates the portfolio asset type reported in EMIR, while at the funds’ asset type reported

in LSEG Lipper IM.

10Less than 1% of the securities (ISINs) in the portfolios of our funds during the Covid-19 period could not be

classified as HQLA because the necessary data were only partially available in the CSDB.

ECB Working Paper Series No 307411Code
Sector
Name

Code
Sector
Name

10
Energy

15
Materials

20
Industrials

25
Consumer
Discretionary

30
Consumer
Staples

35
Health
Care

40
Financials

45

50

Information Communication
Technology

Services

55
Utilities

60
Real
Estate

Table 1: GICS sector codes and names. Source: MSCI and Standard & Poor’s (S&P).

This classification relies on security-level information such as the issuer’s country of residence,

the issuer’s statistical classification of economic activity (NACE), instrument type, maturity, and

asset securitisation type. In our analysis, we use the Global Industry Classification Standard

(GICS) for industry classification, mapping NACE codes to GICS codes based on the tables

in [32]. We choose this classification for its relevance to stock market categorisation, market

index construction, and portfolio management. GICS, developed by Morgan Stanley Capital

International and Standard & Poor’s, classifies publicly traded companies by primary business

activities through a four-level hierarchy that consists of 11 sectors, 25 industry groups, 74

industries, and 163 sub-industries. Table 1 provides the names and codes of the 11 GICS sectors.

The stock of HQLA owned by the fund i is computed following the method and classification

proposed in [27, 33]:

HQLAi = cashi + Level1

i + 0.85Level2A

i + 0.6Level2B

i

.

(5)

No haircut is applied to the market value of the most liquid assets (cash and Level1), while

haircuts of 15% and 40% are applied to Level2A and Level2B assets, respectively.

3 Methods

This section introduces a set of liquidity indicators and a methodological framework for identifying

distressed funds, defined as those experiencing liquidity stress triggered by high and unexpected

derivative margin calls. The proposed indicators quantify margin call exposures relative to HQLA

and cash buffers. Our framework identifies distressed funds, analyses when the occurrence of such

funds is statistically significant, and examines potential differences in HQLA selling behaviour

between distressed and non-distressed funds. Finally, the framework evaluates the potential

ECB Working Paper Series No 307412transmission of liquidity risk from distressed funds to their counterparties, primarily banks.

3.1 Liquidity Indicators

We propose a set of liquidity indicators. The first one (Liqi) is defined as the ratio between the

fund i daily net flow and its liquidity buffer, defined as its HQLA holdings:

Liqα

i (t) =

Flowα
i (t)
HQLAi(t)

(6)

where α denotes a portfolio’s asset class. The numerator has a daily frequency, while the

denominator has a monthly one. Therefore, the ratio is computed by comparing the daily margin

flows within a given month to the HQLA at the end of the preceding month.

As variation margins can only be posted in cash, the first indicator Liqα

i (t) assumes that

high-quality liquid assets can be converted to cash, accounting for possible haircuts but not for

other liquidation costs. But what if the liquidation is too costly? Is the cash buffer adequate?

We thus focus on variation margin calls and propose a second indicator, computed as the ratio

between daily net variation margin flows and cash buffer, similarly to what was done in [31]:

VMLiqα

i (t) =

VMFlowα
cashi(t)

i (t)

(7)

where VMFlowi(t) = (cid:80)

j VMnetflowij(t) = (cid:80)

j VMnetstockij(t) − VMnetstockij(t − 1).

3.2 Detection of funds subject to liquidity stress

From the perspective of a fund’s liquidity risk, it is crucial not only to assess whether the

daily margin to be posted is high relative to the liquidity buffer provided by its high-quality

liquid assets or cash holdings, but also to determine whether these margins to be posted are

unexpected. A fund i is considered to have an outflow on a given day t if it is required to post

some margin, i.e., when the daily net flow is positive. We then restrict our analysis to such

outflows by defining Flowα

indicator as Liqα

i (t)+ = Flowα

i (t)+ = max(Flowα
i (t)+
HQLAi(t) .

i (t), 0), and introduce the corresponding liquidity

We consider a fund i on a certain day t to be subject to liquidity stress (and refer to such a

fund as distressed) if it experiences an extraordinary margin call compared to its past distribution

over the previous month. Within our framework, distressed funds are those that are unprepared

ECB Working Paper Series No 307413for the derivative margin calls they face. The z-score11 z(X) = X−mean(X)

std(X)

is a useful metric

for outlier detection because it standardizes the data, making it easier to identify values that

deviate significantly from past data. This metric extends our framework for assessing liquidity

preparedness beyond the “raw” ratios of equations (6) and (7), which capture the realised

liquidity pressure on a given day. By standardising these ratios relative to their past distributions,

the resulting z-scores enable the identification of extreme values or stress episodes in a manner

that is inherently expectation-based and forward-looking. We consider the fund i to be distressed

at time t when the z-score exceeds two; z(Liqα

i (t)+) > 2. We also investigate the z-score of
i (t)+ to detect unexpected variation margin calls concerning cash reserves. To account
for potential deviations from the Gaussian distribution, we apply the bootstrap method to

V M Liqα

resample the time series of the proposed indicators while removing temporal dependencies. This

approach generates a benchmark distribution for the fraction of funds experiencing liquidity

stress, against which the observed fraction can be compared to assess the statistical significance

of deviations.

We then investigate the distribution of distressed funds across asset classes and countries

to statistically identify the most affected by extreme margin calls. Specifically, we use the

hypergeometric test to assess whether the observed number of funds in a specific category, such as

a particular asset class or country, significantly deviates from what would be expected by chance,

given the overall sample distribution. This approach helps to account for sample heterogeneity

by comparing observed and expected frequencies, ensuring that any identified patterns are not

simply the result of an unbalanced sample. As we test multiple hypotheses, one for each country

and asset class, we apply a false discovery rate (FDR) correction. This ensures the reliability

of the results by accounting for the increased risk of false positives arising from testing many

hypotheses simultaneously.

3.3 Top Sold HQLA-country-sector group

Once distressed funds are identified, we analyse potential differences in HQLA selling behaviour

compared to non-distressed funds. We first group HQLA by their level (Level1, Level2A,

Level2B), their issuer’s country group, and their GICS industry sector for equities. GICS sectors

11We compute the z-score using the mean and standard deviation calculated at the fund level over a time
window of the previous month. Using the mean and standard deviation of past data ensures that the z-score is not
influenced by the outlier itself, provides a clear baseline for comparison, and enables real-time, consistent, and
unbiased anomaly detection.

ECB Working Paper Series No 307414are reported in Table 1 while country groups are in Table 2. To clarify the HQLA-country-sector

group notation, we provide two examples: “2B-NAM-35” refers to Level2B HQLA, whose issuer

is in North America and industry sector is Health Care, while “1-EAC” refers to Level1 HQLA,

whose issuer is in the Euro Area Core.12 We aggregate HQLA-country-sector group holdings at

Euro Area Euro Area
Periphery
EAP

Core
EAC

Europe
Not Euro Area
NEA

International Africa Asia

Entities
XX

AFR ASI

North
America
NAM

Oceania

OCE

South
America
SAM

Table 2: Country group names and their acronyms based on geographic locations. See the Table
A2 in the Appendix for more details on the relative country codes.

the fund level obtaining mktvaluei

g(t) and sharesi
HQLA-country-sector group g, held by the fund i during the considered month t.

g(t), i.e. market values and number of shares of

Our goal is to analyse the top sold HQLA-country-sector groups to detect potential concen-

trations in entities’ selling behaviours, which could trigger fire sales and exacerbate liquidity

issues. Since the portfolio holdings data are reported monthly, we consider funds classified as

distressed at the monthly level. Starting with funds labelled as distressed on a given day t, we

then aggregate the data to the monthly level. Specifically, we consider a fund i to be distressed

in a given month m if it is labelled as distressed on more than 10 business days within that

month.13 Accordingly, for a given month m, a fund i can be classified as distressed dis(i, m) = 1

or not dis(i, m) = 0. We then analysed distressed funds separately from those that are not

distressed to determine whether the selling behaviour of funds under liquidity stress differs from

that of other funds.

