analyzing systemic risk in the european banking

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Analyzing Systemic Risk in the European Banking

System: A Portfolio Approach.∗

Helmut Elsinger†

University of ViennaDepartment of Business Studies

Alfred Lehar‡

University of ViennaDepartment of Business Studies

Martin Summer§

Oesterreichische NationalbankEconomic Studies Division

February 27, 2004

∗We would like to thank Nyeong Lee for valuable research assistance. Martin Summer thanks theBank of England and the London School of Economics for their hospitality during the work on thispaper. The views and findings of this paper are entirely those of the authors and do not necessarilyrepresent the views of Oesterreichische Nationalbank.

†Brunner Strasse 72, A-1210 Wien, Austria, e-mail: [email protected], Tel: +43-1-427738057, Fax: +43-1-4277 38054

‡Corresponding author, Brunner Strasse 72, A-1210 Wien, Austria, e-mail: [email protected],Tel: +43-1-4277 38077, Fax: +43-1-4277 38074

§Otto Wagner Platz 3, A-1011 Wien e-mail: [email protected], Tel: +43-1-404207212,Fax: +43-1-404207299



Abstract

This paper proposes a new method to measure and monitor the risk in a bank-

ing system. Standard tools that regulators require banks to use for their internal

risk management are applied at the level of the banking system to measure the

risk of a regulator’s portfolio of banks. Using a sample of European banks from

1997 until 2003, we estimate the dynamics and correlations between bank asset

portfolios. To obtain measures for the risk of a Europe wide portfolio of banks,

we model the individual liabilities a European regulator would have to face to

each bank as contingent claims on the bank’s assets. The portfolio aspect of the

regulator’s liability is explicitly considered and the methodology allows a compar-

ison of sub-samples from different countries. We obtain empirical results on the

correlation of asset values in our sample. Secondly we obtain quantifications of

expected shortfall in the banking system, i.e. about the amount that would be

neccessary to push one or more insolvent institutions above the default threshold.

This number is interesting to give estimates of the amounts a European regulator

might have to be ready to inject into the system. Thirdly we analyze risk contri-

butions of individual regions to the European banking system and explain these

contributions with individual bank characteristics.

JEL-Codes: C15, E53, G21

2



1 Introduction

The European banking system has recently been confronted with a rapidly changing

environment. The establishment of EMU and the second Banking Directive in the EU

have transformed the institutional framework for European banks considerably.1 Global

integration of financial markets, ongoing financial innovation and international compe-

tition have also radically changed the market environment in which banks operate. This

creates new challenges for supervisors as well. Basing risk assessment on the analysis of

individual institutions and national banking systems in isolation becomes particularly

questionable in such an environment.

To improve on the individual institution approach to banking supervision - which is

to a large extend current practice - we make a proposal for a risk assessment method

that takes a portfolio perspective. Furthermore — to go beyond a national approach —

we study the system of major, publicly traded European banks. We analyze banks at

a system level by taking correlations between individual bank asset risks explicitly into

account. Our method is designed such that it relies on publicly available information

only. It is therefore readily implementable for transeuropean institutions like the Euro-

pean Central Bank or for international organizations like the IMF and does not depend

on proprietary data of national supervisors.

Following Merton (1974), we interpret equity as a call option on a bank’s assets.

Using a time series of equity prices and balance sheet information, we estimate the

market value of a bank’s asset portfolio and its associated dynamics.

Our central idea is to look at the balance sheets of banks not individually (as it is done

in current supervision) but rather as a portfolio of balance sheets. To explore the threat

of a banking crisis, correlation between the values of individual banks’ asset portfolios is

the most important factor. In a banking sector with highly correlated asset portfolios,

the probability of multiple defaults is high, making positive correlation undesirable for

1See Bikker and Wesseling (2003)

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regulators. However, bank fragility is also influenced by financial soundness, because well

capitalized banks are able to absorb larger shocks, reducing the probability of failures,

and the volatility of the banks assets, as more volatile banks face a higher probability of

default.

