Description
The purpose of this paper is to show how network analysis can be used for effective
cross-border financial surveillance, which requires the monitoring of direct and indirect systemic
linkages.
Journal of Financial Economic Policy
Cross-border financial surveillance: a network perspective
Marco A. Espinosa-Vega J uan Solé
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To cite this document:
Marco A. Espinosa-Vega J uan Solé, (2011),"Cross-border financial surveillance: a network perspective",
J ournal of Financial Economic Policy, Vol. 3 Iss 3 pp. 182 - 205
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Cross-border ?nancial
surveillance: a network
perspective
Marco A. Espinosa-Vega and Juan Sole´
International Monetary Fund, Washington, DC, USA
Abstract
Purpose – The purpose of this paper is to show how network analysis can be used for effective
cross-border ?nancial surveillance, which requires the monitoring of direct and indirect systemic
linkages.
Design/methodology/approach – This paper illustrates how network analysis could make a
signi?cant contribution in this regard by simulating different credit and funding shocks to the
banking systems of a number of selected countries. After that, the authors show that the inclusion of
risk transfers could modify the risk pro?le of entire ?nancial systems, and thus an enriched simulation
algorithm able to account for risk transfers is proposed.
Findings – Finally, the authors discuss how some of the limitations of the simulations are a re?ection
of existing information and data gaps, and thus view these shortcomings as a call to improve the
collection and analysis of data on cross-border ?nancial exposures.
Originality/value – This paper is one of the very few to take a cross-border perspective on ?nancial
networks. It is also unique in accounting for risk transfers and in proposing a methodology to include
the analysis (and monitoring) of risk transfers into a network model.
Keywords International lending, Debt problems, International ?nance, International economics,
International policy coordination and transmission,
Macroeconomic aspects of international trade and ?nance, Banks, Other depository institutions,
Micro ?nance institutions, Mortgages, Financial institutions and services, Financial economics
Paper type Research paper
I. Introduction
Effective ?nancial system surveillance requires the monitoring of direct and indirect
?nancial linkages, whose disruption could have important implications for the stability
of the entire ?nancial system. Indeed, the recent ?nancial crisis has underscored the
need to go beyond the analysis of individual institutions’ soundness and assess
whether the linkages across institutions may have systemic implications. Furthermore,
it has become clear that, due to these ?nancial interconnections, during stress events,
even actions geared to enhance the soundness of a particular institution may
undermine the stability of other institutions. Consider, for instance, the depiction of the
Northern Rock case by Morris and Shin (2008):
[. . .] a prudent shedding of exposures from the point of view of Bank 2 is a run from the point
of view of Bank 1. Arguably, this type of run is what happened to the UK bank Northern
Rock, which failed in 2007 (italics added)[1].
Therefore, policymakers and regulators worldwide have become increasingly aware of
the importance of proactively tracking potential systemic linkages. As pointed out by,
for instance, Allen and Babus (2007), network analysis is a natural candidate to aid in
this challenge, as it allows regulators and policymakers to assess externalities
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JFEP
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Journal of Financial Economic Policy
Vol. 3 No. 3, 2011
pp. 182-205
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381111152191
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to the rest of the ?nancial system, by tracking the rounds of spillovers likely to arise
from direct ?nancial linkages[2].
This paper shows how network analysis can be used for cross-border ?nancial
sector surveillance by simulating different credit and funding shocks to the banking
systems of a number of selected countries. In addition, the paper illustrates how the
inclusion of risk transfers (or contingent exposures) can alter the risk pro?le of an
entire ?nancial system. The basis of our analysis is the set of cross-country interbank
exposures (including gross lending and borrowing and risk transfers) estimates of a
selected number of countries that report their banking data to the BIS[3]. Among other
things, the paper tracks the systemic impact unleashed by the execution of
cross-border interbank risk transfers following a credit event. The algorithm designed
to track the domino effects triggered by hypothetical credit and funding shocks to each
banking system in our sample is shown in Figure 1.
The rest of the paper is organized as follows. Section II provides a detailed
explanation of the methodology for the simulations and the data. Section III presents
the results of our simulations. Section IV concludes, while the Appendix presents some
additional results obtained with an additional dataset.
II. A simple interbank exposure model
The point of departure for our analysis is a stylized bank balance sheet that highlights
the role of cross-border interbank exposures. The ?rst set of simulations (simulation 1)
examines the domino effects triggered by the default of a banking system’s interbank
obligations – we call this a credit shock. The second set of simulations (simulation 2)
Figure 1.
Network analysis based
on interbank exposures
Bank 1
N
e
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N
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w
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w
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r
e
Bank 3 Bank 3
Bank N-1 Bank N-1 Bank N-1 Bank N-1
Bank N
Bank 2 Bank 2
Bank N
Bank 1 Bank 1 Bank 1
Bank 2
Bank 3
Bank 2
Bank 3
Bank N Bank N
Trigger
bank
New
failures ...
Trigger failure
(initializes algorithm)
Source: Márquez and Martínez (2007) and authors’ calculations
Contagion rounds
(algorithm internal loop)
Final failures
(algorithm converges)
Cross-border
?nancial
surveillance
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looks at the effects of a credit-plus-funding event, where the default of an institution
also leads to a liquidity squeeze for those institutions funded by the defaulting
institution (i.e. the credit shock is compounded by a funding shock and associated ?re
sale losses). After this, we look at the effects of incorporating risk transfers into our
analysis (simulations 3 and 4).
A. Network simulations of credit and liquidity shocks
To assess the potential systemic implications of interbank linkages, the paper
considers a network of N institutions. The point of departure is the following stylized
balance sheet identity for bank i:
j
X
x
ji
þa
i
¼ k
i
þb
i
þd
i
þ
j
X
x
ij
; ð1Þ
where x
ji
stands for bank i loans to bank j, a
i
stands for bank i’s other assets, k
i
stands
for bank i’s capital, b
i
are long- and short-term borrowing (excluding interbank loans),
d
i
stands for deposits, and x
ij
stands for bank i borrowing from bank j.
Transmission of credit shocks. To analyze the effects of a credit shock, the paper
simulates the individual default of each one of the 18 banking systems in the network,
for different assumptions of loss-given default (denoted by the parameter l), it is
assumed that banking system i’s capital absorbs the losses on impact, and then we
track the sequence of defaults triggered by this event. For instance, after taking
into account the initial credit loss stemming from the default of, say, institution h,
the baseline balance sheet identity of bank i becomes:
a
i
þ
j–h
X
x
ji
þð1 2lÞx
hi
¼ ðk
i
2lx
hi
Þ þb
i
þd
i
þ
j
X
x
ij
; ð2Þ
and bank i is said to fail if its capital is insuf?cient to fully cover its losses (i.e. if
k
i
2 lx
hi
, 0). These losses are shown in light gray in Figure 2[4].
Transmission of credit-plus-funding shocks. The extent to which a bank is able to
replace an unforeseen withdrawal of interbank funding will depend on money market
liquidity conditions. During the 2007-2009 crisis, for instance, we saw a gradual
freezing of money markets that started with ?nancial institutions becoming reluctant
to roll over their funding of counterparties whose portfolios or business models were
perceived to be similar to those of seemingly weak institutions[5]. When liquidity
Figure 2.
Effect of a credit shock on
a bank’s balance sheet
Pre-shock
Balance sheet
k
i
d
i
b
i
k
i
d
i
b
i
a
i
a
i
?
x
ji
j ?
lx
hi
lx
hi
x
ji
j
Post-shock
Balance sheet
?
x
ij
j
?
x
ij
j
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is tight and in the absence of alternative sources of funding, a bank may be forced
to sell part of its assets in order to restore its balance sheet identity. We study the
situation where, as in the 2007-2009 crisis, a bank is able to replace only a fraction of
the lost funding and its assets trade at a discount (i.e. their market value is less than
their book value). We exclude the possibility of institutions raising new capital, and
assume that the loss induced by a funding shortfall is absorbed by the bank’s capital
(Figure 3). Therefore, a bank’s vulnerability not only stems from its direct credit
exposures to other institutions, but also from its inability to roll over (part of) its
funding in the interbank market, and thus having to sell assets at a discount in order to
reestablish its balance sheet identity.
In terms of our stylized model, it is assumed that institutions are unable to replace
all the funding previously granted by the defaulted institutions, which, in turn, triggers
a ?re sale of assets. Thus, we study the situation where bank i is able to replace only a
fraction ð1 2rÞ of the lost funding from bank h, and its assets trade at a discount,
so that bank i is forced to sell assets worth ð1 þdÞrx
ih
in book value terms[6].
The funding-shortfall-induced loss, drx
ih
, is absorbed by bank i’s capital (Figure 3),
and thus the new balance sheet identity for institution i is given by:
a
i
þ
j
X
x
ji
2ð1 þdÞrx
ih
¼ ðk
j
2drx
ih
Þ þb
i
þd
i
þ
j
X
x
ij
2rx
ih
: ð3Þ
Transmission of shocks in the presence of risk transfers. In addition to the type of direct
exposures mentioned so far, it is important to consider the effect that contingent
claims, in particular those stemming from credit guarantees or derivative products
such as credit default swaps (CDS) can have on the stability of a banking system.
Contingent exposures deserve special consideration in times of stress because they
activate dormant linkages across ?nancial institutions and bring new exposures onto
the balance sheet of an institution. As will be discussed below, this paper uses BIS data
on risk transfers to illustrate a possible way in which risk transfers can be incorporated
into ?nancial system surveillance.
Formally, let x
ij
be the direct exposure of institutions j to i. The effective exposure
(i.e. net of risk transfers) of institutions j to i consists of the original exposure x
ij
, “plus”
the amount that institution j needs to pay to all other institutions upon default
of institution i (in other words, bank j’s sale of protection to other banks contingent
on the default of bank i ), “minus” the amounts that institution j receives from all other
Figure 3.
Effect of a funding shock
on a bank’s balance sheet
Pre-shock
Balance sheet
k
i
d
i
b
i
k
i
d
i
b
i
a
i a
i
?
x
ji
j
drx
ih
(1+d) rx
ih
?
x
ji
j
Post-shock
Balance sheet
?
x
ij
j
?
j
x
ij
rx
ih
Cross-border
?nancial
surveillance
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institutions upon default of institution i (i.e. bank j’s purchase of protection from all
other banks against the default of bank i ). Hence, the interbank exposure net of risk
transfers, denoted by z
ij
, can be expressed as:
z
ij
¼ x
ij
þ
h
X
t
i
jh
2
h
X
t
i
hj
; ð4Þ
where t
i
jh
denotes the risk transferred from institutions h to j and referenced on
institution i.
B. The simulation algorithms
A. The algorithm without risk transfers. For each of the simulations, we programmed a
Matlab network algorithm of a system consisting of N nodes (each node representing a
banking system) and with a structure of inter-node (or interbank) loans represented by
the N £ N matrix, X, with a generic element denoted by x
ij
– note that these loans are
direct exposures across nodes. Let F
t
be the set of failed institutions and let NF
t
be the
set of not-failed institutions in round t of the simulations.