First, we select the HQLA-country-sector groups g with a negative variation in the number

of shares, i.e. ∆sd

g(m) < 0,

∆sd

g(m) =





(cid:88)

i|dis(i,m)=d

sharesi

g(m) − sharesi

g(m − 1)



(8)



where d = 1 (d = 0) indicates funds classified as distressed (not). We then compute the monthly

variation in the market value holdings ∆mktvd

g (m) in the HQLA-country-sector group g at month

12The Euro Area core (EAC) includes Germany, France, the Netherlands, Belgium, Luxembourg, Austria,
and Finland, while the Euro Area periphery (EAP) consists of Spain, Italy, Ireland, Slovenia, Slovakia, Greece,
Portugal, Cyprus, Malta, Estonia, Latvia, Lithuania, and Croatia (as of 2023).

13An exception is made for the first month in the time series of each considered case study. Due to the rolling

window, there are fewer data points, so the threshold is adjusted to more than 3 business days.

ECB Working Paper Series No 307415m, but only if the variation of the number of shares is negative. Finally, we sort the values in

ascending order to identify the top sold HQLA-country-sector groups.

We define the fraction of market value holdings in the HQLA-country-sector group g that is

held at the end of month m − 1 by funds in the state d (distressed or not) at month m, similarly

to the portfolio weight, as follows:

M d

g (m − 1) =

(cid:80)
g

(cid:80)

i|dis(i,m)=d mktvi
(cid:80)

i|dis(i,m)=d mktvi

g(m − 1)

g(m − 1)

(9)

3.4 Propagation of liquidity risk to banks and other counterparties

The derivative market can be modelled as a complex network where the nodes represent various

entities, such as banks, CCPs, and NBFIs, involved in trading derivatives, and the links denote
their connections within the market. Specifically, we can represent the daily net flow netflowα,γ

ij (t)
as a network, where the links between nodes correspond to the net posted margin flows from one

entity to another. To obtain a broader understanding of the daily net flow structure, we group

entities by sector and calculate the average across the daily values. As a representative case, we

illustrate here only the Covid-19 period. We distinguish between initial and variation margins,

as well as between centrally cleared markets and bilaterally cleared ones. The results in Figure 1

show that Investment Funds (IFs) primarily trade with banks.14 Limited trading activity among

IFs is observed, with the few connections that exist being negligible in terms of volume compared

to their interactions with other sectors, and these connections are only observed in non-centrally

cleared variation margins. All funds that report both in EMIR and LSEG Lipper IM data are

classified as IFs [29].

In network terminology, a network is bipartite when the entities (or nodes) can be divided

into two distinct groups, and connections only exist between nodes in different groups, not within

the same group. So, from the IFs’ perspective, if we divide the entities into two groups, IFs

and all other sectors, we can say that the IFs’ network is approximately bipartite, with most

of their interactions occurring with entities in the other group, and few connections among IFs

themselves.

Using the methodology described above, a fund i is classified as distressed on day t if it

experiences an unexpected outflow. If unprepared, the fund may face liquidity shortages, posing

14The coefficient of variation of the daily posted margin for equity pure portfolios is in Figure A1 of the Appendix.

ECB Working Paper Series No 307416(a) IM pstdEQU I,N CC

(b) V M pstdEQU I,N CC

(c) IM pstdEQU I,CC

(d) V M pstdEQU I,CC

Figure 1: Average daily posted margin (stock, mln e) for equity pure portfolios from January to
April 2020. Source: EMIR data; ECB and own calculations.

significant risks to its counterparties, primarily banks, as will be discussed in Section 4. The

potential inadequacy of distressed funds’ liquidity buffer could propagate liquidity stress to

counterparties, thereby triggering a systemic crisis across the financial system. An entity j is

subject to an outflow to its counterparty i, when the posted margin is higher than the received

margin so netf lowα

ji(t)+ = max(netf lowα

ji(t), 0). From the perspective of the counterparty i,

inflows from distressed funds (j ∈ D) pose a risk, as funds have to post cash or HQLA to it. To

assess the exposure of bank i to distressed funds, we compute the ratio:

Expα

i (t) =

(cid:80)

j∈D netf lowα
(cid:80)
j netf lowα

ji(t)+
ji(t)+ .

(10)

The higher the exposure, the greater the associated liquidity risk for the counterparties.

4 Results

In the final report [8], the FSB recommends “the scenario design for margin and collateral calls

during normal market conditions, as well as in extreme but plausible stressed market conditions.”

ECB Working Paper Series No 307417We therefore apply our liquidity framework to two case studies: the Covid-19 pandemic and the

monetary policy tightening in 2022–2023. The Covid-19 pandemic serves as a recent example of

an extreme yet plausible spike in margin calls, whereas the monetary policy tightening conditions,

prompted by central bank rate hikes in response to inflationary pressures, represent a more

gradual and expected shift in market conditions driven by monetary policy adjustments.

In the Covid-19 case, we focus on equity pure portfolios, as the most significant margin calls

in the entire derivatives market are observed within these portfolios, as we will also show in this

section. In the other case study, we consider derivatives covering all the assets, including interest

rates, equities, currencies, credits, and commodities.

4.1 Covid-19 Pandemic

The Covid-19 market turmoil significantly impacted the derivatives market, as the increased

volatility in financial markets led to a sharp increase in margin calls. During the week ending on

28 February 2020, equity markets in many countries recorded the greatest single-week decline

since the Global Financial Crisis [30]. The global economic uncertainty caused severe fluctuations

in asset prices, which in turn led to increased margin calls. Consequently, investors experienced

strong liquidity pressure to provide additional collateral to cover potential losses.

As a starting point, we focus on the pivotal five weeks of the COVID-19 pandemic (24 Feb

- 27 Mar 2020). Figure 2 illustrates the relative variation of posted stock margins, defined as

follows:

(cid:16)

(cid:16)

(cid:80)

i,j

(cid:80)

i,j

IMRelα,γ

all (t) =

VMRelα,γ

all (t) =

IMpstdα,γ
(cid:80)

ij (t) − IMpstdα,γ

ij (t0)

(cid:17)

i,j IMpstdα,γ

ij (t0)
ij (t) − VMpstdα,γ

(cid:17)
ij (t0)

VMpstdα,γ
(cid:80)

i,j VMpstdα,γ

ij (t0)

(11)

(12)

where t0 is February 24th.

We report only the cases of equity and interest rate pure portfolios, as these asset classes were

the most impacted by spikes in margin calls during the considered period. The largest spikes of

the relative change are observed in the case of variation margin calls in the non-centrally cleared

markets. In the case of equity pure portfolios, the stock of posted margins increased by roughly

4 times on March 17. The subsequent drop follows the ECB announcement of the e 750 billion

ECB Working Paper Series No 307418(a) Equity pure portfolios

(b) Interest rate pure portfolios

Figure 2: Relative change of posted margin stock in the case of equity (panel a) and interest rate
(panel b) pure portfolios. The colours represent different types of margins, initial or variation,
and whether they are centrally cleared. (24 Feb - 27 Mar 2020). The sample is composed of
20455 entities in the equity case, and of 10552 in the interest one. Source: EMIR data; ECB and
own calculations.

Pandemic Emergency Purchase Programme on March 18.15 Since PEPP was announced late in

the evening, it was incorporated into market prices only on 19 March 2020. This unconventional

measure aimed at stabilising financial markets and supporting the economy by purchasing various

securities. For interest rate pure portfolios, we observe an increase in the relative variation of

posted stock margins, though it is less pronounced, staying below 0.3 for both centrally cleared

and non-centrally cleared trades. Finally, for interest rate pure portfolios, we observe a decreasing

trend in variation margin and an increasing trend in initial margin for non-centrally cleared

trades following the PEPP announcement.

