Andrew G Haldane: On microscopes and telescopes
Speech by Mr Andrew G Haldane, Executive Director and Chief Economist of the Bank of
England, at the Lorentz centre workshop on socio-economic complexity, Leiden,
Netherlands, 27 March 2015.
* * *
The views are not necessarily those of the Bank of England or the Monetary Policy Committee. I would like to
thank Thibaud de Barman, Lucy Canham, Chiranjit Chakraborty, Jeremy Franklin, Rashmi Harimohan, John Hill,
Bradley Hudd, Ben Kempley, Lu Liu, Katie Low, Anna Jernova, Carsten Jung, Damien Lynch, Yaacov Mutnikas,
Tobi Neumann, Paul Robinson and David Ronicle for their assistance in preparing the text. I would also like to
thank Stephen Burgess, Oliver Burrows, Pavel Chichkanov, Nicholas Fawcett, Bob Hills, Ronan Hodge, Glenn
Hoggarth, Catrin Jones, Vas Madouros and Simon Whitaker for their comments and contributions.
Accompanying charts and the table can be found on the Bank of England's website.
At least since the financial crisis, there has been increasing interest in using complexity
theory to make sense of the dynamics of economic and financial systems (Newman (2011),
Arthur (2014)). Particular attention has focussed on the use of network theory to understand
the non-linear behaviour of the financial system in situations of stress (Gai and Kapadia
(2011), Haldane and May (2011), Gai, Haldane and Kapadia (2011)). The language of
complexity theory - tipping points, feedback, discontinuities, fat tails - has entered the
financial and regulatory lexicon.
Some progress has also been made in using these models to help design and calibrate
postcrisis regulatory policy. As one example, epidemiological models have been used to
understand and calibrate regulatory capital standards for the largest, most interconnected
banks - the so-called "super-spreaders" (Craig et al (2014)). They have also been used to
understand the impact of central clearing of derivatives contracts, instabilities in payments
systems and policies which set minimum collateral haircuts on securities financing
transactions (Haldane (2009)).
Rather less attention so far, however, has been placed on using complexity theory to
understand the overall architecture of public policy - how the various pieces of the policy
jigsaw fit together as a whole. This is a potentially promising avenue. The financial crisis has
led to a fundamental reshaping of the macro-financial policy architecture. In some areas,
regulatory foundations have been fortified - for example, in the micro-prudential regulation of
individual financial firms. In others, a whole new layer of policy has been added - for
example, in macro-prudential regulation to safeguard the financial system as a whole
(Hanson, Kashyap and Stein (2010)).
This new policy architecture is largely untried, untested and unmodelled. This has spawned a
whole raft of new, largely untouched, public policy questions. Why do we need both the
micro- and macro-prudential policy layers? How do these regulatory layers interact with each
other and with monetary policy? And how do these policies interact at a global level?
Answering these questions is a research agenda in its own right. Without answering those
questions, I wish to argue that complexity theory might be a useful lens through which to
begin exploring them. The architecture of complex systems may be a powerful analytical
device for understanding and shaping the new architecture of macro-financial policy.
Modern economic and financial systems are not classic complex, adaptive networks. Rather,
they are perhaps better characterised as a complex, adaptive "system of systems" (Gorod et
al (2014)). In other words, global economic and financial systems comprise a nested set of
sub-systems, each one themselves a complex web. Understanding these complex
subsystems, and their interaction, is crucial for effective systemic risk monitoring and
management.
This "system of systems" perspective is a new way of understanding the multi-layered policy
architecture which has emerged since the crisis. Regulating a complex system of systems
calls for a multiple set of tools operating at different levels of resolution: on individual entities
- the microscopic or micro-prudential layer; on national financial systems and economies -
the macroscopic or macro-prudential and monetary policy layer; and on the global financial
and economic system - the telescopic or global financial architecture layer.
The architecture of a complex system of systems means that policies with varying degrees of
magnification are necessary to understand and moderate fluctuations. It also means that
taking account of interactions between these layers is important when gauging risk. For
example, the crisis laid bare the costs of ignoring systemic risk when setting micro-prudential
policy. It also highlighted the costs of ignoring the role of macro-prudential policy in managing
these risks. That is why the post-crisis policy architecture has sought to fill these gaps. New
institutional means have also been found to improve the integration of these
microprudential, macro-prudential, macro-economic and global perspectives. In the UK, the first
three are now housed under one roof at the Bank of England.