To initialize the credit shock simulation (simulation 1), assume that institution h
fails at t ¼ 0, and thus a fraction l of its debts to the rest of institutions will not be
repaid. Then, for each one of the not-failed institutions, j [ NF
t
, the algorithm checks
whether the amount of losses suffered by that institution is larger than the amount of
capital of that particular institution. If that is the case, then that institution is also
driven to bankruptcy. That is:
if
h[F
t
X
lx
hj
. k
j
)j defaults too : j [ F
tþ1
ð5Þ
The algorithm is said to converge once there are no further failures: that is, F
t
¼ F
tþ1
.
For the credit-plus-funding shock simulations (simulation 2), the previous shock is
compounded by the funding-shortfall-induced loss, drx
ih
. That is, at each stage of the
simulation, an institution’s capital may be negatively affected by the asset ?re sale
(recall Figure 3), and hence the default condition is given by:
if
h[F
t
X
lx
hj
þ
h[F
t
X
drx
jh
. k
j
)j defaults too : j [ F
tþ1
: ð6Þ
B. The algorithm with risk transfers. We also programmed an algorithm that takes into
account the effects of risk transfers on the transmission of both the credit and the
credit-plus-funding shocks through the network (simulations 3 and 4, respectively). For
this, the previous algorithm was modi?ed so that the default condition for the credit
shock becomes:
if
h[F
t
X
lx
hj
2
h[F
t
X
i[NF
t
X
lt
h
ij
þu
h[F
t
X
i[NF
t
X
lt
h
ji
. k
j
)j defaults too : j [ F
tþ1
: ð7Þ
The default condition for the credit-plus-funding shock becomes:
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if
h[F
t
X
lx
hj
þ
h[F
t
X
drx
jh
2
h[F
t
X
i[NF
t
X
lt
h
ij
þu
h[F
t
X
i[NF
t
X
lt
h
ji
. k
j;
)j
defaults too : j [ F
tþ1
;
ð8Þ
where the parameter u captures the fraction of risk transfers that have not been
provisioned for. Note that, in the last two equations above, outward and inward
transfers are treated slightly differently: while both transfers are multiplied by l,
inward transfers are also multiplied by the parameter u. This is to recognize the
possibility that institutions may make provisions for those risk transfers that they take
onto their balance sheets[7].
After the default of an institution, the inward risk transfers of that institution are set
to zero, since the defaulted institution will not be able to honor the guarantees it has
extended. In terms of the notation above, this amounts to setting t
i
hj
¼ 0, for all j, after
the default of bank h. For instance, if t
i
hj
¼ $5 billion, this implies that if institution i
defaults, then h needs to make a transfer of $5 billion to institution j.
C. The data[8]
To illustrate the use of network analysis for cross-border ?nancial surveillance, we
focused on cross-country bilateral exposures at end-December 2007, published in the
BIS’ International Consolidated Banking Statistics database. The BIS compiles these
data in two formats:
(1) immediate borrower basis (IBB); and
(2) ultimate risk basis (URB).
While both datasets consolidate the exposures of lenders’ foreign of?ces (i.e. subsidiaries
and branches) into lenders’ head of?ces, the URB dataset also consolidates by residency
of the ultimate obligor (i.e. the party that is ultimately responsible for the obligation in
case the immediate borrower defaults) and includes net risk transfers[9].
Thus, for example, on an IBB, a loan from Deutsche Bank’s subsidiary in London to
Banco Santander’s subsidiary in London would appear as a loan from Germany to the
UK in the IBB statistics (Figure 4). On the other hand, on an URB, the borrower’s
balance sheet is also consolidated (as long as the parent entity explicitly guarantees the
claim), and thus, the same loan described above would be accounted for as a loan from
Germany to Spain.
From a ?nancial stability perspective, one could be interested in using either set of
data depending on the types of shocks and scenarios deemed relevant. In particular,
IBB data provide information on the countries in which risks originate, whereas URB
data allocate claims to the country that would ultimately bear the risks, as long as
these have been explicitly guaranteed (BIS, 2008, pp. 9 and 17). However, using URB
data for network simulations raises the question of how to treat the risk transfers of
failed institutions, since after each round of failures in the network, the risk transfers
embedded in the URB data may not be actual any longer, as the counterparty may be
among the failed institutions. Thus, in order to track more accurately where risks
remain after each round of defaults, disaggregated data on risk transfers at an
individual level need to be added to the simulation (simulations 3 and 4 below show a
way to do this).
Cross-border
?nancial
surveillance
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On the other hand, relying solely on IBBdata may overestimate the systemic role played
by the domestic banks of those countries with a large presence of foreign banks, as is the
case with the UK, for instance. McGuire and von Peter (2009) show that non-UK banks’
of?ces are a much bigger presence in the UK than are UK headquartered banks. This
fact is partly responsible for the important role played by the UK in the IBB simulations
(simulations 1 and 2 below). Nevertheless, as shown in the Appendix, the UK still plays
a very key role in simulations based on URB data.
From an operational standpoint, since both datasets have limitations, our
inclination would be to conduct simulations using both datasets and compare results.
For expository purposes, however, and given that our simulations including risk
transfers build from data on an IBB, we present ?rst the results based on IBB data, and
relegate the results obtained using URB data to the Appendix[10].
We also obtained data on cross-border risk transfers from the BIS. These data
represent inward and outward transfers of risks by reporting banks, and include credit
guarantees and commitments, collateral, and credit derivatives in the banking book
(BIS, 2008, p. 16)[11]. Unfortunately, it was not possible to obtain a further
disaggregation, either by sector and nationality of the referenced entities (i.e. the
entities on whose performance the risk transfer is contingent) or by sector of the party
receiving the risk transfer.
Therefore, we had to assume a distribution of risk transfers across countries of
residence of the referenced institution, andthat all risk transfers were interbanktransfers.
Figure 4.
Cross-border claims on
IBB and URB
Deutsche
Bank
Deutsche
Bank
IBB: loan from Germany to UK
URB: loan from Germany to Spain
Banco
Santander
Banco
Santander
Consolidated only in
URB dataset
Consolidated in
IBB and URB datasets
LOAN
IBB loan
URB loan
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A generalized practice in the literature is to use maximum entropy to generate the
unknown distribution. However, this procedure would be akin to assuming a uniform
distribution of risk transfers across (referenced) countries, which would, in turn, spread
the risk to the maximum amount possible and thus probably give an overly benign
picture of systemic risk in the network. Alternatively, and in order to make an (perhaps
more plausible) approximation of the distribution of risk transfers, we assumed that the
distribution of referenced entities is the same as the distribution of direct credit exposures
(simulation 3). As a robustness check, we also conducted an additional set of simulations
assuming a concentrated distribution of risk transfers for some countries (simulation 4).
Data on country-speci?c bank capital at end-December 2007 were obtained from
Bankscope. To match as closely as possible the number and type of banks in both
datasets (i.e. BISandBankscope), we gathereddata fromonlycommercial andinvestment
banks in Bankscope, excluding smaller entities (such as cooperative banks and savings
banks) which, arguably, are less likely to engage in international transactions.
Finally, the list of the 18 countries analyzed were Australia, Austria, Belgium,
Canada, Finland, France, Germany, Greece, Ireland, Italy, Japan, The Netherlands,
Portugal, Spain, Sweden, Switzerland, the UK, and the USA.
Before proceeding to the description of our simulation results, we would like to
reiterate that due to the data constraints just described, the results of the paper should
be seen as merely illustrative of the potential of network analysis as a tool for
cross-border ?nancial surveillance.
III. Simulation results
We begin this next section by studying how shocks to a country’s resident ?nancial
institutions unravel throughout the network before one considers risk transfers
(simulations 1 and 2). That is, we track shocks to ?nancial institutions that are
incorporated and operate in a given jurisdiction regardless of the residency of the
parent company. Or to put it in terms of our example above, British regulators would
be interested in tracking shocks not only to British banks, but also to the subsidiaries
of foreign entities operating in the UK; hence, a shock to Santander’s subsidiary in
London would be important to track.
After that, we present a second set of simulations that incorporate off-balance sheet
data on risk transfers (simulations 3 and 4). Owing to the lack of granularity of the
data, our analysis is subject to a number of caveats. In particular, it is probably
unrealistic to view the entire external sector of a country’s banking system as suffering
a shock large enough to cause the entire banking system of another country to fail.
Also, these data only cover a subset of bank exposures, namely direct credit exposures,
and do not include over-the-counter derivatives, speci?cally CDS contracts. However,
our study provides a compelling illustration of the type of surveillance analysis that
can be extracted from network analysis, including the identi?cation of systemic and
vulnerable institutions, as well as the detection of data gaps for the identi?cation of
potentially systemic exposures.
A. Simulation 1: the transmission of a credit shock
The ?rst simulation focuses on the transmission of a pure credit shock, thus assuming
that institutions are able to roll over their funding sources and do not need to resort
to ?re sales of assets[12]. The credit shocks we analyze consist of the hypothetical
Cross-border
?nancial
surveillance
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default of a banking system’s debts to foreign banks. The results of this simulation are
reported in Table I. Not surprisingly, what immediately emerges is that the UK and the
US banking systems are systemic players. Speci?cally, as of December 2007, the default
of the UK and the US systems would have led to losses – after all contagion rounds –
of 45 and 96 percent, respectively, of the combined capital in our universe of banking
systems.
The second and third columns in Table I indicate the number of induced failures
and the number of contagion rounds (the aftershocks) triggered by each hypothetical
failure. The two countries that induce the highest number of contagion rounds are the
UK and the USA (Figure 5). The failure of the UK banking system would trigger severe
distress in 11 additional banking systems in three rounds of contagion. Similarly, the
failure of the US banking system would trigger the failure of 14 additional banking
systems in three rounds of contagion.
It is important to emphasize that network analysis is useful not only in identifying
potential failures, but also in estimating the amount of impaired capital after all
aftershocks have taken place. In other words, even when domino effects do not lead to
systemic failures, network analysis provides a measure of the degree to which a
?nancial system will be weakened by the transmission of ?nancial distress across
institutions (Table II). For instance, the failure of Germany would produce a capital
loss to Greek banks of only 1.4 percent of their initial capital, whereas the loss for
Ireland would amount to 38 percent of initial capital.
In addition, this analysis facilitates not only the identi?cation of institutions/systems
whose stress poses systemic risks, but also “vulnerable” systems. For example, while
the UK and the USA were identi?ed as the most systemic systems (i.e. triggering the
Country Failed capital (% of total capital)
Induced
failures
Contagion
rounds
Absolute
hazard
a
Hazard
rate
b
Australia 2.57 – – – 0.0
Austria 0.90 – – 3 17.6
Belgium 1.62 – – 4 23.5
Canada 3.49 – – 1 5.9
Finland 1.24 1 1 – 0.0
France 6.59 – – 3 17.6
Germany 3.65 – – 3 17.6
Greece 0.73 – – – 0.0
Ireland 2.29 – – 2 11.8
Italy 24.57 7 3 2 11.8
Japan 15.02 1 1 1 5.9
The Netherlands 5.64 1 1 3 17.6
Portugal 0.49 – – 2 11.8
Spain 4.19 – – 2 11.8
Sweden 0.77 – – 4 23.5
Switzerland 1.78 – – 4 23.5
UK 45.03 11 3 1 5.9
USA 96.23 14 3 – 0.0
Notes:
a
Number of simulations in which that particular country fails;
b
percentage of failures as a
percent of the number of simulations conducted
Table I.