4.1.1 Liquidity dynamics

When merging the information on liquid assets from LSEG Lipper IM to the derivatives data

in EMIR, we consider only funds with non-zero daily net flows for over 10 business days from

January to April 2020. We collect their monthly (end-of-month) holdings from December to

March 2020, and we filter out monthly observations where the cash buffer is smaller than 1000e to

15https://www.ecb.europa.eu/mopo/implement/pepp/html/index.en.html

ECB Working Paper Series No 307419avoid missing data and reporting errors. Also, we consider only those LEIs whose cash and

HQLA holdings data are available for all the months under consideration to ensure a balanced

sample across the months. We then proceed to detect and remove outliers by computing the

relative variation of HQLA holdings between two consecutive months and filtering out those

whose values are under 0.5 and above 99.5 percentile. This choice also ensures that LEIs’ monthly

relative variation of HQLA cannot be higher than four times, in absolute value.

The sample, denoted by IF, consists of 839 funds. Table 3 reports the number of funds

for each euro-area country and shows that most of them are domiciled in Luxembourg, Ireland,

France, and Germany. Mutual Funds and Exchange Traded Funds (ETFs) make up the vast

majority of the sample, as shown in Table 4, while the asset type at consists mainly of equity,

bond, and mixed assets (see Table 5). The classification of funds’ asset types at, as reported in

Lipper, is based on the primary securities they invest in, helping to categorise funds according to

their investment strategy.

AT BE DE FI FR IE IT LU NL PT Total

Covid-19
2022-2023

4
15

5
1

51
76

0
7

104
21

288
297

15
59

353
890

3
22

16
0

839
1388

Table 3: Funds’ country c. Source: LSEG Lipper IM

Exchange Traded Funds Hedge Funds Mutual Funds Total

Covid-19
2022-2023

275
228

1
3

563
1157

839
1388

Table 4: Funds’ asset universe au. Source: LSEG Lipper IM

Alternatives Bond Commodity Equity Mixed Money Other Total

Assets Market

Covid-19
2022-2023

56
56

68
518

0
8

548
575

167
228

0
2

0
1

839
1388

Table 5: Funds’ asset type at. Source: LSEG Lipper IM

Figure 3 reports the boxplot of the distribution of the relative monthly variation of HQLA

and cash in the considered period (end-of Dec 2019-Mar 2020). The relative variation of HQLA,

in Figure 3a, shows a decreasing trend and increased heterogeneity across funds.16 At the end

16The aggregate distribution of HQLA and cash is reported in the Appendix, see Figure A2.

ECB Working Paper Series No 307420(a) Relative variation of HQLA

(b) Relative variation of cash

Figure 3: Distribution of HQLA and cash relative variation for entities involved in equity pure
derivatives portfolios, end-of-month data from December 2019 to March 2020. Whiskers represent
the 10th and 90th percentiles. The number of funds is 839. Source: LSEG Lipper IM, CSDB,
EMIR, and own calculations.

of January, half of the funds in the sample showed a decrease in HQLA compared to the end

of December, while by the end of March, this proportion had increased to three-quarters. This

result indicates that a large fraction of funds experienced a severe decline in the amount of

available HQLA, possibly in response to margin requirements. A different trend is observed in

the case of the relative variation of cash, see Figure 3b. The median remains close to zero, but

an increasing trend and heterogeneity across funds are also observed.

Figure 4 reports the daily cumulative stock CumN etStockEQU I (t) = (cid:80)

j netstockEQU I
(in blue) and the fraction of the daily cumulative stock due to variation margins, given by

i∈IF

(cid:80)

ij

(t)

V M P ortN etStock(t) =

(cid:80)

(cid:80)

j V M netstockEQU I

ij

(t)

i∈IF

CumN etStockEQU I (t)

, (in yellow). For comparison, the orange line

shows the Euro STOXX 50 Volatility Index (VSTOXX50).17 We observe a sharp increase in

the daily cumulative stock trend beginning in mid-February, followed by a week of fluctuation

after the PEPP announcement on March 18, and then a sharp decline. The daily cumulative

stock volumes return to their January levels only by the end of April. Similarly, the fraction

of daily net stock represented by variation margins (in yellow) follows this trend, increasing

from 5-10% before the pandemic to almost 40% at the peak. The main difference with the daily

cumulative stock volume is during the period from March 12 to 17, when the cumulative stock

continues to grow or remains stable despite a decrease in the portion attributed to variation

17VSTOXX50 represents the implied volatility of options on the Euro STOXX 50 Index, which includes 50
major Eurozone stocks. It reflects market expectations of near-term volatility: higher VSTOXX50 values indicate
greater market uncertainty, while lower values suggest stability.

ECB Working Paper Series No 307421Figure 4: Daily cumulative stock (bln e), the fraction of daily cumulative stock given by variation
margins, Euro STOXX 50 Volatility Index. Equity pure portfolios of the considered sample of
funds, Jan-Apr 2020. Source: LSEG Lipper IM, EMIR, and own calculations.

margins. This can be explained by the fact that variation margins react quickly to high volatility,

leading to a sharp initial increase as market fluctuations intensified in early March. However, as

volatility remained elevated, initial margins were also adjusted upward to account for sustained

risk, causing total margin requirements to rise. As a result, while cumulative stock continued to

grow, the relative contribution of variation margins declined, reflecting the shift toward higher

initial margins during this period. The VSTOXX50 shows a similar overall pattern, anticipating

by a short lag the cumulative stock and the fraction due to VM. This is in line with the idea that

higher volatility requires funds to post more collateral and therefore higher margins. However,

the relationship between daily cumulative stock and volatility is not uniform over time, as there

are periods where margins continue to rise even without a corresponding increase in volatility,

such as in early March, or where margins remain stable despite decreasing volatility, as observed

after the PEPP announcement.

From a liquidity perspective, these results indicate that funds not only had to cope with sharp

increases in margin calls, but also that most of these calls needed to be covered in cash, as they

were variation margins. If buffers were inadequate, funds had to liquidate assets, contributing to

a downward spiral of asset price depreciation and rising haircuts. Recent results in [31] highlight

the risk that investment funds may lack sufficient cash to cover margin calls. This underscores

the importance of funds having diverse and reliable sources of liquidity and collateral.

ECB Working Paper Series No 3074224.1.2 Detection of funds subject to liquidity stress

We now apply the methodology outlined in Section 3.2, which relies on the liquidity metrics

from Eq. (6) and (7), to identify funds that experienced liquidity stress. Figure 5 reports the

fraction of funds classified as distressed according to the two metrics. Specifically, the blue line

in the top panel is the fraction of distressed funds according to the ratio between outflows and
HQLA, i.e. z(LiqEQU I

(t)+) > 2. We notice that almost a quarter of the funds were subject to

i

liquidity stress. Before March 17th, the trend of the fraction mirrors the VSTOXX50 index.

After, additional peaks appear in the curve of the fraction of distressed funds that are not seen in

the cumulative flow. This occurs because, despite the decreasing volatility, some funds still face

high and unexpected margin calls relative to their typical values. The bottom panel shows in

yellow the fraction of distressed funds according to the ratio between variation margin outflows
and the cash buffer, i.e., z(V M LiqEQU I

(t)+) > 2. We observe an even higher magnitude of

i

peaks, up to 40% of funds are distressed, and a larger number of peaks. The differences between

the curves in Figure 5 highlight the importance of HQLA and its orderly conversion to cash.

Figure 5: Fraction of distressed funds (z > 2): liquidity indicator shown in blue and VM-liquidity
indicators in yellow. Equity Pure Portfolio, Jan-Apr 2020. The grey area represents the threshold
determined by the bootstrap method, serving as a benchmark to assess statistically significant
deviations. Source: LSEG Lipper IM, CSDB, EMIR, and own calculations.