To analyze the overall stability of the banking system the portfolio approach has an

important feature: It allows to estimate the probability of a systemic crisis, i.e., that a

certain fraction of financial institutions (both in terms of numbers as well as in terms of

size) will default over a given time horizon. Our framework thus takes into account the

part of systemic risk, which arises from correlated asset portfolios.2

The portfolio view has another attractive feature that is particularly interesting in the

context of European supervision. It allows us to analyze the present value of the expected

future shortfall at individual banks and in individual regions. Within the framework of

our model one can interpret the potential liabilities of the regulator as a portfolio of

put options on correlated assets. Using standard risk management techniques, we can

estimate the current value as well as the volatility of the regulator’s liability. Using

standard value-at-risk tools, the contribution of an individual bank or of a group of

banks to the volatility of a regulator’s liability can be derived. This allows to identify

the banks or bank groups with the highest contribution to systemic risk. Such an analysis

can help to shed light on potential conflicts that can arise in the context of lender of

last resort lending in Europe, where the question of fiscal backup has not yet been really

resolved. If for instance an Italian bank with Spanish owners has lent to German firms

and receives liquidity assistance from the Italian central bank but subsequently turns out

to be insolvent there might be serious issues who picks up the fiscal bill. If the distressed

institution is confined in terms of ownership as well as activities to the national territory,

such conflicts do not arise. To assess their potential severity our analysis of individual

2The portfolio view developed here is similar to the work of Lehar (2003). Our methodology can byconstruction not capture second round effects of bank insolvencies that come from mutual interbankcredit exposures. Such an analysis requires detailed portfolio information which is usually not publiclyavailable. From results in Elsinger, Lehar, and Summer (2003) we furthermore know that the correlationfactor is far more important for the probability of bank insolvencies than domino effect insolvencies.

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contributions to the system’s risk exposure as a whole can be helpful.

Our main findings can be grouped into three categories. We obtain some empirical

results on the correlation of asset values in our sample. Secondly we obtain quantifi-

cations of expected shortfall in the banking system, i.e. about the amount that would

be necessary to push one or more insolvent institutions above the default threshold.

This number is interesting to give estimates of the amounts a European regulator might

have to be ready to inject into the system. Thirdly we analyze risk contributions of

individual regions to the European banking system and explain these contributions with

individual bank characteristics using a regression. As far as correlation is concerned we

find that the median correlation of pairs of asset values is positive for the whole sample

period. An analysis of correlation of the bank asset values in different regions with the

entire bank assets in the European banking system reveals that the correlations become

stronger and more alligned between regions after the introduction of the Euro. Analyz-

ing the expected shortfall a European regulator might face we find that the volatility

of expected shortfall is relatively low and below 10%. The regions in Europe that con-

tributed most to the risk of the system are the banks of Southern Europe and morerecently also the German banks. Banks in the Benelux countries and in the UK and

in northern Europe contributed very little to overall system risk. A regression analysis

of the individual bank risk contributions on their individual characteristics reveals that

the individual contribution depends positively on the return on average assets and on a

time trend and negatively on the book value of equity over total assets and on the bank’s

size. This increasing profitability increases a bank’s contribution to overall risk whereas

the capitalization relative to the risky assets and its size decreases its contribution.

The paper is organized as follows: Section 2 describes our methodology. Section 3

describes our data sample. Section 4 analyzes the asset dynamics in detail. In Section

5 we look at the expected shortfall. The final Section 6 concludes.

5



2 Methodology

A bank’s asset portfolio, consisting of loans, traded securities and many other items, is

re-financed by debt and equity. If the value of the bank’s assets falls below the face value

of its debt, the bank is insolvent. For the estimation of insolvency risk, we therefore

need information on the future development of asset values and the face value of debt.

The problem is that the actual market value of assets is not directly observable.3 What

is however observable is the market value of equity and the face value of debt for each

publicly traded bank. By viewing equity as a call option on the bank’s assets with a

strike price given by the face value of debt, we can make use on this information to get

an estimate of the market value of assets.4.

