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Using large exposure data to gauge the systemic importance of SSM significant institutions

Prepared by Giovanni Covi, Christoffer Kok and Barbara Meller

This article presents stylised facts from the euro area network of large exposures and derives model-based interconnectedness measures of SSM significant institutions. [1] The article has three main findings. First, the interbank network is relatively sparse and suggests a core-periphery network structure. Second, the more complex network measures on average correlate highly with the more simple size-based interconnectedness indicators, constructed following the EBA guidelines on the calibration of O-SII buffers. Third, there is nevertheless value for policymakers to take into account network-based measures in addition to the size-based interconnectedness indicators, as for some individual banks those measures can deviate considerably.

1 Introduction

A bank can be systemic for a variety of reasons, giving rise to “too-big-to-fail” concerns and warranting an appropriate prudential response. For instance, a bank may perform functions that are critical to the smooth functioning of the financial system. If the bank were to fail, it might be difficult to continue these functions. In addition, the more complex an institution is, the more difficult recovery and resolution procedures are to handle. Finally, the systemic footprint of a bank, through its interconnections with other financial institutions, may give rise to broad-based contagion risks. This article focuses on the systemic importance of banks due to this latter aspect.

Before the global financial crisis little attention was paid to financial stability risks related to intra-financial linkages. The crisis, however, revealed the intertwined nature of modern financial systems. It became evident that shocks hitting one part of the system can easily propagate to the broader financial system, whereby risks stemming from interconnectedness can be systemic.

The financial turmoil in the wake of the failure of the US-based investment bank Lehman Brothers, in particular, demonstrated that the consequences of a shock to an interconnected and complex financial system are particularly hard to predict. This highlighted the need to develop analytical tools and indicators to support central banks and other oversight bodies in identifying and monitoring cross-sectional systemic risks.[2]

Consequently, network-based models and indicators have become part of the analytical toolkit of most advanced central banks. These analytical tools serve several purposes. First of all, network tools should take into account the complexity of financial interactions and ideally be able to capture the high degree of interconnectedness across multiple layers of financial institutions.[3] Second, they should allow assessment of contagion risk, both in terms of scope and magnitude of propagation. For instance, in recent years network-based analysis has been incorporated in macroprudential stress test analysis with a view to taking into account second-round contagion effects arising as a result of an adverse scenario.[4] Third, indicators and tools capturing interconnectedness can be useful in identifying systemic institutions, e.g. those that transmit or amplify shocks. Fourth, they can be useful for informing macroprudential policy decisions related to structural systemic risks, in particular interconnectedness.

In order to serve these objectives, however, relevant data availability to properly assess interconnectedness and implied contagion risk in a timely manner is of the essence. Against this background, this article explores SSM significant institutions’ supervisory reporting of large exposures to gauge the interconnections in the interbank network, and beyond, and the related contagion risk.

The article is structured as follows: Section 2 describes the construction of the interbank network based on the large exposure reporting, while Section 3 sets out the main features of the network. Section 4 presents the various macroprudential policy tools available for addressing systemic risks related to interconnectedness. In Section 5, measures of interconnectedness based on the large exposure network are put into the perspective of existing macroprudential measures, and Section 6 concludes.

2 Using large exposure data to measure interconnectedness

Article 392 of the Capital Requirements Regulation (CRR-575/2013) defines “large exposure” as an exposure, before the application of credit risk mitigation (CRM) measures and exemptions, equal or higher than 10% of a bank’s eligible capital vis-à-vis an individual client or group of connected clients.[5] Euro area banks’ large exposures vis-à-vis banks, financial institutions, non-financial institutions, governments, central banks and households are collected and monitored for prudential purposes.[6] This monitoring threshold is combined with a large exposure limit whereby, after taking into account the effect of CRM and exemptions, an exposure should not be higher than 25% of the institution’s eligible capital (Article 395).[7] Overall, the data captures more than 50% of euro area consolidated significant institutions’ (SIs) total assets in terms of gross exposures and roughly 40% of their risk-weighted assets (RWAs) in terms of net exposures. Notably, the large exposure network captures 90% of RWAs vis-à-vis other credit institutions (on a net basis).