ECB Working Paper Series No 307423We now study which funds are more likely to be distressed, both according to their country c

and to the asset type at. Since the sample is unbalanced for these two characteristics (see Tables

3 and 5), we perform the hypergeometric test18, which is particularly useful when the sample

is not evenly distributed across categories. This test evaluates the probability of observing a

specific number of distressed funds in a particular category (such as a given country or asset type)

while accounting for the fact that all categories are not equally represented in the sample. In our

case, the test compares the observed number of distressed funds in a category with the expected

number, based on the overall proportion of funds from that category in the entire set of funds

under investigation. Using this test, we can determine whether the number of distressed funds in

a given category is significantly higher or lower than it would be expected by chance, given the

unbalanced distribution of funds across different countries and asset types. This allows us to

make more robust inferences about which categories are more likely to have distressed funds,

despite the uneven sample composition. Figure 6 presents the test results. The test helps to

detect that Luxembourg, the most represented country in the sample, is robust from a liquidity

perspective, as it is not over-represented among the distressed funds during the Covid-related

period. In contrast, Ireland, the second most represented country in the sample, shows a notable

over-representation among the distressed funds. We also note an over-representation of funds

whose asset type at is Equity. This is expected, as we are considering only equity pure portfolios,

and the margins related to these portfolios exhibit the largest relative variation during the

Covid-19 period, as shown in Figure 2.

4.1.3 Top Sold HQLA-country-sector groups

After classifying funds as distressed or non-distressed, we investigate which HQLA-country-sector

groups experienced the most intense selling activity. We focus only on HQLA-country-sector

groups that experienced negative monthly variations in both the number of shares and the market

value ∆sd

g(m) < 0, ∆mktvd

g (m) < 0 in each month m. We analyse distressed and non-distressed

funds separately as two groups to determine whether their strategies are proportional to each

group’s portfolio composition and whether these selling strategies differ. To assess the former,

we compute the standard deviation of the ratio of the HQLA-country-sector group g’s fraction

of market value in month m to its fraction at the end of month m − 1. This ratio indicates

18We do not test features represented in the sample by fewer than three funds to prevent the risk of indirect

identification and ensure compliance with confidentiality.

ECB Working Paper Series No 307424(a) Country c, z(LiqEQU I

i

(t)+) > 2

(b) Asset type at, z(LiqEQU I

i

(t)+) > 2

(c) Country c, z(V M LiqEQU I

i

(t)+) > 2

(d) Asset type at, z(V M LiqEQU I

i

(t)+) > 2

Figure 6: Hypergeometric test on country c and asset type at representation of distressed funds,
equity pure portfolios, Jan-Apr 2020. A coloured rectangle indicates that a certain group on
a specific day is overrepresented, confidence interval of 95%. Additionally, the false discovery
rate (Benjamini-Hochberg procedure [34]) is applied to correct for multiple testing to ensure the
robustness of our findings. Source: LSEG Lipper IM, CSDB, EMIR and own calculations.

the relative variation in the fraction of the market value of a particular HQLA-country-sector

group from one month to the previous. If selling strategies are proportional to the portfolio

composition, the ratio of the fraction of market value should be the same for all the asset groups

g, thus its standard deviation should be zero. In contrast, a high standard deviation indicates

that the selling strategy deviates from the proportional liquidation hypothesis, suggesting a

selective choice of sold assets. Figure 7 reports the standard deviation of the ratio of the fraction

of market value of the HQLA-country-sector group for distressed and non-distressed funds’

groups separately. We observe that, in all months, the standard deviations are higher for the

distressed funds’ group, indicating that its selling strategies are less proportional to its portfolio

composition compared to the non-distressed funds’ group. We now continue our analysis by

exploring which HQLA-country-sector groups are sold the most and whether these differ between

distressed and non-distressed funds. Figure 8 shows the five top most sold HQLA-country-sector

groups ∀m, d = 0, 1, obtained by sorting them in descending order based on ∆mktvd

g (m), i.e.

ECB Working Paper Series No 307425Figure 7: The standard deviation of the ratio of the HQLA-country-sector group g’s fraction of
market value in month m to its fraction at the end of month m − 1, restricted to groups that
exhibit negative monthly changes in both the number of shares and market value i.e., ∆sd
g(m) < 0,
∆mktvd
g (m) < 0 in each month m. Distressed funds are in blue, and non-distressed funds are in
yellow. Jan-Apr 2020. Source: LSEG Lipper IM, CSDB, EMIR, and own calculations.

the monthly variation in market value. The top panel represents the distressed funds’ group,

while the bottom panel represents the non-distressed funds’ group. Each column corresponds to

a different month m, and the colours indicate the relative fraction of market value at the end

asset groups M d

of the previous month, i.e. the fraction of market value rescaled by its maximum value across
g (m − 1)(cid:1). Thus, the value 1 indicates that the corresponding
asset group was the most present in the portfolio. We examine whether there are any differences

g (m − 1)/maxg

(cid:0)M d

between the top sold HQLA-country-sector groups by distressed and non-distressed funds. In

general, both distressed and non-distressed funds primarily sold Level2B assets, i.e. equities,

but the selling patterns of the two groups differ in terms of country and sectors. January is the

only exception, as the largest negative variation in market values for distressed funds involves

Level2A followed by Level2B HQLA, whereas for non-distressed funds, it involves only Level2B

HQLA. Colours in Figure 8 help determine whether the top sold HQLA-country-sector groups

are also the most represented at the beginning of each month, giving further insights on whether

selling strategies are proportional to fund groups’ portfolio composition. For distressed funds,

February stands out as the only month where the most liquidated HQLA-country-sector group

also holds the highest relative market value in the portfolio. In contrast, January and March show

a different pattern, where the most represented asset groups were not the most sold in terms of

ECB Working Paper Series No 307426Figure 8: Top 5 most sold HQLA-country-sector groups with the largest decrease in the market
value holdings ∆mktvd
g (m) in a given month m. The top table corresponds to distressed funds,
while the bottom table corresponds to non-distressed funds, based on the liquidity indicator Liq.
Colours represent the relative fraction of market value holdings in the HQLA-country-sector
group g held at the end of the previous month m − 1 by funds in state d (distressed or non
distressed) at month m. Jan-Apr 2020. Source: LSEG Lipper IM, CSDB, EMIR, and own
calculations.

market value. In April, the second most sold asset group had the highest portfolio representation,

while the top liquidated asset group had a comparatively smaller presence. For non-distressed

funds, asset sales from January to March closely aligned with portfolio composition, as the most

sold assets also had a high relative market value. However, this trend shifted in April, when

the five most sold asset groups had a lower presence in the portfolio. This pattern hints, on

an aggregate level, at a more reactive or liquidity-driven selling behaviour of distressed funds,

offloading assets in a way that does not always align with their portfolio composition. Their

sales might be influenced by immediate liquidity needs rather than portfolio representation. In

contrast, non-distressed funds seem to demonstrate more stable selling patterns, with liquidations

more closely reflecting portfolio weights. This divergence suggests that distress influences selling

priorities at an aggregate level, potentially amplifying market dislocations and liquidity stress

during periods of turbulence.

ECB Working Paper Series No 307427(a) Kernel Density Estimation

(b) Boxplot at the daily level

Figure 9: Entities’ exposure to distressed funds (ExpEQU I
) during the considered period (Jan–Apr
2020). In the boxplot, for confidentiality reasons, the percentiles of the distribution are calculated
by averaging values across three entities, and the whiskers represent the 10th and 90th percentiles.
Source: LSEG Lipper IM, CSDB, EMIR, and own calculations.

i

4.1.4 Propagation of liquidity risk to banks and other counterparties

We now identify the banks and other institutions that were more exposed to margin calls as being

counterparties of distressed funds. This analysis sheds light on possible systemic risk propagation

mechanisms. We consider the funds classified as distressed by using the indicator Liq at least

one day during the period from January to April 2020. In total, there are 48 counterparties,

of which 44 are classified as banks [29]. The set of Euro Area counterparties C consists of 24

entities: 21 banks and 3 NBFIs. With two exceptions, these banks are either clearing members

of CCPs or are part of a banking group whose parent is a CCP clearing member. In Figure 9,

we report the kernel density estimation (KDE) and the boxplot of the counterparties’ exposure
to distressed funds ExpEQU I

(t), as in Eq. 10. From the KDE plot, we can see that most of the

i

exposure values are below 0.1, but a few observations show higher values. At the counterparty

level, the exposure indicator does not show relevant exposure, except for four entities whose 75th

and 90th percentiles are below 0.02 and 0.15, respectively, and one entity whose 75th percentile

is close to zero but exhibits high values on a few days. The variability in exposure across days,

as indicated by the large whiskers and non-negligible interquartile ranges, suggests that certain

counterparties may be more vulnerable to market shocks and could experience liquidity shortages

or other liquidity stresses, which could have broader implications for financial stability.