Results for simulation 1
(credit channel)
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largest number of contagion rounds and the highest capital losses), Belgium, Sweden,
and Switzerland appear the most vulnerable banking systems, exhibiting the highest
hazard rates in the sample (Table I). In other words, the banking systems of these
countries are severely affected in four out of the 17 simulations in which they were not
the trigger country (Figure 6). This is not to say that a crisis in these countries is
imminent and/or unavoidable. The point is that this type of analysis allows for the
identi?cation of the weakest points within the network.
As suggested by Figure 1, an additional advantage of network simulations is that
the path of contagion can be tracked. To illustrate, consider the case of a hypothetical
default of Italy’s cross-border interbank loans. Figure 7 features the ensuing contagion
path: France would be affected in the ?rst round; Belgium, Germany, and Switzerland
in the second; the combination of these ?ve defaults would be systemic enough to
severely affect Austria, Sweden, and The Netherlands, in the ?nal round of contagion.
B. Simulation 2: the transmission of a credit-plus-funding shock
Next, the paper considers the effects of a joint credit and liquidity shock assuming a
50 percent haircut in the ?re sale of assets and a 65 percent rollover ratio of interbank
debt[13]. Table III summarizes the effects of this type of disturbance for the whole
sample. From a ?nancial stability perspective, it is also important to track the number
of contagion rounds that each simulation yields, as these give an indication of the
sequence of the aftershocks that reverberate throughout the entire network.
Considering scenarios that compound different types of distress allows regulators
to identify new sources of systemic risk that were previously undetected. That is the
case, for instance, for France, Germany, and Spain, where the combined shock
increases the systemic role played by these countries as providers of liquidity
Figure 5.
Number of induced
failures
0
2
4
6
8
10
12
14
16
18
Finland France Germany Italy The
Netherlands
Japan Spain UK US
Credit channel
Credit and funding channel
Note: The number of countries whose banking systems would fail as a result of the initial
failure of each country (e.g. the initial failure of France would induce no failure under the
credit shock scenario and 14 failures under the credit-plus-funding shock scenario)
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Table II.
Country-by-country
capital impairment (credit
channel)
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in addition to their importance as recipients of funding: now, they all induce 14 defaults
compared to none under the credit shock scenario. Similarly, the UK and the USA also
increase their systemic pro?le.
The addition of the funding channel also raises the vulnerability of all banking
systems signi?cantly, as measured by the hazard rate. This fact helps explain why
numerous papers in the network literature – which focus only on credit events – have
shown little contagion as a result of institutions’ defaults. In other words, the
combination of several channels produces a higher number of induced failures.
Here too, the most vulnerable banking systems continue to be the Belgian, Swedish,
and Swiss systems. In addition, the hazard rate for most countries increases several
fold. Table IV features the distribution of capital impairment, highlighting, once again,
the fact that even when stress events do not bring down a banking system, they may
signi?cantly weaken it.
C. Simulations 3 and 4: transmission of shocks in the presence of risk transfers
We now present two simulations that take into account the presence of risk transfers
among the banking systems of the network. The results of these two simulations are
shown in Figures 8-11, along with results from the previous simulations for ease of
comparison.
The point we want to make with these simulations is that the inclusion of risk
transfers could change (sometimes even dramatically) the risk pro?le of the network.
In particular, for the ?rst distribution of risk transfer that we consider, the presence
Figure 6.
Country-by-country
vulnerability level
0
1
2
3
4
5
6
7
8
Credit channel
Credit and funding channel
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Note: Each country's absolute hazard level, defined as the number of simulations in which
the banking system of the country failed as a result of another country's failure (e.g. under
the first scenario Switzerland would be induced to fail in four simulations, whereas under
the second scenario it would be induced to fail in seven simulations)
Cross-border
?nancial
surveillance
193
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of risk transfers improves the resiliency of some countries to certain shocks
(e.g. Belgium, The Netherlands, Sweden, and Switzerland) (Figure 9)[14].
Given that we do not know the true distribution of risk transfers across referenced
entities, and to illustrate that it is key to know this distribution, we assumed an
alternative distribution with a strong concentration of transfers on speci?c countries.
In particular, we assumed that Belgium and Switzerland have only acquired risk
transfers that have France as the referenced entity, and that The Netherlands has only
acquired risk transfers that have Germany as the referenced entity. In other words, all
the risk transfers that Belgium and Switzerland receive from all other countries are
assumed to be referenced on France, and all the risk transfers that The Netherlands
receives from all other countries are assumed to be referenced on Germany.
The results fromassuming this distribution of risk transfers are shown in Figures 10
and 11, and show some differences from before. In particular, notice how, under the
credit shock scenario, the inclusion of risk transfers increases the level of the systemic
Figure 7.
Contagion path triggered
by the Italian failure under
the credit shock scenario
Panel 1 (trigger failure) Panel 2 (1
st
contagion round)
Affected Countries: Italy Affected Countries: Italy, France
Panel 3 (2
nd
contagion round)
Affected Countries: Italy, France,
Belgium, Germany, Switzerland
Affected Countries: Italy, France, Belgium,
Germany, Switzerland, Austria, Sweden,
The Netherlands
Panel 4 (final round)
JFEP
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importance of France – now inducing 11 failures compared to none before. Similarly,
Spain increases its systemic relevance under the credit-plus-funding shock – inducing
now 14 failures compared to only one before. In terms of vulnerability levels (Figure 11),
the results also point to an increased level of risk for almost all countries.
In sum, these examples indicate that having accurate information on risk transfers
across their three relevant dimensions (i.e. the originator of the risk transfers, the
recipient, and the referenced party or entity) is key to the assessment of ?nancial
stability, as different distributions can give rise to very different risk pro?les, as shown
by the difference between Figures 8-9 and 10-11. Thus, having data that include risks
transfers enhance the knowledge of the risks actually present in a network – risks that
could be realized if a distress event were to occur. In other words, we view the variation
in results – which in turn depends on the assumed distribution of risk transfers – as a
call to improve on the collection and analysis of data on risk transfers.
IV. Concluding remarks
Our illustration of network analysis has highlighted its usefulness as a cross-border
surveillance tool. We have also stressed the need to give consideration to off-balance
sheet risks in network analysis and proposed a simulation algorithm for this purpose.
In addition, we emphasized the need for on- and off-balance sheet interbank linkages
and cross-institutional linkages to be made increasingly available to those overseeing
systemic stability. Going forward, ?nancial regulators should continue to develop
ways to systematically collect and analyze these data. Moreover, the global dimension
of the 2007-2009 crisis underscored the need to assess these exposures
Country Failed capital (% of total capital)
Induced
failures
Contagion
rounds
Absolute
hazard
a
Hazard
rate
b
Australia 2.57 – – 6 35.3
Austria 0.90 – – 6 35.3
Belgium 1.62 – – 7 41.2
Canada 3.49 – – 1 5.9
Finland 1.24 1 1 6 35.3
France 48.80 14 4 5 29.4
Germany 48.80 14 5 5 29.4
Greece 0.73 – – 6 35.3
Ireland 2.29 – – 6 35.3
Italy 48.80 14 4 5 29.4
Japan 15.02 1 1 1 5.9
The
Netherlands 5.64 1 1 6 35.3
Portugal 0.49 – – 6 35.3
Spain 48.80 14 5 5 29.4
Sweden 0.77 – – 7 41.2
Switzerland 1.78 – – 7 41.2
UK 48.80 14 3 5 29.4
USA 100.00 17 3 – 0.0
Notes:
a
Number of simulations in which that particular country fails;
b
percentage of failures as a
percent of the number of simulations conducted
Table III.
Results for simulation 2
(credit and funding
channel)
Cross-border
?nancial
surveillance
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Table IV.
Country-by-country
capital impairment (credit
and funding channel)
JFEP
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(
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from a cross-border perspective, which would require further coordination and data
sharing by national regulators.
The analysis of how shocks reverberate throughout the system is important to get a
sense of how a crisis could unravel once the initial shocks have taken place, assuming
the authorities fail, or are too slow, to respond. This is not to say that the analysis
of interconnectedness will unequivocally reveal where the next crisis will arise, but
including an analysis of interlinkages in the supervisors’ repertoire may help identify
institutions that need further scrutiny in terms of their vulnerability and/or level of
systemic risk. If enough data are collected from various types of institutions, the
perimeter of spillovers can be discerned and this could help distinguish which types of
?rms should be under a regulatory net.
The 2007-2009 crisis has proven that interconnectedness across institutions is
present not only within the banking sector, but as importantly, with the non-bank
?nancial sector (such as investment banking, hedge funds, etc.). In particular, the
liquidity problems have demonstrated that rollover risk can spillover to the whole
?nancial system, thus requiring a better understanding and monitoring of both direct
and indirect linkages.
This paper also provides a potential approach to consider how to maintain an
effective perimeter of prudential regulation without unduly sti?ing innovation and
ef?ciency. It illustrates how network models should allow regulators to see which
Figure 8.
Number of induced
failures – uniform
distribution
0
F
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10
12
14
16
18
Credit channel
Credit (with risk transfer)
Credit and funding channel
Credit and funding channel (with risk transfer)
Note: The number of countries whose banking systems would fail, as of December 2007,
as a result of the initial failure of each country
Cross-border
?nancial
surveillance
197
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)
Figure 9.
Country-by-country
vulnerability level –
uniform distribution
0
1
2
3
4
5
6
7
8
Note: Each country's absolute hazard level defined as the number of simulations in which
the banking system of the country failed as a result of another country's failure
Credit channel
Credit (with risk transfer)
Credit and funding channel
Credit and funding channel (with risk transfer)
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d
U
K
U
S
Figure 10.
Number of induced
failures – biased
distribution
0
2
4
6
8
10
12
14
16
18
Credit channel
Credit (with risk transfer)
Credit and funding channel
Credit and funding channel (with risk transfer)
Finland France Germany Italy The
Netherlands
Japan Spain UK US
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institutions are affected in subsequent rounds of spillovers and thus determine relative
levels of supervision. Such an assessment would have to be conducted at regular
intervals, as the structure of the network is likely to change over time. Similarly,
network models can assist policymakers with their tough choices, such as how to
design capital surcharges to lessen the too-connected-to fail problem.
In sum, monitoring global systemic linkages will undoubtedly become increasingly
relevant for ?nancial regulators as well as for the International Monetary Fund. Such
monitoring can be enhanced in several ways:
.
The development of reliable tools for this task should proceed expeditiously.
.
Financial regulators need to strengthen their understanding of systemic linkages
and improve their gathering of relevant data.
.
New information-sharing agreements on cross-border ?nancial exposures
(including regulated and unregulated products and institutions) could strengthen
the capacity of fund members to provide it with the relevant data. In principle,
such agreements could operate on a multilateral or bilateral basis and would
ideally address both the domestic and cross-border dimensions.
Notes
1. While the quote rightly points to the liquidity squeeze suffered by Northern Rock, it is
important to note that the main source of the squeeze came from the wholesale market and
not the interbank market.
2. See Upper (2007) for an insightful survey of the network literature. While most of the network
literature that will be referenced in this chapter is of an applied nature, see Allen and Gale
(2000) and Freixas et al. (2000) for some theoretical underpinnings to the network approach.