The large exposure database fills an important data gap by shedding light on the distribution of euro area SIs’ large exposures by country, sector and type of counterparty. Specifically, the data set encompasses detailed information about the exposure and counterparties, such as the type of risk captured, the instrument and maturity breakdown, as well as counterparty and reporting entity information (legal entity identifier (LEI) code, country and sector), which allows linking of the large exposure dataset to complementary data sources.[8] The large exposure data thus provides a rich set of information that can be exploited for assessing the degree of interconnectedness and systemic risk both of and within the euro area’s financial system.

Use of the large exposure data for network analysis, however, requires substantial data preparation efforts. The large exposure data reporting templates were designed to monitor concentration risk for supervisory purposes and not specifically for constructing a network to assess the degree of interconnectedness and systemic risk of the euro area’s banking system. Therefore, the European Banking Authority’s instructions for reporting large exposures and concentration risk reflect a different purpose and the counterparty code used for identifying each individual client or group of connected clients is not unique across euro area countries. Moreover, in 70% of cases there is no LEI code to identify the counterparty (many entities do not have a LEI code) and for groups of connected clients they are not required to be reported (covering half of the sample). Consequently, the only way to identify counterparties across countries is by the counterparty’s name, which often is reported differently by each reporting entity and in different languages according to the national reporting system. For this purpose, an advanced mapping code was constructed to reconcile counterparties’ names and to fill data reporting gaps.

Figure 1 depicts the resultant SI network of large exposures. The most interconnected banks (in terms of number of counterparties) are placed in the inner circle and banks are clustered and coloured according to their home country. The size of each dot (or “node”, to use network terminology) depicted captures the total number of the institution’s counterparties (exposures to and from), while the thickness of the lines (or edges) represents the exposure value.

The figure is divided into two mirror images. Both images show the same network structure, the difference being that the colour assigned to the edges is aligned with the colour of the institution receiving (Panel a – Borrower perspective) or lending the funds (Panel b – Lender perspective).

This representation clearly shows the home bias pattern, i.e. the dense flow of linkages among banks within the same country. Moreover, it stands out that French banks (blue) within the euro area SI interbank network of large exposures are the most exposed to funding risk since they are highly interconnected on the borrowing side. On the other hand, German and Italian banks are mostly visible in the lender perspective and thereby exposed to counterparty credit risk. Moreover, it is evident that relatively less interconnected banks tend to have lower cross-border activities than inner-circle banks. Overall, the visualisation of the network suggests a core-periphery network structure, where many banks (in the outer circles) are only connected to a few (mostly core) banks. This feature also results in a relatively sparse network. In fact, only 6.3% of all possible links are present.

Figure 1

Intra-euro area SIs’ network of large exposures

a) Borrower perspective

b) Lender perspective

Source: COREP supervisory data, Templates C.27-C.28.
Notes: The institutions represented are euro area significant institutions in the large exposures sample. The cut-off date for data was the third quarter of 2017. The size of the nodes captures the number of an institution’s linkages, while the thickness of the edges represents the exposure value in net terms. The chart is divided into two mirror images, which maintain the same network structure but assign a different colour to the edges according to the colour of the node (institution) receiving (Panel a – Borrower perspective) or lending the funds (Panel b – Lender perspective).

3 Salient features of the large exposure network

The large exposure data provides a comprehensive picture of euro area banks’ large exposures globally. The total amount of euro area SIs’ gross exposures, i.e. before application of credit risk mitigation (CRM) measures and exemptions, was €11.5 trillion in the third quarter of 2017 (see Table 1). The net amount, i.e. after the application of CRM measures and exemptions, is approximately 25% of the gross amount, amounting to around €3 trillion. Moreover, the number of counterparties captured in the data exceeds 3,500 on a consolidated basis. Overall, by adding the less significant institutions (LSIs) to the sample of reporting institutions, the gross exposure increases by almost €1.5 trillion and the net amount by €220 billion.

The most important counterparty sector of euro area SIs is non-financial corporations (NFCs). SIs’ exposure to NFCs amounts, in gross terms, to 33% (€4.36 trillion) of their total exposure, and in net terms to 50% (€1.5 trillion). In comparison, general government is SIs’ second most important counterparty group in terms of gross exposure, accounting for 28% (€3.3 trillion) of the total. Notably, in terms of SIs’ net exposures at risk, exposure to general government amounts to only 7% of the total, or around €280 billion.[9] Lastly, exposures to financial corporations amount to 11% and 12% of the total, in gross and net terms respectively.