ECB Working Paper Series No 3074284.2 Monetary policy tightening 2022-2023

The 2022-2023 period of monetary policy tightening, characterised by significant interest rate

increases by central banks to manage inflation, influenced global financial markets, including

derivatives. These adjustments contributed to greater volatility in interest rate derivatives

and currency swaps. This period was chosen as a second case study for its contrast with the

Covid-19 pandemic: while the pandemic was an unforeseen shock, monetary tightening was a

more predictable response to rising inflation. Both cases underscore distinct liquidity stress

scenarios relevant to NBFI preparedness.

We focus on the period between July 2022 and September 2023 (Q3 2022 – Q3 2023). On

July 21, 2022, the ECB raised its key interest rates for the first time in 11 years, increasing

the deposit facility rate to 0% in response to rising inflation. The ECB continued tightening

monetary policy until September 14, 2023, when the deposit facility rate reached its peak at 4%.

4.2.1 Liquidity dynamics

We first identify euro-area funds that report both in EMIR and LSEG Lipper IM data between

July 2022 and September 2023, and clean the data as explained in Section 4.1.1. We keep only

those entities whose cash and HQLA holdings data are available for all the considered months,

and we end up with 1388 funds.19 Figure 10 shows the distribution of the relative monthly

variation of HQLA and cash, which remained stable over the period July 2022 - Sept 2023 (See

Figure A3 in Appendix).

The relative variation of HQLA in Figure 10a exhibits a decreasing trend in Q3 2022 and a

relatively stable trend in 2023 across funds. A different trend is observed for the relative variation

of cash; while the median remains close to zero, a strong heterogeneity across funds is observed.

Table 3 reports the number of funds for each euro-area country in the monetary policy tightening

2022-2023. Most of them are domiciled in Luxembourg, Ireland, Germany, and Italy, and are

Mutual or Exchange Traded Funds (Table 4). Also, the asset type at consists mainly of equity,

bond, and mixed assets (Table 5).

The cumulative net daily stock, variation stock, and portfolios’ notional reported by investment

funds are shown, respectively, in the top, centre, and bottom panels of Figure 11. Different

colours correspond to distinct derivatives’ asset type portfolios, with purple representing the

19We fill in missing monthly data only if the previous data point is available and no more than two months old.

ECB Working Paper Series No 307429(a) Relative variation of HQLA

(b) Relative variation of cash

Figure 10: Distribution of HQLA and cash relative variation for entities involved in all derivatives
portfolios, end-of-month data from June 2022 to August 2023. Whiskers represent the 10th and
90th percentiles. The number of funds is 1388. Source: LSEG Lipper IM, CSDB, EMIR, and
own calculations.

aggregate. Currency and interest rate portfolios exhibit the highest volatility in terms of margin,

particularly in the variation margin case, while the notional remains relatively stable. The

volatility of the currency market is influenced by fluctuations in key exchange rates between the

euro and other currencies.

4.2.2 Detection of funds subject to liquidity stress

Figure 12 shows the fraction of funds classified as distressed, considering all derivative portfolios.

The blue and yellow lines represent the fraction of distressed funds based on the liquidity indicators

Liq and V M Liq, defined as z(Liqall

i (t)+) > 2, respectively. Focusing
i (t)+) > 2 and z(V M Liqall
on periods when the fraction of distressed funds in blue exceeds the threshold in grey, we observe

different patterns across the time windows defined by the announcement and effective dates. In

September 2022, there is both anticipation and delay, as well as an absence during the interest

change time window. However, until December 2022, the patterns are mostly synchronised with

the interest change time windows. A different pattern emerges when considering the fraction

of distressed funds in yellow, which appears in significant numbers not only during the interest

change time windows but also in the periods between them. We repeat the analysis focusing

on interest rate pure portfolios, as they are more affected by changes in monetary policy. The

fraction of funds classified as distressed in this case is shown in Figure 13. Comparing the

ECB Working Paper Series No 307430(a) Cumulative net daily stock CumN etStockα = (cid:80)

i∈IF

(cid:80)

j netstockα

ij

(b) Cumulative net daily variation margins stock CumN etV M Stockα = (cid:80)

i∈IF

(cid:80)

j VMnetstockα

ij

(c) Cumulative notional (both initial and variation margins)

Figure 11: Aggregated stock margins (bln e), IF sample, Q3 2022 - Q3 2023. Different colours
correspond to distinct derivatives’ asset type α pure portfolios, with purple representing the case
of all asset types. Black, green, and red vertical lines indicate the effective dates of interest rate
changes by the ECB, the Federal Reserve (Fed), and the BoE, respectively. Grey areas represent
the time window between the announcement and effective dates of ECB interest rate changes.
Source: LSEG Lipper IM, EMIR, and own calculations.

ECB Working Paper Series No 307431Figure 12: Fraction of distressed funds (z > 2): liquidity indicator shown in blue and VM-liquidity
indicators in yellow. All Portfolios, July 2022 - Sept 2023. Black, green, and red vertical lines
indicate the effective dates of interest rate changes by the ECB, Fed, and BoE, respectively.
The light blue and yellow areas represent the time window between the announcement and
effective dates of ECB interest rate changes. The grey area denotes the threshold determined by
the bootstrap method, serving as a benchmark for assessing statistically significant deviations.
Source: LSEG Lipper IM, CSDB, EMIR, and own calculations.

blue lines in Figs. 12 and 13, we observe similar trends but higher peaks for interest rate pure

portfolios. A different pattern emerges for the yellow lines, where the trends slightly deviate, yet

higher peaks are also observed for interest rate pure portfolios.

We now aim to analyse how distressed funds are distributed based on funds’ country c and the

asset type at, when derivatives of any asset type are considered. The sample is unbalanced for

these two characteristics, as shown in Tables 3 and 5. To address and mitigate the impact of this

imbalance in the data, we perform the hypergeometric test, as in the previous case study. The

results are presented in Figure 14, where the observed over-representation in distressed funds is

roughly the same for both the Liq and V M Liq indicators. The most over-represented country is

Ireland, followed by Luxembourg, particularly in the case of the V M Liq indicator. Concerning

asset type, the over-representation is evenly distributed across Bond, Equity, and Mixed Assets

categories.

ECB Working Paper Series No 307432Figure 13: Fraction of distressed funds (z > 2): liquidity indicator shown in blue and VM-liquidity
indicators in yellow. Interest Rate pure Portfolios, July 2022 - Sept 2023. Black, green, and
red vertical lines indicate the effective dates of interest rate changes by the ECB, Fed, and
BoE, respectively. The light blue and yellow areas represent the time window between the
announcement and effective dates of ECB interest rate changes. The grey area denotes the
threshold determined by the bootstrap method, serving as a benchmark for assessing statistically
significant deviations. Source: LSEG Lipper IM, CSDB, EMIR, and own calculations.