In addition, Nier et al. (2007) apply network theory to study contagion risk in simulated
Figure 11.
Country-by-country
vulnerability level –
biased distribution
0
1
2
3
4
5
6
7
8
A
u
s
t
r
a
l
i
a
A
u
s
t
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i
a
B
e
l
g
i
u
m
C
a
n
a
d
a
F
i
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l
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n
d
F
r
a
n
c
e
G
e
r
m
a
n
y
G
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c
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Credit channel
Credit (with risk transfer)
Credit and funding channel
Credit and funding channel (with risk transfer)
Cross-border
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banking systems. There are also a number of papers that have focused on domestic banking
systems, e.g. Boss et al. (2005) and Elsinger et al. (2006) for Austria, Fur?ne (2003) for the USA,
Ma´rquez and Mart? ´nez (2008) for Mexico, Memmel and Stein (2008) and Upper and Worms
(2004) for Germany, Sheldon and Maurer (1998) and Mu¨ller (2006) for Switzerland, and Wells
(2002) for the UK, among others. Finally, see Chan-Lau et al. (2009) and Degryse et al. (2010) for
cross-border applications.
3. For purely illustrative purposes, we use BIS cross-country data. Our goal in carrying out this
analysis is not to make speci?c pronouncements about particular countries, but to illustrate
the techniques described in the paper as a useful tool for (macro-) ?nancial surveillance.
4. Subsequent rounds in the algorithm take into account the losses stemming from all failed
institutions up to that point.
5. Indirect linkages among ?nancial institutions may arise when banks hold the same type of
asset in their balance sheets. These linkages can represent an important source of systemic
risk, as the forced sale of assets by some institutions may trigger a decline in the market
value of the other institutions’ portfolios. Models with this type of portfolio linkages can be
found, for instance, in Cifuentes et al. (2005), Elsinger et al. (2006), Lagunoff and Schreft
(2001) and de Vries (2005). In addition, Fur?ne (2003), Nier et al. (2007) and Mu¨ller (2006) also
analyze liquidity shocks.
6. An alternative way to see this is the following. Let rx be the amount of funding that cannot
be replaced. Let p
1
be the current market price for assets and let y be the quantity of assets
sold. That is, p
1
y ¼ rx. Suppose that these assets had been bought at a higher price p
0
, thus
rx ¼ p
1
y , p
0
y ; rxð1 þdÞ. Hence, it is possible to ?nd a relationship between the
parameter d and the change in asset prices: d ¼ ð p
0
2p
1
Þ=p
1
, i.e. d is a parameter re?ecting
the degree of distress in asset markets. Higher d re?ects higher distress in markets.
7. For comparison purposes, and given the lack of reliable estimates for its empirical value, this
parameter was arbitrarily set equal to the loss given default parameter, u ¼ l, in order not to
bias the results in favor of or against the simulations that include risk transfers: simulations
with u , l would produce less defaults and vice versa. Factors that in practice would affect
the numerical value of this parameter include: the fraction of risk transfers that have been
provisioned for or the cheapest-to-deliver option available to the protection seller at the time
a transfer needs to be made.
8. We are indebted to Patrick McGuire and Go¨tz von Peter for extensive discussions on the BIS
statistics.
9. See McGuire and Wooldridge (2005) and BIS (2008) for a detailed description of these data,
and McGuire and Tarashev (2008) for applications of the BIS statistics to monitor the
international banking system. Hattori and Suda (2001) also use BIS data to study the
network topology of the international banking system from a historical perspective.
However, their study does not assess contagion patterns.
10. There is another difference between the IBB and the URB datasets that deserves mention: the
sectoral split (e.g. banks vs other sectors) is not consistent across the two datasets. In the IBB
data, the sectoral split is only available for international claims (cross-border claims plus local
claims in foreign currency), whereas there is no information on the sectoral split for local
claims in local currency. In consequence, we applied the same sectoral fraction available for
international claims to local claims in local currency. In contrast, in the URB data, the sectoral
split applies to total foreign claims (equal to international plus local claims in local currency).
11. The BIS de?nes credit guarantees as contingent liabilities arising from an obligation to pay
to a third party when a client fails to perform a contractual obligation; credit commitments
are irrevocable obligations to extend credit at the request of a borrower (McGuire and
Tarashev, 2008).
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12. The simulations assume that the loss given default parameter equals 100 percent on impact.
That is, when the credit event ?rst materializes, banks are unable to recover any of their
loans, as it takes time for secondary (and distress debt) markets to price recently defaulted
instruments. Thus, the simulation results should be interpreted as the on-impact
transmission of systemic instability. In a similar vein, Wells (2002) argues that network
studies should consider higher loss given default estimates than typically assumed, as banks
tend to face substantial uncertainty over recovery rates in the short run.
13. Corresponding to parameter values of d ¼ 1 and of r ¼ 0:35.
14. Recall that the ?rst distribution we assume is such that the distribution of risk transfers
across referenced entities is the same as the distribution of direct credit exposures.
References
Allen, F. and Babus, A. (2007), “Networks in ?nance: network-based strategies and
competencies”, Working Paper No. 08-07, Chapter 21, Wharton School Publishing,
Philadelphia, PA.
Allen, F. and Gale, D. (2000), “Financial contagion”, Journal of Political Economy, Vol. 108, pp. 1-33.
BIS (2008), Guidelines to the International Consolidated Banking Statistics, Bank for International
Settlements, Basel.
Boss, M., Elsinger, H., Summer, M. and Thurner, S. (2005), “Network topology of the interbank
market”, Quantitative Finance, Vol. 4, pp. 677-84.
Chan-Lau, J., Espinosa, M., Giesecke, K. and Sole´, J. (2009), “Assessing the systemic implications
of ?nancial linkages”, Global Financial Stability Report, Chapter 2, International Monetary
Fund, Washington, DC, April.
Cifuentes, R., Ferrucci, G. and Shin, H. (2005), “Liquidity risk and contagion”, Journal of the
European Economic Association, Vol. 3 Nos 2/3, pp. 556-66.
Degryse, H., Elahi, M.A. and Penas, M.F. (2010), “Cross-border exposures and ?nancial
contagion”, International Review of Finance, Vol. 10 No. 2.
de Vries, C.G. (2005), “The simple economics of bank fragility”, Journal of Banking & Finance,
Vol. 29 No. 4, pp. 803-25.
Elsinger, H., Lehar, A. and Summer, M. (2006), “Risk assessment for banking systems”,
Management Science, Vol. 52 No. 9, pp. 1301-14.
Freixas, X., Parigi, B. and Rochet, J.C. (2000), “Systemic risk, interbank relations and liquidity
provision by the Central Bank”, Journal of Money Credit and Banking, Vol. 32 No. 3 (part 2)
pp. 611-38.
Fur?ne, C.H. (2003), “Interbank exposures: quantifying the risk of contagion”, Journal of Money,
Credit and Banking, Vol. 35 No. 1.
Hattori, M. and Suda, Y. (2001), “Developments in a cross-border bank exposure network”, paper
presented at BIS International Financial Statistics, CGFS Workshop, Research on Global
Financial Stability, BIS Workshop.
Lagunoff, R. and Schreft, S.L. (2001), “A model of ?nancial fragility”, Journal of Economic
Theory, Vol. 99, pp. 220-64.
McGuire, P. and Tarashev, N. (2008), “Global monitoring with the BIS international banking
statistics”, BIS Working Paper No. 244, Bank for International Settlements, Basel.
McGuire, P. and von Peter, G. (2009), “The US dollar shortage in global banking and the
international policy response”, BIS Working Paper No. 291, Bank for International
Settlements, Basel.
Cross-border
?nancial
surveillance
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McGuire, P. and Wooldridge, P. (2005), “The BIS consolidated banking statistics: structure, uses
and recent enhancements”, BIS Quarterly Review, September.
Ma´rquez-Diez-Canedo, J. and Mart? ´nez-Jaramillo, S. (2007), “Systemic risk: stress testing the
banking system”, paper presented at the International Conference on Computing in
Economics and Finance.
Memmel, C. and Stein, I. (2008), Contagion in the German Interbank Market, Deutsche
Bundesbank, Frankfurt am Main.
Morris, S. and Shin, H.S. (2008), “Financial regulation in a system context”, Brookings Papers on
Economic Activity, Fall, pp. 229-74.
Mu¨ller, J. (2006), “Interbank credit lines as a channel of contagion”, Journal of Financial Services
Research, Vol. 29 No. 1, pp. 37-60.
Nier, E., Yang, J., Yorulmazer, T. and Alentorn, A. (2007), “Network models and ?nancial
stability”, Journal of Economics Dynamics & Control, Vol. 31, pp. 2033-60.
Sheldon, G. and Maurer, M. (1998), “Interbank lending and systemic risk: an empirical analysis
for Switzerland”, Swiss Journal of Economics and Statistics, Vol. 134 No. 4, pp. 685-704.
Upper, C. (2007), “Using counterfactual simulations to assess the danger of contagion in interbank
markets”, BIS Working Paper No. 234, Bank for International Settlements, Basel.
Upper, C. and Worms, A. (2004), “Estimating bilateral exposures in the German interbank
market: is there a danger of contagion?”, European Economic Review, Vol. 48, pp. 827-49.
Wells, S. (2002), “UK interbank exposures: systemic risk implications”, Financial Stability Review,
December, pp. 175-82.
Further reading
Degryse, H. and Nguyen, G. (2007), “Interbank exposures: an empirical examination of contagion
risk in the Belgian banking system”, International Journal of Central Banking, Vol. 3 No. 2.
Haldane, A. (2009), “Why banks failed the stress test”, Marcus-Evans Conference on
Stress-Testing, Bank of England, London.
Hartmann, P., Straetmans, S. and de Vries, C.G. (2001), “Asset market linkages in crisis periods”,
CEPR Discussion Paper No. 2916, Centre for Economic Policy Research, London.
Issing, O. and Krahnen, J. (2009), “Why the regulators must have a global ‘risk map’”, Financial
Times, February 20.
Ma´rquez-Diez-Canedo, J. and Mart? ´nez-Jaramillo, S. (2009), “Systemic risk: stress testing the
banking system”, International Journal of Intelligent Systems in Accounting, Finance and
Management, Vol. 16 No. 1.
Perotti, E. andSuarez, J. (2009), “Liquidityinsurance for systemic crises”, CEPRPolicyInsight No. 31.
Stern, G.H. (2008), Repercussions Fromthe Financial Shock, Federal Reserve Bank, Minneapolis, MN.
Stern, G.H. and Feldman, R.J. (2004), Too Big to Fail: The Hazards of Bank Bailouts, Brookings
Institution Press, Washington, DC.
Vassalou, M. and Xing, Y. (2004), “Default risk in equity returns”, Journal of Finance, Vol. 59,
pp. 831-68.
Corresponding author
Juan Sole´ can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
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Appendix. Comparing results based on the IBB and the URB datasets
This appendix brie?y summarizes the results from the simulations using URB data. As
mentioned in Section II.C, URB data are consolidated by residency of the ultimate obligor (i.e. the
party that is ultimately responsible for the obligation in case the immediate borrower defaults)
and include net risk transfers. Therefore, the simulation results could potentially differ
substantially from those using IBB data.
