WP/11/74

A network analysis of global banking:
1978–2009
Camelia Minoiu and Javier A. Reyes

1© 2010 International Monetary Fund

WP/11/74

IMF Working PaperIMF Institute
A network analysis of global banking: 1978–2009
Prepared by Camelia Minoiu and Javier A. Reyes1
Authorized for distribution by Marc Quintyn
April 2011
Abstract
This Working Paper should not be reported as representing the views of the IMF.
The views expressed in this Working Paper are those of the author(s) and do not necessarily represent
those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are
published to elicit comments and to further debate.

In this paper we explore the properties of the global banking network using cross-borderbank lending data for 184 countries over 1978–2009. Specifically, we analyze financial
interconnectedness using network metrics of centrality, connectivity, and clustering. We
document a relatively unstable global banking network, with structural breaks in network
indicators identifying several waves of capital flows. Interconnectedness rankings, especiallyfor borrowers, are relatively volatile over the period. Connectivity tends to fall during andafter systemic banking crises and sovereign debt crises. The 2008–09 global financial crisis
stands out as an unusually large perturbation to the cross-border banking network.
JEL Classification Numbers: E44, F34, G15, F15
Keywords: cross-border bank lending, network analysis, interconnectedness, financial crises,systemic risk
Authors’ e-mail addresses: cminoiu@imf.org; jreyes@walton.uark.edu

1International Monetary Fund (Minoiu) and University of Arkansas Sam M. Walton College of Business (Reyes).
Part of this work was undertaken while Javier A. Reyes was a Visiting Scholar at the IMF Institute (October 2010).
We are grateful to Eugen Tereanu for his invaluable contribution at the early stages of this project. We thank the
Bank of International Settlements (BIS) for providing us with data on BIS locational banking statistics on a bilateralbasis and to Patrick Imam, Tümer Kapan, Patrick McGuire, Swapan-Kumar Pradhan, Marc Quintyn, Mahvash S.
Qureshi, Stefano Schiavo, and INSAC seminar participants for useful comments and discussions. All errors are ourown.

2Contents
I. Introduction ................................................................................................................................. 3
II. Related studies............................................................................................................................ 5
III. Data and definitions .................................................................................................................. 7
A. BIS locational statistics .......................................................................................................... 7
B. The network............................................................................................................................ 8
C. Network indicators ................................................................................................................. 8Country centrality ................................................................................................................... 9
Network density .................................................................................................................... 10
IV. Results..................................................................................................................................... 11
A. Network connectivity ........................................................................................................... 11
B. Country rankings, dynamics, and regional heterogeneity .................................................... 13
C. In-sample dynamics and out-of-sample evolution ............................................................... 14
V. The global banking network during financial crises ................................................................ 16
VI. Conclusions............................................................................................................................. 18
References ..................................................................................................................................... 19
Appendix ....................................................................................................................................... 23
List of tables
Table 1. Summary statistics of network indicators ....................................................................... 26
Table 2. Kolmogorov-Smirnov tests ............................................................................................. 29
Table 3. Country rankings by degree of interconnectedness ........................................................ 30
Table 4. Unit root tests for empirical moments of network indicators ......................................... 33
Table 5. Empirical M-statistics ..................................................................................................... 33
Table 6. Interconnectedness during financial crises: Regression estimates ................................. 40
List of figures
Figure 1. Cross-border financial flows to advanced economies ................................................... 23
Figure 2. Cross-border financial flows to emerging markets and developing economies ............ 23
Figure 3. Network view of cross-border banking, 1980 and 2007 ............................................... 24
Figure 4. Trends in network indicators, 1978–2009 ..................................................................... 27
Figure 5. Empirical distributions of network indicators ............................................................... 28
Figure 6. Ranking stability indices ............................................................................................... 31
Figure 7. Average rankings for the BRICs and emerging Europe ................................................ 31
Figure 8. Regional heterogeneity: degree, strength, and clustering .............................................. 32
Figure 9. Stochastic kernel density estimates ............................................................................... 34
Figure 10. Ergodic distributions for node degree ......................................................................... 35
Figure 11. Global banking network before and during the 2008-09 crisis ................................... 36
Figure 12. Network density during financial crises ...................................................................... 38
Figure 13. Regional clustering for LAC and EAP regions during financial crises....................... 38
Figure 14. Interconnectedness before and after financial crises ................................................... 39
Figure 15. Interconnectedness before and after systemic banking crises (including 2007‒08
episodes) ....................................................................................................................................... 40

