Mapping the interconnectedness between EU banks and shadow banking entities.
Date: 2017-03-20
Institutions: European Systemic Risk Board
Subjects: banking, banking policy, financial institution, financial policy, financial stability, shadow banking

Working Paper Series 

No 40 / March 2017 

Mapping the interconnectedness 
between EU banks and 
shadow banking entities 

by 
Jorge Abad 
Marco D’Errico 
Neill Killeen 
Vera Luz 
Tuomas Peltonen 
Richard Portes 
Teresa Urbano 

Abstract

This paper provides a unique snapshot of the exposures of EU banks to shadow
banking entities within the global ﬁnancial system. Drawing on a rich and novel
dataset, the paper documents the cross-sector and cross-border linkages and con-
siders which are the most relevant for systemic risk monitoring. From a macropru-
dential perspective, the identiﬁcation of potential feedback and contagion channels
arising from the linkages of banks and shadow banking entities is particularly chal-
lenging when shadow banking entities are domiciled in different jurisdictions. The
analysis shows that many of the EU banks’ exposures are towards non-EU entities,
particularly US-domiciled shadow banking entities. At the individual level, banks’
exposures are diversiﬁed although this diversiﬁcation leads to high overlap across
different types of shadow banking entities.

JEL classification: F65, G21, G23.
Keywords:  shadow banking, interconnectedness, financial stability, macroprudential
policy

1

“Policymakers and researchers need better models and data to understand the interconnections
between the banking system and nonbank ﬁnancial institutions.”
- Stanley Fischer, December 2015

1

Introduction

The substantial growth of the shadow banking system in recent years has led to in-

creased monitoring of this part of the ﬁnancial sector.1 Given that ‘shadow banking en-

tities’ can form part of complex ﬁnancial intermediation chains which can also include

banks and insurance corporations (Pozsar et al. 2013, Cetorelli 2014), understanding

the nature of their linkages is important. The global ﬁnancial crisis exposed shortcom-

ings in the assessment of cross-sector and cross-border linkages in the ﬁnancial system.

The interaction of banks and insurance corporations with shadow banking entities led

to the ampliﬁcation of risks and spillovers which were transmitted across sectors and

national borders. As shadow banking entities are subject to varying degrees of su-

pervision, there was a lack of transparency and comprehensive macroprudential risk

monitoring of their linkages and activities.

Owing to their heterogeneous activities, shadow banking entities can be direct

counterparties to banks in a number of markets including derivative and funding mar-

kets.

In addition, banks may be exposed to shadow banking entities through their

common membership of a corporate group, through the provision of explicit or im-

plicit backstops or indirectly through their common exposures to assets.2 Moreover,

liquidity support provided by banks to off-balance sheet entities can further reinforce

the link and potential contagion paths between the regular banking system and the

shadow banking system (BCBS, 2015). As a result of these linkages, signiﬁcant shocks

1For example, according to ESRB (2016a), a broad measure of the shadow banking system in the EU,
comprising total assets of investment funds and other ﬁnancial institutions (OFIs) represented approxi-
mately 36% of total EU ﬁnancial sector assets at the end of 2015 and has grown by 22% since the end of
2012.

2See Claessens and Ratnovski (2014) and BCBS (2015) for a discussion on explicit and implicit back-
stops. The results of Gornicka (2016) lend support to the view that guarantees provided by sponsoring
(2016) examine
institutions are an important element of shadow banking. Furthermore, Clerc et al.
the channels through which indirect contagion can spread across the ﬁnancial system, including fund-
ing and asset markets, and highlight the role of the market price channel and information spillovers in
propagating shocks.

2

in the shadow banking system can have potential spillover effects on other parts of the

ﬁnancial system.

Since the global ﬁnancial crisis, policymakers and regulators have sought to im-

prove the monitoring framework for assessing the interconnectedness of the shadow

banking system with the aim of mitigating systemic risks. The increasing size and

complexity of the shadow banking system, coupled with its interconnectedness with

the banking sector underlines the need for effective ﬁnancial stability monitoring. At

the global level, the Financial Stability Board (FSB) has coordinated efforts in improv-

ing data coverage for monitoring developments within the shadow banking system.3

At the European level, the European Systemic Risk Board (ESRB) has focused on moni-

toring potential ﬁnancial stability risks, including those risks and negative externalities

posed by entities and activities within the shadow banking system.4

While the interconnectedness between banks and shadow banking entities is often

cited as a key ﬁnancial stability concern, we are not aware of other studies which doc-

ument and describe the nature of these linkages using EU bank and exposure-level in-

formation. Fischer (2015) points to the importance of mapping the linkages of shadow

banking entities. He notes “an important area in need of development is economic modelling

on interconnectedness, particularly on the interaction of shadow banking, banks and the broader

ﬁnancial system. . . such research could guide regulatory efforts to collect data and set policies to

limit possible instabilities associated with interconnectedness.” Furthermore, as described in

ESRB (2016b), macroprudential policies beyond the banking sector should focus on the

components of the ﬁnancial system where systemic risks are higher. In order to assess

the potential impact of spillovers, detailed analyses on the interconnections of different

components of the ﬁnancial system are required. Understanding the cross-sector and

cross-border linkages will therefore equip policymakers and regulators with the nec-

essary surveillance tools and can form a key input in the design of macroprudential

3See, for example, FSB (2011) which outlines the FSB’s approach to strengthen the oversight and
regulation of the shadow banking system. The FSB publish annual monitoring reports on the global
shadow banking system. See FSB (2015).

4See ESRB (2016a) and Grillet-Aubert et al. (2016) for a description of the monitoring framework

developed by the ESRB for assessing developments and risks within the EU shadow banking system.

3

policies.

This paper contributes to the growing literature on the monitoring of shadow bank-

ing activities by providing a unique insight into the exposures of EU banks to shadow

banking entities. In this way, the paper ﬁlls an important data gap and sheds new light

by mapping for the ﬁrst time these linkages using granular bank and exposure-level

information. To the best of our knowledge, this is the ﬁrst study which examines the

linkages of EU banks to shadow banking entities at both the country and individual ex-

posure level. In order to understand better the interconnectedness between EU banks

and shadow banking entities and the underlying geography of risk5, the paper asks the

following three questions. (1) Which EU banks are most exposed to shadow banking

entities? (2) What types of shadow banking entities are EU banks exposed to? And (3)

Where are these shadow banking entities domiciled?

In addition, the paper explores the potential contagion paths between the bank-

ing system and particular shadow banking entities drawing on the lessons from the

ﬁnancial crisis. For example, step-in risk exists for banks when they provide implicit

guarantees and sponsor support to securitisation conduits, structured investment ve-

hicles (SIVs) and money market funds (MMFs). The global ﬁnancial crisis exposed

how such backstops of shadow banking entities can have negative externalities for the

banking system and can act as an additional transmission channel of risk (Acharya,

Schnabl and Suarez, 2013; Bengtsson, 2013; Covitz, Liang and Suarez, 2013 and BCBS,

2015). Furthermore, we show that banks have rather diversiﬁed exposures towards

shadow banking entities. However, such diversiﬁcation leads to a high degree of over-

lap among banks’ exposures to different types of shadow banking entities.

The rest of the paper is structured as follows. Section 2 describes the data used

in our analysis and provides a brief overview of our data cleaning procedure. Sec-

tion 3 outlines our analysis and examines the cross-sector and cross-border linkages of

5The cross-border analysis presented in this paper is based on the residency of the shadow banking
entities. However, as noted by Lane (2016a), global ﬁnancial intermediation means that the patterns of
ultimate ownership are quite different to those suggested by the residence of investment vehicles. On
the other hand, residence (domicile) is most directly relevant in determining the geographic locus of
potential supervisory measures.

4

banks’ exposures to shadow banking entities before examining the level of concentra-

tion and overlap of the exposures. Section 4 discusses policy reforms which can assist

in the monitoring of shadow banking interconnectedness while also highlighting the

role of macroprudential policy in mitigating potential ﬁnancial stability risks. Section

5 concludes.