In what follows, I first set out some background on the dynamics of a complex system of
systems using some stylised examples. I then discuss some stylised facts on the "system of
systems" that is today's economic and financial network. Finally, I draw out some tentative
conclusions for future research and policy which follow from viewing the macro-financial
system through this lens.
The architecture of complexity
The literature on complexity theory, and its implication for system dynamics, is now deep and
rich (Newman (2011)). Although there is no generally-accepted definition of complexity, the
one contained in Herbert Simon's classic 1962 article on the Architecture of Complexity -
"one made up of a large number of parts that interact in a non-simple way" - continues to
capture well its everyday essence (Simon (1962)). In complex systems, the whole behaves
very differently than the sum of its parts.
Although there is no single unifying theory of complexity, the dynamic properties of complex
systems are now reasonably well-understood, based on analytical and experimental studies
of networks of all types - physical, natural, social, biological and economic (Ladyman et al
(2013)). These dynamic properties include non-linearity; discontinuities in responses to
shocks; amplifying feedback effects; and so-called "emergent" system-wide behaviour which
is difficult to predict from the behaviour of any one element.
These properties of complex systems typically give rise to irregular, and often highly
nonnormal, statistical distributions for these systems over time. This manifests itself as much
fatter tails than a normal distribution would suggest. In other words, system-wide interactions
and feedbacks generate a much higher probability of catastrophic events than Gaussian
distributions would imply (Newman (2005), Gabaix (2009)). They may also result in
distributions which are multi-modal, consistent with models of multiple equilibria (Bisin et al
(2011)).
The topology and wiring of these complex systems appears, perhaps predictably, to have a
crucial bearing on their resilience to shocks. Many complex networks have been found, in
practice, to exhibit a "scale-free" property (Barabasi and Albert (1999)). That is to say, they
comprise a core set of nodes with a large number of connections and a large set of
peripheral nodes with few connections. There is a core-periphery, or hub-and-spokes,
network configuration. This scale-free property has been found in everything from food webs
to the World Wide Web, from eco-systems to economic systems, from synapses to cities,
from social networks to financial networks (Jackson (2010)).
These scale-free topologies have important, if subtle, implications for system resilience. For
example, core-periphery models have been found to be very robust, at a systemic level, to
random shocks. That is because these shocks are very likely to fall on peripheral nodes
unconnected with, and hence unlikely to cascade through, the system as a whole. But these
systems are also vulnerable to targeted attack on the core nodes - the "super-spreaders" -
whose hyper-connectivity risks generating a systemic cascade (Albert, Jeong and Barabasi
(2000)).
Another typical feature of complex systems is that they tend to organise themselves as a
hierarchy, with a well-defined structure of systems and sub-systems (Simon (1962, 1976)).
Herbert Simon believed hierarchical structures of a particular type were likely to dominate,
namely ones which were "decomposable". By this he meant organisational structures which
could be partitioned such that the resilience of the system as a whole was not reliant on any
one sub-element.1 For evolutionary reasons of survival of the fittest, Simon posited that
decomposable networks were more resilient and hence more likely to proliferate as a species
(Simon (1962)).2
While Simon's evolutionary theory may be a reasonable long-run description of some
realworld complex systems - natural and biological - it may be less good as a description of the
evolution of socio-economic systems. The efficiency of many of these networks relies on
their hyper-connectivity. There are, in the language of economics, significantly increasing
returns to scale and scope in a network industry. These returns increase with network
connectivity (Goldin and Mariathason (2014)). Think of the benefits of global supply chains
and global interbank networks for trade and financial risk-sharing. This provides a powerful
secular incentive for non-decomposable socio-economic systems.
Moreover, if these hyper-connected networks do face systemic threat, they are often able to
adapt in ways which avoids extinction. For example, the risk of social, economic or financial
disorder will typically lead to an adaptation of policies to prevent systemic collapse. These
adaptive policy responses may preserve otherwise-fragile socio-economic topologies. They
may even further encourage the growth of connectivity and complexity of these networks. For
example, policies to support "super-spreader" banks in a crisis may encourage them to
become larger, and more complex, still over time (Haldane (2009)). The combination of
network economies, and policy responses to failure, means socio-economic systems may be
less Darwinian, and hence decomposable, than natural and biological systems.
It is against this backdrop that a complex, socio-economic "system of systems" may emerge.