3
We need to spend much more time modeling and understanding the topology oflinkages among agents, markets, institutions, and countries. (Caballero, 2010, pp.
92)
I. INTRODUCTION
Network analysis is increasingly recognized as a powerful methodological tool for modelinginteractions between economic agents and assessing the resilience of financial systems tosystemic risk.2 Financial interconnectedness is also becoming an important concern in macrofinancial surveillance and has taken center-stage in discussions on prudential regulation policies.3
Network techniques have been used, among others, to describe the global architecture of crossborder financial flows,4 to analyze financial contagion,5 and to examine the dynamics of payment
systems and interbank markets.6 Renewed interest in the application of network analysis tools toanalyze economic interconnectedness was spurred by the 2008–09 global financial crisis, which
due to its voracity and wide reach, provides a fertile experiment in the evolution of financial
networks during times of stress.
In this paper, we explore the properties of the global banking network (henceforth ‘GBN’) using
network analysis techniques. Using bilateral data on cross-border bank lending for 184 countries,
we describe the topology of the network using different metrics of interconnectedness (such ascountry centrality and network clustering) and assess its dynamics over 1978–2009. We also
document changes in the network around periods of financial stress such as systemic banking
crises and sovereign debt crises. Our main findings are as follows. First, network metrics for the
GBN tend to be volatile, with a number of structural breaks identifying waves of capital flows.
Second, interconnectedness rankings of countries, especially for borrowers, are relatively
unstable. Third, connectivity tends to diminish during and after financial crises, with the 2008–
09 crisis standing out as an unusually large perturbation to the network.
Our analysis contributes to the literature in several ways. To our knowledge, this is the first study
that analyzes geographical patterns in the GBN using network techniques. Second, unlike earlier
studies we focus on flows rather than exposures. While stocks of cross-border claims that captureinter-country exposures can be useful indicators of the potential for contagion, cross-borderflows of financial capital reflect liquidity conditions in international markets. As such, they are apotentially informative source of variation during times of financial stress than slowly-evolving
stocks of cross-border claims. Third, we focus not only on the presence or absence of bilateralrelationships (i.e., the binary network), but extend the analysis to a weighted approach by2

For recent contributions, see, Nier et al. (2007), Alessandri et al. (2009), and Cechetti et al. (2010). Goyal (2007)
provides an excellent introduction to network theory. For reviews of its applications in economics and finance, see
Nagurney (2003) and Allen and Babus (2009).
3
Haldane (2009), ECB (2010a, 2010b), and IMF (2009, 2010a, 2010b).
4
Hattori and Suda (2007), Sa (2010), and Kubelec and Sa (2010).
5See, e.g., Soramaki et al. (2007) and Iori et al. (2008) for toplogical descriptions of interbank markets.
6
Allen and Gale (2000), Degryse et al. (2010), Gai and Kapadia (2010), and Kali and Reyes (2010).