2 Data

This section describes the data used in our analysis. In December 2015, the European

Banking Authority (EBA) issued guidelines on the approach that institutions (banks

and investment ﬁrms) should adopt for the purposes of setting appropriate individual

and aggregate limits on exposures to shadow banking entities which carry out bank-

ing activities outside a regulated framework.6 In parallel to the development of the

guidelines, the EBA conducted a data collection to understand better the volume and

distributions of institutions’ exposures to certain types of unregulated and ‘lightly’

regulated entities, and the potential impact of imposing limits to these exposures.7

A sample of institutions were asked to provide information regarding their expo-

sures to counterparties considered as ‘shadow banking entities’. ‘Exposures’ mean any

asset or off-balance sheet item used in the calculation of capital requirements for credit

risk under the standardised approach, without applying risk weights or degrees of

risk. These data were collected at the highest level of consolidation in a Member State,

or individual level if the consolidated level did not apply.

Institutions were asked

to calculate their exposures in the same way as any other exposure reported under the

large exposures’ regime set out in Part 4 of the Capital Requirements Regulation (CRR)

and to report original exposures to individual counterparties, as well as the exposure

before and after taking into account the effect of Credit Risk Mitigation (CRM) and

6https://www.eba.europa.eu/regulation-and-policy/large-exposures/guidelines-on-limits-on-

exposures-to-shadow-banking

7The deﬁnition of ‘shadow banking entity’ used for the purposes of the data collection was broader
than the deﬁnition used in the ﬁnal EBA guidelines, so as to capture as much information as possible
and not to pre-empt future work by the EBA and/ or the European Commission on this topic.

5

large exposures’ exemptions, and also the percentage of the eligible capital.8 The expo-

sures used in the analysis were after CRM and large exposures’ exemptions. We do not

in this paper consider institutions’ liabilities to shadow banking entities. For the pur-

poses of the data collection ‘shadow banking entities’ were deﬁned as undertakings

that met two conditions:

1. They carry out one or more credit intermediation activities. Here ‘credit in-

termediation activities’ means bank-like activities involving maturity transformation,

liquidity transformation, leverage, credit risk transfer or similar activities.

2. They are not excluded undertakings (i.e. mainly undertakings that are subject to

the CRD / CRR or are considered to be regulated in a similar way).

Institutions were asked to identify their counterparties by indicating their identiﬁer

code9, name, and country of residence and to classify them in accordance with their

underlying economic functions (i.e. activities rather than legal form).

Information

was collected on Undertakings for the Collective Investment in Transferable Securities

(UCITS) MMFs, non-UCITS MMFs, non-MMF investment funds, ﬁnance companies,

broker-dealers, credit insurers / ﬁnancial guarantors, securitisations, non-equivalent

banks / insurers and a residual category labelled as ‘other’ for institutions that can

not be classiﬁed according to the types presented above. In addition, institutions were

asked to indicate, to the best of their knowledge, whether a shadow banking entity

was: (A) not supervised on a solo level, but supervised on a consolidated level in the

Union; (B) not supervised on a solo level, but supervised on a consolidated level in a

third country that has a regime at least equivalent to the one applied in the European

Union, or (C) neither of the two.

The sample included 184 reporting institutions (169 banks and 15 investment ﬁrms)

from 22 Member States, and the reference date for the reported data was 31 March

8Article 4(1), point 71 of regulation (EU) No.575/2013 deﬁnes ‘eligible capital’ as the sum of Tier 1
capital as referred to in Article 25 (of the same Regulation) and Tier 2 capital as referred to in Article 71
(of the same Regulation) that is equal to or less than one third of Tier 1 capital.

9In some cases, the identiﬁer code provided were legal entity identiﬁers (LEIs) while others were

internal codes.

6

2015.10 In total, there were 107,621 individual exposures reported, which amounted

to a total exposure of €1,082 billion. As reporting institutions could choose to report

only the exposures to individual shadow banking entities with an exposure value after

exemptions and CRM equal to or in excess of 0.25% of eligible capital, we must there-

fore use the subset of exposures equal to or above this threshold in order to undertake

our analysis using the individual exposure level data. We do this in order to maintain

a sample with information on the individual shadow banking counterparty which is

necessary in order to undertake our analysis of the cross-sector and cross border link-

ages. For conﬁdentiality reasons, the analysis did not identify individual countries

that had a sample composed of three or fewer reporting institutions.11 EBA (2015) pro-

vides further details on the coverage of the sample in each Member State and further

background information on the data collection process.

A signiﬁcant cleaning procedure was applied to the data used in our analysis. As

a ﬁrst step, the national competent authorities (NCAs) made an initial assessment of

the quality of the reported data prior to sending them to the EBA. Subsequently, the

EBA performed several data quality checks in order to assess the completeness and

consistency of the data and liaised with the NCAs where necessary. Some essential

data cleaning was performed prior to conducting the analysis presented in this paper.

For example, we exclude investment ﬁrms from our sample of reporting institutions

and analyse only individual exposures of EU banks equal to or above 0.25% of banks’

eligible capital. The subset of exposures that are equal to or above 0.25% of institu-

tions’ eligible capital represent 3,272 exposures (3% of the total number of exposures)

with a total exposure amount of €568 billion (52% of the total exposure amount for

the full sample of reported exposures) and were reported by 139 EU banks. In ad-

dition, repeated shadow banking counterparties were identiﬁed based on duplicated

counterparty IDs, duplicated counterparty names or on similar names (e.g. ABC Ltd as

10The participating Member States include Austria, Belgium, Cyprus, Czech Republic, Denmark,
France, Germany, United Kingdom, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta,
Netherlands, Norway, Poland, Portugal, Slovakia, Spain and Sweden.

11In the network graphs presented in Section 3, banks from countries who have less than 3 reporting

institutions are labelled ’XZ’.

7

8

TABLE 1. Distribution of EU banks’ exposures to shadow banking entities by
country of reporting institution and domicile of shadow banking entity (in €
billion)

B/SB DE

FR GB

IE

JE

KR KY

LU NL RU TR

US O-EU RW Total

AT

DE

FR

GB

IT

LU

0.2

28.1

0.5

5.2

0.0

1.1

O-EU 0.5

0.0

0.8

16.2

4.8

1.3

0.7

1.4

0.3

5.1

3.7

0.2

9.4

1.9

0.0

1.3

0.2

44.7

19.8

14.7

2.8

0.8

1.5

2.6

0.4

1.1

0.4

0.0

0.5

0.1

0.4

2.9

7.8

1.3

0.0

2.3

0.2

2.9

4.9

0.2

6.9

1.4

24.9

12.0

0.0

0.3

3.2

2.5

2.5

3.3

0.3

1.7

2.0

3.5

0.1

3.0

3.2

0.8

2.3

0.5

1.5

2.1

0.2

2.9

0.1

3.9

0.5

3.0

7.5

0.0

4.5

0.6

33.5

30.1

84.0

0.5

0.1

2.8

3.1

2.2

2.7

4.0

2.9

1.0

12.8

0.7

7.6

6.8

106.0

10.9

78.3

54.5

284.4

2.9

0.4

6.7

26.8

10.4

46.8

Total

35.5

25.1

59.0

35.4

17.0

14.8

36.4

28.8

13.8

10.4

19.5

151.5

28.7

83.5

559.4

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to ‘shadow banking entities’, December 2015. Note: Data refer to individual exposures

equal to or above 0.25 per cent of eligible capital. Country labels on the left hand side of the chart refer to the country of domicile of the reporting institution. Country labels

along the top of the chart refer to country of domicile of shadow banking entity. O-EU refers to other EU countries while RW refers to the rest of the world. The chart excludes

investment ﬁrms and exposures greater than 25% of the institution’s eligible capital (the large exposure limit).