With regard to SIs’ exposures to credit institutions (see the bottom panel of Table 1), the total gross amount is close to €1.9 trillion, while the exposure at risk is almost 41% of the gross amount (€750 billion). SIs’ net exposure towards the 23 non-euro area global systemically important banks (G-SIBs) (around €260 billion) is smaller but close to the net exposure to other euro area SIs; together they account for 76% of the banking network’s exposure value. Adding the large exposures of LSIs to those of other credit institutions (around €570 billion in total gross terms), it can be observed that around half of euro area LSIs’ gross (as well as net) exposures are with euro area SIs.

Table 1

Large exposures by counterparty sector

(EUR billion)

Source: COREP supervisory data, Template C.28.
Notes: Reporting institutions refers to euro area significant institutions (SIs) and less significant institutions (LSIs).Non-EA G-SIBs and non-EA LSIs denote non-euro area global systemically important banks and non-euro area less significant institutions respectively. Additionally, SDBs denotes state development banks and IOs denotes international organisations. Exposure value is reported in gross (G) and net (N) terms, with No denoting the number of counterparties.

Focusing on intra-euro area large exposures of SIs and LSIs broken down by country (see Table 2), it can be observed that France and Germany host the banking sectors with the highest exposure in net terms, €967 billion and €933 billion, respectively. The Italian and Spanish banking sectors hold net exposures of €374 billion and €334 billion respectively.

Table 2

Large exposures of reporting institutions (SIs and LSIs) by country

(EUR billion)

Source: COREP supervisory data, Template C.28.
Notes: Reporting institutions refers to significant institutions (SIs) and less significant institutions (LSIs). Exposure value is reported in gross (G) and net (N) terms.

Figures 2 and 3 illustrate that most of the individual large exposures are clustered at the lower end of the 0-25% range of eligible capital (as observed by the right-skewed distributions). At the same time, it is notable that average exposures to some counterparty sectors tend to be larger than to other sectors. For instance, focusing on interbank exposures (see Figure 2, panel a) in euro, the size of G-SIBs’ large exposures is generally larger than those of smaller banks. At the same time, when measuring interbank exposures in terms of eligible capital (see Figure 2, panel b), interbank exposures are more sizeable for SIs, in particular for LSIs.

Figure 2

Distribution of euro area reporting institutions’ large exposures by size of exposure

Source: COREP supervisory data, Template C.28.
Notes: The cut-off date for data was the third quarter of 2017. The large exposure limit is 25% of a bank’s eligible capital. The figures provides a lender perspective, meaning that they show the size distribution of large exposures held by euro area LSIs, SIs and G-SIBs respectively. LSIs denotes euro area less significant institutions and SIs denotes euro area significant institutions, while G-SIBs denotes euro area global systemically important institutions. A cut-off threshold was set at €3 billion.

Focusing only on the exposures of the SIs, looking at the counterparty sectors, Figure 3 illustrates that, while the size of SIs’ exposures to credit institutions, other financial institutions and general government is broadly similarly distributed, their exposures to NFCs tend to be relatively small, whereas their exposures to households tend to be larger on average (although there are fewer of them and they are also much smaller on aggregate – see Table 1).

Figure 3

Distribution of euro area significant institutions’ large exposures by counterparty sector

Source: COREP supervisory data, Template C.28.
Notes: Reporting institutions are euro area significant institutions. The cut-off date for data was the third quarter of 2017. CIs denotes credit institutions, NFCs non-financial corporations, FCs financial corporations, GG general government, CBs central banks and HHs households.

4 Macroprudential policy instruments

Macroprudential authorities have a number of instruments the purpose of which is to prevent the excessive build-up of systemic risk, making the financial sector more resilient and thereby limiting unintended contagion effects. In particular, these instruments either aim to reduce the risk that an interconnected institution fails by requiring the bank to hold more capital, or they aim to reduce a bank’s exposure to the network or certain assets.[10]

By imposing a buffer on systemically important institutions, authorities can require specific institutions to hold additional capital in order to reduce the likelihood of failure of those banks that are critical to the global or national financial system. In particular, they may apply the global systemically important institution (G-SII) buffer if the bank is deemed important relative to its global peers, or a capital buffer for other systemically important institutions (O-SIIs) if the bank is systemically relevant in its national banking system. The identification of G-SIIs and the calibration of the G-SII buffer are largely regulated in the Capital Requirements Directive (CRD IV), with some discretion via supervisory judgement. The methodology enshrined in European legislation follows the G-SIB methodology developed by the Basel Committee of Banking Supervision.