4adjusting the network statistics to reflect the intensity of financial flows across countries. This
allows us to better capture the heterogeneity in cross-border financial links.7
The international bank lending market has expanded significantly in recent years.8 Net crossborder bank lending (new loans minus repayments) reached a high of $4.3 trillion in 2007 andplummeted to a negative of almost $1 trillion as the subprime crisis unfolded in 2008. Figures 1–
2 show cross-border bank loans relative to other financial flows (FDI and portfolio investment)over 1980–2008. The figures underscore the importance of cross-border lending as a key sourceof finance for emerging and developing economies, having reached levels comparable to FDIprior to the 2008–09 crisis. Advanced economies are the main players in the GBN, with flows
circulating among them that are roughly ten times higher than those to emerging and developing
economies.9 We refer to these economies as the “core” of the network, while emerging anddeveloping countries receiving liquidity from the core form the “periphery.”Why does the network of banking relationships among countries matter above and beyond theactual cross-country bilateral flows? In the wake of the recent crisis it has been argued that
network theories can enrich our understanding of the functioning of financial systems by helping
model complexity, systemic risk, and the factors that cause seizures in financial markets(Haldane, 2009; Caballero, 2010). For instance, higher interconnectedness in the financial
system is believed to improve risk sharing and reduce the risk of contagion (through better
ability to absorb shocks) but to also increase it (through a wider outreach of reverberations).
Financial systems have been shown to display robust-yet-fragile tendencies (Gai and Kapadia,2010) and to react differently to shocks depending on the pattern of interconnectedness (Allenand Gale, 2000).10 A thorough knowledge of topological structures of real world financial
markets could therefore be useful in developing models that can predict the observed patterns
and forecast the reaction of the financial system to shocks.
Network graphs offer a unique perspective on the evolution of interconnectedness in the global
banking market between 1980 and 2007 (Figure 3). Both the core-periphery and the core-core
network have changed substantially over the last three decades, with the web of cross-borderbank relationships expanding markedly as new links were born and cross-border flows increasedin magnitude. More banking activity took place with the periphery in 2007 than in 1980, with
ECA and MENA being the most integrated regions into the global market in 2007 (Panel A). The
7

The importance of analyzing topological features of real-world networks in both binary and weighted terms has
been highlighted, e.g., in Fagiolo et al. (2010) who show that in the binary international trade network countries withmany trade partners typically trade with countries with few partners. However, in the weighted trade network wheretrade intensity is taken into account, countries that exchange large trade volumes have many trade partners, but only
trade intensively with few well-connected partners.
8
We use interchangeably the terms global, international, or cross-border lending to mean cross-border (external)loans extended by banks in a given country to institutions in other countries.
9
Through the paper we assume that the cross-border banking statistics we use (further discussed in Section III.A)
are a good first-order approximation for actual cross-border banking flows among countries. Nevertheless, thecoverage is incomplete because only banks from some 15 advanced economies have submitted data to the BIS since
the early 1980s. Currently, 43 countries report their locational statistics to the BIS.
10
See also Georg (2010) and Fujii and Takaoka (2010) for analyses of how network topology influences contagion
in interbank markets and a hypothetical financial market, respectively.

5
network links have been expanding outwards since 1980 particularly towards emerging Europe
and international banking centers such as Bahrain, Cyprus, and Mauritius.11 Link proliferation isapparent in the core-core network as cross-border lending activity among advanced economies
intensified, too (Panel B). Although Figure 3 clearly depicts the expansion of the GBN, more
could be said about the characteristics and dynamics of the network through a thorough analysis
of its topology. In what follows, we use network metrics to explore the features of the GBN,
while paying special attention to waves of capital flows that took place in recent decades.
The remainder of this paper is structured as follows. In Section II we review previous studies thathave borrowed analytical tools from network theory to analyze cross-border financial flows.
Section III contains a description of our data and network metrics of interest. The topologicalproperties of the GBN are discussed in Section IV, where we focus on both static and dynamic
features of the network and analyze the distributional stability of network statistics, country
rankings and ranking dynamics, and within-sample and out-of-sample dynamics. In Section V
we document the behavior of the network around financial crises. Conclusions are presented in
Section VI.
II. RELATED STUDIES
Our work relates to a rapidly expanding line of research applying network analysis tools toanalyze financial linkages in global markets and their implications for the emergence andmanagement of systemic risk.12 This literature draws on the seminal work of Allen and Gale
(2000), who use network theory to model financial interconnectedness and draw implications for
system stability. Allen and Gale (2000) relate banking system resilience to shocks to its
underlying structure in a stylized four-bank network that is either “complete” (i.e., every bank isconnected to every other banks) or “incomplete” (i.e., every bank is connected with fewer than
all banks). They find that complete networks are more resilient to shocks due to risk sharing andindividual banks bearing a smaller share of the shock, while incomplete networks are morefragile since banks with fewer counter-parties have difficulty diffusing the shock. Using Allenand Gale’s setup, Leitner (2005) shows that interlinkages can be desirable even if they act as
conduits for contagion, because they can motivate banks to bail out one another provided thatthey can coordinate to do so when the threat of contagion arises.
In response to concerns that the network structure considered in Allen and Gale (2000) was too
stylized to reflect real-world financial systems, Nier et al. (2007) model a more complex networkof banks with interlinked balance sheets to examine financial contagion as a function the bankcapitalization, size of cross-exposures, and interconnectedness. They find that bank connectivity
has a non-monotonic effect on contagious defaults: at small levels of connectivity, a small