This can defined as one comprising an interlocking set of individually complex webs ((Gorod
et al (2014)). The system of system concept initially emerged for engineering and enterprise
systems, which involved the multi-layered assembly of component parts. But it has since
found its way into a number of other domains including military planning, ecological
evolution, power grids, transport networks and neurological structures (Gao et al (2014)).
Although still in its infancy, there are some general properties of a "system of systems"
perspective that are worth bringing out. For example, Kurant and Thiran (2006) look at the
behaviour of a particular topology - a layered complex network. Specifically, they focus on
the behaviour of transport networks with a two-layer structure. Simulations of this network
suggest that monitoring risk on a layer-by-layer basis is likely to understate significantly the
risk facing each individual layer.
Layered complex networks may also be less robust to failure than might be apparent from
assessing the resilience of each layer in turn. In other words, the risks in a layered network
are strikingly different than the sum of their parts. The greater the complexity of each layer,
and the stronger the correlation between layers, the greater is this vulnerability (Kurant and
Thiran (2006)). In some respects, this is the counterpart of Simon's "decomposability"
hypothesis in a system of systems context.
Most recently, research has focussed on the controllability of complex, layered networks (Liu,
Slotine and Barabasi (2011), Gao, Liu, D'Souza and Barabasi (2014)). It has tended to find
that, even when the dimensionality of a network is large (a large number of layers), effective
control can be exercised by acting on a relatively small number of key layers or nodes. This
is particularly the case when the network has scale-free properties - in other words, a
coreperiphery-type topology.
These points can be brought to life using some simulations of the statistical distribution of a
simple set of systems of systems. These are constructed by mixing together component
distributions, which proxy the layers or sub-systems. These layers may themselves be
complex. This mixing of layers, or distributions, is done using an assumed distribution of
correlations, which may itself be non-normal and complex. In other words, there are multiple
layers of complexity in this system of systems. The inputs to the simulations are shown in
Table 1.
Chart 1 shows a set of joint (system-wide) distributions from these simulations, where each
line represents a probability contour of the distribution.3 Chart 1a is the baseline case. It
involves a mix of two normal distributions with a low correlation coefficient (0.3) where these
correlations are themselves normally distributed.4 This joint distribution represents a
twolayer system of systems, with neither layer complex (hence the normal distribution for each)
and where the correlation between the sub-systems is weak and regular. The resulting joint
distribution is slightly more elliptical than the normal - meaning a greater likelihood of large
positive or negative outcomes occurring simultaneously - but is otherwise unexceptional.5
We can now add progressively greater degrees of complexity to this base case to assess its
impact on the distribution of systemic risk. Chart 1b raises the correlation between the two
layers to 0.8; there is now strong feedback between the sub-systems. It results in a notable
elongation of the system-wide distribution and an even greater probability of good or bad
news striking simultaneously; it becomes more non-normal, closer to the statistical properties
we expect from natural, biological and social networks.
If we allow one of the layers of this cake to exhibit the properties of a complex system, by
assuming it is t- rather than normally-distributed, then the system-wide distribution is shown
in Chart 1c.6 The tails of this distribution have now further widened. And if we allow for a
nonnormal distribution of correlations between the sub-systems - by assuming correlations rise
when there is a bad draw, as during crises - the lower tail is larger still and the mass is
skewed downwards (Chart 1d).
The final, and perhaps most interesting, case is shown in Chart 1e. This shows a three-layer
network. Two of these layers are themselves complex (they are t-distributed) and all of the
layers are strongly correlated in the network tree. The resulting system-wide distribution is
highly irregular. It is also heavily fat-tailed, meaning that catastrophically good or bad
outcomes are now much more probable than the normal distribution would suggest. Its
topology is significantly more complex than any of its individual layers.
These simulations, although simple, provide some insight into the likely behaviour of a
complex system of systems. For example, they suggest that viewing risk through the lens of
a single layer is likely to provide a significantly distorted picture of the true risk distribution,
with the probability of tail events materially under-estimated. As an example of that, Chart 1f
looks at the unconditional and conditional distributions of one of the sub-systems in Chart 1c.
The conditional distribution is conditioned on outcomes in the second layer lying in the lower
half of the distribution.