Denote by V the market value of the bank’s asset portfolio. Assume that the asset

value V of the bank follows a geometric Brownian motion with drift µ and volatility σ

dV = µV dt+ σVdz (1)

Then equity E t can be seen as a call option on the bank’s assets with a strike price equal

to the current notional value of the bank’s debt Bt, which is assumed to have a maturity

of T . We assume that all bank debt is insured and will therefore grow at the risk-free

rate.6 The value of bank equity is then given by:

E t = V t N (dt) − Bt N (dt − σ√T ) (2)

3The dynamics of the market value of a bank’s liabilities is not important, as the bank is assumed

to default whenever the market value of the assets is below the promised payments, which is the bookvalue of liabilities.4This idea goes back to Black and Scholes (1973) and Merton (1973) and has been widely used

by academics and practitioners to price deposit insurance (Ronn and Verma (1986), Giammarino,Schwartz, and Zechner (1989))5, or to assess credit risk (Ericsson and Reneby (2001), Vassalou andXing (forthcoming), and KMV corporation’s credit risk model). In the banking literature the Mertonframework is also used to evaluate the risk of individual banks over time (Gizycki and Levonian (1993)),to assess the government subsidy to individual banks (Laeven (2002)), and to test for risk shiftingbehavior of banks (Duan, Moreau, and Sealey (1992) and Hovakimian and Kane (2000))

6Relaxing this assumption will not dramatically change the results, since the paper’s focus is noton deposit insurance pricing. From the available data, we can not determine the amount of uninsureddebt for every bank.

6



where

dt =ln(V t/Bt) + (σ2/2)T

σ√T

(3)

In the market one can observe a time series of equity prices E t and read the face

value of bank debt from the balance sheet. With assumptions on the other parameters

it is possible to solve Equation (2) for the market value of the bank’s asset portfolio

V t. We use the maximum likelihood estimator approach developed by Duan (1994) to

estimate the time series of asset values.7 Given a sequence E = (E t), t ∈ {1 . . .m} of

equity values, the parameters (µ, σ) of the asset value process in Equation (1) can be

estimated by maximizing the following likelihood function:

L(E,µ ,σ) = −m − 1

2ln(2π) − m − 1

2lnσ2

−mt=2

lnN (dt)

− 1

2σ2

mt=2

ln

V t(σ)

V t−1(σ)

− µ

2(4)

where V t(σ) is the solution of Equation (2) with respect to V and dt corresponds to dt

in Equation (3) with V t replaced byˆV t(σ).

For each year in the sample period the parameters of the asset process µ and σ are

estimated by assuming the maturity of debt T being equal to one year8 and using a

rolling window of weekly market values of total equity E t of the last year (m = 52).

The procedure gives parameter sets for every bank and every year in the sample, which

can then be used to back out the asset value V t for every given equity price. For each

year the weekly equity prices are used to obtain a time series of 52 corresponding asset

values. By connecting these over the different years of the sample we get a time series

7Ronn and Verma (1986) estimate V by first estimating the volatility of equity σE. They assume alinear relationship between asset volatility σ and σE . This together with Equation 2 defines a systemof two equations, which can be solved for asset value V and asset volatility σ. Duan (1994), however,points out that σE is stochastic when one assumes a geometric Brownian motion for the asset priceprocess. Therefore σE is hard to estimate and it is not linear in the asset volatility. The maximumlikelihood estimator used here overcomes this problem.

8The maturity of debt can also be seen as the time until the next audit of the bank, because thenthe regulator can observe V and close the bank, when it is under-capitalized. In the U.S. the FDICperforms audits every 12 to 18 month.