11This mostly reflects activities by internationally active foreign banks (see, e.g., McGuire and Tarashev, 2006).
A recent foray into network models of systemic risk include Martinez-Jaramillo et al. (2010) who simulatecontagion in a banking system and derive measures of system fragility based on the topological features of theinterbank exposures network. Furthermore, network structures have been incorporated into stress tests of financial
systems to monitor the first- and second-round effects of macroeconomic shocks (see, e.g., Espinosa-Vega and Sole,2010).
12

6
increase raises the likelihood of contagion, but in more interconnected networks, higher
connectivity improves the ability of the financial system to absorb shocks.
The trade-off between shock absorption and shock diffusion in financial networks is a recurring
theme in the economics literature, with complex network structures being seen as both better
able to diversify away idiosyncratic risk and more capable of propagating financial distress.
Battiston et al. (2010) assess how network density (measured as the number of connecting links)
relates to systemic risk in a model of the economy as a credit network, arguing that while higher
connectivity allows for improved risk sharing of distress propagation, it also leads to amechanism of trend reinforcement: when an economic agent suffers a negative shock, her trade
partners react by making her conditions even harder. Thus, financial fragility feeds on itself.
With higher connectivity and trend reinforcement going hand in hand, a highly interconnected
system will be at risk of experiencing avalanches of bankruptcies when negative shocks occur.
Macroeconomic complexity due to network relationships has been recently captured in models of
panic during financial crises. Although not directly modeling the structure of the financial
network, Caballero and Simsek (2009) bring to the fore the relevance of the complexity of abank’s web of relationship by developing a model in which banks assess the health of theirtrading partners by collecting information about them. When financial stress affects the system,banks find it necessary to collect information not only about their immediate trading partners, butalso about the trading partners of those trading partners, and so on. In a complex and highlyinterconnected network, there comes a point when the information gathering process becomestoo costly and is abandoned, with banks withdrawing from loan commitments and illiquid
positions, hence spreading the financial crisis. This complexity is endogenized in the model ofCaballero and Simsek (2010) where banks facing plummeting asset prices and liquidity positions
during severe financial crises, must understand a complex web of inter-connections when
making financial decisions. The model thus displays a “complexity externality” in which market
seizures occur because banks are increasingly reluctant to buy assets in a confusing and uncertain
environment.
These theoretical contributions underscore the importance of documenting the topologicalproperties of real-world economic and financial networks. The most extensively analyzed crossborder network is that of international trade flows (Fagiolo et al., 2009, 2010; Schiavo et al.,2010; Schweitzer et al., 2009). This line of research has documented a trade network with a coreperiphery structure and an emerging “rich club” of countries that share high trade intensities andare highly-interconnected (with one another and the periphery) thus bridging different parts of
the network. The trade network also appears to be stationary. Financial contagion through thetrade network is analyzed in Kali and Reyes (2010), who use network-based measures ofconnectedness to explain abnormal stock market returns during recent crises, and find that
contagion is more likely to occur when the epicenter country is better integrated into the trade
network, while more interconnected downstream countries are better able to dissipate the impact.
Our study most closely relates to topological descriptions of global financial networks. Hattoriand Suda (2007) analyze the core-periphery network of international bank exposures (stocks ofclaims) for 215 countries over 1985–2006. They find that the network has become more tightly
connected and more clustered over time, displaying higher connectivity and shorter average path