In order to assess whether the use of IBB or URB data would substantially alter our policy
conclusions, we ran simulations using URB data. As Table AI shows, the results of the
simulations for the credit and the credit plus funding shocks retain some of their qualitative
implications compared to our previous results: in both instances, the UK and the USA continue to
be the two most systemic banking systems, and some other countries continue to maintain their
(lower) pro?le as sources of contagion (e.g. Finland and The Netherlands).
However, the comparison of IBB and URB data reveals some important differences for some
other countries. For instance, Italy becomes less systemic with URB data in regard to credit
shocks, going from seven to zero induced failures. Similarly, Germany, Italy, and Spain
dramatically reduce their role under the credit-plus-funding shock scenario.
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Table AI.
Comparing simulation
results using IBB and
URB datasets
JFEP
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Table AI.
Cross-border
?nancial
surveillance
205
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This article has been cited by:
1. A Guide to IMF Stress Testing . [CrossRef]
2. Serafin Martinez-Jaramillo, Biliana Alexandrova-Kabadjova, Bernardo Bravo-Benitez, Juan Pablo
Solórzano-Margain. 2014. An empirical study of the Mexican banking system’s network and its
implications for systemic risk. Journal of Economic Dynamics and Control 40, 242-265. [CrossRef]
3. Andreas A. Jobst. 2013. Multivariate dependence of implied volatilities from equity options as measure
of systemic risk. International Review of Financial Analysis 28, 112-129. [CrossRef]
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doc_966295823.pdf
The purpose of this paper is to show how network analysis can be used for effective
cross-border financial surveillance, which requires the monitoring of direct and indirect systemic
linkages.
Journal of Financial Economic Policy
Cross-border financial surveillance: a network perspective
Marco A. Espinosa-Vega J uan Solé
Article information:
To cite this document:
Marco A. Espinosa-Vega J uan Solé, (2011),"Cross-border financial surveillance: a network perspective",
J ournal of Financial Economic Policy, Vol. 3 Iss 3 pp. 182 - 205
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dx.doi.org/10.1108/14757700810853860
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Cross-border ?nancial
surveillance: a network
perspective
Marco A. Espinosa-Vega and Juan Sole´
International Monetary Fund, Washington, DC, USA
Abstract
Purpose – The purpose of this paper is to show how network analysis can be used for effective
cross-border ?nancial surveillance, which requires the monitoring of direct and indirect systemic
linkages.
Design/methodology/approach – This paper illustrates how network analysis could make a
signi?cant contribution in this regard by simulating different credit and funding shocks to the
banking systems of a number of selected countries. After that, the authors show that the inclusion of
risk transfers could modify the risk pro?le of entire ?nancial systems, and thus an enriched simulation
algorithm able to account for risk transfers is proposed.
Findings – Finally, the authors discuss how some of the limitations of the simulations are a re?ection
of existing information and data gaps, and thus view these shortcomings as a call to improve the
collection and analysis of data on cross-border ?nancial exposures.
Originality/value – This paper is one of the very few to take a cross-border perspective on ?nancial
networks. It is also unique in accounting for risk transfers and in proposing a methodology to include
the analysis (and monitoring) of risk transfers into a network model.
Keywords International lending, Debt problems, International ?nance, International economics,
International policy coordination and transmission,
Macroeconomic aspects of international trade and ?nance, Banks, Other depository institutions,
Micro ?nance institutions, Mortgages, Financial institutions and services, Financial economics
Paper type Research paper
I. Introduction
Effective ?nancial system surveillance requires the monitoring of direct and indirect
?nancial linkages, whose disruption could have important implications for the stability
of the entire ?nancial system. Indeed, the recent ?nancial crisis has underscored the
need to go beyond the analysis of individual institutions’ soundness and assess
whether the linkages across institutions may have systemic implications. Furthermore,
it has become clear that, due to these ?nancial interconnections, during stress events,
even actions geared to enhance the soundness of a particular institution may
undermine the stability of other institutions. Consider, for instance, the depiction of the
Northern Rock case by Morris and Shin (2008):
[. . .] a prudent shedding of exposures from the point of view of Bank 2 is a run from the point
of view of Bank 1. Arguably, this type of run is what happened to the UK bank Northern
Rock, which failed in 2007 (italics added)[1].
Therefore, policymakers and regulators worldwide have become increasingly aware of
the importance of proactively tracking potential systemic linkages. As pointed out by,
for instance, Allen and Babus (2007), network analysis is a natural candidate to aid in
this challenge, as it allows regulators and policymakers to assess externalities
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JFEP
3,3
182
Journal of Financial Economic Policy
Vol. 3 No. 3, 2011
pp. 182-205
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381111152191
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to the rest of the ?nancial system, by tracking the rounds of spillovers likely to arise
from direct ?nancial linkages[2].
This paper shows how network analysis can be used for cross-border ?nancial
sector surveillance by simulating different credit and funding shocks to the banking
systems of a number of selected countries. In addition, the paper illustrates how the
inclusion of risk transfers (or contingent exposures) can alter the risk pro?le of an
entire ?nancial system. The basis of our analysis is the set of cross-country interbank
exposures (including gross lending and borrowing and risk transfers) estimates of a
selected number of countries that report their banking data to the BIS[3]. Among other
things, the paper tracks the systemic impact unleashed by the execution of
cross-border interbank risk transfers following a credit event. The algorithm designed
to track the domino effects triggered by hypothetical credit and funding shocks to each
banking system in our sample is shown in Figure 1.
The rest of the paper is organized as follows. Section II provides a detailed
explanation of the methodology for the simulations and the data. Section III presents
the results of our simulations. Section IV concludes, while the Appendix presents some
additional results obtained with an additional dataset.
II. A simple interbank exposure model
The point of departure for our analysis is a stylized bank balance sheet that highlights
the role of cross-border interbank exposures. The ?rst set of simulations (simulation 1)
examines the domino effects triggered by the default of a banking system’s interbank
obligations – we call this a credit shock. The second set of simulations (simulation 2)
Figure 1.
Network analysis based
on interbank exposures
Bank 1
N
e
w
f
a
i
l
u
r
e
N
e
w
f
a
i
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u
r
e
N
e
w
f
a
i
l
u
r
e
Bank 3 Bank 3
Bank N-1 Bank N-1 Bank N-1 Bank N-1
Bank N
Bank 2 Bank 2
Bank N
Bank 1 Bank 1 Bank 1
Bank 2
Bank 3
Bank 2
Bank 3
Bank N Bank N
Trigger
bank
New
failures ...
Trigger failure
(initializes algorithm)
Source: Márquez and Martínez (2007) and authors’ calculations
Contagion rounds
(algorithm internal loop)
Final failures
(algorithm converges)
Cross-border
?nancial
surveillance
183
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looks at the effects of a credit-plus-funding event, where the default of an institution
also leads to a liquidity squeeze for those institutions funded by the defaulting
institution (i.e. the credit shock is compounded by a funding shock and associated ?re
sale losses). After this, we look at the effects of incorporating risk transfers into our
analysis (simulations 3 and 4).
A. Network simulations of credit and liquidity shocks
To assess the potential systemic implications of interbank linkages, the paper
considers a network of N institutions. The point of departure is the following stylized
balance sheet identity for bank i:
j
X
x
ji
þa
i
¼ k
i
þb
i
þd
i
þ
j
X
x
ij
; ð1Þ
where x
ji
stands for bank i loans to bank j, a
i
stands for bank i’s other assets, k
i
stands
for bank i’s capital, b
i
are long- and short-term borrowing (excluding interbank loans),
d
i
stands for deposits, and x
ij
stands for bank i borrowing from bank j.
Transmission of credit shocks. To analyze the effects of a credit shock, the paper
simulates the individual default of each one of the 18 banking systems in the network,
for different assumptions of loss-given default (denoted by the parameter l), it is
assumed that banking system i’s capital absorbs the losses on impact, and then we
track the sequence of defaults triggered by this event. For instance, after taking
into account the initial credit loss stemming from the default of, say, institution h,
the baseline balance sheet identity of bank i becomes:
a
i
þ
j–h
X
x
ji
þð1 2lÞx
hi
¼ ðk
i
2lx
hi
Þ þb
i
þd
i
þ
j
X
x
ij
; ð2Þ
and bank i is said to fail if its capital is insuf?cient to fully cover its losses (i.e. if
k
i
2 lx
hi
, 0). These losses are shown in light gray in Figure 2[4].
Transmission of credit-plus-funding shocks. The extent to which a bank is able to
replace an unforeseen withdrawal of interbank funding will depend on money market
liquidity conditions. During the 2007-2009 crisis, for instance, we saw a gradual
freezing of money markets that started with ?nancial institutions becoming reluctant
to roll over their funding of counterparties whose portfolios or business models were
perceived to be similar to those of seemingly weak institutions[5]. When liquidity
Figure 2.
Effect of a credit shock on
a bank’s balance sheet
Pre-shock
Balance sheet
k
i
d
i
b
i
k
i
d
i
b
i
a
i
a
i
?
x
ji
j ?
lx
hi
lx
hi
x
ji
j
Post-shock
Balance sheet
?
x
ij
j
?
x
ij
j
JFEP
3,3
184
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is tight and in the absence of alternative sources of funding, a bank may be forced
to sell part of its assets in order to restore its balance sheet identity. We study the
situation where, as in the 2007-2009 crisis, a bank is able to replace only a fraction of
the lost funding and its assets trade at a discount (i.e. their market value is less than
their book value). We exclude the possibility of institutions raising new capital, and
assume that the loss induced by a funding shortfall is absorbed by the bank’s capital
(Figure 3). Therefore, a bank’s vulnerability not only stems from its direct credit
exposures to other institutions, but also from its inability to roll over (part of) its
funding in the interbank market, and thus having to sell assets at a discount in order to
reestablish its balance sheet identity.
In terms of our stylized model, it is assumed that institutions are unable to replace
all the funding previously granted by the defaulted institutions, which, in turn, triggers
a ?re sale of assets. Thus, we study the situation where bank i is able to replace only a
fraction ð1 2rÞ of the lost funding from bank h, and its assets trade at a discount,
so that bank i is forced to sell assets worth ð1 þdÞrx
ih
in book value terms[6].
The funding-shortfall-induced loss, drx
ih
, is absorbed by bank i’s capital (Figure 3),
and thus the new balance sheet identity for institution i is given by:
a
i
þ
j
X
x
ji
2ð1 þdÞrx
ih
¼ ðk
j
2drx
ih
Þ þb
i
þd
i
þ
j
X
x
ij
2rx
ih
: ð3Þ
Transmission of shocks in the presence of risk transfers. In addition to the type of direct
exposures mentioned so far, it is important to consider the effect that contingent
claims, in particular those stemming from credit guarantees or derivative products
such as credit default swaps (CDS) can have on the stability of a banking system.
Contingent exposures deserve special consideration in times of stress because they
activate dormant linkages across ?nancial institutions and bring new exposures onto
the balance sheet of an institution. As will be discussed below, this paper uses BIS data
on risk transfers to illustrate a possible way in which risk transfers can be incorporated
into ?nancial system surveillance.
Formally, let x
ij
be the direct exposure of institutions j to i. The effective exposure
(i.e. net of risk transfers) of institutions j to i consists of the original exposure x
ij
, “plus”
the amount that institution j needs to pay to all other institutions upon default
of institution i (in other words, bank j’s sale of protection to other banks contingent
on the default of bank i ), “minus” the amounts that institution j receives from all other
Figure 3.