TABLE 2. Distribution of EU banks’ exposures to shadow banking entities by
country of reporting institution and domicile of shadow banking entity (as a % of
GDP of country of reporting bank)

B / SB DE FR GB IE

JE KR KY LU NL RU TR US O-EU RW Total

AT

DE

FR

GB

IT

LU

0.1

0.0

0.1

0.0

0.0

0.0

0.1

0.1

0.1

0.3

0.0

0.2

1.0

0.0

0.2

0.3

0.0

0.0

0.1

0.3

0.1

0.1

0.1

1.2

0.0

0.8

0.2

0.1

0.0

0.1

0.2

0.1

0.1

0.0

0.0

1.5

0.3

0.3

2.4

1.0

0.8

0.4

1.3

0.6

0.2

0.1

0.2

4.4

0.0

0.1

0.2

0.2

0.0

0.1

0.0

0.2

0.0

0.1

0.5

0.0

2.4

1.5

1.8

1.0

0.0

0.0

0.6

5.5

6.5

0.5

0.0

0.1

O-EU 0.0

0.0

0.0

0.0

0.0

0.1

0.1

0.1

0.1

0.1

0.1

0.1

Total

0.3

0.2

0.5

0.3

0.1

0.1

0.3

0.2

0.1

0.1

0.1

1.2

1.0

0.1

0.1

0.2

0.2

2.1

0.3

0.2

0.2

0.3

0.5

2.9

0.2

0.8

0.1

0.6

2.2

3.9

3.8

15.0

1.7

22.9

1.1

4.3

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to “shadow banking entities”, December 2015. Note: Data refer to individual exposures

equal to or above 0.25 per cent of eligible capital. Country labels on the left hand side of the chart refer to the country of domicile of the reporting institution. Country labels

along the top of the chart refer to country of domicile of shadow banking entity. O-EU refers to other EU countries while RW refers to the rest of the world. The chart excludes

investment ﬁrms and exposures greater than 25% of the institution’s eligible capital (the large exposure limit).

opposed to ABC Limited). Similarly, as shown in EBA (2015), 19% of the shadow bank-

ing counterparties by value of exposures were not identiﬁed by country of residence.

Therefore, we manually clean and match these data to reduce this data limitation to

1% of exposures. This allows us to map the geography of the exposures using network

analysis techniques.

In order to explore the characteristics of EU banks (e.g.

total assets, number of

subsidiaries) in our sample, we also merge the EBA’s data with bank-level information

taken from Bureau van Dijk’s Orbis database. Next, we exclude individual exposures

greater than 25% of banks’ eligible capital which corresponds to the large exposure

limit in Article 395 of the CRR.12 This leaves a ﬁnal sample of 131 banks and 3,182

individual exposures. To gain an overview of the data, Table 1 shows that the total

exposures amount to approximately €560 billion, representing 4.3% of EU GDP (Table

2) with signiﬁcant heterogeneity across countries.

3 Mapping of EU banks’ exposures to shadow banking

entities

This section provides a unique analysis of EU banks’ exposures to shadow banking

entities. First, it examines the characteristics of the EU banks which have exposures

to shadow banking entities.

It then analyses the types of shadow banking entities

to which EU banks are exposed before proceeding to an analysis of the cross-border

nature of these linkages. This section concludes with an analysis of the concentration

and overlap of EU banks’ exposures to shadow banking entities.

3.1 Which? EU banks’ exposures to shadow banking entities

Our analysis of EU banks’ exposures to shadow banking entities ﬁrst examines the

characteristics of the banks involved. As described in Section 2, we complement the

12We do this to exclude outliers in the data. Moreover, in line with the large exposure limit, the sum
of all the exposure values of a bank to a single counterparty or to a group of connected counterparties
must not be higher than 25% of banks’ eligible capital.

9

EBA data using information taken from Bureau van Dijk’s Orbis database.13 Total as-

sets of banks in our sample amount to €27 trillion. The data show that 23% of the

banks in the EBA sample are from Germany, 13% are from the UK and 3% are banks

from France. Weighting by their balance sheet size, however, shows that UK banks ac-

count for 39% of the total assets of the banks in our sample, while German and French

banks account for 11% and 21%, respectively. As noted in EBA (2015), the banks in

our sample cover 56% of total assets of the EU ﬁnancial sector, although the cover-

age is heterogeneous across countries. For example, French and UK banks account for

75% and 85% of their respective ﬁnancial sectors (see EBA, 2015), while Irish banks in

our sample account for only 6% of the Irish ﬁnancial sector. However, as described in

FSB (2015), approximately 80% of the Irish ﬁnancial sector consists of entities outside

of the regular banking system. Reﬂecting this, Irish banks in our sample account for

approximately 60% of the Irish banking sector.

TABLE 3. EU banks’ exposures to shadow banking entities as a % of banks’ (by
country) eligible capital

B / SB DE

FR GB

IE

JE KR KY LU NL RU TR US O-EU RW Total

7.0

39.1

AT

DE

FR

GB

IT

LU

2.2

28.6

0.4

1.4

0.0

0.0

0.8

12.7

1.3

3.4

4.4

5.2

2.9

11.9

7.5

2.0

9.5

1.5

5.3

6.9

0.0

1.2

2.4

1.3

0.4

3.0

0.1

2.3

3.9

3.9

2.1

6.7

1.0

3.5

0.0

2.8

7.0

1.1

3.2

6.6

3.5

1.7

1.6

0.9

0.4

24.7 15.0

18.2

10.1 0.0

0.4

5.9

56.1

66.6

O-EU 0.9

2.7

2.9

2.1

1.0

4.4

6.1

6.3

6.1

10.3

2.3

0.4

0.4

5.6

5.2

5.5

1.5

4.0

0.4

0.8

20.1

0.0

8.4

34.1

23.7

22.4

1.3

1.4

5.3

8.9

7.7

8.6

85.1

107.7

61.7

14.5

75.9

7.7

8.6

71.6

234.2

2.3

2.1

1.1

7.7

21.9

24.2

12.6

88.5

Total

5.1

3.6

8.4

5.0

2.4

2.1

5.2

4.1

2.0

1.5

2.8

21.6

4.1

11.9

79.6

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to ‘shadow banking entities’, December 2015. Note: Data refer to individual exposures

equal to or above 0.25 per cent of eligible capital. Country labels on the left hand side of the chart refer to the country of domicile of the reporting institution. Country labels

along the top of the chart refer to country of domicile of shadow banking entity. O-EU refers to other EU countries while RW refers to the rest of the world. The chart excludes

investment ﬁrms and exposures greater than 25% of the institution’s eligible capital (the large exposure limit).

In terms of exposures to shadow banking entities, UK banks have a total of 779

exposures (24% of the total in our sample), accounting for €284 billion (slightly more

13This allows us to match balance sheet data on total assets for 123 out of the 131 banks in our sample.

10

FIGURE 1. Number of counterparties and total exposures by bank (EUR million)

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to “shadow banking entities”, December 2015. Note: Data refer to individual exposures

equal to or above 0.25 per cent of eligible capital of the reporting bank. The chart excludes investment ﬁrms and exposures greater than 25% of the institution’s eligible

capital (the large exposure limit).

FIGURE 2. Number of counterparties and total exposures by bank (% of eligible
capital)

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to ‘shadow banking entities’, December 2015. Note: Data refer to individual exposures

equal to or above 0.25 per cent of eligible capital of the reporitng bank. The chart excludes investment ﬁrms and exposures greater than 25% of the institution’s eligible

capital (the large exposure limit).

11

than half of the total amount and 15% of UK’s GDP). German and French banks have

a total of 939 and 194 exposures (29% and 6% of the total), accounting for €106 billion

and €78 billion (roughly 4% of their countries’ GDP), respectively. The exposures of

UK banks represent 76% of their aggregate eligible capital, 108% for German banks

and 62% for French banks (Table 3). A comprehensive breakdown of the exposures by

sector and country of the shadow banking institutions is presented in the Sections 3.2

and 3.3.