4.2.3 Top Sold HQLA-country-sector groups

We use the same methodology as in Section 4.1.3, focusing on HQLA-country-sector group that

experienced negative relative variations in both the number of shares and the market value,

i.e., ∆sd

g(m) < 0 and ∆mktvd

g (m) < 0. Figure 15 reports the standard deviation of the ratio

of the HQLA-country-sector group g’s fraction of market value in month m to its fraction at

the end of month m − 1, separately for distressed and non-distressed funds’ groups. As for the

Covid case study, in all months, we observe that the standard deviations are higher for distressed

funds, indicating that their selling strategies are less proportional to portfolio composition

compared to non-distressed funds. To investigate the selling strategies in more detail, we then

analyse which HQLA-country-sector groups are sold the most. The top-sold groups are shown

in Figure 16, where colours indicate the relative fraction of market value at the end of the

M d

previous month, i.e. the fraction of market value rescaled by its maximum value across groups
g (m − 1)(cid:1). We examine whether the selling behaviour of funds subject
to liquidity stress is similar to that of others and find differences in the top-five sold HQLA

g (m − 1)/maxg

(cid:0)M d

ECB Working Paper Series No 307433categories between funds under liquidity stress and those not subject to it. We observe that

Level1 HQLA, primarily consisting of government bonds, is more frequently represented in the

top-five sold HQLA categories for funds subject to liquidity stress compared to those not under

stress. The colours in Figure 8 help determine whether selling strategies are proportional to

portfolio composition. For distressed funds, in September, October, and December 2022, as

well as February and March 2023, the most sold HQLA-country-sector group corresponds to a

high relative fraction of market value. In contrast, during the other months, all five top-sold

groups had a relatively lower presence in the portfolio. For non-distressed funds, the top-sold

HQLA-country-sector groups generally had a high relative fraction of market value over the

entire period.

4.2.4 Propagation of liquidity risk to banks and other counterparties

We identify counterparties of the funds classified as distressed by using the indicator Liq at

least one day during the period from July 2022 and September 2023. In total, there are 102

entities, of which 89 are classified banks [29]. The set of Euro Area counterparties C, consists

of 51 entities: 44 banks and 7 NBFIs. With three exceptions, these banks are either clearing

members of CCPs or are part of a banking group whose parent is a CCP clearing member. The

KDE plot and the boxplot of the counterparties’ exposure to distressed funds Expall

i (t) for the

indicator Liq in Figure 17 shows that most entities have low exposures to distressed funds, with

a few exceptions that are important from a systemic perspective. The KDE plot reveals that

most exposure values are below 0.1, with only a few observations exhibiting higher values, and

just 19 observations exceeding 0.4. At the counterparty level, the exposure indicator does not

show significant exposure, except for four entities whose 75th and 90th percentiles are below 0.01

and 0.05, respectively; one entity whose 75th percentile is near zero but displays high values

on certain days; and one entity whose 75th and 90th percentiles are below 0.05 and 0.2. The

variability in exposure across days, reflected by the large whiskers and notable interquartile

ranges on certain days, suggests that some counterparties may be more vulnerable to liquidity

shocks driven by distressed funds.

ECB Working Paper Series No 307434(a) Country c, z(Liqi(t)+) > 2

(b) Country c, z(V M Liqi(t)+) > 2

(c) Asset type at, z(Liqi(t)+) > 2

(d) Asset type at, z(V M Liqi(t)+) > 2

Figure 14: Hypergeometric test on country c and asset type at representation of distressed funds,
portfolios of any asset type, July 2022 - September 2023. A coloured square indicates that a
certain group on a specific day is overrepresented, confidence interval of 95%. Additionally, the
false discovery rate (Benjamini-Hochberg procedure [34]) is applied to correct for multiple testing
to ensure the robustness of our findings. Source: LSEG Lipper IM, CSDB, EMIR and own
calculations.

ECB Working Paper Series No 307435Figure 15: The standard deviation of the ratio of the HQLA-country-sector group g’s fraction of
market value in month m to its fraction at the end of month m − 1, restricted to groups that
exhibit negative monthly changes in both the number of shares and market value i.e., ∆sd
g(m) < 0,
∆mktvd
g (m) < 0 in each month m. Distressed funds are in blue, and non-distressed funds are in
yellow. Jul 2022-Sep 2023. Source: LSEG Lipper IM, CSDB, EMIR, and own calculations.

ECB Working Paper Series No 307436Figure 16: Top 5 most sold HQLA country-sector groups with the largest decrease in the market
value holdings ∆mktvd
g (m) in a given month m. The top table corresponds to distressed funds,
while the bottom table corresponds to non-distressed funds, based on the liquidity indicator Liq.
Colours represent the relative fraction of market value holdings in the HQLA-country-sector group
g held at the end of the previous month m − 1 by funds in state d (distressed or non-distressed)
at month m. Jul 2022-Sep 2023. Source: LSEG Lipper IM, CSDB, EMIR, and own calculations.

ECB Working Paper Series No 307437(a) Kernel Density Estimation

(b) Boxplot at the daily level

Figure 17: Entities’ exposure to distressed funds (Expall
i (t)) during the considered period (Jul
2022 – Sep 2023), all asset portfolios. The inset of subfigure (a) displays only exposure values
below 0.4, excluding 19 observations that exceed this threshold. In the boxplot, for confidentiality
reasons, the percentiles of the distribution are calculated by averaging values across three entities,
and the whiskers represent the 10th and 90th percentiles. Black, green, and red vertical lines
indicate the effective dates of interest rate changes by the ECB, Fed, and BoE, respectively. Grey
areas represent the time window between the announcement and effective dates of ECB interest
rate changes. Source: LSEG Lipper IM, CSDB, EMIR, and own calculations.

ECB Working Paper Series No 3074385 Conclusions and policy implications

This paper introduces a novel set of liquidity indicators along with a methodological framework

designed to assess the ability of NBFIs to meet derivative margin calls and to promptly identify

entities facing liquidity stress. Large margin calls can give rise to asset fire sales by market

participants scrambling for liquidity and can transmit stress to other parts of the financial

system. Our methodological framework can be integrated into micro- and macro-prudential

policy toolkits supporting various applications: monitoring NBFIs’ liquidity preparedness for

margin calls, conducting liquidity stress tests and scenario analyses, and calibrating adequate

entity-specific liquidity levels, depending on the granularity and frequency of available data.

We apply our methodology to two case studies: the Covid-19 Pandemic and the 2022-2023

monetary policy tightening. Our analysis shows that the extreme spikes in margin calls in equity

derivatives during the Covid-19 turmoil placed significant liquidity pressures on NBFIs, leading to

widespread asset sales. In contrast, the effects of margin calls on interest rate derivatives during

the monetary policy tightening were more gradual, yet still revealed notable liquidity stress

among certain funds. We identify distressed funds and compare the composition of their liquid

assets sold with their peers in the two periods. Distressed funds primarily sold equities (Level 2B

HQLA) during the Covid-19 crisis, while government bonds (Level 1 HQLA) dominated the asset

sales during monetary policy tightening. To assess the implications for systemic risk, we further

examine the potential spillover effects on NBFI counterparties, particularly banks. Our findings

indicate that while most counterparties had limited exposure to distressed funds, a subset of

them exhibited vulnerabilities to NBFI liquidity shocks. This concentration suggests that while

systemic contagion risk from margin calls is generally contained, certain market participants

remain exposed to sudden liquidity strains.

Our findings reinforce the importance of adequate liquidity preparedness, which can contribute

to ensuring financial stability. Margin and collateral calls serve as crucial safeguards against

counterparty risk, but if they arise unexpectedly and affect a large portion of market participants,

they can significantly amplify liquidity demand and lead to fire sales, as recent episodes of market

stress have shown. This underscores the need for financial institutions to proactively manage

liquidity risks and for authorities to better monitor NBFI liquidity preparedness, particularly in

times of market stress, and run stress tests and scenario analyses.

Our indicators and methodology provide an easy to use framework that aligns with the recent

ECB Working Paper Series No 307439international policy recommendations from the Financial Stability Board, which emphasises the

integration of margin requirements into broader liquidity risk frameworks [8]. Future research

could further expand our approach by incorporating collateral margin calls, offering a more

comprehensive perspective on NBFI liquidity resilience.

In this context, a crucial aspect for the effectiveness of these indicators as early-warning

tools is the availability of granular, frequent, and high-quality data. While daily, entity-level

data on margin calls is available under EMIR reporting, obtaining high-frequency, high-quality

data on NBFIs’ broader liquidity positions remains a challenge. Asset holdings and balance-

sheet data are often accessible only to regulators and supervisors, limiting the ability to fully

assess systemic liquidity risks across jurisdictions. Improved data-sharing mechanisms among

authorities, potentially under an internationally coordinated framework, would enable a more

comprehensive view of liquidity vulnerabilities in the NBFI sector. Addressing these data gaps

would significantly enhance the ability of regulators to monitor and mitigate liquidity risks

effectively.