A risk manager looking at the unconditional distribution (a single layer perspective) would
significantly under-estimate the true tail risk they were facing. This has direct parallels with
the pre-crisis situation, when the actions of individual risk managers at banks, and individual
supervisors of those banks, ignored the role of systemic risk when gauging individual firm
risk. This led to a material under-estimation of individual firm risk. The micro-prudential
microscope was wrongly focussed.
Ignoring a layer, or sub-system, is likely to be equally distorting. That can be seen by
comparing the risk distributions in Chart 1d (two layers) and 1e (three layers). If the world
involves three layers of complexity, then missing a layer will lead to a significant
underestimation of risk. This, too, has a parallel with the pre-crisis situation where the
macroprudential layer was essentially ignored by both micro-prudential and macro-economic
policymakers. This led both to under-estimate the policy risk they faced.
Pulling this together, what are the public policy implications which follow from this complex
system of systems perspective? First, it underscores the importance of accurate data, and
timely mapping, of each layers in a system of systems. This is especially important when
these layers are themselves complex. Granular data is needed to capture the interactions
within and between these complex sub-systems.
Second, modelling of each of these layers, and their interaction with other layers, is likely to
be important, both for understanding system risks and dynamics and for calibrating potential
policy responses to them.
Third, in controlling these risks, something akin to the Tinbergen Rule is likely to apply. There
is likely to need to be at least as many policy instruments as there are complex
subcomponents of a system of systems if risk is to be monitored and managed effectively. Put
differently, an under-identified complex system of systems is likely to result in a loss of
control, both system-wide and for each of the layers.
The architecture of macro-financial systems
How, then, does this theory relate to real-world, macro-financial systems? These systems
are likely to contain many moving parts. Moreover, these moving parts are likely to be
significantly more tightly-coupled than in the past as a result of financial and global
integration. In other words, the global economic and financial system may, over recent
decades, have become a "system of systems", with multiple, interacting layers each a
complex system in its own right.
Chart 2 provides a stylised characterisation of those layers, decomposed four ways. At the
highest resolution - the "micro-prudential" layer - are individual financial firms. These are, if
you like, the atoms of the financial system. Like atoms, however, some individual banks are
themselves complex entities, with many moving and interacting business parts.
At one lower level of resolution - the "macro-prudential" layer - is the financial system. This
comprises interactions between financial firms in the network, as might arise from
counterparty relationships in interbank, repo and derivatives markets. This layer is akin to an
organ, like the brain, whose behaviour is the result of interactions between complex
neurological sub-components.
At a lower level of resolution still - the "macro-economic" layer - is the national economy.
This comprises complex interactions between the financial sector and the wider economy -
the flows of funds which intermediates money from owners to borrowers. It is akin to the
physiology of a human, whose behaviour in the result of complex interactions among its
organs, themselves complex sub-components.
Finally, at the lowest level of resolution of all - the "telescopic" layer - is the global economic
and financial system. This involves cross-border trade and financial interactions between
countries, with flows of goods, services or information at increasing volumes and velocities. It
is akin to interactions within a social network or across the World Wide Web.
At least in principle, each of these individual layers could be complex. These layers are also
likely to interact. For example, there are likely to be strong interactions between the financial
system and the macro-economy and between individual national financial systems and the
global financial system. This coupling between sub-systems adds to the degree of complexity
of the system as a whole.
Let me present some stylised facts on each of these layers, from microscopic through
telescopic, to illustrate why each has the characteristics of a complex sub-component. I will
also present some evidence on interactions between these sub-systems, which means we
have a genuine macro-financial system of systems.
(a) Complexity among individual firms
There is no off-the-shelf measure of organisational complexity, whether within banks or more
generally, but a number of proxies have recently been constructed. For example, the Basel
Committee has recently devised a range of complexity metrics for the world's so-called SIFIs
- systemically important financial institutions.7 So far, 30 institutions globally have been
designated by the Financial Stability Board (FSB) as SIFIs, from across a range of countries.
Chart 3 looks at a set of four complexity proxies for each of the designated SIFIs in 2006, the
year before the financial crisis broke: total balance sheet size; the notional value of their
derivatives portfolio; the number of legal entities in the group; and their trading assets. They
are shown by their country of incorporation.
In 2006, the average SIFI had a balance sheet of $1.35 trillion - roughly the same as the
annual GDP of a small G7 country. The largest SIFI had a balance sheet totalling
$3.0 trillion. This is many multiplies of the largest non-financial firm. It also only covers
onbalance sheet positions. Turning to off balance sheet positions, the derivatives portfolio of an
average SIFI totalled $19 trillion in notional terms in 2006.8 For the largest SIFI, it was $60
trillion. This, too, is many multiples of the largest non-financial firm's position.