Turning to the bank-level data, Figures 1 and 2 illustrate the positive relationship

between the number of counterparties of each bank and their total exposure to shadow

banking entities (both measured in € millions and as a % of their eligible capital). Fol-

lowing Cetorelli and Goldberg (2014), we construct a measure of banks’ geographic

complexity and check whether more complex banks tend also to have a greater geo-

graphical diversiﬁcation of their exposures to shadow banking entities.14 As in their

paper, this complexity measure is constructed using data on the location of the sub-

sidiaries of the banks in our sample. Figure 3 shows that the relationship between the

banks’ geographic diversiﬁcation of exposures to shadow banks and their geographic

complexity is positive, and it is also correlated with the banks’ size (depicted as the

size of each of the circles). A more comprehensive analysis of the geography of the

exposures is presented in Section 3.3.

3.2 What? The types of shadow banking counterparties

Table 4 shows that EU banks are exposed to many different types of shadow banking

entities.15 Importantly, there are signiﬁcant differences in the business models and risk

proﬁles across entities. For example, entities such as non-equivalent banks / insurers

and credit insurers / ﬁnancial guarantors are less likely to be captured in shadow bank-

ing surveillance exercises depending on the deﬁnition of shadow banking employed.

14The geographic complexity measure is constructed as a Herﬁndahl concentration index. See Ce-

torelli and Goldberg (2014) for further details.

15Regarding non-MMF investment funds, the data are even more granular and are split into hedge
funds, equity funds, real-estate funds, ﬁxed income funds, other investment funds and ‘not identiﬁed’
funds.

12

FIGURE 3. Geographic diversiﬁcation of EU banks’ exposures to shadow banking
entities and banks’ geographic complexity

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to ‘shadow banking entities’, December 2015. Note: Data refer to individual exposures

equal to or above 0.25 per cent of eligible capital. The chart excludes investment ﬁrms and exposures greater than 25% of the institution’s eligible capital (the large exposure

limit).

The ﬁnal dataset we employ shows that EU banks had around 65% of their exposures

to securitisations (26%), investment funds other than MMFs (22%) and ﬁnance compa-

nies (18%). Regarding the top ﬁve exposures by type and country of domicile of the

shadow banking entities (in EUR billions), Table 4 highlights that EU banks are most

heavily exposed to ﬁnance companies domiciled in the US, followed by US securitisa-

tion vehicles, ‘other’ US shadow banking entities, securitisation vehicles domiciled in

Ireland and US non-MMF investment funds.

While some of the linkages between banks and shadow banking entities may sim-

ply capture standard ﬁnancial intermediation ﬂows, others may reﬂect the increased

complexity of intermediation chains across the ﬁnancial system. In this paper we do

not explore the motives for the linkages between banks and shadow banking enti-

ties. Rather, taking a macroprudential perspective, we document the different types of

linkages and consider which are the most relevant for systemic risk monitoring. For

example, as the ﬁnancial crisis showed, a fundamental source of shadow banking risk

relates to regulatory arbitrage across borders and sectors, including from the bank-

13

14

TABLE 4. Distribution of EU banks’ exposures to shadow banking entities by
country of domicile and type of shadow banking entity (weighted by size of
exposure)

Country / type

1

2

3

4

5

6

7

8

9

Total

DE

ES

FR

GB

IE

LU

NL

O-EU

HK

JE

JP

KR

KY

RU

TR

US

RW

0.0

0.0

0.0

0.0

0.7

0.0

0.0

0.4

0.0

0.0

0.3

0.0

0.0

0.0

0.1

0.0

0.1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.7

0.2

0.0

0.1

3.7

0.0

0.5

2.5

0.8

2.3

0.2

2.0

0.5

0.2

0.2

0.0

3.4

0.0

0.0

4.0

1.9

1.0

0.3

0.6

2.0

0.7

0.7

0.6

1.4

0.0

0.1

0.1

0.2

0.7

0.1

0.4

8.2

1.2

0.0

0.0

0.0

0.1

0.0

0.0

0.4

0.5

0.0

0.0

0.2

0.0

0.0

0.0

0.0

0.0

0.6

0.0

0.0

0.0

0.6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.3

0.3

0.6

0.6

1.2

0.3

2.2

2.2

4.5

1.2

1.5

0.4

0.0

2.7

0.0

0.0

1.8

0.0

0.0

7.1

0.8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.1

0.0

0.0

0.4

1.9

0.1

1.7

3.0

1.6

4.4

0.4

0.1

0.4

2.4

0.3

0.4

0.1

0.3

0.1

0.0

0.2

0.5

0.5

0.1

0.0

4.7

2.5

6.3

0.7

4.5

10.5

6.3

5.2

2.5

4.4

1.3

3.0

1.5

2.6

6.5

1.9

3.5

27.1

12.1

Total

2.0

0.9

22.3

18.2 2.8

1.4

26.2 13.3

13.0

100.0

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to ‘shadow bank-

ing entities’, December 2015. Note: Data refer to individual exposures equal to or above 0.25 per cent

of eligible capital. Country labels on the left hand side of the chart refer to the country of domicile of

the shadow banking entity. O-EU refers to other EU countries while RW refers to the rest of the world.

The numbers along the top of the chart refer to the type of shadow banking entity as in EBA (2015).

1 = UCITS MMF; 2 = Non-UCITS MMF; 3 = Non-MMF investment fund; 4 = ﬁnance companies; 5 =

broker-dealers; 6 = credit insurers/ ﬁnancial guarantors; 7 = securitisation; 8 = non-equivalent banks /

insurers; 9 = other.

ing system. Fragmented regulatory regimes and a lack of information and disclosure

can lead to regulatory arbitrage and can impede systemic risk monitoring of shadow

banking interconnectedness.

A growing number of studies including Adrian and Ashcraft (2012) and Adrian,

Ashcraft and Ceterolli (2013) document some of the incentives of ﬁnancial institutions

to avoid taxes, accounting rules and capital requirements. Adrian (2014) highlights

regulatory and tax arbitrage as a potential motivation for the increasing role of shadow

banking entities within the ﬁnancial system. Similarly, Acharya, Schnabl and Suarez

(2013) show that regulatory arbitrage was an important motive in the setting up of

asset-backed commercial paper conduits. They ﬁnd that such shadow banking entities

had insufﬁcient risk transfer as losses remained with the banks themselves. The banks

had structured the guarantees to these entities so as to reduce regulatory capital re-

quirements. The results of Gornicka (2016) also provide insights into the role of guar-

antees between banks and shadow banking entities, while Claessens and Ratnovski

(2014) consider the reliance on backstops as a crucial feature of shadow banking.

Our data also show that 13% of EU banks’ total exposures are to entities that could

not be further identiﬁed and are labelled as ‘other’ shadow banking entities, highlight-

ing the information limitations for some types of entities. Moreover, the data illustrate

that the reporting banks possess limited information about the supervisory treatment

of their shadow banking counterparties. By value of the exposures, almost 90% of the

shadow banking counterparties were reported as either not supervised or not further

identiﬁed by the reporting institution.

The existence and scope of regulatory regimes for shadow banking entities can vary

both across but also within jurisdictions. This further reinforces the need for a detailed

mapping of the cross-sector linkages. For example, consider the three types of shadow

banking entities that EU banks are most exposed to: non-MMF investment funds, ﬁ-

nance companies, and securitisations. While investment funds and fund managers are

regulated and supervised in the EU through the UCITS and Alternative Investment

Fund Managers Directive (AIFMD) respectively, there is large variation with respect

15

to the regulatory treatment of ﬁnance companies and securitisations. Similarly, the

regulatory treatment of investment funds, ﬁnance companies and securitisations may

vary across jurisdictions outside the EU and may not be equivalent to EU applicable

regimes. In light of these differences, we proceed by examining the types of shadow

banking entities that EU banks are most exposed to and discuss potential ﬁnancial sta-

bility risks associated with the business models or particular features of these entities.

