DEB
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an
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ks20
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ww
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aR
an
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ort
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ry.
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ing
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ase
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ly.
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ate
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de
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tive
an
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da
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be
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ilab
leso
on
.
0.2
0.4
0.6
0.8
10
0.1
0.2
0.3
0.4
0.5
0.6
Glo
bal re
lative losses
Relative frequencies
2008 −
Var
level =
5%
5%
−G
lob
al V
aR
, 2008
His
togra
m (
2nd r
ound)
VaR
CV
aR
His
togra
m (
1st ro
und)
VaR
1st r
ou
nd
ES
1st r
ou
nd
VaR
2n
d r
ou
nd
ES
2n
d r
ou
nd
HS
BC
BN
P
DB
Barc
lays
Cre
d. A
gric.
Soc. G
en.
RB
S
Santa
nder
ING
Llo
yds
Unic
red.
Nord
ea
Inte
sa
Banco
Bilb
ao
Com
merz
bank
Natixis
Sta
nd. C
hart
. Danske
EU
In
terb
an
k N
etw
ork
2008 t
op
20
HS
BC
BN
P
DB
Barc
lays
Cre
d. A
gric.
Soc. G
en.
RB
S
Santa
nder
ING
Llo
yds
Unic
red.
Nord
eaInte
sa
Banco
Bilb
ao
Com
merz
bank
Natixis
Sta
nd. C
hart
.
Danske
EU
In
terb
an
k N
etw
ork
2013 t
op
20
0.2
0.4
0.6
0.8
10
0.1
0.2
0.3
0.4
0.5
0.6
Glo
ba
l re
lative
lo
sse
s
Relative frequencies
20
13
− V
ar
leve
l =
5%
5%
−G
lob
al
Va
R,
20
13
His
tog
ram
(2
nd
ro
un
d)
Va
RC
Va
RH
isto
gra
m (
1st
rou
nd
)
Va
R 1
st r
ou
nd
ES
1st r
ou
nd
Va
R 2
nd r
ou
nd
ES
2n
d r
ou
nd
00
.20
.40
.60
.81
0
0.1
0.2
0.3
0.4
0.5
HS
BC
BN
P
DB
Barc
lays
Cre
d. A
gric.
vu
lne
rab
ilit
y
impact
2008
Hig
h−
imp
ac
t &
H
igh
−v
uln
era
ble
00
.20
.40
.60
.81
0
0.1
0.2
0.3
0.4
0.5
HS
BC
BN
P
DB
Barc
lays
Cre
d. A
gric.
vu
lne
rab
ilit
y
impact
2013
Hig
h−
imp
ac
t &
H
igh
−v
uln
era
ble
Sy
ste
mic
im
pa
ct
impact
2008
2009
2010
2011
2012
2013
0
0.1
0.2
0.3
0.4
0.5
0.6
DEBTRANK “FLIGHT DECK”
Figure 1: DebtRank flight deck. The DebtRank flight deck for the first 10 banks by asset size in the years 2008 and 2013.Circle size reflects vulnerability. The red pie wedges represent the impact of each node on the system. Large nodes withlarge red wedges are both vulnerable and impactful. On the right side, we observe the level of initial equity in green andthe 5% VaR losses in red.
INTERCONNECTEDNESS
HSBC
BNP
DB
Barclays
Cred. Agric.
Soc. Gen.
RBS
Santander
ING
LloydsUnicred.
Nordea
Intesa
Banco Bilbao
CommerzbankNatixis
Stand. Chart.
Danske
2008
HSBC
BNP
DB
Barclays
Cred. Agric.
Soc. Gen.
RBS
Santander
ING
Lloyds
Unicred.
Nordea
Intesa
Banco Bilbao
CommerzbankNatixis
Stand. Chart.
Danske
2013
Figure 2: A closer look at the interbank network structure. Due the lack of data, mutual exposures need to be estimated.We plot, for sake of clarity, only the first 18 nodes by total assets (reflected by node size) in the year 2008 (left) and 2013(right) for one network estimation. More impactful nodes are placed in the center. Circle size reflects asset size. Colorsreflect the median vulnerability of the node: blue nodes have lower vulnerability and red nodes have higer vulnerability.
VULNERABILITY AND IMPACT
impact
2008 2009 2010 2011 2012 20130
0.1
0.2
0.3
0.4
0.5
0.6
Figure 3: The dynamics of systemic risk. Global systemic vulnerability (left) and the individual impact (left). We observethe decomposition of the total relative equity loss in first order (shock on assets), second order (reverberation on theinterbank market) and third order (fire sales) effects. On the right, we track the individual impact of each institution in time.We observe a certain level of consistency across time, although in general the individual impact is decreasing.
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
HSBC
BNP
DB
Barclays
Cred. Agric
.
vulnerability
impact
2008
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
HSBCBNP
DB
Barclays
Cred. Agric
.
vulnerability
impact
2013
Figure 4: Impact vs vulnerability. We divide the set of banks into four main subsets. Institutions in the upper-right quadrantar both vulnerable and impactful, therefore warning about the potential consequences of their distress. We observe noinstitutions in that quadrant in 2013, although a certain number of very impactful banks (which can affect up to 15% of thetotal initial equity) is still present.
LOSS DISTRIBUTION AND VALUE AT RISK
0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
Global relative losses
Re
lative
fre
qu
en
cie
s
2008 − Var level = 5%
Histogram (2nd
round)
VaR 2nd
round
CVaR 22nd
round
Histogram (1st
round)
Var 1st
round
Var 2nd
round
0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
Global relative losses
Re
lative
fre
qu
en
cie
s
2013 − Var level = 5%
Histogram (2nd
round)
VaR 2nd
round
CVaR 22nd
round
Histogram (1st
round)
Var 1st
round
Var 2nd
round
Figure 5: Loss distribution. Histograms of the loss distribution for the entire system for different initial shock levels drawnfrom a Beta distribution, and the respective VaR (5%) and CVaR (5%). We observe a sharp increase from the first to thesecond round in both years, with extremely high levels in 2008, leading to the collapse of nearly the whole system.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Loss distribution
Rela
tive fre
quencie
s
2013 − bank: HSBC, 5% VaR
Histogram (2nd
round)
VaR (2nd
round)
CVaR (2nd
round)
Histogram (1st round)
VaR (1nd
round)
CVaR (2nd
round)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Loss distribution
Rela
tive fre
quencie
s
2013 − bank: IntesaSanPaolo, 5% VaR
Histogram (2nd
round)
VaR (2nd
round)
CVaR (2nd
round)
Histogram (1st round)
VaR (1nd
round)
CVaR (2nd
round)
Figure 6: Focus on two insitutions. We select two banks (year 2013): HSBC, which ranks 1st by asset size and Intesa SanPaolo, which ranks 13th. Despite having similar levels of VaR (5% level) at the first round, the difference between the VaRof the two banks at the second round is much higher. This shows that risk might be underestimated also from an individualperspective in case second round effects are not taken into account.
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