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Asset commonality of European banks☼

Sonia Dissem*

University of Lille and Skema Business School

Abstract

In this paper, we investigate the notion of asset commonality. We describe the evolution of asset

commonality, for 43 European banks over 15 countries; by comparing the 2011 and 2016 EU

wide stress test reporting. We determine the main variables that influence asset commonality and

its evolution. We notice that asset commonality can be used as a complementary measure to other

market systemic risk measures. Furthermore, we find that asset commonality can influence

negatively the returns and positively the volatility of the bank. We also find that some banks,

which have no problems of funding and fire sales, have experienced a decrease in their

performance. Asset commonality can be seen as an interesting tool than can be used by

regulators.

This version:

July 2017

Keywords: Asset commonality, bank regulation, systemic risk

JEL codes: G21, G28

* Corresponding author: Sonia Dissem, Univ. Lille - EA 4112 - LSMRC, F-59000 Lille, France

Email: sonia.dissem@univ-lille2.fr

☼The author is grateful to Pr. Diane Pierret for her valuable comments and availability during the exchange semester

at HEC Lausanne. All remaining errors are my own.

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1. Introduction

One of the reasons of failure of some financial institutions is related to the contagion process (Gai

and Kapadia, 2010; Gai et al., 2011) called also domino effect. Indeed, the financial system has

become less hierarchical, less modular and more interconnected. Therefore, it became more apt to

systemic failure. The popular contagion was the US subprime crisis, where a lot of sectors and

countries faced big losses (Hellwig, 2009). Thus, the financial crisis of 2007-2009 has shown

how a collapse of some financial institutions was followed by worldwide economic downturn.

One of the ways that leads to contagion among financial institutions is fire sales (Schleifer and

Vishny, 1992; Cifuentes et al., 2005). Indeed, fire sales create endogenous risk and are

considered as channel of loss contagion across asset classes and across financial institutions

holding these assets.

In these recent years, the theoretical literature is not only related to the contagion effect on the

financial sector but also related to asset commonality between financial institutions. Indeed, the

linkages among financial institutions are considered as the condition of creation of systemic risk.

We can, for instance, refer to the speech of the Federal Reserve Chairman Bernanke at the

Conference on Bank Structure and Competition in 2013: “Examples of vulnerabilities include

high levels of leverage, maturity transformation, interconnectedness, and complexity, all of which

have the potential to magnify shocks to the financial system”.

To better understand the term of “asset commonality”, we can interpret it as the common

exposures between banks’ portfolios. Asset commonality between banks is a measure that is not

very widespread in the empirical literature. Allen et al. (2012) analyze the systemic risk that

comes from different structures of asset commonality among banks. However, the topic of

interconnectedness was investigated by many researchers and most of them agree that

interconnectedness between banks increases systemic risk. Cai et al. (2017) find a positive and

significant correlation between interconnectedness and various market systemic risk measures.

In this paper, we determine the main banking variables that affect asset commonality results and

its evolution. We also evaluate asset commonality to forecast the actual ranking of banks’

realized outcomes (realized loss, realized volatility and realized return) during a crisis.

Our paper contributes to the existing literature along a number of dimensions. As far as we know,

it is the first study where we use asset commonality of the EU-wide stress testing reporting. In

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addition, very few empirical articles deal with this topic. It is also the first study where asset

commonality by asset classes is weighted by the risk of each asset class. The risk of each asset

class is chosen according to the current literature. For instance, for the sovereign asset class, the

best indicator of risk is CDS prices. We also have little evidence how banks are interconnected.

We propose to show that asset commonality can be considered as a systemic risk measure that

can be used together with other market systemic measures (SRISK (Acharya et al. (2010,2012)),

CoVaR ( Adrian and Brunnermeier (2016)),…). Indeed, instead of using asset prices, we use

actual risk exposures. Thus, we want to emphasize the role of asset commonality in funding

shocks across banks in a downturn.

In our paper we have focused on asset commonality at the bank level in Europe between asset

classes (Sovereign, Corporate, Institution and Retail). We have examined the common exposures

of banks from the European Banking Authority (EBA) stress tests reporting in 2011 and 2016.

We chose these two dates because of their relation to a period of crisis. Indeed, we faced in 2011

the sovereign debt crisis, and at the beginning of 2016 the market capitalization had fallen by

more than 40%. Moreover, comparing recent available data is necessary in order to understand

the evolution of asset commonality between 2011 and 2016. In addition, the European bank

exposures data disclosed by the European Banking Authority is an occasion to understand how

unconventional monetary policy affects asset commonality. We have also collected the market

data (CDS prices, stock prices) to compute the weighted asset commonality and the financial

information (balance sheet ratios). We test the implications of asset commonality on asset prices

(sovereign, institution, corporate and retail) by controlling for bank and country characteristics.

Thus, we use asset commonality to predict banks’ realized outcomes in a period of recession.

In a first step, we analyze the risk indicator chosen for each asset class. To that end, we

distinguish between (i) the GIIPS countries (Greece, Ireland, Italy, Portugal and Spain); (ii) non-

GIIPS Eurozone countries; and (iii) non-Eurozone European countries. Our analysis is also based

on two different periods of time (before and after the publication of stress test results in 2011 and

2016). We find that for the sovereign, corporate and retail asset classes, GIIPS countries of the

banks have the highest risk. However, for the institution asset class, it is the non-Eurozone

European countries of the banks which have the highest risk.

Our descriptive statistics of the bank characteristics shows a striking result which is almost a

doubled standard deviation for the Exposures at Default (EAD) as of December 2015 in

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comparison to the standard deviation as of December 2010. We find, that for the recent published

stress test reporting, there are outliers with big extreme exposures.

For the systemic risk measures, we notice that the descriptive statistics are lower for the data

collected (SRISK, LRMES…) as of December 2015 than as of December 2010. Thus, there are

higher capital excesses even in a crisis scenario for the banks as of December 2015 than as of

December 2010.

Our descriptive statistics of asset commonality (AC) show that non-weighted AC went up, when

we compare both disclosures, especially for large Eurozone banks. However, AC went down for

non-Eurozone European banks. In addition, the results of non-weighted AC as of December 2015

are higher than the results as of December 2010. Then, we can notice that weighted (by the risk

of different asset classes) AC is larger than non-weighted AC. Weighted AC went up for the

same banks. For the weighted by the median AC (the median risk of all asset classes), we notice

that it can be both larger and smaller than weighted AC and always larger than non-weighted AC.

So, the comparison between weighted AC and weighted by the median AC might be interesting.

We also notice that there is sufficient variation for empirical tests since the standard deviation of

AC is between 2.88 and 5.03 for the different types of AC (non-weighted, weighted and weighted

by the median).

To determine the main variables that have a significant influence on asset commonality we use

the Spearman ranking correlation methodology. What is striking is that results are not significant

when we focus on the whole sample. However, when we focus separately on the three banking

groups we notice that some results are significant. We find that the size of the bank (total assets)

and the diversification index are the main variables that have significant influence on asset

commonality since the ranking correlation is positive and significant. So, being a large bank and

having a well-diversified portfolio increases asset commonality with other banks. However, the

bank’s credit risk is negatively correlated with asset commonality as of December 2010 since

during the sovereign debt crisis; banks tend to decrease their investment in riskier categories of

asset classes. We also find that an increase of the Core Tier 1 Capital has a negative influence on

asset commonality. This means that banks that are well capitalized have less asset commonality.

Moreover, an increase of RWA has a positive influence on asset commonality. However, the

level of profitability and the funding have no influence on asset commonality. These results are

confirmed by running linear regressions.

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To determine if an exposure similar to assets enhances systemic risk, we use the Spearman

ranking correlation methodology. We find that ranking correlation between asset commonality

and SRISK is positive and statistically significant. Thus, we confirm again that asset

commonality comes from the banks that are undercapitalized. However, we find that asset

commonality comes from highly leveraged banks. In addition, asset commonality does not

necessarily come from banks that have a high volatility of returns. But, it comes from banks that

have a high correlation of returns.

To determine which factors impact the evolution of asset commonality, we use the Spearman

ranking correlation methodology. We find that the determinants of the evolution are the

diversification index, the bank’s size and the credit risk. We find that a high level of

diversification influences negatively the evolution of asset commonality. On the opposite, the

credit risk especially for non-GIIPS Eurozone banks influences positively the evolution of asset

commonality.

To determine the predictive power of asset commonality on realized outcomes, we run linear

regressions with realized outcomes (realized loss, return and volatility) as dependent variables.

We find that, in 2010, asset commonality predicts the ranking of bank’s six-months realized loss

and six-months realized volatility. However, in 2015, we find that asset commonality predicts the

ranking of bank’s six-months realized return and six-months realized loss.

In a final step, we compute the differences of EADs between June 2016 (data of the capital

transparency exercise) and December 2015 (data of the stress testing exercise) in order to take

into account the notion of fire sales. To that end, we distinguish between (i) No assets sold, and

(ii) Assets sold. We notice for both groups that the results are significant especially for the

ranking correlation of asset commonality with the diversification index, the bank’s size and the

bank’s capitalization. We also find that asset commonality is a good predictor of the bank’s

realized returns especially for the “Assets sold” group.

In summary, asset commonality can give a better picture of the risk position of a bank than some

market systemic risk measures. In addition, the determinants of asset commonality’ evolution are

period-specific. Finally, asset commonality influences negatively the return and positively the

volatility of the bank.

The paper proceeds as follows. In Section2, we present the literature review. The empirical

methodology used is disclosed in Section 3. Data used and summary statistics are disclosed in

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Section 4. Section 5 discusses our empirical results on the determinants of asset commonality and

its forecasting power. So, we have focused on the implications of asset commonality for systemic

risk. We make a robustness check in Section 6. Some concluding remarks and perspectives are

given in section 7.

2. Asset commonality literature review

Our paper relates the effect of diversification on systemic risk. Cai et al. (2017) proposed a novel

measure of interconnectedness based on the distance between two banks’ syndicated loan

portfolios. They find that interconnectedness is positively linked to diversification. Even the

correlation between market systemic risk measure and interconnectedness is positive. Their final

and important result is that interconnectedness increases systemic risk during recessions. We also

know that banks face a trade-off between idiosyncratic and systemic risk. Thus, according to

Shafer (1994), Wagner (2010), and Ibragimov et al. (2011), diversification is seen as a trigger of

systemic risk since it increases the portfolios’ overlaps between banks. According to them,

diversification is good when we focus on each bank individually. So, for these authors, the best

decision to take is limiting diversification. Ibragimov et al. (2011) define a diversification

threshold of the point at which individual benefit from diversification begins to be offset by the

systemic risk from diversification. Our paper is an empirical complement to these theoretical

papers.