Securitisations: This classiﬁcation includes securitisation-based credit intermedi-

ation and funding of ﬁnancial entities including CLOs, ABCP and SIVs. These enti-

ties played a disruptive role during the global ﬁnancial crisis and have since received

increased attention from researchers, policymakers and regulators alike. Regarding

empirical studies, Acharya, Schnabl and Suarez (2013) show that such shadow bank-

ing entities had insufﬁcient risk transfer during the crisis as losses remained with the

banks themselves. Similarly, Covitz, Liang and Suarez (2013) document runs in the

ABCP market.16 Their ﬁndings suggest that banks may be exposed to liquidity vul-

nerabilities through their implicit and explicit support of ABCP programmes or other

conduits in the shadow banking system.

The interconnectedness of banks and shadow banking entities can lead to step-

in risk when banks provide sponsor support to securitisation conduits and SIVs. As

noted by BCBS (2015), the majority of securitisation vehicles and SIVs that encountered

difﬁculties during the global ﬁnancial crisis were supported by banks through liquidity

provisions that the banks were not contractually obliged to make. In most cases, the

sponsoring bank absorbed the losses of these vehicles, which resulted in bringing these

shadow banking entities back onto the bank’s balance sheet. Therefore, although cross-

sector interconnectedness can be unobservable or indirect in nature, step-in risk may

still exist.

Our data show that, by value of exposure, approximately 27% of EU banks’ expo-

16From a ﬁnancial stability perspective, bank step-in risks can refer to either liquidity risk (i.e. runs) or
solvency issues (i.e. losses) or both. Covitz, Liang and Suarez (2013) deﬁne a run on a commercial paper
program as when short-term creditors refuse to roll over their positions. Similarly, Acharya, Schnabl
and Suarez (2013) examine whether an investor that was holding ABCP at the start of the ﬁnancial crisis
suffered losses by not rolling over maturing ABCP.

16

sures to securitisations are to entities domiciled in the US, while they also have signiﬁ-

cant exposures to entities domiciled in Ireland, Jersey, France and the United Kingdom.

Moreover, EBA (2014) points to an inconsistent regulatory treatment of securitisation

vehicles across EU Member States. In some Member States, securitisation vehicles are

subject to solo prudential requirements, while in others they remain outside the reg-

ulatory perimeter. Table 4 shows that 36% of EU banks’ exposures to securitisation

vehicles are to entities which are domiciled in jurisdictions where no solo prudential

requirements are in place (e.g. Ireland, Luxembourg, the Netherlands and the United

Kingdom). Similar to ﬁnance companies, however, securitisation vehicles may be pru-

dentially consolidated in large banking groups or may be subject to some form of reg-

ulatory or statistical oversight. For example, euro area securitisation entities known

as ﬁnancial vehicle corporations (FVCs) have been subject to quarterly ECB statistical

reporting obligations since the end of 2009. In addition, jurisdictions such as Ireland

have recently introduced new reporting requirements on a wider sample of SPVs in

order to understand better their activities and their associated cross-sector and cross-

border linkages.17 The exposures of EU banks to securitisations captured in our ﬁnal

dataset may also include retained transactions, namely those that are not placed in

the market but held on balance sheet and used for collateral purposes in central bank

reﬁnancing operations.

Finance companies: Finance companies comprise entities that engage in loan pro-

vision that is dependent on short-term funding and include entities such as leasing

companies and ﬁnance companies. Pozsar et al. (2013) document the role played by

ﬁnance companies in the shadow banking system, which typically involves loan orig-

ination funded through commercial paper or medium term notes. Similarly, Grillet-

Aubert et al. (2016) note that these entities are relevant from a shadow banking per-

spective if they engage in credit intermediation outside of the regulatory perimeter and

are interconnected with the regular banking system.

Regarding their regulatory treatment, EBA (2014) highlights the fragmentation of

17See Godfrey, Killeen and Moloney (2015).

17

national regulatory regimes applicable to ﬁnance companies in the EU. For example,

in some EU Member States, ﬁnance companies are subject to solo prudential require-

ments (e.g. France, Italy, Portugal and Spain), whereas in others they are not (e.g.

Belgium, Bulgaria, Czech Republic, Denmark, Greece, Ireland, Lithuania, Latvia, the

Netherlands, Slovakia, the United Kingdom). As shown in Table 4, 45% of EU banks’

exposures to ﬁnance companies (in € billions) are towards US-domiciled entities, while

a signiﬁcant share of exposures to ﬁnance companies within the EU relate to jurisdic-

tions that do not report solo prudential regulatory requirements for ﬁnance companies

in EBA (2014). It is important to note, however, that some of the ﬁnance companies

domiciled in these jurisdictions may be consolidated in large banking groups. As ex-

plained below, however, a lack of data on the supervisory treatment of these entities

does not allow for such a detailed assessment.

Non-MMF investment funds: Our data include categories for hedge funds, equity

funds, real-estate funds, ﬁxed income funds, other investment funds and ‘not iden-

tiﬁed’ funds. There is a large diversity of business models within the sector which

affects their susceptibility to ﬁnancial stability risks, including run risk. For example,

alternative investment funds like hedge funds can employ signiﬁcant leverage and are

therefore relevant for shadow banking monitoring, while others such as equity funds

are less exposed to shadow banking risks. As noted by Grillet-Aubert et al (2016), ow-

ing to the diversity of the investment fund sector, the underlying liability structures,

investor proﬁles and regulatory constraints should be assessed when monitoring risks

within this sector.

More recently, liquidity and leverage within the investment fund sector and busi-

ness models such as open-ended real estate funds and high-yield corporate bond funds

have attracted increasing attention owing to their potential for amplifying ﬁnancial

stability risks. Therefore, understanding the exposures of EU banks to these types of

investment funds is important. Overall, our data show that 22% of total exposures of

EU banks are to non-MMF investment funds with many of these entities domiciled in

the US, Cayman Islands, Germany, Luxembourg and the United Kingdom.

18

3.3 Where? The cross-border exposures of EU banks to shadow bank-

ing entities

Next, we explore the cross-border nature of the exposures of EU banks to shadow

banking entities to understand better the internationalisation of the shadow banking

system and the cross-border complexity of the linkages. Our data highlight the global

and cross-border nature of the interconnectedness of shadow banking entities, as ap-

proximately 60% of EU banks’ total exposures to shadow banking entities are towards

non-EU domiciled entities. In particular, these data show the strong links between EU

banks and US-domiciled shadow banking entities, which account for approximately

27% of the total exposures in our ﬁnal dataset. A number of studies including, for

example, IMF (2014) and Maes (2014) highlight the interaction of EU banks and US-

domiciled shadow banking entities during the ﬁnancial crisis.

Figures 4 and 5 map the international exposures of EU banks to shadow banking en-

tities using granular bank-level and individual exposure level information. The green

nodes represent the reporting banks, while their size is determined by the number of

individual counterparties or shadow banking entities to which they are exposed to (de-

gree centrality). Orange nodes represent non-EU-domiciled shadow banking entities

while the purple nodes represent EU domiciled shadow banking entities. Moreover,

the orange links in the network in Figure 4 show that EU banks have a large number of

exposures to non-EU domiciled shadow banking entities. The purple links show that

EU banks have exposures to a number of EU domiciled shadow banking entities while

the blue links represent domestic exposures (EU bank exposure to a shadow banking

entity domiciled in the same country as the bank).

Figure 5 shows a network where the positioning is identical to the network pre-

sented in Figure 4. However, the node size in Figure 5 is proportional to total expo-

sures (i.e. sum of all individual exposures) while the colour of links range from green

(smaller exposures) to orange (larger exposures) depending on the size of the individ-

ual exposure in € billions. Taken together, these network graphs show that many of the

most interconnected EU banks (those with the largest number of linkages measured by

19

degree centrality) are typically the banks with the largest individual exposures.

In Figure 6, we examine the geography of the exposures of the top twenty-ﬁve

banks by their exposures to shadow banking entities. While there is some heterogene-

ity across banks, it is clear the EU banks are heavily exposed to shadow banking entities

located outside of the EU. The monitoring of such linkages is particularly challenging if

authorities lack an holistic view of banks’ and shadow banks’ international activities or

if they lack information on some aspects of their activities. Moreover, Figure 7 presents

a global map of the exposures based on the country of domicile of the shadow bank-

ing entity. As highlighted by Lane (2016a), information sharing amongst regulators

and policymakers can be an important component in the surveillance of international

shadow banking activities.