For the identification of O-SIIs and the calibration of O-SII buffers, CRD IV allows more national discretion.[11] In particular, the systemic importance of O-SIIs is to be judged on the basis of at least one of the following four criteria: size, importance to the economy of the European Union or the relevant Member State, significance of cross-border activities, and interconnectedness of the institution or group with the financial system. In practice, most EU authorities follow the EBA guidelines to identify their respective O-SIIs.[12] The EBA guidelines take into account all four criteria but leave it to the national authority to identify, in a second step, further O-SIIs based on additional criteria. For the calibration of the O-SII buffer, the buffers computed by the ECB’s floor methodology serve as a lower bound.[13] The O-SII and G-SII buffers are natural candidates for tools that increase the resilience of those banks that are crucial to the interbank network. However, it might be that other dimensions relevant in determining systemic importance, such as size and cross-border activities, dilute the buffer requirements targeting the bank’s systemic importance due to its interconnectedness. In this case, authorities might want to use alternative macroprudential measures.

As an alternative to the SII buffers, the systemic risk buffer (SRB) or Pillar 2 additional own funds requirements may currently be used to increase the resilience of a bank or group of banks that pose high contagion risk.[14] Unlike for the G-SII and O-SII buffers, the CRD does not provide specific criteria for determining the SRB. Rather generally, the SRB aims to reduce systemic risks of a structural nature that are not covered by the Capital Requirements Regulation (CRR).[15] In addition, authorities may set different SRB levels for different institutions or sets of institutions. Alternatively, or additionally, authorities may currently impose Pillar 2 additional own funds requirements to address contagion risks related to a specific bank, which are then added to the structural buffers. Notably, the SRB, G-SII and O-SII buffers are not cumulative, rather the highest of the three is applicable, if the three buffers are applied at the same level of consolidation.

Besides capital-based requirements, authorities have the option to increase liquidity requirements for O-SIIs or G-SIIs, or to target risky exposures directly.[16] Additional liquidity requirements could, for example, take the form of an add-on to the minimum requirement for the liquidity coverage ratio. Exposure-based measures include an increase of risk weights for certain risk exposures (e.g. exposures to other financial institutions). In addition, authorities have the possibility to tighten the large exposure limits on certain risk exposures. As currently designed, the SRB is a very flexible (residual) tool that can also be used to require banks to hold more capital for certain risk exposures.[17] Risk exposures may comprise all exposures to another bank or to any other counterparty and/or may apply to specific products such as covered bonds.

5 Comparing measures of interconnectedness used for calibration of policy instruments

As previously described, there are several macroprudential policy measures that can cater for contagion risks arising from interconnectedness. A key question in this regard is how to calibrate these policy measures such that they appropriately address the systemic risk that they are meant to target.

Currently, most national authorities make use of a score methodology, as prescribed by the EBA guidelines, when calibrating their O-SII buffers. The score is computed as the average of four indicators that capture the size, importance, complexity and interconnectedness of a bank. The interconnectedness indicator is relatively easy to construct and allows transparency and comparability across countries but does not make use of information for the whole network. In the following, the relatively simple EBA interconnectedness indicator will be compared with four standard topographic network measures and two model-based interconnectedness measures, which are derived using the large exposure network presented above.

The EBA’s O-SII score takes the domestic banking system as a reference since, by definition, an O-SII has to be systemically important for its domestic banking system. In contrast, the variant of the EBA’s O-SII score and O-SII interconnectedness indicator (OII) used in this article takes the banks in the large exposure sample of reporting euro area SIs as the reference point, rather than the respective national banking sector.[18] The amendment with regard to the reference point is motivated by two considerations. First, Figure 1 shows that interconnectedness is not bound by borders, in particular in the case of those banks which form the core and are likely to be O-SIIs. In order to avoid contagion, not only within the national borders but also across borders, it is important to identify those banks that form the core and to ensure that they are especially resilient to shocks. Second, the data set consists of the largest banks in the euro area but is not representative from a national perspective. It is therefore not possible to take the national banking system as a reference point. Overall, the analysis at hand complements the assessment carried out by national authorities and takes a different perspective. At the same time, the conclusions on the relative performance of the different interconnectedness measures and their comparison with the EBA’s O-SII interconnectedness indicator are likely to be indicative for those national banking systems with similar features to the network analysed.