Our paper is also related to the banking network. Beale et al. (2011) find that banks should

diversify in different asset classes if systemic costs are high. Allen et al. (2012) developed a

model where short-term debt and asset commonality of banks are linked to create high systemic

risk. They show that the asset structure is not important in determining systemic risk when banks

use long-term debt. Moreover, according to Adachi-Sato and Vithessonthi (2017), the effect of

bank asset commonality on corporate investment is inconclusive. Indeed, they use three measures

that capture “bank systemic risk”. One of these measures is used to capture “bank asset

commonality”. The relationship between this measure and corporate investment is positive but

not robust. Nevertheless, they find a positive and statistically significant result between corporate

investment and the “bank asset commonality” with respect to non-core banking activities. In a

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similar study, Paltalidis et al. (2015) show that the interconnectedness in the banking network

participates to systemic risk of banks in 16 Eurozone countries.

Many researchers show that the effect of bank-level interconnectedness is very ambiguous.

Indeed, Allen and Gale (2000) support that a complete interbank market in which each bank is

connected to all the other banks is superior in terms of stability to incomplete ones in which

banks are connected to only a part of other banks. Chen and Hasan (2016) find similar results.

Gai and Kapadia (2010) find a threshold for interconnectedness above which more

interconnectedness is destabilizing and below which it is stabilizing. In the related result,

Acemoglu et al. (2015) find a threshold of the shock amount at which the contagion is not low

anymore even with a complete network. With big shocks, the networks with low connections are

less fragile than the ones with more links.

Gai et al. (2011) also explain that a greater complexity and a greater concentration increase the

fragility of the financial system. During the last years two third of the banks’ balance sheets

growth was attributed to claims with other financial agents, rather than with the non-financial

sector.

Farhi and Tirole (2012) show that one of reasons for the rise of AC between banks is the rigid

and constraining regulation. In addition, in order to avoid bankruptcy in case of systemic risk,

banks opt for the same allocation strategies.

3. Methodology

Cai et al. (2017) developed a measure of interconnectedness on loan portfolio. We have used

their methodology to compute asset commonality between European banks. Before presenting

their methodology, we need to explain that asset commonality imply a small distance between

these two institutions. So, we need to compute this distance using the Euclidean distance defined

by:

���������,�,� = ��(��,�,� − ��,�,�)�

�

���

(���ℎ � ≠ �)

(1)

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Where ��,�,� is the exposure to sector j of bank k normalized by the bank total exposure. More

precisely, it is the weight of Exposure at Default (EAD) of each category j of asset class

relatively to total EAD of the bank k.

For each bank m we have the following: ∑ ��,�,����� = 1

Note that the distance measure must lie between the range of 0 and √2 due to the definition of

Euclidean distance. A small distance between two banks implies that they have similar

allocations in their portfolio and are more prompt to react to the same kind of shocks. Given the

weights in each category of asset classes, we can compute a diversification index for each bank.

It is defined for a bank m with j categories of asset classes as:

���������,� = [1 − �(��,�,�)�] × 100

�

���

(2)

The notion behind the measure is that as a bank becomes more diversified, ∑ (��,�,�)�����

becomes smaller, so the diversification index grows larger. As diversification increases, the risk

of individual failure (i.e. the idiosyncratic risk) decreases while systemic risk increases.

Cai et al. (2017) transform the weighted average distance in an asset commonality measure that is

normalized to a scale of 0-100 with 0 being the least interconnected and 100 being the most

interconnected. More specifically, the ����� ������������,�of bank m in time t, is equal to:

����� ������������,� = (1 −∑ ��,�.��� ���������,�,�

√2) × 100

(3)

Where ���������,�,� is the distance between bank m and bank n at time t as defined in (1), and

��,� is the weight given to bank n.

Two kinds of weights are adopted. The first method is to give the same weight to each bank of

the sample that is the equally weighted method. The second method is the size-weighted method

where specific weights are defined by the following equation:

��,� =����� ������ �,�

��� �� ����� �������

(4)

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We point out that these two methods present several similarities in the results. For this reason we

report, in our tables, the size weighted method.

We also have the first idea to compute the weighted Euclidean distance and to notice if it is

different from the non-weighted Euclidean distance as defined in (1). The notion of weight is

related to the risk of the different asset classes. More specifically, the weighted distance between

a bank m and a bank n, ����������,�,� , equals:

����������,�,� = �� ���(��,�,� − ��,�,�)�

�

���

(5)

Where W is a (J×J) diagonal matrix of weights.

The Euclidean distance can be written as a quadratic form. This is the reason why we can

compute the ����������,�,� using the previous formula. Indeed the quadratic form defined by:

���������,�,� = [(��,� − ��,�)������,� − ��,��]�/� (6)

Where ��,� and ��,� are J dimensional vectors of asset holdings, and ��is a (J×J) identity matrix.

Thus, the asset commonality weighted of a bank m at time t is equal to:

�� ��,� = (1 −∑ ��,�. ���� ���������,�,�

√2) × 100

(7)

In addition, the asset commonality weighted by the median of a bank m at time t is equal to:

�� ���,� = (1 −∑ ��,�. ����� ���������,�,�

√2) × 100

(8)

Where �����������,�,� is the weighted by the median Euclidean distance and W is the

diagonal matrix of the median weight of all asset classes.

The above formulas are computed by using a MATLAB code1.

1 Created by the author

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4. Data and Summary Statistics

In this section, we discuss our data sources and provide summary statistics.

4.1 Variables Definitions and Sources

To compute asset commonality measure, we need to understand the notion of Exposure at Default

(EAD). We know that the calculation of Risk-Weighted Assets (RWA) relies on four quantitative

inputs: Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD) and

Maturity. The RWA can be decomposed into three categories: credit risk, operational risk and

market risk. The credit risk is the major component of RWA (80%). It is computed from the EAD

weighted by risk weights. Risk weights are determined by risk parameters: PD and LGD. Thus,

the EAD is used to calculate the RWA for credit risk exposure by banks. According to the Basel

Committee on Banking Supervision, EAD can be defined as: “which for loan commitments

measures the amount of the facility that is likely to be drawn if a default occurs”2. More

specifically, “EAD is equal to the current amount outstanding in case of fixed exposures like term

loans. For revolving exposures like lines of credit, EAD measures the amount of the facility that

is likely to be drawn further if a default occurs (Conversion Factor (CF))”3.

Thus, EAD is a publicly available data disclosed by the European Banking Authority (EBA). So,

the results are taken from the EBA website. More precisely, we have focused on stress tests

reporting: 2011 stress test reporting which includes EADs of 90 banks, as of December 2010 and

2016 stress test reporting which includes EADs of 53 banks, as of December 2015. The banks in

common between these two reporting are 43 banks. We decided to focus only on these 43 banks

in order to see the evolution of asset commonality when we compare both stress tests reporting.

More precisely, we took the non-defaulted assets and not the defaulted ones, as in 2011, we don’t

have the detail of the defaulted assets for each asset class. In the interest of fair comparison

between the two years, we have also kept the same asset classes which are the following:

Institutions, Corporate, Retail, Sovereign and Others. In the last asset class “Others, we selected

exposures that did not match any other asset class. For instance, in 2011, the asset class

“commercial Real Estate” is put into the asset class “Others”, since in 2016, we don’t have

obviously this asset class.

2 Definition from BIS Consultative Document “Overview of the New Basel Capital Accord” , April 2003 3 Definition available in the following website : https://en.wikipedia.org/wiki/Exposure_at_default

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As mentioned in the previous section, the weight used to compute the weighted Euclidean

distance represents the risk of the different asset classes. So, for each class we choose the best

indicator of risk. Thus, for the sovereign asset class, we use daily five-year CDS of European

countries during 2011 (between January and July 2011) and 2016 (between January and July

2016) from Thomson Reuters. We chose to work with CDS because it is the best measure that

can be used to measure the risk of this asset class. We know that CDS are insurance contracts that

allow bondholders to be paid back if a country is facing a default. Thus, this data is very

interesting in order to know how investors worry about potential countries' defaults and how,

especially, some countries can have difficulty in paying back what is owed. In 2011, investors bet

that Portugal would be the next member of the European Union to fall, with a default probability

of 66%4. For Ireland, Spain and Italy, the probability of default is: 51%, 33% and 28%

respectively, according to Markit5. For the institution asset class, we use daily banking price

index for the different countries that we have in common from Datastream. We compute the

volatility of the prices which is the measure of risk for this asset class. We know that the Greek

banking system was negatively affected by the Greek debt crisis. Before the publication of ST

results in 2016 (during the first half of 2016), the average annualized volatility was high because

of non-performing assets in some countries and low nominal growth environment. During the

second half of 2016, the volatility decreased because of the strengthening of yield curves that

provided some support for euro area banks’ profitability. More precisely, the banks’ share prices

decreased after the “Brexit” referendum on 23 June and, to a much lesser degree, after the

disclosure of EU-wide stress test results in late July. It is only in October and early November,

euro area banks’ stock prices recovered. One of the main problems is the hard banking regulation

which continues to decrease the ability of the banks to lend6. For the corporate asset class, we use

daily industrial price index for the different countries that we have in common from Datastream.

We computed the volatility of the prices which is the measure of risk for this asset class. In 2011,

the decrease of daily prices is mainly due to the European sovereign debt crisis. During this

period, SMEs are facing a poor economic performance. Since the beginning of 2015, most of

European countries experienced good growth. SME employment grew in 2014 by 1.1%. In 2015,

4 Source: ESMA Working Paper No. 1, 2014 "Monitoring the European CDS Market through networks: Implications for contagion risks" of Laurent Clerc, Silvia Gabrieli, Steffen Kern, and Yanis El Omari 5 Markit Ltd. is a global financial information and services company.

6 Source : https://www.ecb.europa.eu/pub/fsr/shared/pdf/3financialstabilityreview201611.en.pdf

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SME employment increased by 1.5%7. One of the reasons that may explain the increase in

volatility for Sweden in 2011 is the low inflation. It was the lowest inflation in Europe during the

period of 2011-2012. In addition, the krona appreciated substantially between 2010 and 2012. For

the retail asset class, we use a monthly banking interest rate for the 13 countries from the ECB.

This monthly rate represents: revolving loans and overdrafts convenience credit debt for

households. This asset class is mainly represented by households. For this asset class, we find

interesting indicators but most of them are yearly and we need either monthly or quarterly data.

The data used to control for bank characteristics (Core Tier 1 Capital, RWA and Net Income)

are from the EBA stress tests reporting. The core Tier 1 Capital Ratio is a proxy for bank

capitalization. It is computed as the bank’s core equity capital over its total RWA. It should be

larger than 5% under the adverse scenario in order to allow the bank to pass the test. The ROA is

a proxy for bank profitability. It is equal to the net income over total assets. The total assets, total

liabilities and short term funding data (without deposits) data are taken from SNL8 since there is

no disclosure of this information from EBA after 2010. As a proxy for credit risk we use the ratio

of RWA to total assets. To control for country characteristics we use data from the World

DataBank. We download GDP per capita in dollars and current account as a percentage of GDP,

for 2010 and 2015. The current account balance is the “sum of net exports of goods and services,

net primary income and net secondary income”9, where net primary income is “receipts and

payments of employee compensation paid to nonresident workers”10 and net secondary income

represents the moves in the balance of payments without reciprocity11. The concentration of the

banking sector is computed using the idea of Beck et al. (2003). The ratio is equal to the sum of

total assets of the three largest banks in the country to the sum of total assets of all the banks from

the country using the sample of the 43 banks. Thus, the concentration ratio is between 0 and 1.