While our analysis provides a unique insight into the geography of the exposures of

EU banks to shadow banking entities, it is subject to a number of limitations. The data

underpinning our analysis show the domicile of the shadow banking entity. As noted

by Grillet-Aubert et al. (2016) the “country of domicile is important in determining the

geographic locus of potential supervisory measures”. However, such geographic data

are based on a ﬁrst counterparty basis and may not reﬂect the ultimate risk bearers

within the shadow banking entity. For example, while a shadow banking entity may

be domiciled in country A owing to a favourable business or tax environment, the end

investor may be located in country B, and therefore such ﬂows of potential risk would

not be captured in our analysis. In addition, we lack information on the regulatory

treatment of the shadow banking counterparty which would be required to map the

cross-border risks in more detail.

20

FIGURE 4. Network of EU banks’ exposures to shadow banking entities

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to “shadow banking entities”, December 2015. Note: Data refer to individual exposures

equal to or above 0.25 per cent of institutions’ eligible capital. Green nodes: reporting institutions (banks) labelled by country of residence. Purple and orange nodes: EU and

non-EU domiciled shadow banking entities, respectively. The chart excludes investment ﬁrms and exposures greater than 25% of the institution’s eligible capital (the large

exposure limit). Node size is proportional to degree centrality (the number of counterparties). Blue links represent domestic exposures (EU institution to a domestic shadow

banking entity); purple links represent EU exposures (EU institution to EU-domiciled shadow banking entity) and orange links represent non-EU exposures (EU institution

to non-EU domiciled shadow banking entity).

FIGURE 5. Network of EU banks’ exposures to shadow banking entities

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to “shadow banking entities”, December 2015. Note: Data refer to individual exposures

equal to or above 0.25 per cent of institutions’ eligible capital. Green nodes: reporting institutions (banks) labelled by country of residence. Purple and orange nodes: EU

and non-EU domiciled shadow banking entities, respectively. The chart excludes investment ﬁrms and exposures greater than 25% of the institution’s eligible capital (the

large exposure limit). Node size is proportional to total exposures (sum of all individual exposures). Colour of link ranges from green to orange depending on the size of the

individual exposure (green links: smaller exposures, orange links: larger individual exposures).

21

22

FIGURE 6. Top 25 EU banks by their exposures to shadow banking entities

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to ‘shadow

banking entities’, December 2015. Note: Data refer to individual exposures equal to or above 0.25 per

cent of eligible capital.

23

FIGURE 7. Geography of risk: distribution of exposures by domicile of shadow
banking entities

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to ‘shadow

banking entities’, December 2015. Note: Data refer to individual exposures equal to or above 0.25 per

cent of eligible capital.

3.4 Concentration / Overlap Analysis

We now move to analyse to what extent EU banks’ exposures to shadow banking enti-

ties are concentrated and overlapped. By “overlap”, we intend to measure the common

exposures of EU banks towards shadow banking entities, and we introduce a speciﬁc

measure to do so.

There is a natural relationship between concentration and overlap, which we il-

lustrate in the following example, where we analyse three different ﬁnancial systems

composed of 2 banks and 3 shadow banks. For illustrative purposes, for each bank, we

consider its portfolio structure as the normalised total exposures (so that they sum up

to one).

Example Consider the three ﬁnancial systems represented by the following portfolio

matrices:

π1 =











0 1 0

0 0 1

π2 =











0 0 1

0 0 1






π3 =

1/3 1/3 1/3

1/3 1/3 1/3






where the element πs

ik refers to the exposure from bank i to shadow banking entity k

in the s-th system.

The ﬁrst system (matrix π1) presents zero overlap because of the high concentra-

tion of the two banks. Each of the banks has no diversiﬁcation and concentrates its

exposures to one entity. A shock to any of the shadow banking entities will not impact

the two banks at the same time. The second system (matrix π2) has maximal over-

lap because of the concentration of its banks. Banks have a concentrated portfolio of

exposures (no diversiﬁcation) but they are both exposed to the same shadow bank-

ing entity. A shock to the third shadow banking entity will affect both banks at the

same time. The third system, captured by the matrix π3, presents a completely dif-

ferent picture: the two banks achieve maximal diversiﬁcation by being exposed to all

shadow banking entities in the system. The third system has therefore maximal over-

lap because of low concentration (maximal diversiﬁcation) of its banks. Any shock will

24

impact equally both banks at the same time.

Overall, this example highlights that the increased diversiﬁcation of exposures may

increase overlap and therefore reduce the beneﬁt of diversiﬁcation owing to the com-

monalities of the sources of shocks. In this sense, banking systems may be potentially

fragile to certain types of shocks while being robust to others: a system with a high

degree of common exposures due to diversiﬁcation may be resilient to small common

shocks, but vulnerable to shocks beyond a speciﬁc threshold (see, e.g., Acemoglu et

al. 2015). This systemic feature of the ﬁnancial system has received increased attention

in recent years. As ﬁnancial systems increase their levels of complexity and intercon-

nectedness, they face a trade-off between diversiﬁcation and systemic risk (Battiston et

al., 2012). This stream of research shows that more connections may increase individual

diversiﬁcation while, at the same time, making the system more susceptible to common

shocks which may lead to wide-scale systemic failures.

Some studies argue that a certain degree of individual concentration can be bene-

ﬁcial. Stomper (2006), for instance, links concentration to the expertise lenders have

in certain sectors. On the other hand, a more locally concentrated system does not

beneﬁt from diversiﬁcation and individual institutions may become riskier. Concen-

tration risk therefore still represents one of the main possible causes of major losses in

a credit institution. The global ﬁnancial crisis brought to light many examples of risk

concentrations within ﬁnancial institutions. Since it can directly affect the survival of

an institution, concentration risk requires special attention by supervisors. This has led

policymakers to focus speciﬁcally on concentration.18 Understanding whether overlap

is ascribable to either concentration or diversiﬁcation or both is therefore important

from a ﬁnancial stability perspective.

3.4.1 Setup

Hence we seek to understand the type and geography of the shadow banking entities

for which overlap occurs, which we hereby analyse. To this end, consider the set of

18See, e.g., the BIS (BCBS, 2006) and EBA study on Concentration (CEBS Guidelines on the manage-

ment of concentration risk).

25

n banks (indexed by i = 1, 2, . . . , n) and the set of the m shadow banks (indexed by

k = 1, 2, . . . , m). Given an exposure xik of bank i to shadow bank k, the total expo-
sure to shadow banking entities of each bank i is therefore (cid:80)

k xik. Further, deﬁne the

following two ratios:

πik =

exposure of i to k
total shadow bank exposures of i

=

xik
k xik

(cid:80)

and

lik =

exposure of i towards k
capital of i

=

xik
ci

where πik can be thought of as the elements of the portfolio matrix of the system (with

(cid:80)

k πik = 1, ∀i), and lik are individual exposures as a fraction of bank i’s capital. More-

over the total exposure of i as a fraction of its capital is simply given by li = (cid:80)

k lik and,

therefore, it holds that lik = li × πik. A simple, yet useful interpretation of the term lik

is the percentage of equity lost by i given a shock on k. In this sense, if lik > 1, i may

itself default from the default of k.

Concentration A well-known measure of concentration is the Herﬁndahl index (HHI)

of concentration, computed in our case as follows:

Hi =

(cid:88)

π2
ik

k

which measures the concentration of the portfolio of exposures to shadow banking en-

tities of bank i. The top panel of Figure 8 reports the values of Hi for each bank versus

their normalised degree (i.e. the number of shadow banking entities to which bank i

is exposed). It shows that concentration is inversely related to the number of shadow

banking counterparties, i.e. banks with less concentration in their exposures to shadow

banking entities also have a larger set of counterparties. The bottom panel of Figure 8

reports the values of Hi versus the number of different countries to which each bank

is exposed. It suggests that banks with low levels of concentration have their expo-

26

sures split across many different countries. This has an important implication for our

cross-border analysis if diversiﬁcation is achieved by having exposures to jurisdictions

or sectors where prudential regulation may not apply.