For the O-SII interconnectedness indicator, the EBA guidelines prescribe the following computation: I n t e r c o n n e c t e d n e s s i = A V G ( I F S L i i = 1 N I F S L i + I F S A i i = 1 N I F S A i + D S i i = 1 N D S i ) * 10.000

First, each bank’s intra-financial system liabilities (IFSL), intra-financial system assets (IFSA) and debt securities outstanding (DS) are divided by their respective sum across all banks in our sample. Then, the average of the three sub-indicators is taken and multiplied by 10,000 to express the indicator in basis points. The O-SII score is calculated on a bank basis as the simple average of the size, importance, complexity and interconnectedness indicators.[19]

To assess how well the EBA’s interconnectedness indicator captures contagion risk due to interconnectedness, it is compared with the following four standard interconnectedness measures, which take into account the whole network structure: page rank, centrality, degree and weighted degree. In addition, two model-based measures derived from the euro area SI interbank network of large exposures are computed: the Espinoza-Sole indicator (ESI) and the Systemic Probability Index (SPI).[20] For a better comparison of these different measures, the network-based indicators for each bank are divided by the sum across all banks in the sample and multiplied by 10,000 to express the indicators in basis points.

Box 1
Model-based estimates and methodology

The estimated induced losses in the large exposure network given the default of each euro area significant institution are computed following the methodology developed by Covi, Gorpe and Kok (2018), which is built on the paper of Espinosa-Vega and Sole (2010). This augmented contagion modelling framework includes bank-specific ( i ) and/or exposure-specific ( j ) parameters to precisely estimate credit and funding shocks conditional on four key assumptions: loss given default ( λ i   o r   λ j ) , funding shortfall ( δ i ) , fire sale parameter ( ρ i ) and a bank-specific default threshold ( k i ) .

D e f a u l t :                                               k i - ( λ i x h i + δ i ρ i x i h ) < 0                                                                                                                                                   ( 1 )

These parameters have been calibrated by exploiting additional information embodied in the large exposure data, which have been complemented by other COREP and FINREP supervisory templates. In the paper, a uniform estimate for each parameter, equal to the average across all reporting banks, is used, while the default threshold is set at 4.5% of a bank’s risk-weighted assets.

Overall, the model tests the system for a given bank’s default. The simulation exercise continues with a second round if there is at least one additional failure in response to the initial induced default and stops when there are no additional failures. In the end, a contagion index is developed to rank banks in terms of their contribution to the systemic risk of euro area SIs.

The Systemic Probability Index (SPI) is a model-based indicator measuring the likelihood of the contagion spreading across the banking system after a default of a given bank on its interbank exposures (Hałaj and Kok, 2013). It differs mainly from the Espinoza-Sole approach because it is a probabilistic systemic risk measure based on a Cauchy distribution drawing from a set of parameters. For the sake of comparability, the loss given default and the default threshold were set equal to the Espinoza-Sole model specification. Although funding risk is not taken into consideration in this methodology, the model captures risks stemming from the volatility of the capital base, which is set at 10%.

Table 3 shows the correlation between the different interconnectedness measures, both indicator and network-based ones. Overall, the different measures are highly correlated. Notably, the model-based Espinoza-Sole measure has the lowest correlation with the other measures. However, the lowest correlation is still quite high at 0.80. Overall, this result is reassuring in the sense that, on average, the rather simple O-SII interconnectedness indicator seems to capture banks’ interconnectedness well, particularly when compared with the standard network measures.[21]

Table 3

Correlation matrix of intra-euro area large exposure systemic risk measures

Notes: The sample comprises 84 euro area significant institutions (SIs) reporting large exposures. The cut-off date for data was the third quarter of 2017. All correlation coefficients are statistically different from 0 at a 1% confidence level. Model-based estimates refer to euro area SIs’ induced capital losses. Espinoza-Sole estimates are calculated following the methodology used by Covi, Gorpe and Kok (2018), while the Systemic Probability Index (SPI) is based on Halaj and Kok (2013). PageRank assigns a probability to each institution according to how often a user following links will non-randomly reach that institution (an edge’s weight matters). Centrality measures node importance in a network based on a node’s connections. It assigns relative scores to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes. Degree refers to the number of a node’s links in the network, while weighted degree refers to the total amount of a node’s exposures.