Low values denote a banking sector with many small banks.

To estimate the realized outcomes of the banks we have focused on the public banks which

represent a sample of 34 banks. Thus, we have computed the market capitalization using

information (number of shares outstanding and stock price) from WRDS. Then, we compute the

market-to-book ratio which is equal to the market capitalization divided by the common equity.

7 Source : http://ec.europa.eu/growth/smes/business-friendly-environment/performance-review-2016_en 8 SNL Financial LC provides industry-specific financial market data feed of public and private companies worldwide

9Source: http://data.worldbank.org/indicator/BN.CAB.XOKA.GD.ZS 10Source: http://data.worldbank.org/indicator/BN.GSR.FCTY.CD 11 Source: http://data.worldbank.org/indicator/BN.TRF.CURR.CD

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The bigger the ratio, the more the investors believe that the bank is able to create value in the

future compared to its peers.

The information about market systemic risk measures of financial institutions is publicly

available. We took the data from the Volatility Laboratory (V-Lab) website12. The data concerns

the whole world for which financial data is publicly available. Since the 2008 crisis, many

researchers proposed systemic risk measures. One of the commonly used is SRISK as defined by

Acharya et al. (2012) and Brownlees and Engle (2016). SRISK represents the systemic capital

shortfall of a bank measured in billions of U.S. dollars. More precisely, SRISK indicates the

expected capital a firm would have to raise to reconstitute a certain capital ratio if a financial

crisis occurs. We also decide to compute SRISK% which is a relative measure that indicates the

systemic risk contribution of a firm. It determines the percentage of the capital shortfall

for the whole financial sector that is due to the firm in the case of a crisis. The higher the

SRISK%, the higher the losses for the firm in a crisis and the higher the

contribution of the firm to the crisis.

4.2 Summary Statistics

4.2.1 Risk of asset class

For the Sovereign Asset class, in figure 1, we notice that Portugal has the highest average daily

five-year CDS after the publication of ST results in 2011 while Ireland has the highest average

daily five-year CDS before the publication of ST results in 2011. We can notice a huge decrease

(more than 90%) in the average daily five-year CDS for Ireland in 2016. The result is the same

for Portugal where we can notice a decrease of 80% in 2016. The Non Eurozone countries have

the second highest average daily five-year CDS (after GIIPS countries). We also notice that the

GIIPS countries (without Greece) have the highest average daily five-year CDS before and after

the publication of ST results in 2011 and 2016. After the second half of 2011, there is a huge

difference between GIIPS countries (without Greece) and the two other groups (non-GIIPS

Eurozone countries; and non-Eurozone countries). Indeed, there is a difference of almost 80%.

Furthermore, we notice a kind of parallelism in the average daily five-year CDS between the

three groups (GIIPS, Euro Non GIIPS and Non Euro) starting from the beginning of 2014.

12 Source : http://V-Lab.stern.nyu.edu/ Created by Robert Engle, Rob Capellini, Michael Robles and Hseu-Ming Chen

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For the Institution asset class we notice that most of the European countries have a peak for the

annualized volatility after the publication of stress test results in 2011 (see figure 2). For Greece,

the annualized volatility was equal to 94% after the publication of ST results in 2011 and to more

than 100% before the publication of ST results in 2016. The GIIPS countries have the highest

average annualized volatility in 2011 and 2016. After the publication of ST results in 2016, we

notice a high decrease of the average annualized volatility for all European countries. For

instance, for GIIPS countries the decrease is about 45% after the publication of ST results in

2016. When we focus on the average daily prices, what is striking is that non Eurozone countries

have the highest daily prices during the whole period (between 2010 and 2016). This is mainly

due to UK daily prices because three quarters of non-euro area financial sector assets are located

in UK. But, there is a kind of parallelism with the two other curves of GIIPS countries and Euro

non GIIPS countries.

For the corporate asset class, we notice a peak of the annualized volatility for all European

countries after the publication of ST results in 2011 (see figure 3). The highest peaks concern the

following countries: Finland, Italy, Ireland and Sweden. The three groups (GIIPS, Non-Euro and

Euro non GIIPS) have almost the same percentage (35%) of average annualized volatility after

the publication of ST results in 2011. The average annualized volatility decreased by more than

50 % after the publication of ST results in 2016. When we focus on the average daily prices

chart, we notice that GIIPS countries have the highest average daily prices for the whole period

of interest. Moreover, what is striking is the decrease of daily prices right after the publication in

2011 and the increase of daily prices right after the publication in 2016.

For the retail asset class, we notice from figure 4 that the highest average monthly rate of

overdraft for households is the one of Hungary. The situation of Hungary may be explained by an

increase of poverty in 2011. However, between 2012 and 2014, poverty decreased from 26% to

23% thanks to public work schemes and other large-scale efforts on the part of the government to

improve living standards13. We can also notice that in 2011, non-Eurozone countries have the

highest average monthly rate and in 2016, GIIPS countries have the highest one. From the

beginning of 2014, we notice that the average monthly banking rate decreased for all the

European countries used in the sample.

13 Source: http://budapestbeacon.com/public-policy/tarki-poverty-in-hungary-decreased-between-2012-and-2014/23079

15

4.2.2 Bank & country characteristics and systemic risk measures

Table A.11 in the appendix section compares the descriptive statistics of EAD, bank and country

characteristics, public banks and systemic risk measures between data as of December 2010 and

December 2015. On average the total EAD as of December 2015 (900.91 EUR bn) is higher than

the average total EAD as of December 2010 (471.65 EUR bn). Even the standard deviation is

almost doubled in 2015 (835.02 EUR bn). As the median is lower than the mean for both years,

the distribution is skewed to the right (i.e. it has a longer right tail) with more big extreme

positive values for EAD than low values. We precise that all asset classes’ distributions are

skewed to the right: outliers with big extreme exposures are more common than outliers with low

exposure values.

In 2011 and 2016 EBA stress test reporting, the common equity Tier 1 Capital ratio of the bank

(the capital divided by the total risk exposure) has to be larger than 5% under the adverse

scenario to allow the bank to pass the test. The firms that failed the test need either to increase

their capital (increase the numerator) or to sell assets (decrease the denominator). The last

solution is mostly chosen during a crisis, due to the difficulty to refinance and increase capital.

This leads to fire sales and a worsening of the crisis (Acharya et al. 2014). The Core Tier 1

capital ratio (as of December 2010) is the lowest for Allied Irish Banks plc (3.71%) and the

highest for DekaBank Deutsche Girozentrale, Frankfurt (13.03%). When we focus more in

detail, we can notice that Eurozone non-GIIPS banks (except Norddeutsche Landesbank ) and

Non Eurozone banks have succeeded to the 2011 EBA stress test adverse scenario. The Core Tier

1 capital ratio is higher than 5% for all the 43 banks used in the sample as of December 2015. So,

all of the banks succeeded to the EBA stress test adverse scenario. For the banking size, we

notice that the bank with the highest total assets is BNP Paribas and HSBC Holdings as of

December 2010 and December 2015 respectively. For the amount of RWA, we notice that on

average banks have 224.51 EUR bn and 200.07 EUR bn of RWA as of December 2010 and

December 2015 respectively. The bank which has the maximum amount of RWA for both years

is HSBC Holdings. As a proxy for credit risk we use the ratio of RWA to Total assets. In 2011,

the highest ratio which is equal to 1 concern: Royal Bank of Scotland and PKO Bank Polski. The

lowest ratio is for Deutsche Bank. The bank with the highest net income is BNP Paribas with

9.16 EUR bn in 2011 and HSBC Holdings with 33.76 EUR bn in 2016. The bank with the

16

biggest loss is again Allied Irish Banks plc (-10.10 EUR bn) in 2011 and DekaBank Deutsche

Girozentrale, Frankfurt (0.33 EUR bn). In 2011, the less profitable bank is Allied Irish Banks plc

(-7.69%) and the most profitable is PKO Bank Polski (2.28%) while in 2016, the less profitable

bank is DekaBank Deutsche Girozentrale (0.30%) and the most profitable is OTP Bank (5.11%).

The banks in common between 2011 and 2016 come from 15 different countries. In 2011, the

GDP per capita is the lowest for Poland (12 598 USD) and the highest for Norway (87 646 USD).

In 2016, the GDP per capita is the lowest for Hungary (14 517 USD) and the highest for Norway

(89 493 USD). In 2011, the countries with the highest ratios are Norway (10.90% of the GDP)

and Netherlands (7.40% of the GDP): the twice are net creditors (providing resources to the rest

of the world). Poland (-5.40% of the GDP) and Spain (-3.90% of the GDP) are net borrowers. In

2016, the countries with the highest ratios are Netherlands (9.10% of the GDP) and Germany

(8.80%). UK (-5.20% of the GDP) and France (-1.40% of the GDP) are net borrowers. In 2011,

Spain and Germany with a ratio of 0.63 are the countries with the less concentrated banking

sector. The countries with a number of banks smaller or equal to 3, in the sample, have a ratio of

1. These countries have a concentrated banking sector with a few large banks. The countries with

a concentrated banking sector are: Austria, Belgium, Finland, Hungary, Ireland, Norway and

Poland. In 2016, France and Germany with a ratio of 0.80 and 0.70 respectively are the countries

with the less concentrated banking sector. The countries with the highest concentrated banking

sector are the same as in 2011.

For the public banks, we focus on the two main important indicators which are the market

capitalization which is converted in Euro for all non-Eurozone banks and the market to book

ratio. There are only 34 publicly traded banks out of the sample of 43 banks. The average market

capitalization is 23.20 EUR bn and 28.95 EUR bn in 2011 and 2016 respectively. The highest

amount for both years concerns the bank HSBC Holdings. Moreover, the average market to book

ratio is equal to 1.19 and 0.80 in 2011 and 2016 respectively. In 2011, the lowest is equal to 0.33

and it concerns Dexia. Whereas, in 2016, the lowest ratio is equal to 0.01 and it concerns the

same bank. This indicates either a negative feeling of the investors about the future growth of the

bank or an undervaluation. The highest ratios are equal to 3.24 for PKO Bank Polski and to 2.20

for Swedbank as of December 2010 and December 2015 respectively. It is not surprising for

PKO Bank Polski, which has the best ROA and also a negative SRISK as of December 2010. It is

the same for Swedbank which has a negative SRISK as of December 2015.

17

For the systemic risk measures, SRISK and LRMES are converted in euro since the data, in the

V-Lab website, is in dollar. On average, SRISK is equal to 45.60 EUR bn and 23.08 EUR bn as

of December 2010 and December 2015 respectively. There are only two banks with a negative

SRISK: OTP Bank and PKO Bank Polski as of December 2010. But, as of December 2015, there

are 6 banks with a negative SRISK. It indicates an excess of capital even in a crisis scenario.

