Asset commonality of European banks☼
Transcript of Asset commonality of European banks☼
1
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: [email protected]
☼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.
2
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
3
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
4
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.
5
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
6
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
7
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)
8
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)
9
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
10
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
11
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
12
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
13
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
14
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
References
Acemoglu, D., A. Ozdaglar and A. Tahbaz-Salehi (2015): “Systemic Risk and Stability in
Financial Networks”, American Economic Review 105(2), 564-608.
Acharya, V., R. Engle and D. Pierret (2014): "Testing macroprudential stress tests: The risk of
regulatory risk weights", Journal of Monetary Economics 65, 36-53.
Acharya, V., R. Engle and M. Richardson (2012): "Capital shortfall: a new approach to rankings
and regulating systemic risks", American Economic Review 102(3), 59-64.
Acharya, V., L. H. Pedersen, T. Philippon and M. Richardson (2010): "Measuring systemic risk",
Working paper. Available at http://ssrn.com/abstract=1595075.
Adachi-Sato, M. and C. Vithessonthi (2017): “Bank systemic risk and corporate investment:
Evidence from the US”, International Review of Financial Analysis, Forthcoming.
Allen, F., A. Babus and E. Carletti (2012): “Asset Commonality, Debt Maturity and Systemic
Risk”, Journal of Financial Economics, Vol. 104 N°. 3, 519-534.
Allen, F. and D. Gale (2000): “Financial Contagion”, Journal of Political Economy, 108(1), 1-33.
Beale, N., D.G. Rand, H. Battey, K. Croxson, R.M. May, R. and M.A. Nowak (2011):
“Individual versus systemic risk and the Regulators Dilemma”, Proceedings of the National
Academy of Sciences 108(31), 12647-12652.
Bernanke, Ben S. (2013): “Monitoring the Financial System”, Remarks at the 49th Annual
Conference on Bank Structure and Competition.
Brownlees, C. and R. Engle (2016): "SRISK: A conditional capital shortfall measure of systemic
risk", Working paper, Available at http://ssrn.com/abstract= 1611229.
Cai, J., A. Saunders, F. Eidam and S. Steffen (2017): “Syndication, Interconnectedness, and
Systemic Risk”, Working paper, Available at http://ssrn.com/abstract=1508642
28
Chen, Y. and I. Hasan (2016): “Interconnectedness Among Banks, Financial Stability, and Bank
Capital Regulation”, Working paper, Available at http://ssrn.com/abstract_id=2869823
Cifuentes, R., H.S. Shin and G. Ferrucci (2005): “Liquidity Risk and Contagion”, Journal of the
European Economic Association 3(2-3), 556-566.
Farhi, E. and J. Tirole (2012): “Collective Moral Hazard, Maturity Mismatch, and Systemic
Bailouts”, American Economic Review, 102 (1): 60-93.
Gai, P., A. Haldane and S. Kapadia (2011): “Complexity, Concentration and Contagion”, Journal
of Monetary Economics, Vol. 58, N°.5, 453-470.
Gai, P. and S. Kapadia (2010): “Contagion in Financial Networks”, Bank of England Working
paper n°383 Available at https://ssrn.com/abstract=1577043.
Hellwig, M.F. (2009): “Systemic Risk in the Financial Sector: An analysis of the Subprime
Mortgage Financial Crisis”, De Economist, 157 (2): 129-207.
Ibragimov, R., D. Jaffee and J. Walden (2011): “Diversification disasters”, Journal of Financial
Economics 99, 333-348.
Paltalidis, N., D. Gounopoulos, R. Kizys and Y. Koutelidakis (2015): “Transmission channels of
systemic risk and contagion in the European financial network”, Journal of Banking &
Finance 61, Supplement 1, S36-S52.
Shaffer, S. (1994): “Pooling intensifies joint failure risk”, Research in Financial services 6, 249-
280.
Schleifer, A. and R. Vishny (1992): “Liquidation Values and Debt Capacity: A Market
Equilibrium Approach”, Journal of Finance 47(4), 1343-1366.
Wagner, W. (2010): “Diversification at financial institutions and systemic crises”, Journal of
Financial Intermediation 19, 333-354.
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).