7



Table 1: Summary statistics of all banks included in the sample

Country Number Total Assets(book values in mill, USD)of banks Sample 2002 max (2002) median (2002) min (2002)

AUSTRIA 7 128,968 90,285 6,346 1,201BELGIUM 5 1,411,762 498,910 272,119 32,745DENMARK 38 266,040 217,249 349 45FINLAND 3 15,195 13,275 1,769 150FRANCE 34 1,811,056 866,080 3,779 13GERMANY 14 1,948,694 751,643 35,259 493GREECE 10 181,620 55,250 15,952 1,293IRELAND 5 238,157 91,155 36,111 6,441

ITALY 28 1,437,691 328,618 13,214 712LUXEMBOURG 4 77,950 44,861 16,425 240NETHERLANDS 6 1,384,378 739,972 8,630 9PORTUGAL 5 167,218 66,063 28,719 5,993SPAIN 14 846,625 373,495 7,825 1,042SWEDEN 4 612,465 253,488 127,532 103,911UNITED KINGDOM 36 2,630,030 686,533 1,801 57Total 213 13,157,854

of asset values for all banks over the entire sample period.

3 Sample

We have included data on 324 EU banks in our sample. The accounting data are from

Bankscope whereas the market values have been collected from Datastream. Equity

prices are recorded on a weekly basis from January 1992 until December 2002 andbalance sheet information is collected annually starting in 1996. We deleted all banks

for which less than three years of data was available (111 banks).

The largest bank in the sample — BNP Paribas — has assets of USD 811 billion,

the smallest bank has USD 9 million, and the median bank size is USD 4,8 billion.

Table 1 shows summary information for the included banks. For parts of our analysis

8



we have grouped banks in six different regions: Benelux (Netherlands, Belgium, Lux-

embourg), France, UK & IR (United Kingdom and Ireland), DE & AT (Germany and

Austria), Southern Europe (Greece, Italy, Portugal, and Spain), and Scandinavia (Den-

mark, Finland, and Sweden). Appendix A contains additional summary statistics of

bank accounting variables used in the subsequent analysis.

4 Dynamics of bank asset values

With the methodology explained in Section 2, it is possible to extract a time seriesof market values of bank assets. To measure the correlations and volatilities of the

banks’ portfolios, for each week in the sample period a variance- covariance matrix Σ t

of the asset-returns is estimated using a simple exponentially weighted moving average

(EWMA) model with a decay factor λ of 0.949 The covariance σij,t at time t between

the asset portfolios of bank i and j are estimated by

σij,t = λσij,t−1 + (1 − λ) ln V itV it−1

ln V jt

V jt−1

(5)

Figure 1 plots the correlations between bank asset portfolios over the sample period.10

The asset correlations give superior information compared to equity correlations as they

are not influenced by changes in the capital structure. For the whole sample the median

9The EWMA model was chosen because it is used in the RiskMetrics framework, which is a standardin market risk management. Since the time series is relatively short, it is hard to evaluate alternativevolatility models and identify the best fitting one. For this reason the decay factor was also chosen as

in the RiskMetrics set- up. Following the RiskMetrics specification I also neglect the mean return. Themean return for the asset portfolio is quite small, on average 0.0014 for the whole sample. Includingthe mean does not significantly change results. For robustness a simple equally weighted 24 monthmoving average was also tested but the results were very similar. One could also use the estimate σ

from equation (4) for bank volatility. This volatility is equal to a simple moving average (taken overthe last 24 month) at the end of every fiscal year. I did not use this measure for two reasons: first, itcorresponds to the simple average at the end of every fiscal year, and the results of the simple averageare similar to the EWMA model. Second, there is just one estimate available for every year and thevolatility would have to be assumed constant during that year and then jump to a new level.

10Correlations in market values of liabilities do not have to be considered here. The focus of correla-tions of asset values is consistent with the theoretical assumptions of the model, as default is assumedto occur when the market value of the bank’s assets is below the face value of the liabilities.

9



Figure 1: Weekly correlation estimates between banks from Jan. 1997 until Dec. 2002.From all pair wise correlations, the median correlation as well as the 10% and the 90%

quantile are shown.

correlation was positive for the whole time period. It was declining until the beginning

of 1999 where the median correlation was close to zero. Note that during that period

the distance between the 10% and the 90% quantile of the correlations was growing. In

early 1999 the median correlation began to increase. It reached its peak of 38% at the

end of 2000. In the following two years the level decreased quite steadily to 16%.