Thus, these banks would be potential buyers of other banks in distress and they lower the total

systemic risk. As of December 2010 and 2015, SRISK% is on average equal to 2%. For the

LRMES, we notice that all the descriptive statistics are lower for the data collected as of

December 2015 than as of December 2010. The LRMES is the average of a firm’s returns where

the market return falls by over 40% over 6 months. In addition, for both years, Beta is on average

higher than 1. This means that investments in the banks are on average more volatile than the

market. The highest LRMES and Beta, as of December 2010, concern Irish banks (Bank of

Ireland and Allied Irish Bank). For the data collected as of December 2015, it is Raiffeisen Bank

International which has the highest LRMES and Beta. Correlation indicates the strength of the

relationship between the stock return of the bank and the market-value weighted index. It is equal

to 0.52 and 0.38 on average as of December 2010 and December 2015 respectively. There are

only positive correlations in our sample. Volatility represents the annualized volatility of the

stock returns of the bank. Italian, Irish and Spanish banks have the highest volatility for both

years. The volatility is higher than 100%. Finally, for the leverage, we notice that the highest

leverage is equal to 268.42 for Allied Irish Bank and 2683.73 for Dexia as of December 2010 and

December 2015 respectively. It is a strong indicator of high financial risk.

4.2.3 Euclidean Distance & Asset Commonality

We provide in table 1 the Euclidean distances and asset commonality (AC) between the banks

with total assets higher than 1000 EUR bn that we described in section 3. Panel A and B

summarize non-weighted Euclidean distance and non-weighted asset commonality respectively.

Euclidean distances are weighted by one in non-weighted measure. The closest banks are Credit

Agricole and BPCE as of December 2010 (distance of 0.10 on an average of 0.51 for all the

banks of the sample) and BNP and Societe Generale as of December 2015 (distance of 0.10 on an

average of 0.31 for all the banks of the sample). The highest distances are between Lloyds and

BPCE for both disclosures. When we compare both disclosures, we can notice that AC went up,

18

especially for large banks in the Eurozone (e.g. DB, BNP, Soc.Gen). AC went down for

European non-Eurozone banks. In addition, we notice that in 2015 the AC is higher than in 2010.

Panel C and D summarize weighted Euclidean distance and weighted asset commonality

respectively. The closest banks are BNP and Societe Generale for both disclosures. The highest

distances are between Lloyds and BPCE for both disclosures. We notice that weighted AC is

larger than non-weighted AC. In addition, there is less cross-sectional variation in weighted AC

than non-weighted AC. Weighted AC went up too for the same banks. The correlation between

the two measures (AC non-weighted & AC weighted) is high. Panel E and F summarize weighted

by the median Euclidean distance and weighted by the median asset commonality respectively.

Once again, the closest banks are BNP and Societe Generale and the highest distances are

between Lloyds and BPCE for both disclosures. In 2010, the median of the market risk weight of

all asset classes is equal to 0.83. In 2015, the median of the market risk weight of all asset classes

is equal to 0.68. So, the AC weighted by the median can be both larger and smaller than AC

weighted and always larger than AC non-weighted. Thus, we believe that the comparison

between AC weighted and AC weighted by the median might be interesting.

In table A.12, we provide descriptive statistics of the above-mentioned measures. Panel A is

related to the non-weighted data. While Euclidean distance must lie within the range of 0 and √2

and our AC measure must be within 0 and 100 by definition, the standard deviation of these

measures – 0.08-0.19 for Euclidean distance measures and 2.88-5.03 for size weighted AC (SW

AC) measure – implies that there is sufficient variation for empirical tests. Panel B is related to

the weighted data. Panel C is related to the weighted by the median data. The highest standard

deviation for AC concerns Non Euro banks which is equal to 9.90, 8.61 and 9.04 for AC non W,

AC W and AC WM respectively in 2010. In 2015, the highest standard deviation also concerns

Non Euro banks.

4.2.4 Other descriptive statistics

In table 2, we demonstrate which asset class influences the most AC. The exposures towards

institution and sovereign asset classes have a significant positive correlation with AC as of

December 2010 and December 2015. The exposures towards retail asset class have a significant

negative correlation with AC as of December 2010. In 2010, the correlation is negative (-0.51)

and significant at the 1% level between AC and the exposures towards the asset class Retail. For

19

the institution asset class, the correlation with AC is positive and significant at the 1% level. It is

equal to 0.45 and 0.32 as of December 2010 and December 2015 respectively. For GIIPS (IIS)

banks, we notice that it is the asset class “Others” which influences negatively AC. Indeed, the

ranking correlation is negative (-0.85) and significant at the 1% level.

In table 3, we reported the ranking correlation between EBA risk weight by asset class as of

December 2015 and AC W & AC WM for all the banks. We notice a positive and statistically

significant correlation for the sovereign asset class. The sovereign asset class risk weight is

highly correlated with AC between banks. So, AC comes from the sovereign asset class. The

ranking correlation is negative for the institution asset class. Another important finding is that the

ranking is positive for Eurozone non GIIPS banks. For GIIPS banks, the result is statistically

significant only between the risk weight of the retail asset class and AC WM. Finally, for Non

Euro banks, the result is statistically significant only between the risk weight of the sovereign

asset class and AC WM. This is another indicator that the comparison between AC W and AC

WM is interesting.

5. Determinants of Asset Commonality and forecasting power

In this section, we determine the main variables that have a significant influence on asset

commonality and its evolution. We have also focused on the correlation between asset

commonality and systemic risk measures. At the end of this section, we focus on the predictive

power of asset commonality on realized outcomes (return, loss and volatility).

5.1 Determinants of Asset commonality

We chose to use the Spearman ranking methodology in order to determine the ranking correlation

between asset commonality and bank characteristics. In table 4, we report only the size weighted

AC since there are small differences with the equally-weighted AC. Thus, we focused on the

determinants of AC as of December 2010 and December 2015. We point out that the ranking

correlation between AC and country characteristics is not significant. This is the reason why

these results are not reported. In table 4, we can notice that the results are not significant when we

focus on the whole sample (43 banks). However, when we focus separately on the 3 groups

(GIIPS, Euro non GIIPS and Non Euro) we notice that some results are significant. Indeed, the

variables with the most significant ranking correlation with AC are Diversification Index and

20

total assets. The size of the bank and its level of diversification have a positive relationship with

the asset commonality. The strength of these relationships is higher with the AC WM (weighted

by the median) than with the AC W (weighted). The Diversification Index is the variable with the

highest correlation with AC: 0.95 and 0.83 as of December 2010 and December 2015

respectively. Being more diversified increases the asset commonality. The size of the bank (total

assets) shows the same positive relationship with AC. The bigger the bank, the larger is the asset

commonality with other banks. The biggest banks invest in the same way and have more ways to

diversify through different businesses or countries than the smallest banks. Unlike size or

diversification, the credit risk of the bank (RWA/Assets) is negatively correlated with AC as of

December 2010 but positively correlated with AC as of December 2015. The banks have a higher

level of RWA (and consequently of credit risk) when they invest in riskier asset categories. The

negative correlation might be explained by the fact that during the sovereign debt crisis, banks

tend to decrease their investment in riskier categories of asset classes. The Core Tier 1 capital

ratio has a positive and significant influence on the ranking of asset commonality for GIIPS

banks for the collected data as of December 2010. However, for the collected data as of

December 2015, the ranking correlation is negative and significant for Euro non GIIPS banks.

This means that the Euro non GIIPS banks, which are well capitalized (high Core Tier 1 capital

ratio), have less asset commonality. Thus, increasing the Core Tier 1 has a negative influence on

AC and increasing RWA has a positive influence on AC. We point out also that the level of

profitability has no influence on AC. It is the same case when we focused on the funding (Short

term /Total Liabilities) as shown in table 4.

In order to better confirm these results we run linear regressions with the size weighted AC as

dependent variable on the bank characteristics for both disclosures (table 5). The R² is high in

2010 and 2015. So, the model fits the data. The intercept is highly positive (54.35) and

statistically significant for GIIPS (IIS) banks when the dependent variable is “AC WM” as of

December 2010. This means that AC is high between GIIPS banks. However, in 2010, the

intercept is highly negative (-29.05) and statistically significant for Non Euro zone banks. This

confirms that AC is not very important between non Euro banks. When the diversification is high

the AC between banks is high especially in 2010. An increase of the core tier 1 capital ratio by

one unit will decrease AC by 51.81 in 2015. The core Tier 1 capital ratio confirms that AC

concerns banks that are undercapitalized. The Non Eurozone banks are well capitalized since the

21

coefficient of the independent variable core tier 1 capital ratio is highly positive (238) and

statistically significant in 2010. We can notice that the coefficient is higher for “AC W” than for

“AC WM” (equal to 65).

After having explored the determinants of AC, by looking at the bank’s characteristics, we focus

on systemic risk measures. We have used the same methodology (Spearman Ranking

correlation). We wanted to determine if an exposure to similar assets enhance the systemic risk.

Thus, we wanted to know if systemic risk measures and AC are related. We find that ranking

correlation between SRISK and AC W and AC WM is positive and statistically significant for all

the banks (table 6). Indeed, it is equal to 0.69 and 0.73 for AC W and AC WM respectively in

2010. For the leverage, results are statistically significant only in 2015. Indeed, the ranking

correlation between leverage and AC W is equal to 0.38. Thus, AC comes from banks that are

undercapitalized since SRISK is the difference between the required and the available capital. AC

comes from highly leveraged banks. For the volatility of returns, results are negative and

statistically significant in 2015. Thus, AC comes from banks that not necessarily have a high

volatility of returns. For the correlation of returns, results are positive and statistically significant

in both 2010 and 2015. Thus, AC comes from banks that have a high correlation of returns.

GIIPS banks have the particularity to have statistically significant result for ranking correlation

between, SRISK and AC WM and LRMES and AC WM in 2010. We may conclude that AC has

a positive correlation with SRISK, but its level has no influence on systemic risk when we

consider other more significant determinants of SRISK. SRISK and AC can be seen as

complementary tools to assess the risk of a bank, as they are not dependent on the same

determinants. With AC, we can provide a better picture of the risk position of a bank than with

SRISK.

5.2 Determinants of the evolution of asset commonality

In order to know which factors impact the evolution of AC, we perform a Spearman ranking

correlation analysis of AC (variation of AC between data of December 2015 and December

2010) with various variables measured at the beginning of the period. The most significant

ranking correlations are displayed in table 7. We notice that the determinants of the evolution are

the diversification index, the bank’s size and the credit risk. An increase of 1% of the credit risk

will increase the AC WM by 0.41. We then perform the regression of AC on various variables

22

measured at the beginning of the period. The regressions with highest levels of significance are

found in table 8. The R² is significant for all the regressions (between 0.76 and 0.96 of the

variability of the evolution of AC explained). Over the period (2010-2015), a high level of

diversification has a significant negative influence on the evolution of AC. On the contrary, the

evolution is amplified when the bank has a high capitalization ratio (high Core Tier 1 capital

ratio) and has a high credit risk (especially for Euro non GIIPS banks). Finally, consistently with

what we have already displayed, it is difficult to draw general conclusions on the evolution of

AC. The determinants of its evolution are period-specific. We run the same regression and

compared the period (December 2012 and June 2013) and we have found different results.