These massive SIFI balance sheets were, in turn, spread across a very large array of distinct
legal entities within each group. In 2006, the average SIFI had around 328 legal entities
within the group. The largest had almost 900 distinct legal entities. Most of the largest global
SIFIs had numbers of legal entities running to four figures. By comparison, the world's largest
non-financial firms had less than half this number of legal affiliates (Carmassi and Herring
(2014)).
A final complexity metric is the proportion of so-called trading assets. This is a diagnostic on
the diversity of a bank's business model - whether it is engaged in investment as well as
commercial banking. Some of these trading assets will also be complex and hence difficult to
price and trade - so-called Level 2 and 3 assets. As Chart 3 shows, the average SIFI had a
trading book of around a quarter of assets in 2006. For the largest, this was closer to around
50% of assets.
Since the crisis, there is little evidence of these complexities having reversed. Chart 4
updates the four metrics using end-2013 data. The balance sheet of the average SIFI has
not shrunk. In fact, it had risen to around $1.8 trillion by end-2013. For the largest SIFI, it was
around $4.0 trillion. The pattern is even more striking off balance sheet. For the average
SIFI, the derivatives book has risen by over 50% to over $30 trillion by 2013. For the largest,
it had risen to $75 trillion.
On the other complexity metrics, the number of legal entities for the average SIFIs is
essentially unchanged comparing 2006 and 2013. And despite a significant rise in risk
weights, there had been only a modest slimming of SIFI trading books by 2013, with the
average share of assets going from 22% to 19%.
An alternative, more micro-economic, approach to gauging organisational complexity is to
look at internal structures. For example, Collinson and Jay (2012) use a five-way
classification of complexity: governance; internal structures; staff capabilities; roles and
responsibilities; and corporate culture. They use this to evaluate the world's largest
200 companies, including 26 banks.
Financial services rank sixth overall as an industry on complexity grounds. Within this,
however, there is a striking diversity in performance, with a number of banks standing out as
having high levels of complexity and low performance. Chart 5 plots banks' complexity and
performance, based on a study by Simplicity Consulting in 2012.9 It shows an inverted
U-shaped pattern. While modest degrees of bank complexity are good for performance,
beyond a threshold these complexity benefits go into reverse and performance worsens.
This U-shaped pattern is consistent with micro-economic theory. It suggests economies of
scale and scope in banking can give way to diseconomies beyond some threshold. There is
empirical evidence suggesting diseconomies of scale may operate for some SIFIs, consistent
with them being "too complex to manage" (Davies and Tracey (2014)). Taken together, this
evidence suggests the world's SIFIs are, and remain, highly complex entities in their own
right.
(b) Complexity within the financial system
As with individual banks, there are no perfect metrics of financial system complexity. But
maps of the topology or wiring of the financial sector can be revealing about the strength,
direction and complexity of the counterparty links between, and interactions among, financial
firms. As one example, Chart 6 looks at the interbank connections between UK banks in
2013.10
This suggests the wiring of the interbank network is messy and dense. The network's
epicentre is a small set of large banks with a very large number of connections -
"superspreader" banks. There are, in addition, a large number of peripheral banks with few
connections. In other words, the interbank system has all the properties of a classic
scalefree network.
Chart 7 looks at the pattern of interbank funding exposures among UK banks. It, too,
suggests a complex web with scale-free properties. Networks with these properties are prone
to failures among the super-spreader nodes. Since these are likely to be the entities most
exposed to "too complex to manage" problems, this topology harbours a potential systemic
vulnerability, the type of which was exposed in 2008.
Complex interactions are not confined to the banking sector. The non-bank ("shadow"
banking) sector has become increasingly important over recent years. In the US, estimates
suggest it was broadly equivalent in scale to banking in the run-up to the crisis. At a global
level, non-bank intermediation is currently estimated at around $75 trillion, around half of the
assets of the global banking system.
Because many of these entities sit outside of the regulatory perimeter, data on their
exposures and interactions is thin on the ground. But work in the US to map these
interactions suggests they too are dense and complex (Pozsar et al (2010)). Chart 8
presents a stylised map of the shadow banking sector and its interaction with the banking
sector. These interactions are highly complex and multi-layered.