7
length13 in the recent period. Against the backdrop of an increasingly dense network, the authors
posit that systemic risk is building up while risk and capital are also being allocated more
efficiently.14 A key finding is that network features remain largely unperturbed during major
events such as the LTCM near-collapse or the 1997–98 Asian crisis. In contrast, we will
document a network of financial flows that is more turbulent than that of financial exposures―
one that behaves differently during times of financial stress compared to tranquil periods.
In a similar vein, Kubelec and Sa (2010) and Sa (2010) assemble a large dataset of bilateralcross-border exposures by asset class (FDI, portfolio equity, debt, and foreign exchange
reserves) for 18 advanced and emerging market economies, documenting a marked increase infinancial interconnectedness over 1980–2005. The financial network has become more clusteredwith a lower average path length over time, and its central hubs are the United States and theUnited Kingdom. Comparisons with the international trade network reveal that both networks
have experienced increased connectivity over time, despite the fact that trade openness (total
trade over GDP) has risen less than financial openness (total assets and liabilities over GDP).
Both trade and financial exposures distributions are long tailed,15 with a few countries
exchanging large flows while most countries exchange much smaller flows––a pattern that hints
at a core-periphery structure.
III. DATA AND DEFINITIONS
A. BIS locational statistics
Our data are the BIS locational statistics on exchange-rate adjusted cross-border bank credit over
1978–2009 (on a quarterly basis: 1978Q1–2009Q3) for 184 countries. Locational statistics arecompiled on the basis of residence of BIS reporting banks (in a number of countries)16 and cover“the cross-border positions of all banks domiciled in the reporting area, including positions vis-àvis their foreign affiliates” (Wooldridge, 2002, p. 80). These positions include loans, deposits,
debt securities, and other assets provided by banks. Cross-border flows are then estimated aschanges in cross-border exposures (stocks). Since locational cross-border exposures are also
reported by currency, changes in stocks are estimated after accounting for exchange ratechanges. Thus, exchange-rate adjusted estimates of cross-border flows, which we use in thispaper, are considered a better approximation of true flows than unadjusted changes in stocks(Wooldridge, 2002).

13

Average path length is defined as average of the shortest path between all pairs of nodes in the network. It
measures the “degrees of separation” among nodes.
14
Using similar data on cross-border financial exposures, Degryse et al. (2010) use simulations to examine the risk
and speed of potential contagion in the global financial network over 1999–2006. They find that a shock that affects
the liabilities of one country may have rippling effects through the system. Furthermore, the network topology in2006 suggests that a shock affecting emerging Europe, Turkey or Russia would affect most countries in the world.
15
Using data from the Coordinated Portfolio Investment Survey (CPIS) for 2001-06, Song et al. (2009) show thatthe distribution of global financial exposures are well approximated by a Weibull distribution.
16
In contrast, another often-used source of cross-border financial data are the BIS consolidated statistics, which are
based on the nationality of the reporting bank, and net out intragroup positions. For a comparison between
consolidated and locational banking statistics, see, e.g., Waysand, Ross, and Guzman (2010).