5.3 Asset commonality forecasting power

To have a broader picture of the potential influence of asset commonality on the performance of

the banks during the second half of 2011 and the second half of 2016, we also perform

regressions with realized outcomes as dependent variables. The realized outcomes are realized

loss, realized return and realized volatility. The realized loss represents the

capital loss on securities held in a portfolio of a bank that has become actual by the sale or other

type of surrender of one or many securities. The formula is the following:

�������� ���� = −���� ∗ � ln (���

��� − 1)

�����

���

(9)

Where ���� is the market-value of equity of bank � (all converted in Euros), with �=

07/15/2011 and 07/29/2016 and �=130 (six months).

The realized return represents the returns earned by a bank during a period of time (6 months).

The realized stock return of bank m at time t is defined by

�������� ������ = − � ln (���

��� − 1)

�����

���

(10)

Where, ��� is the stock price of the bank.

23

The six-month realized volatility is defined by:

�������� ���������� = �1

130� (��� − �̅

�����

���

mt, 130)²

(11)

Where �̅mt, 130 is the six months forward average return of bank � at date �.

From table 9, we notice that the comparison between AC W and AC WM gives almost the same

results. The ranking correlation is high and statically significant in 2010 between AC W and

realized loss (0.56) and AC W and realized volatility (0.36). Thus, in 2010, the AC W and AC

WM predict the ranking of bank's six-months realized loss and six-months realized volatility. The

ranking correlation between AC W and the six-months realized return is positive (0.65) and

statistically significant at the 5% level in 2010. More precisely, in 2010, AC W for GIIPS (IIS)

banks predicts the ranking of bank's six-months realized return. We may conclude that being a

bank from a GIIPS country would largely improve the bank’s return during the sovereign debt

crisis. The ranking correlation between AC WM and the six-months realized loss is positive

(0.48) and statistically significant at the 10% level in 2010. Specifically, in 2010, AC WM for

Non Eurozone banks predicts the ranking of bank's six-months realized loss. In 2015, the ranking

correlation is positive (0.35) and statistically significant at the 5% level between AC W and

banks' six-month realized return. So, logically, the ranking correlation is negative (-0.65) and

statically significant at the 1% level between AC W and banks' six-month realized loss. In 2015,

AC W and AC WM predict the ranking of bank's six-months realized return and six-months

realized loss. The ranking correlation between AC WM and the six-months realized volatility is

negative (-0.64) and statistically significant at the 5% level in 2015. More precisely, in 2015, for

GIIPS banks AC W predicts the banks' six-months realized volatility. The results of regression of

realized outcomes with asset commonality weighted and weighted by the median show that it is

mainly the results of the banks' six-month realized loss that are statistically significant. Indeed, in

2010, the intercept is negative and statically significant at the 5% level (table 10). In 2015, the

intercept is positive and statistically significant at the 1% level. The realized loss is expected to

be negative when we don't take into account AC in 2010. The realized loss is expected to be

positive when we don't take into account AC in 2015. The coefficient of the AC W is positive

24

(0.54) and statistically significant at the 5% level in 2010 (the same for AC WM). The coefficient

of AC W is negative (-1.31) and statistically significant at the 1% level (the same for AC WM).

Once AC is taking into account, it is expected that the banks' six-months realized loss will

increase in 2010 and banks' six-months realized loss will decrease in 2015. In 2015, results are

statistically significant for the 3 groups (Euro non GIIPS, GIIPS and Non Euro). This is not the

case in 2010. When we run linear regressions (table A.13), it is banks' capitalization (core tier 1

capital ratio) and the banks’ size (ln(TA)) that predicts the six-months realized loss in 2010. In

2015, it is only the banks’ size (ln(TA)) that predicts banks' six-months realized loss. An increase

by one unit of the book to market ratio will increase the six months realized loss by 5.04

(regression 4). In 2015, when stocks are undervalued, it is expected that the six-months realized

loss increase.

To sum up, the AC by asset classes is not significant to forecast performance. Nevertheless, even

it is not significant; the AC would have a negative effect on bank’s return. It would increase

volatility and it would raise the risk of the bank. Even if AC is not the best predictor of bank’s

realized outcomes, it can indeed be a helpful measure to assess the banks’ risk and

interconnectedness.

6. Robustness Check

In order to better know if AC is able to predict the bank’s realized outcomes, we run some linear

regression by taking into account the notion of fire sales. We collect the data (EADs) of the

transparency exercise in 2016 (published in December 2016) and the same data of the EU wide

stress testing exercise of 2016. The variation concerns differences between EADs as of June 2016

and as of December 2015. We point out that we didn’t take into account data of the transparency

exercise in 2011 since only the information about Sovereign’ EADs is available. Thus, we have

subtracted these 2 data (EADs) and we have divided data into 2 groups “No Assets sold” and

“Assets Sold”. The list of banks of the two groups is presented in table A.14. From table A.15,

we used the same methodology of Spearman ranking correlation to notice if the ranking of AC

and the ranking of some important banking characteristics is consistent. We notice that for both

groups, the results are significant at the 1% and 5% level especially for the ranking of AC W and

AC WM with the diversification index, the bank’s size (TA) and the bank capitalization (Core

Tier 1 capital ratio). When we run linear regressions with the same variables, we notice that it is

25

mainly the group of “Assets sold” which has significant results. Indeed, from table A.16, we

notice that an increase of 1% of the credit risk will decrease AC W by 6.55. In addition, an

increase of 1% of the ratio (Short term funding/Total liabilities) will decrease AC W by 7.86. The

previous ratio is, logically, significant since we are taking into account the “Assets sold” group.

Then, when we focus on the ability of AC to predict the performance of banks realized outcomes;

we notice that for the “Assets sold” group the realized are significant with the realized return and

realized loss. Thus, AC is a good predictor of the bank’s realized return especially for the “Assets

sold” group (table A.17). The results are confirmed in table A.18, when we run linear regressions

of realized outcomes with AC W and AC WM.

We can conclude that when we take into account the “Assets sold” group the results are more

significant concerning the prediction of AC of the banks realized performance. Thus, from

another angle, we notice that AC can forecast performance if we focus on the banks for which

some assets were sold. From another point of view, we may focus on the banks for which there

are not assets sold between the two periods, for which there is no problem of funding but there is

a decrease of performance (table A.19). For these banks, we may notice the interest of AC as a

good predictor of systemic risk.

7. Summary and Conclusion

It is interesting to understand asset commonality among financial institutions in order to

understand systemic risk. In this paper, we measure asset commonality of European banks,

interconnectedness through the common exposures between banks’ portfolios to the same types

of assets, as of December 2010 and December 2015. On average, we have 70% of similarity

between banks’ portfolios when they are decomposed by asset classes. Asset commonality is

significantly related to the diversification level of the bank (major determinant), its size, its credit

risk and its capitalization. In addition, significant results are mainly related to GIIPS banks. Asset

commonality decreases during the European sovereign debt crisis, banks reducing the similarities

by adopting a tailor-made portfolio composition. Nevertheless, in 2015, asset commonality

started to increase again. We also find that asset commonality can influence negatively the

performance and positively the volatility of the bank. The level and rapidity of contagion are

increased when banks are interconnected and diversified, due to the portfolios’ overlaps. The

asset commonality and SRISK can be seen as complementary tools to assess the risk of a bank, as

26

they are not dependent on the same determinants. With our asset commonality measures, we can

have a broader and better picture of the interconnection of the banks.

We can propose new policy implications. The asset commonality measures presented in this

paper can be used as complementary measures to other popular systemic risk measures (SRISK,

∆CoVaR…) to monitor risks. We have to pay more attention to the negative externalities related

to diversification. As pointed out in the related literature, there is a diversification threshold at

which the individual benefit from diversification begins to be offset by the increase in systemic

risk from the diversification. The regulators also have maybe to regulate the proportion of some

asset classes in the banks’ balance sheets. The regulators could take measures, for example a

ceiling for the proportion of sovereign exposures held by the banks, with the purpose to reduce

the contagion and asset commonality.

27

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29

Figure 1: Sovereign risk This figure shows the average daily five-year sovereign CDS prices of IIPS countries (Ireland, Italy, Portugal, and Spain), non-GIIPS Eurozone countries, and non-Eurozone countries. Vertical bars indicate the dates of data collected as of December 2010 and December 2015 and the announcement dates of stress test results (7-15-2011 & 7-29-2016).

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(c) Average daily five-year CDS prices

30

Figure 2: Institution risk This figure shows the average annualized volatility of daily banking price index by periods (Panel A), the average annualized volatility of GIIPS countries (Greece, Ireland, Italy, Portugal, and Spain), non-GIIPS Eurozone countries, and non-Eurozone countries (Panel B) and the average daily prices (Panel C). Vertical bars indicate the dates of data collected as of December 2010 and December 2015 and the announcement dates of stress test results (7-15-11 & 7-29-16).

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31

Figure 3: Corporate risk This figure shows the average annualized volatility of daily industrial price index by periods (Panel A), the average annualized volatility of GIIPS countries (Greece, Ireland, Italy, Portugal, and Spain), non-GIIPS Eurozone countries, and non-Eurozone countries (Panel B) and the average daily prices (Panel C). Vertical bars indicate the dates of data collected as of December 2010 and December 2015 and the announcement dates of stress test results (7-15-11 & 7-29-16).

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32

Figure 3: Retail risk This figure shows the average monthly banking interest rates of revolving loans and overdrafts for households by periods (Panel A), the same average monthly banking interest rates of GIIPS countries (Greece, Ireland, Italy, Portugal, and Spain), non-GIIPS Eurozone countries, and non-Eurozone countries (Panel B) and the average monthly rates (Panel C). Vertical bars indicate the dates of data collected as of December 2010 and December 2015 and the announcement dates of stress test results (7-15-11 & 7-29-16).