0
200
400
600
800
1000
1200
GER
MA
NY
FRA
NC
E
AU
STR
IA
BEL
GIU
M
NET
HE
RLA
ND
S
FIN
LAN
D
ITA
LY
IREL
AN
D
SPA
IN
PO
RTU
GA
L
DEN
MA
RK
SWED
EN
NO
RW
AY UK
HU
NG
AR
Y
PO
LAN
D
Before p. ST 2011 After p. ST 2011
Before p. ST 2016 After p. ST 2016
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
Before p. ST2011
After p. ST2011
Before p. ST2016
After p. ST2016
GIIPS (without Greece) Eurozone non GIIPS
Non Eurozone
0
100
200
300
400
500
600
700
800
2010-01-02 2011-05-17 2012-09-28 2014-02-10 2015-06-25 2016-11-06
Eurozone Non GIIPS GIIPS(without Greece) Non Eurozone
Data ST 2011 Results ST 2011 Results ST 2016Data ST 2016
(a) Average daily five-year CDS prices by periods (b) Average daily five-year CDS prices by countries
(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).
020406080
100120
AU
STR
IA
BEL
GIU
M
FIN
LAN
D
FRA
NC
E
GER
MA
NY
NET
HE
RLA
ND
SPA
IN
GR
EEC
E
IREL
AN
D
ITA
LY
PO
RTU
GA
L
DEN
MA
RK
HU
NG
AR
Y
NO
RW
AY
PO
LAN
D
SWED
EN UK
Before p. ST 2011 After p. ST 2011
Before p. ST 2016 After p. ST 2016
0
10
20
30
40
50
60
70
Before p. ST2011
After p. ST2011
Before p. ST2016
After p. ST2016
GIIPS Eurozone non GIIPS Non Eurozone
0
200
400
600
800
1000
1200
2010-01-02 2011-05-17 2012-09-28 2014-02-10 2015-06-25 2016-11-06
Eurozone Non GIIPS GIIPS Non Eurozone
Data ST 2011 Results ST 2011 Results ST 2016Data ST 2016
(a) Average annualized volatility by periods (a) Average annualized volatility by countries
(c) Average daily banking price index
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).
0
10
20
30
40
50
AU
STR
IA
BEL
GIU
M
FIN
LAN
D
FRA
NC
E
GER
MA
NY
NET
HE
RLA
ND
GR
EEC
E
IREL
AN
D
ITA
LY
PO
RTU
GA
L
SPA
IN UK
DEN
MA
RK
HU
NG
AR
Y
NO
RW
AY
PO
LAN
D
SWED
EN
Before p. ST 2011 After p. ST 2011
Before p. ST 2016 After p. ST 2016
0
5
10
15
20
25
30
35
40
Before p. ST2011
After p. ST2011
Before p. ST2016
After p. ST2016
GIIPS Eurozone non GIIPS Non Eurozone
0
500
1000
1500
2000
2500
3000
2010-01-02 2011-05-17 2012-09-28 2014-02-10 2015-06-25 2016-11-06
Eurozone Non GIIPS GIIPS Non Eurozone
Data ST 2011 Results ST 2011
Results ST 2016Data ST 2016
(a) Average annualized volatility by periods (b) Average annualized volatility by countries
(c) Average daily industrial price index
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).
02468
1012
Ger
man
y
Fran
ce
Luxe
mb
ou
…
Fin
lan
d
Net
her
lan
d
Au
stri
a
Ital
y
Gre
ece
Po
rtu
gal
Irel
and
Spai
n
Hu
nga
ry
Po
lan
d
Den
mar
k
UK
Before p. ST 2011 After p. ST 2011
Before p. ST 2016 After p. ST 2016
0
2
4
6
8
Before p. ST2011
After p. ST2011
Before p. ST2016
After p. ST2016
GIIPS Eurozone non GIIPS Non Eurozone
0
1
2
3
4
5
6
7
8
9
2010-06-16 2011-10-29 2013-03-12 2014-07-25 2015-12-07
Eurozone Non GIIPS GIIPS Non Eurozone
Data ST 2011 Results ST 2011 Results ST 2016Data ST 2016
(a) Average monthly rates by periods (b) Average monthly rates by countries
(c) Average monthly banking interest rates of revolving loans and overdrafts
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