8
Since, the BIS locational banking statistics capture “net flows of financial capital between any
two regions channeled through the banking system” (McGuire and Tarashev, 2006, p. 34), they
are best suited for analyzing geographical interlinkages, but less so for analyzing the global
balance sheets of banks or funding risk (Fender and McGuire, 2010). These location-based data
capture activities of all banks (domestic and foreign) operating in a particular location. Thus, tothe extent that foreign banks are more active than domestic ones in any given location, the data
will reflect that. The BIS locational banking statistics are thus well-suited for studying temporal
patterns in financial linkages across countries, the subject of our study, but will have little to say
about the drivers of such patterns (Fender and McGuire, 2010).
B. The network
Each of the 184 countries in our dataset is a node within the network. We model the financial
flows as a directed network, with nodes being linked through cross-border lending. Banking
flows between countries are the links. We work both with quarterly data and annual data
(obtained by adding up flows across quarters), and model each quarter or year over the sample
period (1978–2009) as a separate network.
The sample comprises 15 lenders (BIS reporting countries) and 169 borrowers. Thus, our full
network has a core-periphery structure, with the “core” comprising the economies that act as
lenders (and represent a sample of BIS reporting countries), and the “periphery” comprisingcountries that act as borrowers (non-BIS reporting countries). Since intra-core lending activity
dwarfs in magnitude exchanges between the core and the periphery, in the remainder of the paper
we focus on the core-periphery network (in which core countries act as lenders only) as opposedto the full network (in which core countries are borrowers as well). We also analyze the corecore network made up solely of “core” countries that are both lenders and borrowers (see theappendix for the list of countries).
From the dataset of bilateral banking flows, we construct our network of interest as follows. We
retain all positive flows (corresponding to net increases in cross-border exposure or “netinvestments”) and replace the negative flows with zeros.17 The resulting matrix––one of net
financial investments––thus comprises positive values (i.e., net investments) and zeros (i.e., net
repayments or no flows).
C. Network indicators
The network metrics we use to study interconnectedness in the GBN include measures of country
centrality (degree and strength) and network density (connectivity and clustering). We describe
the network focusing not only on binary country-pair lending relationships, but also through aweighted approach to take into account the magnitude of flows across countries.18

1718

The analysis of the “net repayments” network is left for future research.
See Barrat et al. (2004).

9
We use bilateral (net) bank lending flows (adjusted for inflation using the US CPI) to buildtmatrices Mt where rows represent lenders and columns represent borrowers. Each entry mij isthe value of the flow from lender i to borrower j at time t. These matrices can be transformed intottheir binary counterparts (At) where each cell aij takes value 1 if the flow from country i tocountry j at time t is positive and 0 otherwise. The dimensions of these matrices( 15  N t for 15 lenders and Nt borrowers) change through the sample period as new countries
enter the network.
Country centrality
Node degree counts the number of connections (links) for each country (node). Since we workwith a directed network, we have in-coming links for borrowers and out-going links for lenders.
Therefore, we compute the out-degree (the number of outgoing links) for each country bycounting the countries to which it lends (its debtors) and in-degree (the number of incoming
links) for each country by counting the countries from which it borrows (its creditors), as
follows:

NDitout  A(ti )1
NDitin  ( A(ti ) )'1tt

where A(i ) denotes the ith row of matrix At, ( A(i ) )’ denotes the ith row of the transpose of matrix
At and 1 is a unitary vector with Nt elements. The maximum value for in-degree is 15 as the
sample of lenders does not change over time. The maximum number for out-degree (Nt) varies
by year as countries enter the network. Note that node degree only uses information from thebinary representation of the network.19
Node strength is the total value of flows originating or terminating in a given node. In our case,in-strength for country i ( NSitin ) is the total amount of cross-border credit it receives, whereas
out-strength for country i ( NSitout ) is the total amount of cross-border credit it lends. Out-strengthand in-strength are computed by substituting matrix At for matrix Mt in the node degree formulas
presented above. Node strength is the simplest weighted network indicator that captures theintensity of financial relationships among countries.
Relative node strength focuses on the relative importance of lenders as providers of financial
capital, and respectively, that of borrowers as destinations for financial investment in the
network. Borrower j’s dependence on lender i is the share of inflows it receives from i in her
total borrowing. Hence, relative node out-strength increases with the lender’s relative importance
19

Node degree captures the extent to which a country is well connected or “in the thick of things.” A high nodedegree simply means that the node has a large neighborhood of local contacts, be it lenders or borrowers, and it
relatively prominent in its neighborhood. Thus, node degree is an indicator of “local centrality.” More sophisticatedindicators (such as shortest path length) measure the node’s strategic significance in the overall network, or its“global centrality” (see Scott, 2009, pp. 82–93.) We are limited in undertaking such an analysis, however, by thecore-periphery structure of the dataset, with countries in the periphery not being interlinked except via the core.