Overlap We focus on a simple measure for portfolio overlap between two banks:

sπ
ij =

(cid:88)

m

min{πim, πjm}

(1)

where the index m refers to each of the nine shadow banking categories in our sample

(as described in Section 3.2), aggregated by their country of domicile. The quantity sπ
ij

can be interpreted as the intersection of the two portfolios, i.e. the common amount

of exposures to the same combinations country/type of shadow bank entities. We

compute this pairwise quantity for all the banks in our sample. Naturally, given the

large number of shadow banking entities in our sample, many banks will have zero

overlap, in that they will have no shadow bank exposure in common. It is therefore

useful to compute the overlap both conditional on the existence of common exposures

and unconditionally. Also, given the granularity of our dataset, we can analyse the

levels of overlap at two levels of aggregation: by country of shadow banking entity

and by type of shadow banking entity. The results are reported in Figure 9, where we

plot the empirical cumulative density functions (ECDF) of the overlaps for these two

levels of aggregation. In both cases, the aggregation by type of shadow banking entity

provides higher levels of overlap. The conditional overlap (ECDF) on the top panel

shows that, once strictly positive overlaps are computed, these overlaps can be high

for a relatively large amount of bank pairs. For example the overlap by country is at

least 0.6 for ten percent of the bank pairs. An important fraction of banks may therefore

be exposed to common shocks originating from the same set of countries.

3.5 Vulnerability

Shocks from the shadow banking system may potentially engender distress in the

banking sector and can lead to signiﬁcant losses. To explore this, we build on the

27

28

FIGURE 8. Concentration.

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to “shadow bank-

ing entities”, December 2015. Note: the horizontal axes represent concentration indices for each bank

as measured by Equation (1). The vertical axes represent normalised degree (top panel) and number of

countries to which each bank is exposed (bottom panel).

llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll0.00.20.40.60.81.00.00.20.40.60.81.0individual Herfindahl indexnormalised degreellllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll0.00.20.40.60.81.005101520253035individual Herfindahl indexnumber of countries29

FIGURE 9. Overlap.

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to “shadow bank-

ing entities”, December 2015. Note: empirical cumulative density functions (ECDF) of the overlap mea-

sure is computed by Equation (1). Blue lines represent the overlap in terms of countries to which banks

are exposed. Green lines represent the overlap in terms of the types of shadow banking entities (as de-

ﬁned in Section 3.2). Top panel: distribution of overlap conditional on the existence of commonalities.

Bottom panel: unconditional distribution.

0.00.20.40.60.81.00.00.20.40.60.81.0overlap distributionoverlapP(overlap <= t)overlap by country of sboverlap by type of sb0.00.20.40.60.81.00.00.20.40.60.81.0overlap distributionoverlapP(overlap <= t)overlap by country of sboverlap by type of sbframework of Battiston et al. (2016) to understand potential capital losses owing to

shadow banking activities. First, deﬁne the following quantity:

(cid:40)

hi = min

1,

(cid:88)

k

(cid:41)

hik

, where hik = likrk

which measures the potential relative equity loss due to a shock rk ∈ [0, 1] on a shadow

banking entity k. The total relative equity loss for the banking system of a country C

is:

where wi = ci
(cid:80)
j cj

the system.

(cid:40)

HC = min

1,

(cid:32)

wi

(cid:88)

likrk

(cid:33)(cid:41)

k

(cid:88)

i∈C

is the relative capital of i with respect to the total aggregate capital in

Within this framework, it is possible to understand what are the types of shadow

banking entities, aggregated by their country of domicile, that could lead to the largest

losses for the EU banking system, by computing the following quantity:

(cid:40)

Hm = min

1,

(cid:41)

wilimrm

,

(cid:88)

i

(2)

where m represents one of the nine shadow banking categories in our sample (as de-

scribed in Section 3.2), aggregated by their country of domicile. By assuming a com-

mon shock to all sectors (rm = r, ∀k), we can rank them in terms of the potential equity

loss they would cause in the EU banking system. Table 5 presents the ranking for

the top ten results. We observe that ﬁnance companies (Type 4) domiciled in the US

represent the top source of vulnerability for the EU banking sector, followed by US-

domiciled securitisation vehicles (Type 7) and US-domiciled shadow banking entities

that could not be further deﬁned or classiﬁed as ‘other’ (Type 9). This shows that

the top three largest exposures for the EU banking system are indeed US-domiciled

shadow banking entities.

Last, we analyse how these different sources of vulnerability may impact the same

30

TABLE 5. Vulnerability

Rank Source

Rank Source

1

2

3

4

5

US Finance companies

US Securitisation

US ‘Other’

IE Securitisation

6

7

8

9

DE Non-MMF investment funds

KY Non-MMF investment funds

TR Non-equivalent banks/insurers

JE Securitisation

US Non-MMF investment funds

10

GB ‘Other’

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to “shadow bank-

ing entities”, December 2015. Note: Rank of the top ten sources of vulnerability (from highest to lowest)

for the EU banking system (in terms of potential equity loss). The sources of vulnerability are shadow

banking categories as classiﬁed in Section 3.2, aggregated by their country of domicile.

banks by computing a leverage-based overlap measure:

Sl

ij =

(cid:88)

k

min{lik, ljk}

(3)

which measures the common relative equity loss to the banking systems of any pair of

countries following a given shock.19 We plot the network associated to Sl

ij in Figure 10.

The colour of the nodes reﬂects the level of concentration computed as the Herﬁndahl

index, ranging from blue (smaller concentration) to red (larger concentration). It shows

that less concentrated banking systems tend have more overlap: more diversiﬁcation

leads to more overlap between banking systems of different EU countries. Given that

a common source of vulnerability is represented by US-domiciled entities, this high-

lights the potential transmission channels stemming from the US shadow banking sys-

tem which were exposed during the ﬁnancial crisis (Maes, 2014).

19By banking system of a country, we refer to the aggregation of all banks in a given country, weighted

by the size of their equity.

31

32

FIGURE 10. Common vulnerability overlap: less concentrated systems tend to be
more overlapped

Source: Authors’ calculations based on EBA (2015), Report on institutions’ exposures to “shadow bank-

ing entities”, December 2015. Note: each node in the network represents a banking system, deﬁned as

the aggregation of the banks by country. The size of the nodes is proportional to the system’s total ex-

posures to shadow banking entities. The size of the weights reﬂects the level of leverage-based overlap

as computed in Equation 3. The colour of the nodes reﬂects the level of concentration computed as the

Herﬁndahl index, ranging from blue (smaller concentration) to red (larger concentration).

4 Policy

Recent policy initiatives such as the Capital Markets Union (CMU) seek to increase the

role of market-based ﬁnance in the EU ﬁnancial system but also reinforce the need for

detailed monitoring frameworks for the shadow banking system. The global ﬁnan-

cial crisis highlighted the risks associated with an overbanked ﬁnancial system (ESRB

2014), while empirical evidence suggests that a heavily bank-based ﬁnancial system

can exert a negative effect on long-run economic growth (Langﬁeld and Pagano 2016).

But credit intermediation through lightly regulated or non-regulated non-bank ﬁnan-

cial institutions can also pose risks to ﬁnancial stability if left unmonitored.