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33

Table 1: Euclidean Distances and Asset Commonality Matrices These tables report the Euclidean distances and the Asset Commonality between the banks with total assets higher than € 1000 bn as of December 31, 2010, and as of December 31, 2015 when EAD is decomposed by asset classes

Panel B: Non-Weighted Asset Commonality

December 2010 December 2015

BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE

BNP Paribas 100.00 100.00

Deutsche Bank 82.10 100.00 88.92 100.00

HSBC 83.49 79.79 100.00 90.27 89.13 100.00

Barclays 78.54 77.50 82.88 100.00 86.81 86.07 89.52 100.00

Credit Agricole 82.65 69.55 71.81 67.59 100.00 86.07 81.02 81.42 81.18 100.00

Banco Santander 75.61 72.73 78.73 82.53 66.00 100.00 86.85 84.40 85.62 89.65 82.64 100.00

Societe Generale 90.15 78.88 79.88 75.34 86.31 72.06 100.00 92.98 87.70 89.59 85.36 88.45 83.94 100.00

Lloyds 61.46 60.02 67.67 78.14 54.58 72.45 59.31 100.00 73.59 72.94 75.57 84.26 73.29 82.69 72.59 100.00

BPCE 77.60 65.27 66.96 63.52 93.18 62.04 82.68 51.60 100.00 87.18 80.08 80.76 79.85 91.32 82.25 87.75 71.67 100.00

Panel A: Non-Weighted Euclidean Distances December 2010 December 2015

BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE

BNP Paribas 1 1 Deutsche Bank 0.25 1 0.16 1 HSBC 0.23 0.29 1 0.14 0.15 1 Barclays 0.30 0.32 0.24 1 0.19 0.20 0.15 1 Credit Agricole 0.25 0.43 0.40 0.46 1 0.20 0.27 0.26 0.27 1 Banco Santander 0.34 0.39 0.30 0.25 0.48 1 0.19 0.22 0.20 0.15 0.25 1 Societe Generale 0.14 0.30 0.28 0.35 0.19 0.40 1 0.10 0.17 0.15 0.21 0.16 0.23 1 Lloyds 0.55 0.57 0.46 0.31 0.64 0.39 0.58 1 0.37 0.38 0.35 0.22 0.38 0.24 0.39 1 BPCE 0.32 0.49 0.47 0.52 0.10 0.54 0.24 0.68 1 0.18 0.28 0.27 0.29 0.12 0.25 0.17 0.40 1

34

Panel C: Weighted Euclidean Distances

December 2010 December 2015

BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE

BNP Paribas 1

1

Deutsche Bank 0.24 1 0.14 1

HSBC 0.22 0.28 1 0.13 0.13 1

Barclays 0.27 0.29 0.23 1 0.14 0.14 0.13 1

Credit Agricole 0.24 0.42 0.38 0.43 1 0.18 0.24 0.24 0.21 1

Banco Santander 0.30 0.35 0.26 0.20 0.44 1 0.16 0.19 0.19 0.13 0.20 1

Societe Generale 0.12 0.28 0.26 0.31 0.19 0.34 1 0.09 0.15 0.13 0.16 0.16 0.19 1

Lloyds 0.49 0.52 0.42 0.29 0.59 0.34 0.52 1 0.28 0.28 0.28 0.18 0.26 0.18 0.29 1

BPCE 0.30 0.47 0.44 0.48 0.09 0.49 0.24 0.63 1 0.17 0.26 0.25 0.24 0.12 0.21 0.17 0.30 1

Panel D: Weighted Asset Commonality

December 2010 December 2015

BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE

BNP Paribas 100.00 100.00

Deutsche Bank 82.75 100.00 90.20 100.00 HSBC 84.26 80.33 100.00 91.01 90.74 100.00

Barclays 80.70 79.48 83.82 100.00 90.02 89.96 91.07 100.00

Credit Agricole 83.22 70.64 73.02 69.87 100.00 87.12 83.13 83.11 84.90 100.00 Banco Santander 78.83 75.40 81.51 85.55 69.04 100.00 89.00 86.60 86.39 90.62 85.77 100.00

Societe Generale 91.33 80.45 81.53 78.28 86.59 75.78 100.00 93.62 89.70 91.13 89.01 89.04 86.26 100.00

Lloyds 65.10 63.45 70.30 79.84 58.27 75.94 63.40 100.00 80.43 80.04 80.53 87.42 81.44 87.52 79.57 100.00 BPCE 78.73 66.89 68.63 66.18 93.95 65.38 83.00 55.55 100.00 88.14 81.94 82.19 83.10 91.67 85.07 88.06 79.02 100.00

35

Panel E: Euclidian distances weighted by the median December 2010 December 2015

BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE

BNP Paribas 1 1

Deutsche Bank 0.23 1 0.13 1

HSBC 0.21 0.26 1 0.11 0.13 1

Barclays 0.28 0.29 0.22 1 0.15 0.16 0.12 1

Credit Agricole 0.22 0.39 0.36 0.42 1 0.16 0.22 0.22 0.22 1

Banco Santander 0.32 0.35 0.27 0.23 0.44 1 0.15 0.18 0.17 0.12 0.20 1

Societe Generale 0.13 0.27 0.26 0.32 0.18 0.36 1 0.08 0.14 0.12 0.17 0.13 0.19 1

Lloyds 0.50 0.52 0.42 0.28 0.59 0.36 0.53 1 0.31 0.32 0.28 0.18 0.31 0.20 0.32 1

BPCE 0.29 0.45 0.43 0.47 0.09 0.49 0.22 0.63 1 0.15 0.23 0.22 0.24 0.10 0.21 0.14 0.33 1

Panel F: Asset Commonality weighted by the median

December 2010 December 2015

BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE BNP D. B. HSBC Barc. C.A. Sant. S.G. Lloy. BPCE

BNP Paribas 100.0 100.00

Deutsche Bank 83.65 100.00 90.86 100.00 HSBC 84.92 81.54 100.00 91.98 91.04 100.00

Barclays 80.40 79.45 84.36 100.00 89.12 88.51 91.36 100.00

Credit Agricole 84.16 72.19 74.26 70.41 100.00 88.51 84.35 84.68 84.48 100.00

Banco Santander 77.72 75.10 80.57 84.05 68.95 100.00 89.16 87.14 88.14 91.46 85.68 100.00 Societe Generale 91.01 80.71 81.62 77.48 87.50 74.49 100.00 94.21 89.86 91.41 87.93 90.48 86.75 100.00

Lloyds 64.80 81.54 70.48 80.03 58.52 74.84 62.84 100.00 78.22 77.69 79.85 87.02 77.97 85.73 77.40 100.00

BPCE 79.54 68.29 69.82 66.69 93.77 65.33 84.18 55.80 100.00 89.42 83.58 84.14 83.38 92.84 85.36 89.89 76.64 100.00

36

Table 2. Ranking correlation This Table summarizes the Spearman ranking correlation of the size weighted asset commonality by asset classes as of December 31, 2010 and as of December 31, 2015 with different portfolio’s categories. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. * indicates that the correlation is significant at the 10% level, ** at the 5% level and *** at the 1% level.

2010 ALL GIIPS Eurozone non GIIPS Non Eurozone

Portfolio Allocation AC W AC WM AC W AC WM AC W AC WM AC W AC WM

Institution 0.45*** 0.42*** 0.45 0.40 0.10 0.08 0.15 0.17

Corporate 0.23 0.13 -0.05 -0.47 0.34 0.32 0.08 0.11

Retail -0.51*** -0.47*** -0.20 0.06 -0.34 -0.32 -0.04 -0.12

Sovereign 0.31** 0.26* -0.01 -0.19 0.35 0.33 0.01 0.02

Others 0.14 0.24 -0.35 0.07 0.06 0.08 -0.22 -0.10

Table 3. Ranking correlation This Table summarizes the Spearman ranking correlation of the size weighted asset commonality by asset classes as of December 31, 2015 with different portfolio’s categories risk weight. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. * indicates that the correlation is significant at the 10% level, ** at the 5% level and *** at the 1% level.

Risk Weight ALL Eurozone non GIIPS GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

Sovereign 0.40*** 0.42*** 0.31 0.37 0.25 0.05 0.38 0.57**

Institution -0.04 -0.12 0.28 0.22 -0.43 -0.40 -0.22 -0.32

Corporate 0.09 0.13 0.10 0.13 0.15 0.29 -0.05 0.20

Retail 0.14 0.14 0.05 0.04 0.37 0.65** 0.19 0.21

2015 ALL GIIPS Eurozone non GIIPS Non Eurozone

Portfolio Allocation AC W AC WM AC W AC WM AC W AC WM AC W AC WM

Institution 0.32** 0.41*** 0.52 0.60* 0.07 -0.03 0.65** 0.78***

Corporate 0.17 0.12 0.52 0.33 -0.12 -0.09 0.50** 0.29

Retail -0.12 -0.20 -0.02 -0.18 0.05 0.08 -0.37 -0.60

Sovereign 0.39*** 0.44*** 0.39 0.57* 0.08 0.09 0.16 0.45

Others 0.02 0.09 -0.85*** -0.66** 0.08 0.09 -0.20 0.11

37

Table 4. Ranking correlation This Table summarizes the Spearman ranking correlation of the size weighted asset commonality by asset classes as of December 31, 2010 and as of December 31, 2015 with different bank characteristics. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. * indicates that the correlation is significant at the 10% level, ** at the 5% level and *** at the 1% level.

2010 ALL GIIPS Eurozone non GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

- Diversification Index 0.16 0.18 0.82*** 0.95*** 0.76*** 0.77*** 0.93*** 0.95***

Total Assets -0.03 -0.01 0.65** 0.77*** 0.64*** 0.65*** 0.64** 0.75***

Core Tier 1 Capital ratio 0.22 0.26* 0.76*** 0.62** -0.15 -0.13 0.30 0.15

RWA/assets -0.07 -0.14 -0.35 -0.44 -0.40* -0.42* -0.17 -0.18

Short Term/TL 0.25 0.21 -0.53 -0.47 -0.40* -0.43* -0.31 -0.26

2015 ALL GIIPS Eurozone non GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

- Diversification Index 0.00 -0.04 0.69** 0.83*** 0.65*** 0.75*** 0.71*** 0.93***

Total Assets 0.07 0.01 0.55* 0.57* 0.65*** 0.67*** 0.81*** 0.72***

Core Tier 1 Capital ratio -0.20 -0.18 -0.05 0.00 -0.67*** -0.69*** -0.35 -0.30

RWA/assets 0.12 0.12 0.21 0.36 0.04 0.04 -0.06 -0.03

Short Term/TL 0.07 0.04 -0.38 -0.19 -0.02 -0.12 -0.28 -0.34

38

Table 5.Linear regression This Table summarizes the regression of the size weighted asset commonality by asset classes as of December 31, 2010 and as of December 31, 2015 with different bank characteristics. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. * indicates that the correlation is significant at the 10% level, ** at the 5% level and *** at the 1% level.

2010 ALL GIIPS Eurozone non GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

Intercept 21.41** 8.11 38.11 54.35* 24.57*** 12.85 -29.05** -12.00**

Diversification Index 0.49*** 0.54*** 0.70 0.92* 0.45*** 0.49*** 0.47*** 0.58***

Core Tier 1 capital ratio -29.02 -20.02 -184.59 -210.93 -30.90* -35.73* 238.36*** 64.77**

Log(total assets) 0.97* 1.47*** -1.29 -3.57 1.11** 1.67*** 2.48*** 1.92***

RWA/Assets 2.62 1.59 18.28 14.04 -0.15 0.16 6.00* 3.70**

Short Term/TL -7.74 -0.96 -18.38 -33.86 -12.47 -9.32 6.75 5.74*

N 34 34 7 7 15 15 12 12

R² 0.90 0.97 0.99 0.99 0.97 0.98 0.97 0.99

2015 ALL GIIPS Eurozone non GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

Intercept 59.32*** 38.00** 39.78 49.50 -26.47 -8.00 42.85 9.44

Diversification Index 0.17 0.32 0.03 0.08 0.86* 0.80*** 0.57** 0.73***

Core Tier 1 capital ratio -51.81*** -18.27 64.13 -4.55 4.62 -5.01 -68.39* -14.8

Log(total assets) 1.45*** 1.58*** 1.81 1.61 2.49** 1.72** 0.51 1.08**

RWA/Assets -2.82 2.92 33.01 20.97 7.38 3.62 -15.97 -3.36

Short Term/TL -3.35 -2.48 -21.78 -12.89 -7.65 -6.89 2.01 4.04

N 34 34 7 7 15 15 12 12

R² 0.60 0.73 0.58 0.73 0.79 0.91 0.83 0.98

39

Table 6. Ranking correlation This Table summarizes the Spearman ranking correlation of the size weighted asset commonality as of December 31, 2010 and as of December 31, 2015 with SRISK and some related measures. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. * indicates that the correlation is significant at the 10% level, ** at the 5% level and *** at the 1% level.