10~~ t represents theas a creditor in the network. Defining a new matrix ( M t ) where each cell mijratio of inflows received by borrower j from lender i to j’s total inflows, relative node outstrength is given by:
RNS itout  M (ti ) 1
Similarly, lender i’s dependence on borrower j as a destination for its financial capital isdetermined by the share of lending to j in her total lending. Defining matrix ( M̂ t ) where eachtcell m̂ij is the ratio of the inflow received by j from lender i to i’s total lending, relative node instrength is given by:

RNSitin  ( Mˆ (ti ) ) '1
Note that the maximum relative out-strength is equal the total number of borrowers (in a network
where one lender provides all the credit) while the maximum relative in-strength is the total
number of lenders (in a network in which a borrower receives all the loans).20
Network density
Connectivity. Network connectivity is the number of links that exist between countries (or total
node degree) expressed as a share of the total possible number of links. It represents thelikelihood of connection between two countries in the GBN. Let mt be the observed number oftlinks (corresponding to positive flows mij  0 in the matrix Mt or 1’s in the matrix At). With 15
lenders and Nt borrowers in the core-periphery network, the degree of connectivity is given by mt 
 15  N t  .
Unweighted/binary clustering. We consider two clustering coefficients—a binary and aweighted clustering coefficient. The binary measure is the ratio between the total number ofcomplete triangles (in which every country lends to every other country) and the total possible
number of such triangles (see Text figure 1). For the core-core network, we take complete
triangles to mean bi-directional lending among three countries i, j, and k from the core. For the
core-periphery network, complete triangles are those where countries i and j from the core lend
to one another and they both lend to periphery country k. (Complete triangles for the two
networks are shown in Panel A.) The binary clustering coefficient ranges between 0 and 1, withhigher values representing a more clustered network––one that displays a greater share of
tripartite relationships. This particular definition also enables us to compute regional clustering
coefficients for the core-periphery network by restricting k to belong to a certain region.
Weighted clustering. Our weighted clustering coefficient is inspired by the generalized nodeclustering coefficient proposed by Opsahl and Panzarasa (2009), which counts complete triangles
as defined above and calculates their total value as the geometric mean of their weights (i.e.,cross-country flows). The weighted clustering coefficient is obtained by dividing the total value
20

By construction, average RNSout (across borrowers) is the average number of borrowers per lender, while average
RNSin (across lenders) is the average number of lenders per borrower.

11of complete triangles by the total value of complete (three-sided) and incomplete (two-sided)
triangles (as shown in Panel B).
Text figure 1. Triangles for clustering coefficients
Panel A. Complete triangles
Panel B. Complete and incomplete triangles
Core-core
Core-periphery++

Notes: Panel A shows complete triangles used to compute binary clustering coefficients. For the core-core network,
all countries belong to the core and there are bi-directional links among all three. For the core-periphery network,countries i and j are in the core, while country k is in the periphery. Panel B shows the three types of triangles usedin the denominator of the weighted clustering coefficient for both networks.