While, on average, the O-SII interconnectedness indicator seems to capture even contagion risk from more complex network features, Figure 4 illustrates the indicator’s performance with regard to individual banks. When comparing the OII with the standard network measures for individual banks (left-hand panel), it becomes evident that the OII assigns higher values to the more interconnected banks (those with an OII of greater than 500 basis points) than the page rank and the (weighted) degree. In contrast, the centrality measure is either as conservative as or more conservative than the OII for those banks. For the less connected banks (those with an OII of less than 500 basis points), the picture is less clear-cut but the network measures tend to signal a higher degree of interconnectedness than the OII.

When comparing the interconnectedness indicator with the standard network measures for individual banks (see Figure 4, right-hand panel), the SPI follows a pattern similar to the standard network measures, while the ESI does not. The SPI assigns lower scores than the OII to institutions that are highly and medium connected (institutions with an OII above 150 basis points), and higher scores to less connected banks. The difference between the scores assigned to individual banks by the OII and the ESI is less pronounced and less systematic. Still, it can be deduced from the figure that, for a few banks, the ESI is much higher than the OII.

Figure 4

Euro area-based O-SII interconnectedness indicator and intra-euro area large exposure systemic risk measures

Source: FINREP supervisory data, Templates F. 01.02, F. 10.00, F. 11.00, F 20.04 and F 20.06.
Notes: The institutions represented are euro area significant institutions in the large exposures sample. The cut-off date for data was the third quarter of 2017.

6 Conclusion

This article utilises the large exposure data from supervisory reporting to present stylised facts from the euro area large exposure network and to derive network-based measures of the systemic importance of SSM SIs.

When focusing on interbank exposures, the network is relatively sparse and suggests a core-periphery network structure, where most banks are in the periphery and are only connected to a few core banks. The core banks are highly connected with one another. Similarly, the less interconnected periphery banks tend to have lower cross-border activities than the core banks.

When comparing different interconnectedness measures, it is demonstrated that the standard network measures, as well as model-based network measures, correlate strongly with the size-based measure of individual banks’ interconnectedness (OII), as prescribed by the EBA guidelines. The latter indicator feeds into the EBA’s O-SII score, which is commonly used to motivate O-SII capital buffers. The high correlation between the network measures and the OII is reassuring, as it suggests that, on average, the systemic footprint of banks due to their interconnectedness is appropriately taken into account in the calibration of structural buffers.

There are, however, also cases where network-based measures deviate significantly from sized-based measures. Network-based measures are able to take into account the complexity and multi-layered nature of banks’ interrelations, unlike size-based measures. This would suggest that prudential supervisors should also make use of this type of information when calibrating structural buffers (even if only on a judgemental basis), keeping in mind that interconnectedness indicators only capture one element of the systemic footprint of financial institutions.

For network-based measures to be informative for structural buffer calibration, up-to-date and comprehensive network data are however needed. The large exposure reporting used to derive network snapshots, as described in this article, provides the ECB with a reliable data source that is regularly updated.


Covi, G., Gorpe, M.Z. and Kok, C., “Contagion Risk in the Euro Area Interbank Network. A granular investigation of the euro area banks’ large exposures and their systemic risk implications”, Working Paper Series, forthcoming, ECB, Frankfurt am Main, 2018

European Banking Authority, Guidelines on the criteria to determine the conditions of application of Article 131(3) of Directive 2013/36/EU (CRD) in relation to the assessment of other systemically important institutions (O-SIIs), GL/2014/10, EBA, 2014

Espinosa-Vega, M.A. and Sole, J., “Cross-Border Financial Surveillance: A Network Perspective”, IMF Working Paper, No 10/105, 2010.

Hałaj G. and Kok, C., “Assessing interbank contagion using simulated networks”, Working Paper Series, No 1506, ECB, Frankfurt am Main, 2013 and Computational Management Science, Vol. 10(2), pp. 157-186.

© European Central Bank, 2018

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All rights reserved. Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged.

ISSN 2467-1770 DOI 10.2866/351597
ISBN 978-92-899-3195-3 EU catalogue No QB-CA-18-001-EN-Q