Overall, this study contributes to the ongoing policy debate on non-bank financial inter-

mediation by proposing a framework that enhances the monitoring of liquidity preparedness.

Strengthening the resilience of NBFIs is essential to mitigating systemic risks and ensuring the

stability of the financial system.

ECB Working Paper Series No 307440References

[1] European Systemic Risk Board. Liquidity risks arising from margin calls. Technical

report, European Systemic Risk Board, 2020.

https://www.esrb.europa.eu/pub/

pdf/reports/esrb.report200608_on_Liquidity_risks_arising_from_margin_calls_

3~08542993cf.en.pdf.

[2] Audrius Jukonis, Elisa Letizia, and Linda Rousov´a. The impact of derivatives collateralisation

on liquidity risk: evidence from the investment fund sector. ECB Working Paper Series No.

2756, European Central Bank, 2022.

[3] Maddalena Ghio, Linda Rousov´a, Dilyara Salakhova, and German Villegas Bauer. Derivative

margin calls: a new driver of MMF flows. Working Paper Series No. 2800, European Central

Bank, 2023.

[4] Thomas Carraro, Linda Fache Rousov´a, Oana Furtuna, Maddalena Ghio, Kristina Kallage,

Charles O’Donnell, Francesco Vacirca, and Sebastiano Michele Zema. Lessons learned from

initial margin calls during the march 2020 market turmoil. Box 9, European Central Bank,

2021. Financial Stability Review, November 2021.

[5] Yajun Wang. Why can margin requirements increase volatility and benefit margin constrained

investors? Review of Finance, 20(4):1449–1485, 2016.

[6] Robin Greenwood, Augustin Landier, and David Thesmar. Vulnerable banks. Journal of

Financial Economics, 115(3):471–485, 2015.

[7] Emanuele Franceschi, Maciej Grodzicki, Benedikt Kagerer, Christoph Kaufmann, Francesca

Lenoci, Luca Mingarelli, Cosimo Pancaro, and Richard Senner. Key linkages between

banks and the non-bank financial sector. Special Feature B, European Central Bank, 2023.

Financial Stability Review, November 2023.

[8] Financial Stability Board. Liquidity preparedness for margin and collateral calls: Final

report. https://www.fsb.org/uploads/P101224-1.pdf, 2024.

[9] Financial Stability Board.

Enhancing the resilience of non-bank financial

in-

termediation:

Progress

report, April

2024.

https://www.fsb.org/2024/04/

liquidity-preparedness-for-margin-and-collateral-calls-consultation-report/.

ECB Working Paper Series No 307441[10] European Central Bank. Financial stability review. May 2025. https://www.ecb.europa.

eu/pub/financial-stability/fsr/html/index.en.html.

[11] Bank of England. Financial stability review. June 2024. https://www.bankofengland.

co.uk/financial-stability-report/2024/financial-stability-report-june-2024.

pdf.

[12] Bank of England.

Financial

stability review.

Nov 2024.

https://www.

bankofengland.co.uk/-/media/boe/files/financial-stability-report/2024/

financial-stability-report-november-2024.pdf.

[13] Piergiorgio Alessandri, Prasanna Gai, Sujit Kapadia, Nada Mora, and Claus Puhr. A

framework for quantifying systemic stability. International Journal of Central Banking,

5(3):47–81, 2009.

[14] Matthias Sydow, Aurore Schilte, Giovanni Covi, Marija Deipenbrock, Leonardo Del Vecchio,

Pawel Fiedor, G´abor Fukker, Max Gehrend, R´egis Gourdel, Alberto Grassi, Bj¨orn Hilberg,

Michiel Kaijser, Georgios Kaoudis, Luca Mingarelli, Mattia Montagna, Thibaut Piquard,

Dilyara Salakhova, and Natalia Tente. Shock amplification in an interconnected financial

system of banks and investment funds. Journal of Financial Stability, 71:101234, 2024.

[15] Katarzyna Barbara Budnik, Aurea Ponte Marques, Carla Giglio, Alberto Grassi, Agha

Durrani, Juan Manuel Figueres, Paul Konietschke, Catherine Le Grand, Julian Metzler,

Francisco Javier Poblaci´on Garc´ıa, et al. Advancements in stress-testing methodologies for

financial stability applications. ECB Occasional Paper Series, 348, 2024.

[16] Matthias Sydow, G´abor Fukker, Tomasz Dubiel-Teleszynski, Fabio Franch, S´ebastien Gallet,

Helmut Gr¨undl, Stelios Kotronis, Debora Miccio, Michela Pellegrino, Sebastian Schl¨utter,

and Matteo Sottocornola. Banks and non-banks stressed: liquidity shocks and the mitigating

role of insurance companies. Ecb working paper series no. 3000, European Central Bank,

2024.

[17] Laura Alfaro, Saleem A Bahaj, Robert Czech, Jonathon Hazell, and Ioana Neamtu. Lash

risk and interest rates. Technical Report Staff Working Paper No. 1073, Bank of England,

2025.

ECB Working Paper Series No 307442[18] Antoine Bouveret, Massimo Ferrari, Michael Grill, Luis Molestina Vivar, Daniel Jonas

Schmidt, and Christian Weistroffer. Leveraged investment funds: A framework for assessing

risks and designing policies. ECB Macroprudential Bulletin, 26, 2025.

[19] Annalaura Ianiro, Agnese Leonello, and Dario Ruzzi. Measuring synthetic leverage in interest

rate swaps. ECB Macroprudential Bulletin, 26, 2025.

[20] Kristy A.E. Jansen, Sven Klingler, Angelo Ranaldo, and Patty Duijm. Pension liquidity

risk. DNB Working Paper 801, De Nederlandsche Bank, February 2024.

[21] Sebastiano Michele Zema. Uncovering the network structure of non-centrally cleared

derivative markets: evidence from large regulatory data. Empirical Economics, 65(4):1799–

1822, 2023.

[22] Argyris Kahros, Alessandro Pioli, Thomas Carraro, Marios Gravanis, and Francesco Vacirca.

A descriptive analysis of the client clearing network in the european derivatives landscape.

Journal of Financial Market Infrastructures, 9(1), 2021.

[23] Marco Bardoscia, Gerardo Ferrara, Nicholas Vause, and Michael Yoganayagam. Simulating

liquidity stress in the derivatives market. Journal of Economic Dynamics and Control,

133:104215, 2021.

[24] Pawel Fiedor, Sarah Lapschies, and Lucia Orsz´aghov´a. Networks of counterparties in the

centrally cleared eu-wide interest rate derivatives market. Technical Report Working Paper

Series No. 54, European Systemic Risk Board (ESRB), September 2017. ESRB Working

Paper Series.

[25] Claudio Barbieri, Maciej Grodzicki, Grzegorz Ha(cid:32)laj, and Riccardo Pizzeghello. System-wide

implications of counterparty credit risk. ECB Macroprudential Bulletin, 26, 2025.

[26] Bank for International Settlements (BIS). Basel framework. LCR - Liquidity Coverage

Ratio, LCR30 - High-quality liquid assets, 2023. https://www.bis.org/basel_framework/

chapter/LCR/30.htm.

[27] Pierce Daly, Elisa Telesca, and Christian Weistroffer. Assessing liquidity vulnerabilities in

open-ended bond funds: A fund-level redemption coverage ratio approach. Box 6, European

Central Bank, 2023. Financial Stability Review, November 2023.

ECB Working Paper Series No 307443[28] Gemma Agostoni, Annalaura Ianiro, Audrius Jukonis, Francesca D Lenoci, Elisa Letizia,

and Grzegorz Skrzypczynski. Using trade-level derivatives data for macroprudential analysis.

Ips 224, evolving statistics in support of central bank policies, European Central Bank, 2023.