In Figure 2 we display the estimates of correlations of bank asset values in differentregions with the entire asset portfolio of banks across Europe. The asset portfolios in

the regions are value weighted. Regional groups are as defined in Section 3.

The graphic at the top of Figure 2 shows that the correlations for the first three

regions. We see that correlation becomes more aligned and stronger across all three

regions displayed in the graph soon after the introduction of the Euro. Whereas the UK

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and Ireland had a strong correlation of asset values during the entire period. Scandinavia

and and the region consisting of Germany and Austria show a small correlation before

1998. A similar pattern can be observed for the second group where we see increased

correlation after the Euro introduction. While the Benelux banks and the banks of

southern Europe had a fairly high correlation throughout the sample period, France has

a low correlation during the first year of the Euro introduction. Finally the last chart

shows the correlations of countries outside the Eurozone with the asset value of the

European banking portfolio. While correlations increased recently only the UK had a

persistent high correlation with the overall European asset values.

Figure 3 shows weekly estimates of annual volatilities of bank’s asset portfolios from

January 1997 until December 2002. The graph shows the median volatilities as well as

the 10% and the 90% quantiles of the volatility distribution. The median volatility of

bank asset values is more or less constant over time. Even during the East Asian crises

in 1998 the median volatility remains unaffected. The 90% quantile of the volatility

distribution shows however an increase during that period.

Data on the financial soundness of the European banking sector are displayed in

Figure 4. The median capitalization ratio, which is measured as market value of assets

V over face value of debt B, is increasing over time, which is — as one can see from the

sub-samples — mainly driven by a moderate increase at the beginning of the sample

period and a decline since 2001. Comparing capitalization with a benchmark of 8%

(i.e. capitalization ratio 1.08), the median bank in the European banking system is

under-capitalized in terms of market values of assets.

5 Expected shortfall

From a regulator’s perspective it is interesting to look at the expected shortfall in the

banking system under his supervision. The expected shortfall is the present value of

the amount of debt that can not be covered by the assets of the bank in case of default

11



Figure 2: Weekly correlation estimates of the correlation of the value-weighted portfolioof all bank assets in a given region to the portfolio of all bank assets held by European

Banks.

12



Figure 3: Weekly estimates of annual volatilities of banks’ asset portfolios from Jan. 1997until Dec. 2002. The volatilities are on a monthly base, the median volatilities as well

as the 10% and the 90% quantiles of the volatility distribution are shown.

(i.e. max(B − V, 0)). In the simple Merton (1977) framework, this is given by the value

of a put option. If all the debt is insured then the expected shortfall is equal to the

future liability of the deposit insurance, as the regulator must pay the difference between

the face value of deposits and the proceeds from selling the banks assets at the market

value.11 Previous studies such as Giammarino, Schwartz, and Zechner (1989) or Duan

and Simonato (2002) use the same methodology to compute the value of the deposit

insurance liability. Formally we compute the expected shortfall S it of bank i at time t

for a horizon of T years as the value of a put option

S it = BitN (−dt + σ

√T ) − V it N (−dt) (6)

11Note that in this analysis we do not distinguish between insured and uninsured bank debt

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Figure 4: Capitalization ratios from Jan. 1997 until Dec. 2002. The ratio is computedby dividing the market value of the banks assets by the face value of debt. The graph

shows the median capitalization ratio as well as the 10% and the 90% quantiles of thedistribution.

where Bit is the face value of the bank’s debt, V it is the market values of the asset

portfolio, and dt is defined as in Equation (3). The expected shortfall for all banks the

sample is therefore S t =

iS it . This measure will inform the deposit insurance agency

of the value of its liabilities. Figure 5 shows this future liability for the whole sample

of all banks. The expected shortfall varies considerably over time as one would expect

after looking at the time variations in bank asset volatility and in bank capitalization.

Because of this variation, the regulator might thus not only be concerned about the level

of the expected shortfall but also about its dynamics. In an economy with uncorrelated

bank portfolios a shock to the assets of one bank will increase the volatility of expected

shortfall of this bank directly but it will not affect costs due to failures of other banks.