(c) Complexity of macro-financial interactions
The links and interactions between the financial sector and the wider economy are, in some
respects, even less well mapped than the shadow banking sector. They, too, have evolved
significantly over recent decades in scale and complexity.
As an example of that evolution, Chart 9 looks at a representation of the flow of funds
between various sectors in the UK - companies, households, the rest of the world and
government. It also shows the role of the financial sector in intermediating these flows of
funds between ultimate asset-owners (shown at the top) and liability-holders (shown at the
bottom). The intermediation sector is further decomposed into banking and "other" financial
intermediaries such as pension, hedge and other funds and insurance companies. The Bank
of England's is also shown.
These balance sheets are scaled relative to GDP at two dates, 1978 and 2011. The changes
over time in the scale and pattern of intermediation are striking. In 1978, the UK banking
system was dominated by foreign banks. Banks' assets summed to a little over 100% of
GDP. Non-bank intermediaries, meanwhile, accounted for little more than 50% of GDP and
were dominated by traditional real-money investors, such as pension funds and insurance
companies. The Bank of England's balance sheet was a mere 6% of GDP.
Fast forwarding to 2011, this flow of funds picture has been transformed. The balance sheets
of ultimate asset-owners/liability-holders has risen from around 200% of GDP to around
600% of GDP - a threefold rise. The scale and pattern of intermediation between these
sectors has also been transformed. The banking sector has also risen substantially over this
period. UK universal banks now dominate the landscape, accounting for 80% of total banking
assets. They are the new "super-spreaders" of the UK financial sector.
Non-bank intermediation has also seen a dramatic scaling-up of its balance sheets, rising
sixfold to 300% of GDP. Within this, growth has been particularly strong within the
nontraditional sectors (such as hedge funds and unauthorised funds) and among new types of
vehicle (such as ETFs and private equity funds). These are a new strain of potential
"superspreader". The Bank of England's balance sheet has also grown rapidly, rising to over 25%
of GDP, largely courtesy of QE.
Overall, the picture is of a much larger, much more diverse and potentially considerably more
complex set of channels of intermediation than in the past. Of course, this stylised map offers
only the most cursory of indications of the true degree of complexity in individual interactions
within and between sectors.
(d) Complexity of global macro-financial interactions
A final dimension to network complexity is the global one. There has been a dramatic rise in
the scale of global trade and, in particular, capital market integration over the past few
decades. Both are now at their highest levels for several centuries and, most probably, ever
(Chart 10).
For global trade, Chart 11 compares trade flows across 70 countries constituting around 80%
of world exports at two dates - 1995 and 2013. The size of the nodes reflects the importance
of a country to world trade and the thickness of the links the size of trade flows. It is clear that
the wiring of the international trading systems has become considerably more dense and
complex over this period, reflecting the deepening and lengthening of global value chains
(OECD (2013), de Backer and Miroudet (2014)).
This highly interconnected and complex set of global supply chains has the potential to
propagate disturbances much more virulently than in the past, as two recent events illustrate.
The first was the correlated collapse of world trade during the financial crisis. Between
2008Q1 and 2009Q1, real world trade fell by almost 15%. Correlations between regional
import volumes rose to close to unity at the height of the crisis (Chart 12). The strength and
length of global supply chains caused a synchronous collapse in trade (Bems et al (2012)).
The second was the Japanese earthquake and tsunami in 2011. Because Japan plays a
central role in global supply chains as a producer of high-value intermediate goods, this had
a cascading effect on global production. For example, as Japanese car producers ceased
production, their foreign affiliates were unable to compensate because they depending on
inputs from Japan (OECD (2013)). Deepening global supply chains mean a large number of
countries are now potential "super-spreaders" of global trade contagion.
The story is similar, if on an even more dramatic scale, when it comes to global banking.
Chart 13 plots the network of cross-border bank lending flows across a wide range of
countries, in 1980 and in 2007. As with trade, the wiring of the international banking network
has become significantly more dense and complex (Minoiu and Reyes (2013)).11 And as with
domestic banking networks, there is a clear core-periphery pattern to the network, with a
small number of nodal "super-spreader" banking sectors serving as international lenders to
the periphery banks.
The global financial crisis provided a telling illustration of the susceptibility of such a network
to a weakening, or failure, of the core banking sector. As these core sectors came under
stress, it caused a significant cascading of global i