[29] Francesca D Lenoci and Elisa Letizia. Classifying counterparty sector in emir data. In

Data Science for Economics and Finance: Methodologies and Applications, pages 117–143.

Springer International Publishing Cham, 2021.

[30] Basel Committee on Banking Supervision (BCBS), Bank for International Settlements’

Committee on Payments and Market Infrastructures (CPMI), and International Organization

of Securities Commissions (IOSCO). Review of margining practices. BIS publications,

September 2022.

[31] Margherita Giuzio, Annalaura Ianiro, Fabrizio Lillo, Valentina Macchiati, Elisa Telesca,

and Andrzej Sowi´nski. Assessing the liquidity preparedness of investment funds to meet

margin calls in derivatives markets. Box 5, European Central Bank, 2024. Financial Stability

Review, May 2024.

[32] European Commission. Sustainable finance: Teg benchmark handbook, 2020. Publications

Office of the European Union.

[33] Margherita Giuzio, Francesca Lenoci, and Christian Weistroffer. Portfolio rebalancing

by euro area investment funds following outflows. Box 7, European Central Bank, 2019.

Financial Stability Review, November 2019.

[34] Yoav Benjamini and Yosef Hochberg. Controlling the false discovery rate: a practical and

powerful approach to multiple testing. Journal of the Royal statistical society: series B

(Methodological), 57(1):289–300, 1995.

ECB Working Paper Series No 307444A Appendix

A.1 List of Abbreviations

Abbreviation Meaning

NBFI
ECB
FSB
Fed
BoE
EU
EA
UK
OTC
PEPP
LASH
HQLA
EMIR
ESCB
CSDB
LEI
ISIN
TR
CCP
IM
VM
INTR
EQUI
CURR
CRDT
COMM
MIXT
CC
NCC
NACE
GICS
FDR
IF
BANK
GOVT
IC
MMMF
NCB
NFC
OFI
OTHR
PF
ETF
VSTOXX50
KDE

Non Bank Financial Institutions
European Central Bank
Financial Stability Board
Federal Reserve
Bank of England
European Union
Euro Area
United Kingdom
Over-The-Counter
Pandemic Emergency Purchase Programme
Liquidity After Solvency Hedging
High-Quality Liquid Asset
European Market Infrastructure Regulation
European System of Central Banks
Centralised Securities Database
Legal Entity Identifier
International Securities Identification Number
Trade Repository
Central Counterparty
Initial Margin
Variation Margin
Interest Rate
Equity
Currency
Credit
Commodity
Mixed Assets
Centrally Cleared
Not Centrally Cleared
Statistical Classification of Economic Activities
Global Industry Classification Standard
False Discovery Rate
Investment Funds
Banks
Government
Insurance Companies
Money Market Mutual Funds
National Central Banks
Non Financial Corporations
Other Financial Institutions
Others
Pension Funds
Exchange Traded Funds
Euro STOXX 50 Volatility Index
Kernel Density Estimation

Table A1: List of Abbreviations.

ECB Working Paper Series No 307445A.2 Country group definition

Country Group Names Acronyms Country Codes

Euro Area Core

Euro Area Periphery

Non-Euro Area

International Entities

Africa

EAC

EAP

NEA

INT

AFR

Asia

ASI

North America

NAM

Oceania

South America

OCE

SAM

DE, FR, NL, BE, LU, AT, FI

ES, IT, IE, SI, SK, GR, PT, CY, MT, EE, LV, LT,
HR(from 2023)

AL, AD, BA, BG, CZ, DK, FO, GB, HU, IS, LI, MD,
MK, ME, NO, PL, RO, RS, SE, UA, CH, HK, GG,
GI, IM, JE, HR(until 2023)

XX (international entities)

DZ, AO, BJ, BW, BF, BI, CM, CV, CF, TD, KM, CD,
CG, DJ, EG, GQ, ER, SZ, ET, GA, GM, GH, GN,
GW, KE, LS, LR, LY, MG, MW, ML, MR, MU, MA,
MZ, NA, NE, NG, RW, ST, SN, SC, SD, TZ, TG, TN,
UG, ZM, ZW, ZA

AF, AM, AZ, BH, BD, BT, BN, KH, CN, CY, GE, IN,
ID, IR, IQ, IL, JP, JO, KZ, KP, KR, KW, KG, LA,
LB, MV, MN, MM, MY, NP, OM, PK, PH, QA, SA,
SG, LK, SY, TJ, TH, TM, AE, UZ, VN, YE, TW, RU

US, CA, MX, BM, BS, BB, HT, JM, KY, PR, AG, AI,
AW, CU, DM, DO, GD, GP, HT, HN, MQ, MS, NI,
SR, SX, TT, TC, VC, VG, VI

AS, AU, CK, FJ, FM, GU, KI, MH, NC, NZ, PG, PW,
WS, SB, TV, VU

AR, BO, BR, CL, CO, EC, GY, PY, PE, SR, UY, VE,
CW, PA

Table A2: Description of country groups in terms of country codes.

ECB Working Paper Series No 307446A.3 Derivative Market Structure by Sector

(a) IM pstdEQU I,N CC

(b) V M pstdEQU I,N CC

(c) IM pstdEQU I,CC

(d) V M pstdEQU I,CC

Figure A1: Coefficient of variation (ratio between mean and standard deviation) of the daily
posted margin for equity pure portfolios from January to April 2020. Source: EMIR; ECB and
own calculations.

ECB Working Paper Series No 307447A.4 Distribution of HQLA and cash holdings

A.4.1 Covid-19 Pandemic

(a) Distribution of HQLA

(b) Distribution of cash

Figure A2: Distribution of HQLA and cash of entities that are involved in equity pure derivatives
portfolios, end-of-month data from December 2019 to March 2020. Whiskers and yellow scatter
represent the 10th and 90th percentiles and the mean, respectively. The number of funds is 839.
Source: LSEG Lipper IM, CSDB, EMIR; ECB and own calculations.

A.4.2 Monetary policy tightening 2022-2023

(a) Distribution of HQLA

(b) Distribution of cash

Figure A3: Distribution of HQLA and cash of entities that are involved in derivatives portfolios,
end-of-month data from June 2022 to August 2023. Whiskers and yellow scatter represent the
10th and 90th percentiles and the mean, respectively. The number of funds is 1388. Source:
LSEG Lipper IM, EMIR, CSDB; ECB and own calculations.

ECB Working Paper Series No 307448Acknowledgements 

The authors acknowledge the comments and suggestions provided by an anonymous referee and the editorial board of the ECB 
Working Paper Series, which contributed to improving the manuscript. The authors thank the participants of the workshop, “EMIR Data 
Analytics for Research, Financial Stability, and Supervision”, held at Banca d’Italia in June 2024, as well as the participants of the DGMF 
Seminar at the ECB in February 2025. They also thank L. Rousová, A. Sowiński, and E. Telesca for the valuable discussion. This paper 
was completed while Valentina Macchiati was affiliated with the European Central Bank. 
The views expressed are those of the authors and do not necessarily reflect those of the European Central Bank, or the Eurosystem. 

Valentina Macchiati 
Scuola Normale Superiore, Pisa, Italy; European Central Bank, Frankfurt am Main, Germany; email: valentina.macchiati@sns.it 

Lorenzo Cappiello 
European Central Bank, Frankfurt am Main, Germany; email: lorenzo.cappiello@ecb.europa.eu 

Margherita Giuzio 
European Central Bank, Frankfurt am Main, Germany; email: margherita.giuzio@ecb.europa.eu 

Annalaura Ianiro 
European Central Bank, Frankfurt am Main, Germany; email: annalaura.ianiro@ecb.europa.eu 

Fabrizio Lillo 
Scuola Normale Superiore, Pisa, Italy; University of Bologna, Bologna, Italy; email: fabrizio.lillo@sns.it 

© European Central Bank, 2025 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

+49 69 1344 0 
www.ecb.europa.eu 

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

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

PDF 

ISBN 978-92-899-7391-5 

ISSN 1725-2806 

doi:10.2866/7681183 

QB-01-25-165-EN-N