  1. Input from B. Hansen is gratefully acknowledged.
  2. The importance of “cross-sectional” systemic risks has been highlighted in a number of special features in issues of the ECB’s Financial Stability Review; see “The concept of systemic risk” (December 2009), “Financial networks and financial stability” (June 2010), “Systemic risk methodologies” (June 2011), “Evaluating interconnectedness in the financial system on the basis of actual and simulated networks” (June 2012), “Predicting bank distress and identifying interdependencies among European banks” (December 2012) and “Gauging the effectiveness of cross-sectional macro-prudential tools through the lens of interbank networks” (November 2013).
  3. See, for example, Montagna, M. and Kok, C., “Multi-layered interbank model for assessing systemic risk”, Working Paper Series, No 1944, ECB, Frankfurt am Main, August 2016.
  4. See, for example, Henry, J. and Kok, C., “A macro stress testing framework to assess systemic risks in the banking sector”, Occasional Paper Series, No 152, ECB, Frankfurt am Main, October 2013; Dees, S., Henry, J. and Martin, R., “STAMP€: Stress-Test Analytics for Macroprudential Purposes in the euro area”, ECB, Frankfurt am Main, February 2017.
  5. Moreover, institutions that report FINREP supervisory data are also requested to report large exposure information with a value equal to or above €300 million.
  6. This data is collected via the COREP C.27-C.30 supervisory templates.
  7. Eligible capital refers to the sum of the full amount of Tier 1 capital and a share of Tier 2 capital less than or equal to 33% of Tier 1 – see Article 4(71). However, in the next CRR update, eligible capital for large exposure reporting and limits will refer exclusively to Tier 1 capital.
  8. Regarding risk typology, the exposure may be directed towards a single client or a group of connected clients. In the latter case, the reporting bank is required to consider not only the risk embodied in the exposure but also the cascade effect that default of that exposure might produce on other connected entities following the criteria defined as control relationship and economic dependency (EBA/GL/2017/15).
  9. The high ratio of gross to net exposures to general government is due to Article 400 of the CRR, which defines “exemptions” attributes a 0 risk weight to exposures to general government, with the exception of exposures to regional and local governments, which have a 20% risk weight.
  10. A more general and comprehensive review of macroprudential tools, not only in the context of contagion, can be found in The ESRB Handbook on Operationalising Macro-prudential Policy in the Banking Sector, European Systemic Risk Board.
  11. Powers concerning the identification of systemically important institutions should be clearly assigned to national macroprudential authorities, as well as to the ECB, to counter potential inaction bias and ensure a level playing field for banks across participating Member States; see the ECB contribution to the European Commission’s consultation on the review of the EU macroprudential policy framework, p. 3.
  12. See the EBA’s Guidelines on the criteria to determine the conditions of application of Article 131(3) of Directive 2013/36/EU (CRD) in relation to the assessment of other systemically important institutions (O-SIIs). The EBA also provides a compliance table in Annex 1 of the document.
  13. The ECB methodology is summarised in the chapter “ECB floor methodology for setting the capital buffer for an identified Other Systemically Important Institution (O-SII)” of the June 2017 issue of the ECB’s Macroprudential Bulletin. The methodology will be reviewed in 2019.
  14. While these instruments can currently be used to top up or replace the O-SII buffer, a clear delineation of their scope is needed to avoid overlaps and double counting of risks. Moreover, Pillar 2 requirements are not suitable to address systemic risks because of Pillar 2’s idiosyncratic nature.
  15. Regulation (EU) No 575/2013 of the European Parliament and of the Council of 26 June 2013 on prudential requirements for credit institutions and investment firms (OJ L 176, 27.6.2013, p. 1–337)
  16. See Article 458 of the CRR.
  17. If the policy purpose between the SRB and the O-SII buffer becomes clearly delineated in the legal text, the SRB can become a targeted rather than residual instrument.
  18. This article uses the term (interconnectedness) indicator, while the EBA guidelines refer to (interconnectedness) category. The main difference between the standard O-SII methodology and the variant used in this article lies in the calculation of the denominator. In this article, the summation is across all banks in the sample, while in the standard methodology the denominator is computed using the sample of domestic banks. Our sample comprises those banks (at consolidated level) appearing in the large exposure dataset for which we had available FINREP data (a total of 84 banks).
  19. The O-SII score and the interconnectedness indicator outlined in the EBA guidelines are constructed using FINREP supervisory data. Notably, we do not make use of payment data. For the importance category, we therefore do not incorporate the indicator measuring the value of domestic payment transactions owing to a lack of data.
  20. Espinoza-Sole estimates are calculated following the methodology used by Covi, Gorpe and Kok (2018), which builds on the framework by Espinosa-Vega and Sole (2010), while the Systemic Probability Index is based on Hałaj and Kok (2013). Box 1 provides a short description of the two modelling frameworks.
  21. It should be borne in mind that these results are specific to the network used here and could be different in a network with different features or at a different time.