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2009

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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.