In a low correlation banking system, in which the shocks to the bank asset portfolios are

14



Figure 5: The present value of the regulator’s expected shortfall for a holding horizonof one year plotted from Jan. 1997 until Dec. 2002 (in million USD).

mainly idiosyncratic, the expected shortfall should be low. With highly correlated asset

portfolios a shock will again hit the regulator directly but will also adversely affect the

expected shortfall at other banks. Thus, high systemic risk in the banking system will

imply high volatility of expected shortfall.

It is thus important to look at the potential future shortfall in a banking system from

a portfolio perspective and not just at the level of individual banks. The regulator’s

portfolio can be defined at multiple levels. Deposit insurers and national regulators can

apply the methodology of this section to the banks under their supervision. Supra-

national institutions like the European Central Bank, the IMF or the BIS may want to

include banks from several countries in their analysis.

When we look at the regulator’s exposure to expected shortfall, we have a portfolio of

put options written on the individual banks’ asset portfolios. We can then use standard

methods from the risk management literature (see, e.g., Jorion (2000)) to compute the

volatility of the expected shortfall in the banking system.

15



Let Σt be the variance-covariance matrix of the returns on the banks’ asset portfolios,

an δt the vector of partial derivatives (V it ∂S it/∂V

it ). Then, using first order terms, the

Dollar-volatility of the expected shortfall zt can be approximated by12:

zt = δtΣtδt (7)

To break down the contribution of an individual bank or a group of banks to the reg-

ulator’s risk exposure, we decompose the volatility of the expected shortfall using the

standard concept of component value at risk.13 Define

ζ t =1

zt(Σtδ

t) ∗ δt (8)

as the vector of contributions to the expected shortfall risk, where ∗ is the element-wise

product of two vectors. Due to the nice property that the sum of the elements of ζ t is

equal to zt, the elements of this vector are the contributions of individual banks to the

overall volatility in expected shortfall. These contributions can also be negative, when

a bank reduces the risk of the regulator’s portfolio.

Figure 6 shows the volatility of the regulator’s expected shortfall in percent.14 The

volatility is relatively low (in general below 10%), which is surprising given the fact that

the regulator’s portfolio consists of out of the money put options that are very sensitive

to changes in the underlying (i.e. the value of a bank’s asset portfolio). Maybe our

findings are due to the fact that the European banking sector is not that integrated yet.

When we look at the different regions, we find that banks in Belgium, the Netherlands,

and Luxembourg contribute almost nothing to the risk of the European banking sys-

tem, whereas banks in Southern Europe contribute most. Recently German banks have

contributed a lot to the risk in the European banking sector, which reflects the recent

12By taking into account second order effects (Gamma), the accuracy of the value-at-risk estimationcould be enhanced. The simpler method used in this section is used because it allows us to computethe contribution of each bank to the value-at-risk, which is used in the subsequent analysis.

13see e.g. Jorion (2000) p. 159.

14i.e. the graph shows ztSt

for the whole sample and

j∈J ζjt

Stfor the subset of banks in sub-sample J

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Table 2: Results from a fixed effects panel regression explaining the contribution of anindividual bank to the risk of a global deposit insurers portfolio (ζ ) standardized by the

bank’s liabilities (B). The explanatory variables are a time trend and return on averageassets (ROAA) the book value of equity over total assets in percent (EQBK) and thelog of the book value of total assets (SIZE).

Variable Coefficient p-valueROAA 0.003023 0.0016EQBK -0.634585 0.0368SIZE -0.210554 0.0003T 0.013261 0.0376Nobs 971Cross Sections 213Sample 1997-2002R2 0.220309Prob(F-statistic) 0.000000

period of distress in the German banking sector.

To conclude this section we perform an econometric analysis of volatility contribu-

tions. We try to explain the contributions of banks by their characteristics. In order

to do so it is useful to standardize regulatory risk by a bank specific variable, which is

quite stable over time. Since deposit insurance premiums are often expressed per dollar

of insured deposits, the liabilities of the bank are a natural candidate to standardize the

risk of the deposit insurer as well.