2010 ALL GIIPS Eurozone non GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

- SRISK/TA 0.37** 0.39** -0.27 0.14 -0.20 -0.15 0.38 0.48*

SRISK 0.69*** 0.73*** 0.65** 0.81*** 0.39 0.41 0.65** 0.75***

LRMES 0.29* 0.30* 0.37 0.69** -0.11 -0.04 -0.02 -0.09

Beta 0.29* 0.30* 0.37 0.69** -0.12 -0.04 -0.02 -0.09

Correlation 0.48*** 0.50*** 0.48 0.35 0.50 0.49 0.12 0.14

Volatility 0.00 -0.01 0.13 0.38 -0.17 -0.10 0.01 -0.07

Leverage 0.26 0.28 -0.52 -0.10 0.13 0.14 0.41 0.52*

2015 ALL GIIPS Eurozone non GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

SRISK/TA 0.37** 0.38** -0.05 -0.28 -0.08 -0.06 0.46 0.23

SRISK 0.70*** 0.68*** 0.49 0.38 0.62* 0.60* 0.82*** 0.64**

LRMES 0.31* 0.31* 0.49 0.24 0.28 0.39 0.12 0.09

Beta 0.32* 0.31* 0.49 0.24 0.28 0.39 0.12 0.09

Correlation 0.69*** 0.66*** 0.77*** 0.60* 0.77*** 0.81*** 0.65** 0.57**

Volatility -0.46*** -0.37** -0.65** -0.61** -0.52 -0.44 -0.38 -0.22

Leverage 0.38** 0.39** -0.19 -0.35 -0.09 -0.08 0.69*** 0.38

40

Table 7. Ranking correlation This Table summarizes the Spearman ranking correlation of the variation of the size weighted asset commonality by asset classes between December 31, 2010 and December 31, 2015 with different bank characteristics at the beginning of the period of interest. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. * indicates that the correlation is significant at the 10% level, ** at the 5% level and *** at the 1% level.

Variation (2015-2010) ALL GIIPS Eurozone non GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

- Diversification Index -0.82*** -0.86*** -0.55* -0.91*** -0.71*** -0.78*** -0.85*** -0.85***

Total Assets -0.32** -0.48*** -0.50 -0.77*** -0.09 -0.30 -0.35 -0.51*

Core Tier 1 Capital ratio -0.15 -0.24 -0.35 -0.51 0.28 0.31 -0.44 -0.36

RWA/assets 0.30** 0.41*** 0.56* 0.53* 0.30 0.43* -0.02 0.03

Short Term/TL 0.29* 0.25 0.58* 0.50 0.11 0.14 0.34 0.23

Table 8. Linear regression This Table summarizes the regression of the variation of the size weighted asset commonality by asset classes between December 31, 2010 and December 31, 2015 with different bank characteristics at the beginning of the period of interest. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. * indicates that the correlation is significant at the 10% level, ** at the 5% level and *** at the 1% level.

Variation (2015-2010) ALL GIIPS Eurozone non GIIPS Non Eurozone

W WM W WM W WM W WM

Intercept 20.00** 40.98*** 21.53 -2.60 -2.68 26.99*** 50.03*** 63.47***

Div. Index -0.31*** -0.37*** -0.35 -0.69* -0.33*** -0.40*** -0.34*** -0.36***

Core T1 capital r. 32.04 4.86 166.13 181.90 54.75 28.03 -89.76 -84.16

Log(TA) 0.92** 0.17 0.77 4.50* 2.37** 1.08 0.05 -0.64

RWA/Assets 3.54 2.68 -4.96 -5.60 18.14** 13.28** -1.73 -2.00

Short Term/TL 10.28** -2.89 5.76 30.63 -2.49 -9.74* 0.88 -10.62

N 38 38 9 9 16 16 13 13

R² 0.78 0.86 0.76 0.96 0.83 0.95 0.95 0.89

41

Table 9. Rank correlations: Asset commonality vs. realized outcomes This table presents the rank correlations of asset commonality weighted and weighted by the median and realized outcomes (the six-month realized return, volatility and loss on 07/15/2011 and 07/29/2016). The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. ***, ** and* indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

2010 ALL GIIPS Eurozone non GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

- Realized Return -0.15 -0.21 0.65** 0.52 0.25 0.26 0.07 0.12

Realized Loss 0.56*** 0.62*** -0.04 -0.06 0.28 0.25 0.44 0.48*

Realized Volatility 0.36** 0.42** -0.05 0.12 0.24 0.31 0.03 0.10

2015 ALL GIIPS Eurozone non GIIPS Non Eurozone

AC W AC WM AC W AC WM AC W AC WM AC W AC WM

- Realized Return 0.35** 0.44** 0.36 0.28 0.22 0.30 0.17 0.29

Realized Loss -0.65*** -0.67*** -0.75** -0.72** -0.75** -0.70** -0.55* -0.57**

Realized Volatility 0.09 0.09 -0.64** -0.54 -0.05 0.09 0.23 0.01

42

Table 10. Linear regression: Asset commonality vs. realized outcomes This table summarizes the results of regression realized outcomes (the six-month realized return, volatility and loss on 07/15/2011 and 07/29/2016) with asset commonality weighted and weighted by the median. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. ***, ** and* indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

Asset Commonality Realized Return Realized Volatility Realized Loss All Banks 2010 2015 2010 2015 2010 2015 Constant 0.48 0.48 0.66 0.33 -0.57 -0.49 3.37 3.37 -33.13** -29.91** 103.07*** 127.43*** AC W 2010 -0.01 0.01 0.54** AC WM 2010 -0.01 0.01* 0.50** AC W 2015 -0.01 -0.03 -1.31*** AC WM 2015 0.00 -0.03 -1.59***

Asset Commonality Realized Return Realized Volatility Realized Loss Euro Non GIIPS 2010 2015 2010 2015 2010 2015 Constant -3.44 -4.20 -4.01 -5.45 0.00 -0.04 1.31 1.56 -57.74 -69.09 143.78** 206.88*** AC W 2010 0.04 0.00 0.90 AC WM 2010 0.05 0.00 1.05 AC W 2015 0.05* -0.01 -1.75** AC WM 2015 0.06 -0.02 -2.47***

Asset Commonality Realized Return Realized Volatility Realized Loss GIIPS 2010 2015 2010 2015 2010 2015 Constant -5.90* -4.18* 8.31 3.44 -3.61 -3.52 20.14 15.00 20.60 20.75 217.42* 279.98** AC W 2010 0.08* 0.07 -0.27 AC WM 2010 0.06 0.06 -0.28 AC W 2015 -0.09 -0.23 -2.65* AC WM 2015 -0.03 -0.17 -3.40**

Asset Commonality Realized Return Realized Volatility Realized Loss Non Eurozone 2010 2015 2010 2015 2010 2015 Constant -0.16 -0.28 0.13 -0.08 0.21 0.21 -0.12 0.00 -21.19 -19.19 95.57 119.68* AC W 2010 -0.01 0.00 0.38 AC WM 2010 0.00 0.00 0.35 AC W 2015 0.01 0.00 -1.25 AC WM 2015 0.01 0.00 -1.53*

43

Appendix A. Table A.11. Descriptive statistics: All Banks This table reports descriptive statistics of the 43 common banks as of December 31, 2010 and December 31, 2015. The statistics are related to the Exposure at Default (EAD), the bank and country characteristics, the public banks and the systemic risk measures. There are 34 public banks for which we have information about systemic risk measures. December 2010 December 2015

Obs. Mean Std. Dev. Min. Median Max. Mean Std. Dev. Min. Median Max.

Total EAD in bn € 43 471.65 402.81 28.57 256.25 1494.51 900.91 835.02 67.89 450.32 3757.31

Bank characteristics

Core tier 1 capital (in bn €) 43 20.05 18.74 1.70 11.50 86.90 25.98 24.92 2.81 12.41 120.18

Core tier 1 capital ratio 43 8.97% 2.11% 3.71% 8.76% 13.03% 14.31% 2.86% 10.47% 13.30% 24.14%

Total assets (in bn €) 43 556.68 544.74 32.75 316.35 1998.16 580.65 573.43 33.92 252.36 2218.84

RWA (in bn €) 43 224.51 197.33 14.09 120.54 825.56 200.07 210.07 20.97 87.33 1012.13 Credit risk 43 48.36% 18.51% 18.19% 44.08% 100% 36.81% 12.21% 18.05% 33.31% 69.86%

Net income (in bn €) 43 1.61 3.11 -10.10 0.89 9.16 7.28 7.94 0.33 3.68 33.76

ROA 43 0.21% 1.29% -7.69% 0.40% 2.28% 1.40% 0.80% 0.31% 1.25% 5.11%

Country characteristics

GDP per capita (in $) 43 42826 11825 12600 41788 87646 44738 13005 14517 45133 89493

Current account (% of GDP) 43 1.60% 4.37% -5.40% 1.20% 10.90% 3.38% 4.33% -5.20% 2.60% 9.10%

Concentration 43 0.85 0.14 0.62 0.87 1 0.88 0.10 0.73 0.84 1

Public banks

Market capitalization (in bn €) 34 23.20 25.76 0.53 13.68 133.78 28.95 28.14 0.09 22.97 143.80

Market-to-book ratio 34 1.19 0.55 0.33 1.16 3.24 0.80 0.66 0.01 0.77 2.20

Systemic risk measures

SRISK (in bn €) 34 45.60 48.63 -6.53 25.61 165.47 23.08 29.99 -5.92 7.54 101.32

SRISK % 34 2.08% 2.20% 0.00% 1.17% 7.50% 2.02% 2.56% 0.00% 0.65% 8.73%

LRMES (in €) 34 75.80 11.69 56.04 75.63 110.29 44.35 8.52 23.55 44.94 62.88

Beta 34 1.69 0.46 1.06 1.64 3.42 1.04 0.26 0.48 1.04 1.69

Correlation 34 0.52 0.09 0.20 0.52 0.66 0.38 0.15 -0.04 0.43 0.64

Volatility 34 38.21% 27.46% 18.80% 33.65% 147.00% 39.39% 32.71% 19.90% 27.40% 140.30%

Leverage 34 42.08 48.33 3.74 28.03 268.42 101.38 449.71 6.77 18.42 2683.73

44

Table A.12: Euclidean Distances and Asset commonality Summary Statistics This Table provides the summary statistics of the variables (ED and Size Weighted AC), as of December 2010 and December 2015.

Panel A: Non-Weighted Panel B:Weighted Panel C: Weighted Median

Variable Obs. Mean S.D. Min. Median Max. Mean S.D. Min. Median Max. Mean S.D. Min. Median Max.