Effect of a funding shock
on a bank’s balance sheet
Pre-shock
Balance sheet
k
i
d
i
b
i
k
i
d
i
b
i
a
i a
i
?
x
ji
j
drx
ih
(1+d) rx
ih
?
x
ji
j
Post-shock
Balance sheet
?
x
ij
j
?
j
x
ij
rx
ih
Cross-border
?nancial
surveillance
185
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institutions upon default of institution i (i.e. bank j’s purchase of protection from all
other banks against the default of bank i ). Hence, the interbank exposure net of risk
transfers, denoted by z
ij
, can be expressed as:
z
ij
¼ x
ij
þ
h
X
t
i
jh
2
h
X
t
i
hj
; ð4Þ
where t
i
jh
denotes the risk transferred from institutions h to j and referenced on
institution i.
B. The simulation algorithms
A. The algorithm without risk transfers. For each of the simulations, we programmed a
Matlab network algorithm of a system consisting of N nodes (each node representing a
banking system) and with a structure of inter-node (or interbank) loans represented by
the N £ N matrix, X, with a generic element denoted by x
ij
– note that these loans are
direct exposures across nodes. Let F
t
be the set of failed institutions and let NF
t
be the
set of not-failed institutions in round t of the simulations.
To initialize the credit shock simulation (simulation 1), assume that institution h
fails at t ¼ 0, and thus a fraction l of its debts to the rest of institutions will not be
repaid. Then, for each one of the not-failed institutions, j [ NF
t
, the algorithm checks
whether the amount of losses suffered by that institution is larger than the amount of
capital of that particular institution. If that is the case, then that institution is also
driven to bankruptcy. That is:
if
h[F
t
X
lx
hj
. k
j
)j defaults too : j [ F
tþ1
ð5Þ
The algorithm is said to converge once there are no further failures: that is, F
t
¼ F
tþ1
.
For the credit-plus-funding shock simulations (simulation 2), the previous shock is
compounded by the funding-shortfall-induced loss, drx
ih
. That is, at each stage of the
simulation, an institution’s capital may be negatively affected by the asset ?re sale
(recall Figure 3), and hence the default condition is given by:
if
h[F
t
X
lx
hj
þ
h[F
t
X
drx
jh
. k
j
)j defaults too : j [ F
tþ1
: ð6Þ
B. The algorithm with risk transfers. We also programmed an algorithm that takes into
account the effects of risk transfers on the transmission of both the credit and the
credit-plus-funding shocks through the network (simulations 3 and 4, respectively). For
this, the previous algorithm was modi?ed so that the default condition for the credit
shock becomes:
if
h[F
t
X
lx
hj
2
h[F
t
X
i[NF
t
X
lt
h
ij
þu
h[F
t
X
i[NF
t
X
lt
h
ji
. k
j
)j defaults too : j [ F
tþ1
: ð7Þ
The default condition for the credit-plus-funding shock becomes:
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if
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i[NF
t
X
lt
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h[F
t
X
i[NF
t
X
lt
h
ji
. k
j;
)j
defaults too : j [ F
tþ1
;
ð8Þ
where the parameter u captures the fraction of risk transfers that have not been
provisioned for. Note that, in the last two equations above, outward and inward
transfers are treated slightly differently: while both transfers are multiplied by l,
inward transfers are also multiplied by the parameter u. This is to recognize the
possibility that institutions may make provisions for those risk transfers that they take
onto their balance sheets[7].
After the default of an institution, the inward risk transfers of that institution are set
to zero, since the defaulted institution will not be able to honor the guarantees it has
extended. In terms of the notation above, this amounts to setting t
i
hj
¼ 0, for all j, after
the default of bank h. For instance, if t
i
hj
¼ $5 billion, this implies that if institution i
defaults, then h needs to make a transfer of $5 billion to institution j.
C. The data[8]
To illustrate the use of network analysis for cross-border ?nancial surveillance, we
focused on cross-country bilateral exposures at end-December 2007, published in the
BIS’ International Consolidated Banking Statistics database. The BIS compiles these
data in two formats:
(1) immediate borrower basis (IBB); and
(2) ultimate risk basis (URB).
While both datasets consolidate the exposures of lenders’ foreign of?ces (i.e. subsidiaries
and branches) into lenders’ head of?ces, the URB dataset also consolidates by residency
of the ultimate obligor (i.e. the party that is ultimately responsible for the obligation in
case the immediate borrower defaults) and includes net risk transfers[9].
Thus, for example, on an IBB, a loan from Deutsche Bank’s subsidiary in London to
Banco Santander’s subsidiary in London would appear as a loan from Germany to the
UK in the IBB statistics (Figure 4). On the other hand, on an URB, the borrower’s
balance sheet is also consolidated (as long as the parent entity explicitly guarantees the
claim), and thus, the same loan described above would be accounted for as a loan from
Germany to Spain.
From a ?nancial stability perspective, one could be interested in using either set of
data depending on the types of shocks and scenarios deemed relevant. In particular,
IBB data provide information on the countries in which risks originate, whereas URB
data allocate claims to the country that would ultimately bear the risks, as long as
these have been explicitly guaranteed (BIS, 2008, pp. 9 and 17). However, using URB
data for network simulations raises the question of how to treat the risk transfers of
failed institutions, since after each round of failures in the network, the risk transfers
embedded in the URB data may not be actual any longer, as the counterparty may be
among the failed institutions. Thus, in order to track more accurately where risks
remain after each round of defaults, disaggregated data on risk transfers at an
individual level need to be added to the simulation (simulations 3 and 4 below show a
way to do this).
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On the other hand, relying solely on IBBdata may overestimate the systemic role played
by the domestic banks of those countries with a large presence of foreign banks, as is the
case with the UK, for instance. McGuire and von Peter (2009) show that non-UK banks’
of?ces are a much bigger presence in the UK than are UK headquartered banks. This
fact is partly responsible for the important role played by the UK in the IBB simulations
(simulations 1 and 2 below). Nevertheless, as shown in the Appendix, the UK still plays
a very key role in simulations based on URB data.
From an operational standpoint, since both datasets have limitations, our
inclination would be to conduct simulations using both datasets and compare results.
For expository purposes, however, and given that our simulations including risk
transfers build from data on an IBB, we present ?rst the results based on IBB data, and
relegate the results obtained using URB data to the Appendix[10].
We also obtained data on cross-border risk transfers from the BIS. These data
represent inward and outward transfers of risks by reporting banks, and include credit
guarantees and commitments, collateral, and credit derivatives in the banking book
(BIS, 2008, p. 16)[11]. Unfortunately, it was not possible to obtain a further
disaggregation, either by sector and nationality of the referenced entities (i.e. the
entities on whose performance the risk transfer is contingent) or by sector of the party
receiving the risk transfer.
Therefore, we had to assume a distribution of risk transfers across countries of
residence of the referenced institution, andthat all risk transfers were interbanktransfers.
Figure 4.
Cross-border claims on
IBB and URB
Deutsche
Bank
Deutsche
Bank
IBB: loan from Germany to UK
URB: loan from Germany to Spain
Banco
Santander
Banco
Santander
Consolidated only in
URB dataset
Consolidated in
IBB and URB datasets
LOAN
IBB loan
URB loan
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A generalized practice in the literature is to use maximum entropy to generate the
unknown distribution. However, this procedure would be akin to assuming a uniform
distribution of risk transfers across (referenced) countries, which would, in turn, spread
the risk to the maximum amount possible and thus probably give an overly benign
picture of systemic risk in the network. Alternatively, and in order to make an (perhaps
more plausible) approximation of the distribution of risk transfers, we assumed that the
distribution of referenced entities is the same as the distribution of direct credit exposures
(simulation 3). As a robustness check, we also conducted an additional set of simulations
assuming a concentrated distribution of risk transfers for some countries (simulation 4).
Data on country-speci?c bank capital at end-December 2007 were obtained from
Bankscope. To match as closely as possible the number and type of banks in both
datasets (i.e. BISandBankscope), we gathereddata fromonlycommercial andinvestment
banks in Bankscope, excluding smaller entities (such as cooperative banks and savings
banks) which, arguably, are less likely to engage in international transactions.
Finally, the list of the 18 countries analyzed were Australia, Austria, Belgium,
Canada, Finland, France, Germany, Greece, Ireland, Italy, Japan, The Netherlands,
Portugal, Spain, Sweden, Switzerland, the UK, and the USA.
Before proceeding to the description of our simulation results, we would like to
reiterate that due to the data constraints just described, the results of the paper should
be seen as merely illustrative of the potential of network analysis as a tool for
cross-border ?nancial surveillance.
III. Simulation results
We begin this next section by studying how shocks to a country’s resident ?nancial
institutions unravel throughout the network before one considers risk transfers
(simulations 1 and 2). That is, we track shocks to ?nancial institutions that are
incorporated and operate in a given jurisdiction regardless of the residency of the
parent company. Or to put it in terms of our example above, British regulators would
be interested in tracking shocks not only to British banks, but also to the subsidiaries
of foreign entities operating in the UK; hence, a shock to Santander’s subsidiary in
London would be important to track.
After that, we present a second set of simulations that incorporate off-balance sheet
data on risk transfers (simulations 3 and 4). Owing to the lack of granularity of the
data, our analysis is subject to a number of caveats. In particular, it is probably
unrealistic to view the entire external sector of a country’s banking system as suffering
a shock large enough to cause the entire banking system of another country to fail.
Also, these data only cover a subset of bank exposures, namely direct credit exposures,
and do not include over-the-counter derivatives, speci?cally CDS contracts. However,
our study provides a compelling illustration of the type of surveillance analysis that
can be extracted from network analysis, including the identi?cation of systemic and
vulnerable institutions, as well as the detection of data gaps for the identi?cation of
potentially systemic exposures.
A. Simulation 1: the transmission of a credit shock
The ?rst simulation focuses on the transmission of a pure credit shock, thus assuming
that institutions are able to roll over their funding sources and do not need to resort
to ?re sales of assets[12]. The credit shocks we analyze consist of the hypothetical
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default of a banking system’s debts to foreign banks. The results of this simulation are
reported in Table I. Not surprisingly, what immediately emerges is that the UK and the
US banking systems are systemic players. Speci?cally, as of December 2007, the default
of the UK and the US systems would have led to losses – after all contagion rounds –
of 45 and 96 percent, respectively, of the combined capital in our universe of banking
systems.
The second and third columns in Table I indicate the number of induced failures
and the number of contagion rounds (the aftershocks) triggered by each hypothetical
failure. The two countries that induce the highest number of contagion rounds are the
UK and the USA (Figure 5). The failure of the UK banking system would trigger severe
distress in 11 additional banking systems in three rounds of contagion. Similarly, the
failure of the US banking system would trigger the failure of 14 additional banking
systems in three rounds of contagion.
It is important to emphasize that network analysis is useful not only in identifying
potential failures, but also in estimating the amount of impaired capital after all
aftershocks have taken place. In other words, even when domino effects do not lead to
systemic failures, network analysis provides a measure of the degree to which a
?nancial system will be weakened by the transmission of ?nancial distress across
institutions (Table II). For instance, the failure of Germany would produce a capital
loss to Greek banks of only 1.4 percent of their initial capital, whereas the loss for
Ireland would amount to 38 percent of initial capital.