The potential leakage of bank-like activities outside the regulatory perimeter poses

challenges for policymakers and regulators. Regulatory arbitrage – the circumvention

of ﬁnancial regulation or tax or disclosure rules – reduces the overall transparency of

the ﬁnancial system and can lead to the build-up of vulnerabilities. As revealed during

the ﬁnancial crisis, the build-up of systemic risks went unnoticed in less regulated parts

of the ﬁnancial system until it was too late. Even when policymakers and regulators

identiﬁed these risks, they lacked appropriate data to understand the extent of the

linkages across institutions, markets and borders. Therefore, as noted by Cecchetti

and Tucker (2016), regulatory arbitrage and the behaviour of institutions and activities

which lie outside the regulatory perimeter should be a central consideration in the

design of macroprudential regimes aimed to enhance ﬁnancial stability and secure the

resilience of the ﬁnancial system.

A growing number of studies have highlighted the role of macroprudential policy

in mitigating ﬁnancial stability risks (Borio, 2009., Hanson et al. 2011., Claessens et

al, 2013., Schoenmaker and Wierts, 2016). While macroprudential policy in the bank-

ing sector in Europe is operational, the toolkit for macroprudential policy beyond the

banking sector is still in its infancy. As noted by ESRB (2016b), a broader set of macro-

prudential instruments can assist in mitigating potential ﬁnancial stability risks arising

from the increased interconnectedness of the ﬁnancial system. Lane (2016b) posits that

the rise of the shadow banking system and the scope for cross-border spillovers and

33

substitution effects requires careful coordination of macroprudental policies through

authorities such as the ESRB. The importance of international cooperation in the de-

sign and implementation of macroprudential policy has also been highlighted by Cec-

chetti and Tucker (2016). Moreover, recent empirical evidence supports the view that

activities can leak across borders and sectors (Claessens et al. 2013., Aiyar et al. 2014.,

Reinhardt and Sowerbutts. 2015., Cizel et al. 2016).

ESRB (2016b) provides an overview of macroprudential policy options to address

risks from non-bank ﬁnancial institutions and describes existing leverage and liquid-

ity requirements (e.g. UCITS and AIFMD) which have a macroprudential dimension.

New regulatory data through the AIFMD, the European Market Infrastructure Regula-

tion (EMIR) and the Securities Financing Transaction Regulation (SFTR) can allow for

a more detailed assessment of the interconnectedness of banks and shadow banking

entities in a number of markets. These data can also be used to monitor the use of syn-

thetic leverage by non-bank ﬁnancial institutions and allow for the mapping of link-

ages between banks and non-banks in derivative markets (Abad et al., 2016; D’Errico

et al., 2016).

Recent policy measures can act to enhance the resilience of the ﬁnancial system

and mitigate some of the ﬁnancial stability risks associated with the increased inter-

connectedness of the banking and shadow banking systems. For example, the EBA’s

guidelines on limits on exposures to shadow banking entities, effective from 1 January

2017, introduce an approach that will allow EU institutions to set internal limits for

their exposures to shadow banking entities. For those institutions that do not have

sufﬁcient information on their exposures to shadow banking counterparties, the EBA

requires a fallback approach involving a ﬁxed limit (25% of eligible capital) to all or

some of the aggregated exposures. In addition, limits on concentration of exposures

can also assist in mitigating risks across sectors (ESRB, 2016b).

34

5 Conclusions

The increasing size of the shadow banking system underlines the need for a better

understanding of the linkages of shadow banking entities with different parts of the

ﬁnancial system, including the banking system. Despite the increased focus on as-

sessing the interconnectedness between banks and shadow banking entities since the

ﬁnancial crisis, to the best of our knowledge no study documents these linkages using

bank- and exposure-level information in Europe. In light of this gap in the literature,

this paper examines the exposures of EU banks to shadow banking entities drawing

on a unique data collection exercise by the EBA in March 2015.

Our analysis conﬁrms that the exposures of EU banks to shadow banking entities

is global and spans regional and national borders. Consistent with the literature, our

analysis shows that EU banks have signiﬁcant exposures to shadow banking entities

globally and, in particular, to entities domiciled in the US, which represent approxi-

mately 27% of the total exposures. Moreover, our analysis ﬁnds that EU banks’ expo-

sures to shadow banking entities are concentrated by type of counterparty, with ap-

proximately 65% of the exposures to securitisations, non-MMF investment funds and

ﬁnance companies. The global ﬁnancial crisis showed how such linkages can act as

contagion paths and can lead to the ampliﬁcation of shocks across borders and sectors.

Therefore, for the purposes of systemic risk monitoring, our ﬁndings further reinforce

the need for coordinated responses, which can be facilitated through greater coopera-

tion and information sharing amongst regulators.

We also examine the categories of shadow banking entities that can potentially act

as a source of vulnerability to the EU banking system, when the former are grouped

by their country of residence and type. When examining the level of concentration

and overlap of the exposures, we ﬁnd that EU banks have low levels of individual

concentration towards shadow banking entities. However, this diversiﬁcation leads

to high overlap where different banks are commonly exposed to the same types of

shadow banking entity. This high but shared diversiﬁcation across types of shadow

banking entities may potentially lead to common sources of vulnerability in times of

35

stress.

Our results also suggest a number of avenues for future research. The analysis in

this paper presents EU banks’ exposures to shadow banking entities and is therefore

based on the asset side of banks’ balance sheets. Building on this, future work can

seek to investigate the liability side of banks’ balance sheets and speciﬁcally the role of

shadow banking entities as a source of funding for banks. Moreover, understanding

the linkages of shadow banking entities to other non-bank ﬁnancial institutions is also

an important part of the ﬁnancial ecosystem which needs to be monitored. A mapping

of these linkages and potential contagion paths between sectors and jurisdictions will

provide a more complete picture of the interconnectedness of the banking and shadow

banking systems. Other potential areas for further work include an examination of

the supervision and regulation of individual shadow banking entities, including their

potential prudential consolidation, in order to understand better which entities lie fully

outside of the regulatory perimeter.

36

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41

Imprint and acknowledgements 

The views expressed in this paper are our own, and do not necessarily reflect the views of the ESRB, EBA or 
their member institutions. Any remaining errors are our own. The authors would like to thank Claudia Guagliano, 
Olaf Weeken, the ESRB’s Joint ATC-ASC Expert Group on Shadow Banking (JEGS), co-chaired by Steffen 
Kern and Richard Portes, and the ESRB’s Advisory Scientific Committee (ASC) for helpful comments and 
discussion. In addition, the authors thank participants at the 5th EBA Policy Research Workshop including our 
discussant Nicola Garbarino (Bank of England) and participants at the National Bank of Belgium / ECB / ESRB 
Workshop 2017 “Taking stock of the analytical toolkit for macroprudential analysis” including our discussant Jan 
Klacso (National Bank of Slovakia) for helpful comments on earlier drafts. Marco D’Errico acknowledges support 
from: Swiss National Fund grant no PP00P1-144689, FET Projects SIMPOL (no 610704) and DOLFINS 
(no 640772). 

Jorge Abad 
CEMFI; email: jorge.abad@cemfi.edu.es 

Marco D’Errico 
IBF – University of Zurich; European Systemic Risk Board Secretariat; email: marco.derrico@esrb.europa.eu 

Neill Killeen 
European Systemic Risk Board Secretariat; email: neill.killeen@esrb.europa.eu 

Vera Luz 
European Banking Authority; email: vera.luz@eba.europa.eu 

Tuomas Peltonen 
European Systemic Risk Board Secretariat; email: tuomas.peltonen@esrb.europa.eu 

Richard Portes 
London Business School and European University Institute; email: rportes@london.edu 

Teresa Urbano 
European Banking Authority; email: teresa.urbano@eba.europa.eu 

© European Systemic Risk Board, 2017 

Postal address 
Telephone 
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+49 69 1344 0 
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All rights reserved. Reproduction for educational and non-commercial purposes is permitted provided that the 
source is acknowledged. 

Note: 
The views expressed in ESRB Working Papers are those of the authors and do not necessarily reflect 
the official stance of the ESRB, its member institutions, or the institutions to which the authors are 
affiliated. 

ISSN 
ISBN 
DOI 
EU catalogue No 

2467-0677 (online) 
978-92-95081-99-4 (online) 
10.2849/713713 (online) 
DT-AD-17-007-EN-N (online)