All banks

ED 2011 903 0.51 0.15 0.10 0.53 0.87 0.45 0.12 0.09 0.45 0.78 0.47 0.13 0.09 0.48 0.79

AC 2011 43 67.41 7.36 50.26 68.64 78.95 71.07 6.13 53.99 72.50 80.87 70.24 6.72 54.58 71.36 80.78

ED 2016 903 0.31 0.08 0.08 0.31 0.52 0.26 0.07 0.06 0.26 0.45 0.25 0.07 0.07 0.26 0.43

AC 2016 43 80.46 3.91 71.80 80.61 87.01 83.47 3.59 76.45 83.93 88.93 83.89 3.22 76.74 84.01 89.29

Euro.Non GIIPS

ED 2011 153 0.43 0.15 0.10 0.42 0.82 0.39 0.13 0.09 0.38 0.69 0.40 0.14 0.09 0.39 0.74

AC 2011 18 72.82 7.27 53.95 73.53 82.71 75.21 5.99 61.03 75.25 84.00 75.18 6.64 57.94 75.83 84.21

ED 2016 153 0.28 0.08 0.08 0.28 0.45 0.25 0.07 0.08 0.25 0.43 0.23 0.06 0.07 0.23 0.37

AC 2016 18 82.78 3.92 76.09 83.77 88.92 84.74 3.69 79.02 85.76 90.17 85.80 3.23 80.28 86.62 90.87

GIIPS

ED 2011 55 0.48 0.19 0.11 0.54 0.75 0.39 0.15 0.10 0.44 0.57 0.44 0.17 0.10 0.50 0.69

AC 2011 11 70.83 5.03 63.64 69.39 78.29 76.06 4.13 68.56 74.96 81.79 73.37 4.59 66.80 72.05 80.18

ED 2016 55 0.25 0.08 0.08 0.28 0.36 0.20 0.06 0.06 0.21 0.29 0.21 0.06 0.07 0.23 0.30

AC 2016 11 84.84 2.88 81.72 83.90 89.04 87.93 2.57 84.61 87.27 91.31 87.50 2.37 84.92 86.73 90.96

Non Euro.

ED 2011 91 0.52 0.17 0.15 0.54 0.87 0.47 0.15 0.15 0.49 0.78 0.47 0.15 0.14 0.50 0.79

AC 2011 14 68.75 9.90 52.33 70.14 82.25 71.36 8.61 55.68 72.06 83.46 71.46 9.04 56.46 72.73 83.79

ED 2016 91 0.32 0.09 0.08 0.33 0.49 0.27 0.08 0.07 0.28 0.45 0.26 0.08 0.07 0.27 0.40

AC 2016 14 80.70 5.48 73.15 81.13 88.85 83.91 4.73 77.69 84.21 90.79 84.09 4.52 77.86 84.44 90.81

45

Table A.13. Linear Regression: Realized loss vs. banking control variables This table summarizes the results of regression the six-month realized loss (on 07/15/2011 and on 07/29/2016) with the banking control variables. ***, ** and* indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

2010 ALL GIIPS

1 2 3 4 1 2 3 4

- Constant -41.29*** -46.10*** -53.76*** -34.37** -10.07 32.01 -335.72** -123.03

DivIndex 0.02 -0.062 -0.16 -0.06 -0.23 0.04 -8.40** -3.71

CT1.R. 125.63** 119.64** 137.37* 111.59 -96.99 46.03 1795.34**

Ln (TA) 2.69*** 2.43** 2.80** 2.65** 2.88 4.86 62.19** 31.37*

AC W 0.22 0.35 0.07 -1.38 1.51 0.50

B to M -0.32 -0.13 -9.38** -7.00

S. T./TL -20.47

R² 0.26 0.27 0.28 0.23 0.05 0.08 0.87 0.60

N 34 34 30 24 11 11 9 9

2010 Eurozone non GIIPS Non Eurozone

1 2 3 4 1 2 3 4

Constant -60.09 -98.68 -135.37 16.38 -62.03* -65.16* -66.70 -56.16

DivIndex 0.27 0.27 0.48 0.11 -0.10 0.12 0.09 0.31

CT1.R. 137.67 116.46 139.65 240.93* 303.03* 295.51* 291.41

Ln (TA) 2.56 2.15 3.71 3.40 4.12** 4.88** 4.86** 5.45*

AC W 0.60 0.53 -0.58 -0.44 -0.36 -0.77

B to M 0.79 -0.99 -0.64 0.16

S. T./TL -71.07 -36.11

R² 0.25 0.30 0.53 0.64 0.54 0.55 0.56 0.78

N 10 10 8 7 13 13 13 11

46

2015 ALL GIIPS

1 2 3 4 1 2 3 4

Constant 56.89*** 47.56** 40.50 25.18 44.00 104.59 137.29 222.74

DivIndex -0.06 -0.09 -0.14 -0.22 -0.22 -0.39 -0.30 0.00

CT1.R. 43.58 49.30 85.56* 125.17 345.35 402.86 623.02

Ln (TA) -5.07*** -5.39*** -5.21*** -5.21*** -5.88** -2.49 -4.18 -9.20

AC W 0.18 0.16 0.36 -1.14 -1.54 -1.11

B to M 4.05* 5.04* -7.49 -11.99

S. T./TL -12.72

R² 0.49 0.49 0.51 0.49 0.70 0.74 0.86 0.70

N 34 34 30 24 11 11 9 9

2015 Eurozone non GIIPS Non Eurozone

1 2 3 4 1 2 3 4

Constant 175.82 166.31 81.71 -448.03 70.63 54.23 60.57 100.48

DivIndex -1.38 -1.50 -1.58 6.19 -0.38 -0.69 -0.54 -0.82

CT1.R. 49.48 66.04 151.48 60.58 71.87 107.26 208.12

Ln (TA) -4.79*** -5.11** -6.50** 3.58 -4.37** -5.70* -6.06 -5.30*

AC W 0.25 1.35 -1.93 0.71 0.37 -0.16

B to M 3.11 3.85 7.95 21.38

S. T./TL -36.84 -86.22

R² 0.80 0.80 0.94 0.90 0.55 0.57 0.61 0.75

N 10 10 8 7 13 13 13 11

47

Table A.14. Assets Sold and no Assets Sold groups List of banks: Variation of Exposure at Defaults between 06/30/2016 and 12/31/2015.

No Assets Sold Assets Sold

ABN AMRO Groep N.V. Allied Irish Banks, Plc

Banca Monte dei Paschi di Siena SpA Banco Bilbao Vizcaya Argentaria, SA

Banco de Sabadell, SA Banco Popular Español SA

Banco Popolare Società Cooperativa Banco Santander SA

Bank of Ireland Barclays Plc

BNP Paribas SA Bayerische Landesbank

Danske Bank Belfius Banque SA

DekaBank Deutsche Girozentrale Commerzbank AG

Deutsche Bank AG Coöperatieve Rabobank U.A.

DNB ASA Groupe BPCE

Erste Group Bank AG HSBC Holdings Plc

Groupe Credit Agricole Intesa Sanpaolo SpA

ING Groep N.V. KBC Group NV

Jyske Bank Lloyds Banking Group Plc

Landesbank Baden-Württemberg NORD/LB Norddeutsche Landesbank Girozentrale

Nordea Bank - group Bank Polski Spolka Akcyjna

Nykredit Realkredit Société Générale SA

OP-Pohjola Group Svenska Handelsbanken - group

OTP Bank Nyrt. The Royal Bank of Scotland Group PLC

Raiffeisen-Landesbanken-Holding GmbH UniCredit SpA

Skandinaviska Enskilda Banken - group Swedbank - group Unione di Banche Italiane SCpA

48

Table A.15. Ranking correlation This Table summarizes the Spearman ranking correlation of the variation of the size weighted asset commonality by asset classes between December 31, 2015 and June 30, 2016 for banks with assets sold and no assets sold with different bank characteristics at the beginning of the period of interest. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. * indicates that the correlation is significant at the 10% level, ** at the 5% level and *** at the 1% level. No Assets Sold Assets Sold

Variation 12/31/2015 and 06/30/2016 AC W AC WM AC W AC WM

Diversification Index 0.75*** 0.83*** 0.41* 0.60***

Total Assets 0.42** 0.55*** 0.83*** 0.77***

Core Tier 1 Capital ratio -0.46** -0.48** -0.58*** -0.55**

RWA/assets 0.01 0.003 -0.06 0.02

Short Term/TL -0.24 -0.39* -0.23 -0.14

Table A.16. Linear regression This Table summarizes the regression of the variation of the size weighted asset commonality by asset classes between December 31, 2015 and June 30, 2016 for banks with assets sold and no assets sold with different bank characteristics at the beginning of the period of interest. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. * indicates that the correlation is significant at the 10% level, ** at the 5% level and *** at the 1% level.

Variable No Assets Sold Assets Sold

AC W 2015 AC WM 2015 AC W 2015 AC W M2015

Intercept 50.83 26.36 0.09 -5.65

Div. Index 0.16 0.25 0.75*** 0.86***

Core T1 ratio -42.32 -1.04 2.38 9.96 Ln(Total Assets) 1.79 2.47** 1.71*** 1.27*** RWA/Assets 1.17 11.37 -6.55* -5.57* Short Term/TL 3.07 0.30 -7.86** -5.45** N 21 21 17 17 R² 0.57 0.76 0.93 0.96

49

Table A.17. Rank correlations: Asset commonality vs. realized outcomes This table presents the rank correlations of asset commonality non weighted and weighted and realized outcomes (the 6-month realized return, volatility and loss on 07/29/2016). The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. ***, ** and* indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

Asset Commonality Realized return Realized volatility Realized Loss

No Assets Sold AC W 2015 0.17 0.19 -0.47** AC WM 2015 0.27 0.11 -0.59**

Assets Sold AC W 2015 0.64*** -0.23 -0.75*** AC WM 2015 0.74*** -0.14 -0.74***

Table A.18. Linear regression: Asset commonality vs. realized outcomes This table summarizes the results of regression realized outcomes (the 6-month realized return, volatility and loss on 07/29/2016) with asset commonality non weighted and weighted. The AC W stands for asset commonality weighted by the risk of the different asset classes. The AC WM stands for asset commonality weighted by the risk’s median of the different asset classes. ***, ** and* indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

Asset Commonality Realized Return Realized Volatility Realized Loss No Assets Sold Assets Sold No Assets Sold Assets Sold No Assets Sold Assets Sold Constant 4.21 4.17 -6.65* -7.35 5.54 6.48 -1.56 -2.04 48.86* 73.52** 180.25** 199.36** AC W 2015 -0.04 0.08* -0.06 0.02 -0.65* -2.23** AC WM 2015 -0.04 0.09* -0.07 0.03 -0.94** -2.44**

50

Table A.19. List of banks for which there is no problem of funding, no fire sales and a decrease of returns.

BNP Paribas SA

Deutsche Bank AG

Erste Group Bank AG

Jyske Bank

Raiffeisen-Landesbanken-Holding GmbH

Skandinaviska Enskilda Banken - group

Swedbank - group