In addition, this analysis facilitates not only the identi?cation of institutions/systems
whose stress poses systemic risks, but also “vulnerable” systems. For example, while
the UK and the USA were identi?ed as the most systemic systems (i.e. triggering the
Country Failed capital (% of total capital)
Induced
failures
Contagion
rounds
Absolute
hazard
a
Hazard
rate
b
Australia 2.57 – – – 0.0
Austria 0.90 – – 3 17.6
Belgium 1.62 – – 4 23.5
Canada 3.49 – – 1 5.9
Finland 1.24 1 1 – 0.0
France 6.59 – – 3 17.6
Germany 3.65 – – 3 17.6
Greece 0.73 – – – 0.0
Ireland 2.29 – – 2 11.8
Italy 24.57 7 3 2 11.8
Japan 15.02 1 1 1 5.9
The Netherlands 5.64 1 1 3 17.6
Portugal 0.49 – – 2 11.8
Spain 4.19 – – 2 11.8
Sweden 0.77 – – 4 23.5
Switzerland 1.78 – – 4 23.5
UK 45.03 11 3 1 5.9
USA 96.23 14 3 – 0.0
Notes:
a
Number of simulations in which that particular country fails;
b
percentage of failures as a
percent of the number of simulations conducted
Table I.
Results for simulation 1
(credit channel)
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largest number of contagion rounds and the highest capital losses), Belgium, Sweden,
and Switzerland appear the most vulnerable banking systems, exhibiting the highest
hazard rates in the sample (Table I). In other words, the banking systems of these
countries are severely affected in four out of the 17 simulations in which they were not
the trigger country (Figure 6). This is not to say that a crisis in these countries is
imminent and/or unavoidable. The point is that this type of analysis allows for the
identi?cation of the weakest points within the network.
As suggested by Figure 1, an additional advantage of network simulations is that
the path of contagion can be tracked. To illustrate, consider the case of a hypothetical
default of Italy’s cross-border interbank loans. Figure 7 features the ensuing contagion
path: France would be affected in the ?rst round; Belgium, Germany, and Switzerland
in the second; the combination of these ?ve defaults would be systemic enough to
severely affect Austria, Sweden, and The Netherlands, in the ?nal round of contagion.
B. Simulation 2: the transmission of a credit-plus-funding shock
Next, the paper considers the effects of a joint credit and liquidity shock assuming a
50 percent haircut in the ?re sale of assets and a 65 percent rollover ratio of interbank
debt[13]. Table III summarizes the effects of this type of disturbance for the whole
sample. From a ?nancial stability perspective, it is also important to track the number
of contagion rounds that each simulation yields, as these give an indication of the
sequence of the aftershocks that reverberate throughout the entire network.
Considering scenarios that compound different types of distress allows regulators
to identify new sources of systemic risk that were previously undetected. That is the
case, for instance, for France, Germany, and Spain, where the combined shock
increases the systemic role played by these countries as providers of liquidity
Figure 5.
Number of induced
failures
0
2
4
6
8
10
12
14
16
18
Finland France Germany Italy The
Netherlands
Japan Spain UK US
Credit channel
Credit and funding channel
Note: The number of countries whose banking systems would fail as a result of the initial
failure of each country (e.g. the initial failure of France would induce no failure under the
credit shock scenario and 14 failures under the credit-plus-funding shock scenario)
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Table II.
Country-by-country
capital impairment (credit
channel)
JFEP
3,3
192
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in addition to their importance as recipients of funding: now, they all induce 14 defaults
compared to none under the credit shock scenario. Similarly, the UK and the USA also
increase their systemic pro?le.
The addition of the funding channel also raises the vulnerability of all banking
systems signi?cantly, as measured by the hazard rate. This fact helps explain why
numerous papers in the network literature – which focus only on credit events – have
shown little contagion as a result of institutions’ defaults. In other words, the
combination of several channels produces a higher number of induced failures.
Here too, the most vulnerable banking systems continue to be the Belgian, Swedish,
and Swiss systems. In addition, the hazard rate for most countries increases several
fold. Table IV features the distribution of capital impairment, highlighting, once again,
the fact that even when stress events do not bring down a banking system, they may
signi?cantly weaken it.
C. Simulations 3 and 4: transmission of shocks in the presence of risk transfers
We now present two simulations that take into account the presence of risk transfers
among the banking systems of the network. The results of these two simulations are
shown in Figures 8-11, along with results from the previous simulations for ease of
comparison.
The point we want to make with these simulations is that the inclusion of risk
transfers could change (sometimes even dramatically) the risk pro?le of the network.
In particular, for the ?rst distribution of risk transfer that we consider, the presence
Figure 6.
Country-by-country
vulnerability level
0
1
2
3
4
5
6
7
8
Credit channel
Credit and funding channel
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Note: Each country's absolute hazard level, defined as the number of simulations in which
the banking system of the country failed as a result of another country's failure (e.g. under
the first scenario Switzerland would be induced to fail in four simulations, whereas under
the second scenario it would be induced to fail in seven simulations)
Cross-border
?nancial
surveillance
193
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of risk transfers improves the resiliency of some countries to certain shocks
(e.g. Belgium, The Netherlands, Sweden, and Switzerland) (Figure 9)[14].
Given that we do not know the true distribution of risk transfers across referenced
entities, and to illustrate that it is key to know this distribution, we assumed an
alternative distribution with a strong concentration of transfers on speci?c countries.
In particular, we assumed that Belgium and Switzerland have only acquired risk
transfers that have France as the referenced entity, and that The Netherlands has only
acquired risk transfers that have Germany as the referenced entity. In other words, all
the risk transfers that Belgium and Switzerland receive from all other countries are
assumed to be referenced on France, and all the risk transfers that The Netherlands
receives from all other countries are assumed to be referenced on Germany.
The results fromassuming this distribution of risk transfers are shown in Figures 10
and 11, and show some differences from before. In particular, notice how, under the
credit shock scenario, the inclusion of risk transfers increases the level of the systemic
Figure 7.
Contagion path triggered
by the Italian failure under
the credit shock scenario
Panel 1 (trigger failure) Panel 2 (1
st
contagion round)
Affected Countries: Italy Affected Countries: Italy, France
Panel 3 (2
nd
contagion round)
Affected Countries: Italy, France,
Belgium, Germany, Switzerland
Affected Countries: Italy, France, Belgium,
Germany, Switzerland, Austria, Sweden,
The Netherlands
Panel 4 (final round)
JFEP
3,3
194
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importance of France – now inducing 11 failures compared to none before. Similarly,
Spain increases its systemic relevance under the credit-plus-funding shock – inducing
now 14 failures compared to only one before. In terms of vulnerability levels (Figure 11),
the results also point to an increased level of risk for almost all countries.
In sum, these examples indicate that having accurate information on risk transfers
across their three relevant dimensions (i.e. the originator of the risk transfers, the
recipient, and the referenced party or entity) is key to the assessment of ?nancial
stability, as different distributions can give rise to very different risk pro?les, as shown
by the difference between Figures 8-9 and 10-11. Thus, having data that include risks
transfers enhance the knowledge of the risks actually present in a network – risks that
could be realized if a distress event were to occur. In other words, we view the variation
in results – which in turn depends on the assumed distribution of risk transfers – as a
call to improve on the collection and analysis of data on risk transfers.
IV. Concluding remarks
Our illustration of network analysis has highlighted its usefulness as a cross-border
surveillance tool. We have also stressed the need to give consideration to off-balance
sheet risks in network analysis and proposed a simulation algorithm for this purpose.
In addition, we emphasized the need for on- and off-balance sheet interbank linkages
and cross-institutional linkages to be made increasingly available to those overseeing
systemic stability. Going forward, ?nancial regulators should continue to develop
ways to systematically collect and analyze these data. Moreover, the global dimension
of the 2007-2009 crisis underscored the need to assess these exposures
Country Failed capital (% of total capital)
Induced
failures
Contagion
rounds
Absolute
hazard
a
Hazard
rate
b
Australia 2.57 – – 6 35.3
Austria 0.90 – – 6 35.3
Belgium 1.62 – – 7 41.2
Canada 3.49 – – 1 5.9
Finland 1.24 1 1 6 35.3
France 48.80 14 4 5 29.4
Germany 48.80 14 5 5 29.4
Greece 0.73 – – 6 35.3
Ireland 2.29 – – 6 35.3
Italy 48.80 14 4 5 29.4
Japan 15.02 1 1 1 5.9
The
Netherlands 5.64 1 1 6 35.3
Portugal 0.49 – – 6 35.3
Spain 48.80 14 5 5 29.4
Sweden 0.77 – – 7 41.2
Switzerland 1.78 – – 7 41.2
UK 48.80 14 3 5 29.4
USA 100.00 17 3 – 0.0
Notes:
a
Number of simulations in which that particular country fails;
b
percentage of failures as a
percent of the number of simulations conducted
Table III.
Results for simulation 2
(credit and funding
channel)
Cross-border
?nancial
surveillance
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Table IV.
Country-by-country
capital impairment (credit
and funding channel)
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from a cross-border perspective, which would require further coordination and data
sharing by national regulators.
The analysis of how shocks reverberate throughout the system is important to get a
sense of how a crisis could unravel once the initial shocks have taken place, assuming
the authorities fail, or are too slow, to respond. This is not to say that the analysis
of interconnectedness will unequivocally reveal where the next crisis will arise, but
including an analysis of interlinkages in the supervisors’ repertoire may help identify
institutions that need further scrutiny in terms of their vulnerability and/or level of
systemic risk. If enough data are collected from various types of institutions, the
perimeter of spillovers can be discerned and this could help distinguish which types of
?rms should be under a regulatory net.
The 2007-2009 crisis has proven that interconnectedness across institutions is
present not only within the banking sector, but as importantly, with the non-bank
?nancial sector (such as investment banking, hedge funds, etc.). In particular, the
liquidity problems have demonstrated that rollover risk can spillover to the whole
?nancial system, thus requiring a better understanding and monitoring of both direct
and indirect linkages.
This paper also provides a potential approach to consider how to maintain an
effective perimeter of prudential regulation without unduly sti?ing innovation and
ef?ciency. It illustrates how network models should allow regulators to see which
Figure 8.
Number of induced
failures – uniform
distribution
0
F
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l
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d
F
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K
U
S
2
4
6
8
10
12
14
16
18
Credit channel
Credit (with risk transfer)
Credit and funding channel
Credit and funding channel (with risk transfer)
Note: The number of countries whose banking systems would fail, as of December 2007,
as a result of the initial failure of each country
Cross-border
?nancial
surveillance
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Figure 9.
Country-by-country
vulnerability level –
uniform distribution
0
1
2
3
4
5
6
7
8
Note: Each country's absolute hazard level defined as the number of simulations in which
the banking system of the country failed as a result of another country's failure
Credit channel
Credit (with risk transfer)
Credit and funding channel
Credit and funding channel (with risk transfer)
A
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Figure 10.