Table 2 shows the results from a fixed effects panel regression of ζ it/Bit on a time

trend (T), the return of average assets (ROAA), the book value of equity over total

assets in percent (EQBK), and the log of the book value of total assets (SIZE). Weinclude bank specific fixed effects because the risk that a specific bank contributes can

also be influenced by factors such as the location of the bank, the local regulator’s

policies, accounting and auditing standards or listing requirements.

The regression yields that an increasing profitability makes banks riskier. Increasing

book values of equity over total assets, the prime instrument of bank regulation, and

increasing total assets lower the risk contributions of a bank to the global regulator’s

17



Figure 6: Volatility p.a. of the regulator’s expected shortfall from Jan. 1997 untilDec. 2002. The graphs of the six sub-samples (UK and Ireland, Benelux, Germany andAustria, France, Scandinavia, and Southern Europe) show the volatility contributionsof each sub-sample to the portfolio volatility.

18



portfolio. These findings are robust under different specifications. Less convincing is the

positive time trend that is mainly driven by the tremendous increase of riskiness in the

second half of 2002.

6 Conclusion

Traditional banking supervision relies mostly on the analysis of single institutions. The

idea behind this approach and the current regulatory framework which is focused on

individual bank balance sheets is, that there is little insolvency risk in the bankingsystem as long as the default of individual banks is low. While individual institutions

are encouraged by regulators to take a portfolio perspective on their internal financial

operations, regulators have not yet implemented this portfolio perspective at the level of

the banking system. They do not see the banks under their jurisdiction as a portfolio,

they do not consider correlations between them, and the ideas and tools of modern risk

management have not found their way into prudential banking supervision.

This paper closes this gap and attempts to measure risk at the level of the banking

system rather than at the level of individual banks using standard tools of modern risk

management similar to those applied by major banks in their internal operations. Our

method provides a forward looking risk assessment tool that is applicable in developed

financial markets by the use of publicly available information only. The method is able

to study asset correlations, and the contribution of individual institutions to the overall

risk of the system.

We apply this method to the European banking system and get a couple of insights

into the systemic dynamics of bank asset values. Our first set of results apply to cor-

relation of asset values. We find that the median correlation between all possible pairs

of correlated asset values in our sample is positive over the whole sample period. They

decline until 1999 and reach a peak at the end of 2000. Studying the correlation of bank

asset values in different regions with the entire bank portfolio we find that correlations

19



get stronger and more alligned after the introduction of the Euro. Our analysis of volatil-

ity reveals that the median asset value volatility was largely constant over the sample

period. Our analysis of soundness shows that European banks are under-capitalized in

terms of market values of assets. The final set of results exploit the portfolio perspective

to study the volatility and the amount of expected shortfall for a European regulator

as well as the contributions of individual regions to the correlation of asset portfolios

and thus to the system’s risk exposure. The volatility of expected shortfall is relatively

low and largely below 10%. The European regions that contribute most to the overall

risk of the system are southern Europe and recently also German banks. Finally we

regress individual bank characteristics on the contribution of banks to the risk of the

system and find that profitability is positively correlated with risk contribution whereas

the ratio of equity over total assets and the bank’s size are negatively correlated with

the bank’s risk contribution.

Clearly our results are only a first step to analyze bank risk at a system level. The

attractive feature of our approach is that we take standard tools from risk management

that are applied daily within financial institutions to the level of the banking system bylooking at banks as a portfolio of contingent claims of a regulator. Of course the method

gives only a coarse picture because it has to disregard non publicly traded banks and

inter-linkages. The analysis of inter-linkages usually requires a set of non public data 15

and are not readily integrated in the framework used here. We hope to make progress on

this line in future research. In a first step, we believe that the approach presented here

should provide an attractive tool to institutions that are involved in financial system

risk assessment but don’t have full access to national supervisory data. The methodis applicable with publicly available informations and allows regulators to draw on all

the insights an techniques from modern portfolio theory and risk management in their

challenging task to keep a macro-prudential eye on the stability of an entire banking

system.

15Elsinger, Lehar, and Summer (2003) develop a method that analyzes correlation of asset values andinter-linkages within a different framework for the Austrian banking system.

20



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A Summary statistics of regression variables

The following table contains summary statistics of the included banks. Bank size (SIZE)is measured as log of the book value of total assets measured in million US- Dollars,Return on Average Assets (ROAA) as included in the Bankscope database and EQBKis the book value of equity over total assets in percent.

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Table 3: Summary statistics of all banks included in the sample

SIZE min 0.25 median 0.75 max mean stdevBenelux 2.23 7.58 10.40 12.67 13.51 9.79 3.48France 2.60 6.43 8.24 8.87 13.67 7.96 2.41UK & IR 4.04 6.49 7.79 9.65 13.44 8.14 2.68DE & AT 6.20 9.09 10.17 11.41 13.53 10.09 1.85Southern Europe 6.57 8.38 9.46 10.51 12.83 9.54 1.60Scandinavia 3.81 5.01 6.09 7.48 12.44 6.73 2.32ROAABenelux -0.46 0.47 0.56 0.69 2.60 0.64 0.65France 0.04 0.93 1.20 2.29 15.48 1.91 2.65

UK & IR -80.55 -15.43 -4.52 0.81 10.46 -8.72 15.33DE & AT -0.21 0.13 0.25 0.41 1.06 0.27 0.26Southern Europe -2.56 0.51 0.73 0.97 3.94 0.82 0.78Scandinavia 0.08 0.74 1.06 1.28 3.73 1.13 0.63EQBKBenelux 0.03 0.03 0.04 0.06 0.99 0.14 0.26France 0.02 0.07 0.12 0.23 0.91 0.19 0.20UK & IR 0.04 0.07 0.64 0.84 0.97 0.50 0.37DE & AT 0.01 0.02 0.04 0.05 0.06 0.04 0.01Southern Europe 0.03 0.05 0.07 0.09 0.18 0.08 0.04

Scandinavia 0.04 0.07 0.11 0.13 0.94 0.12 0.13Total EquityBenelux 9.21 155.47 1642.67 12065.75 26756.22 6902.25 8824.46France 6.93 108.93 454.45 840.75 30105.99 2118.48 6043.05UK & IR 35.66 362.66 846.12 2776.81 52889.00 5001.32 11029.11DE & AT 25.92 330.57 603.95 2123.91 27041.66 2875.66 6281.52Southern Europe 80.70 276.63 878.27 2478.43 28231.71 2895.79 5336.32Scandinavia 5.95 19.16 69.56 133.31 12442.02 864.31 2407.68Total AssetsBenelux 9.28 3308.74 32745.30 320378.47 739971.67 191606.04 254990.68

France 13.43 621.52 3779.04 7105.44 866080.39 53266.36 172596.82UK & IR 56.85 660.52 2414.10 15500.30 686533.00 69955.86 168275.00DE & AT 493.02 8834.40 26152.90 90285.44 751643.40 98936.32 190343.50Southern Europe 711.62 4348.52 12864.94 36846.15 373495.22 46195.68 84219.89Scandinavia 44.97 150.49 440.37 1769.34 253488.30 19860.00 55943.38Total LiabilitiesBenelux 0.07 3153.27 31190.26 308312.73 713215.45 184703.79 246303.10France 1.15 428.43 3221.48 6182.29 835974.39 51147.87 166568.78UK & IR 2.50 78.74 388.61 14731.22 633644.00 64954.55 157700.72DE & AT 467.10 8408.96 25822.33 87032.22 724601.74 96060.66 184186.07

Southern Europe 591.04 4066.53 12148.60 34616.12 345263.51 43299.90 79017.03Scandinavia 9.76 129.83 366.38 1688.63 241046.28 18995.68 53560.3023

analyzing systemic risk in the european banking

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