Number of induced
failures – biased
distribution
0
2
4
6
8
10
12
14
16
18
Credit channel
Credit (with risk transfer)
Credit and funding channel
Credit and funding channel (with risk transfer)
Finland France Germany Italy The
Netherlands
Japan Spain UK US
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institutions are affected in subsequent rounds of spillovers and thus determine relative
levels of supervision. Such an assessment would have to be conducted at regular
intervals, as the structure of the network is likely to change over time. Similarly,
network models can assist policymakers with their tough choices, such as how to
design capital surcharges to lessen the too-connected-to fail problem.
In sum, monitoring global systemic linkages will undoubtedly become increasingly
relevant for ?nancial regulators as well as for the International Monetary Fund. Such
monitoring can be enhanced in several ways:
.
The development of reliable tools for this task should proceed expeditiously.
.
Financial regulators need to strengthen their understanding of systemic linkages
and improve their gathering of relevant data.
.
New information-sharing agreements on cross-border ?nancial exposures
(including regulated and unregulated products and institutions) could strengthen
the capacity of fund members to provide it with the relevant data. In principle,
such agreements could operate on a multilateral or bilateral basis and would
ideally address both the domestic and cross-border dimensions.
Notes
1. While the quote rightly points to the liquidity squeeze suffered by Northern Rock, it is
important to note that the main source of the squeeze came from the wholesale market and
not the interbank market.
2. See Upper (2007) for an insightful survey of the network literature. While most of the network
literature that will be referenced in this chapter is of an applied nature, see Allen and Gale
(2000) and Freixas et al. (2000) for some theoretical underpinnings to the network approach.
In addition, Nier et al. (2007) apply network theory to study contagion risk in simulated
Figure 11.
Country-by-country
vulnerability level –
biased distribution
0
1
2
3
4
5
6
7
8
A
u
s
t
r
a
l
i
a
A
u
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C
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Credit channel
Credit (with risk transfer)
Credit and funding channel
Credit and funding channel (with risk transfer)
Cross-border
?nancial
surveillance
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banking systems. There are also a number of papers that have focused on domestic banking
systems, e.g. Boss et al. (2005) and Elsinger et al. (2006) for Austria, Fur?ne (2003) for the USA,
Ma´rquez and Mart? ´nez (2008) for Mexico, Memmel and Stein (2008) and Upper and Worms
(2004) for Germany, Sheldon and Maurer (1998) and Mu¨ller (2006) for Switzerland, and Wells
(2002) for the UK, among others. Finally, see Chan-Lau et al. (2009) and Degryse et al. (2010) for
cross-border applications.
3. For purely illustrative purposes, we use BIS cross-country data. Our goal in carrying out this
analysis is not to make speci?c pronouncements about particular countries, but to illustrate
the techniques described in the paper as a useful tool for (macro-) ?nancial surveillance.
4. Subsequent rounds in the algorithm take into account the losses stemming from all failed
institutions up to that point.
5. Indirect linkages among ?nancial institutions may arise when banks hold the same type of
asset in their balance sheets. These linkages can represent an important source of systemic
risk, as the forced sale of assets by some institutions may trigger a decline in the market
value of the other institutions’ portfolios. Models with this type of portfolio linkages can be
found, for instance, in Cifuentes et al. (2005), Elsinger et al. (2006), Lagunoff and Schreft
(2001) and de Vries (2005). In addition, Fur?ne (2003), Nier et al. (2007) and Mu¨ller (2006) also
analyze liquidity shocks.
6. An alternative way to see this is the following. Let rx be the amount of funding that cannot
be replaced. Let p
1
be the current market price for assets and let y be the quantity of assets
sold. That is, p
1
y ¼ rx. Suppose that these assets had been bought at a higher price p
0
, thus
rx ¼ p
1
y , p
0
y ; rxð1 þdÞ. Hence, it is possible to ?nd a relationship between the
parameter d and the change in asset prices: d ¼ ð p
0
2p
1
Þ=p
1
, i.e. d is a parameter re?ecting
the degree of distress in asset markets. Higher d re?ects higher distress in markets.
7. For comparison purposes, and given the lack of reliable estimates for its empirical value, this
parameter was arbitrarily set equal to the loss given default parameter, u ¼ l, in order not to
bias the results in favor of or against the simulations that include risk transfers: simulations
with u , l would produce less defaults and vice versa. Factors that in practice would affect
the numerical value of this parameter include: the fraction of risk transfers that have been
provisioned for or the cheapest-to-deliver option available to the protection seller at the time
a transfer needs to be made.
8. We are indebted to Patrick McGuire and Go¨tz von Peter for extensive discussions on the BIS
statistics.
9. See McGuire and Wooldridge (2005) and BIS (2008) for a detailed description of these data,
and McGuire and Tarashev (2008) for applications of the BIS statistics to monitor the
international banking system. Hattori and Suda (2001) also use BIS data to study the
network topology of the international banking system from a historical perspective.
However, their study does not assess contagion patterns.
10. There is another difference between the IBB and the URB datasets that deserves mention: the
sectoral split (e.g. banks vs other sectors) is not consistent across the two datasets. In the IBB
data, the sectoral split is only available for international claims (cross-border claims plus local
claims in foreign currency), whereas there is no information on the sectoral split for local
claims in local currency. In consequence, we applied the same sectoral fraction available for
international claims to local claims in local currency. In contrast, in the URB data, the sectoral
split applies to total foreign claims (equal to international plus local claims in local currency).
11. The BIS de?nes credit guarantees as contingent liabilities arising from an obligation to pay
to a third party when a client fails to perform a contractual obligation; credit commitments
are irrevocable obligations to extend credit at the request of a borrower (McGuire and
Tarashev, 2008).
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12. The simulations assume that the loss given default parameter equals 100 percent on impact.
That is, when the credit event ?rst materializes, banks are unable to recover any of their
loans, as it takes time for secondary (and distress debt) markets to price recently defaulted
instruments. Thus, the simulation results should be interpreted as the on-impact
transmission of systemic instability. In a similar vein, Wells (2002) argues that network
studies should consider higher loss given default estimates than typically assumed, as banks
tend to face substantial uncertainty over recovery rates in the short run.
13. Corresponding to parameter values of d ¼ 1 and of r ¼ 0:35.
14. Recall that the ?rst distribution we assume is such that the distribution of risk transfers
across referenced entities is the same as the distribution of direct credit exposures.
References
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competencies”, Working Paper No. 08-07, Chapter 21, Wharton School Publishing,
Philadelphia, PA.
Allen, F. and Gale, D. (2000), “Financial contagion”, Journal of Political Economy, Vol. 108, pp. 1-33.
BIS (2008), Guidelines to the International Consolidated Banking Statistics, Bank for International
Settlements, Basel.
Boss, M., Elsinger, H., Summer, M. and Thurner, S. (2005), “Network topology of the interbank
market”, Quantitative Finance, Vol. 4, pp. 677-84.
Chan-Lau, J., Espinosa, M., Giesecke, K. and Sole´, J. (2009), “Assessing the systemic implications
of ?nancial linkages”, Global Financial Stability Report, Chapter 2, International Monetary
Fund, Washington, DC, April.
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European Economic Association, Vol. 3 Nos 2/3, pp. 556-66.
Degryse, H., Elahi, M.A. and Penas, M.F. (2010), “Cross-border exposures and ?nancial
contagion”, International Review of Finance, Vol. 10 No. 2.
de Vries, C.G. (2005), “The simple economics of bank fragility”, Journal of Banking & Finance,
Vol. 29 No. 4, pp. 803-25.
Elsinger, H., Lehar, A. and Summer, M. (2006), “Risk assessment for banking systems”,
Management Science, Vol. 52 No. 9, pp. 1301-14.
Freixas, X., Parigi, B. and Rochet, J.C. (2000), “Systemic risk, interbank relations and liquidity
provision by the Central Bank”, Journal of Money Credit and Banking, Vol. 32 No. 3 (part 2)
pp. 611-38.
Fur?ne, C.H. (2003), “Interbank exposures: quantifying the risk of contagion”, Journal of Money,
Credit and Banking, Vol. 35 No. 1.
Hattori, M. and Suda, Y. (2001), “Developments in a cross-border bank exposure network”, paper
presented at BIS International Financial Statistics, CGFS Workshop, Research on Global
Financial Stability, BIS Workshop.
Lagunoff, R. and Schreft, S.L. (2001), “A model of ?nancial fragility”, Journal of Economic
Theory, Vol. 99, pp. 220-64.
McGuire, P. and Tarashev, N. (2008), “Global monitoring with the BIS international banking
statistics”, BIS Working Paper No. 244, Bank for International Settlements, Basel.
McGuire, P. and von Peter, G. (2009), “The US dollar shortage in global banking and the
international policy response”, BIS Working Paper No. 291, Bank for International
Settlements, Basel.
Cross-border
?nancial
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banking system”, paper presented at the International Conference on Computing in
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Economic Activity, Fall, pp. 229-74.
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Degryse, H. and Nguyen, G. (2007), “Interbank exposures: an empirical examination of contagion
risk in the Belgian banking system”, International Journal of Central Banking, Vol. 3 No. 2.
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Stress-Testing, Bank of England, London.
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CEPR Discussion Paper No. 2916, Centre for Economic Policy Research, London.
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banking system”, International Journal of Intelligent Systems in Accounting, Finance and
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Stern, G.H. and Feldman, R.J. (2004), Too Big to Fail: The Hazards of Bank Bailouts, Brookings
Institution Press, Washington, DC.
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pp. 831-68.
Corresponding author
Juan Sole´ can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
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Appendix. Comparing results based on the IBB and the URB datasets
This appendix brie?y summarizes the results from the simulations using URB data. As
mentioned in Section II.C, URB data are consolidated by residency of the ultimate obligor (i.e. the
party that is ultimately responsible for the obligation in case the immediate borrower defaults)
and include net risk transfers. Therefore, the simulation results could potentially differ
substantially from those using IBB data.
In order to assess whether the use of IBB or URB data would substantially alter our policy
conclusions, we ran simulations using URB data. As Table AI shows, the results of the
simulations for the credit and the credit plus funding shocks retain some of their qualitative
implications compared to our previous results: in both instances, the UK and the USA continue to
be the two most systemic banking systems, and some other countries continue to maintain their
(lower) pro?le as sources of contagion (e.g. Finland and The Netherlands).
However, the comparison of IBB and URB data reveals some important differences for some
other countries. For instance, Italy becomes less systemic with URB data in regard to credit
shocks, going from seven to zero induced failures. Similarly, Germany, Italy, and Spain
dramatically reduce their role under the credit-plus-funding shock scenario.
Cross-border
?nancial
surveillance
203
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Table AI.
Comparing simulation
results using IBB and
URB datasets
JFEP
3,3
204
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Table AI.
Cross-border
?nancial
surveillance
205
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This article has been cited by:
1. A Guide to IMF Stress Testing . [CrossRef]
2. Serafin Martinez-Jaramillo, Biliana Alexandrova-Kabadjova, Bernardo Bravo-Benitez, Juan Pablo
Solórzano-Margain. 2014. An empirical study of the Mexican banking system’s network and its
implications for systemic risk. Journal of Economic Dynamics and Control 40, 242-265. [CrossRef]
3. Andreas A. Jobst. 2013. Multivariate dependence of implied volatilities from equity options as measure
of systemic risk. International Review of Financial Analysis 28, 112-129. [CrossRef]
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