IV. RESULTS
Here we describe the topology of the network using the measures of connectivity defined above,
focusing on the time-evolution of their empirical distributions. The aim is to determine how the
web of bilateral bank lending relationships has changed over 1978‒2009. Occasionally we also
draw comparisons between the GBN and the international trade network (henceforth ‘ITN’), thus
contrasting two key channels of financial contagion––financial and trade linkages (Van
Rijckeghem and Weder, 2001).
A. Network connectivity
Table 1 gives the summary statistics of our key network indicators for the full, core-periphery,
and core-core networks. In the full GNB, countries borrow on average from 4.4 lenders up to a
maximum of 15 (that is, all lenders), while the core countries lend to 48 borrowers, of which 8
are in the core and 40 in the periphery. Some $293 million are lent out on average across borders
each quarter––$81 million to periphery countries and $212 to the core. The likelihood that two
countries are connected through cross-border flows ranges between 26 and 44 percent in the full
network, reaching 79 percent within the core before the current crisis. Similarly, the binary
clustering coefficient in the full network––the probability that two countries are connected with
one another if they both have a relationship with a third country––is about 11 percent. In thecore, where clustering is defined more restrictively by requiring that relationships be bidirectional, the probability is 13 percent and has reached 27 percent before the 2008–09 crisis.
The time-evolution of summary statistics for network indicator confirms that the last three
decades have experienced several waves of financial globalization (Figure 4). The average
number of outgoing links for each lender steadily increased from about 40 to a peak of 60
countries in 2007 before sharply dropping in 2008. Node strength (the total flows per country)
has followed a similarly steep increase during the 2000s, followed by a drop in 2008 that is of

12historically unprecedented magnitude. Connectivity and clustering indicators for the network as awhole have been volatile, too.21 These features stand in contrast with the ITN, for which network
metrics have remarkably stable empirical moments over the past decades (Fagiolo et al., 2010).
Looking closer at the fluctuations in network metrics, we can identify three global waves ofcross-border bank lending. First, the build-up of financial interlinkages between the early 2000s
upto to the current crisis stands out for all indicators. Second, a smaller surge in connectivity isevident prior to the 1997–98 Asian crisis. Third, the charts depict part of the build-up of crossborder lending that came to an end with the debt crisis of the early 1980s. The shifts in the
topology of the GBN confirm our prior knowledge about the global waves of private capital
flows as documented, for instance, in the IMF’s World Economic Outlook (2007).22
Another way to detect shifts in network connectivity over time is by analyzing the shape of the
distributions of network metrics. Nonparametric kernel density estimates for degree and relative
strength in 1980 and 2007 are shown in Figure 5. On the lender side, the density estimate for outdegree (the number of outgoing links) has preserved its shape over time, but has shifted to theright as lenders have provided financial capital to an increasing number of countries. Similarly,
the distribution of in-degree (the number of incoming links) has shifted to the right as borrowers
tap into a larger pool of lenders. The bimodality of the 2007 distribution of in-degree suggeststhat a large group of borrowers are emerging as particularly well-connected, i.e., borrowing fromabout 10 of the available lenders. The distribution of relative out-strength suggests that mostlenders in 1980 provided about 5 percent of flows to any given borrower; by 2007, that number
had shifted to about 10 percent, as relative importance has been shared more equitably among
lenders. In contrast, the density of relative in-strength reveals a great degree of heterogeneity interms of the importance of borrowers as destinations for liquidity. Inequality in terms of relative
strength among borrowers has risen between 1980 and 2007, hinting at a more polarized
periphery. As further documented in the next section, highly-connected borrowers share theperiphery with a plethora of less relevant nodes.
We wrap up the analysis of the evolution of the empirical distributions through two-sample
Kolmogorov-Smirnov tests. The null hypothesis is that the observed empirical distributions (attwo points in time) are close enough to conclude that they are drawn from the same data
generating process. We undertake the test for each indicator by comparing its empirical
distribution function at the beginning of each decade with that in subsequent decades. We
summarize the results in Table 2, where we report the proportion of years each decade with
empirical distributions of network metrics that are unlike those at the beginning of the decade.
Interestingly, the empirical distributions of lender centrality metrics are more stable than those ofborrower centrality. Borrower connectivity, strength, and relative strength show muchturbulence, with the distributions in 1980 being poor predictors of future ones. For instance, thelandscape for borrowers through the 2000s is unlike that in previous decades, but seems
21

Interestingly, network connectivity for the GBN based on cross-border flows appears less persistent than that for
the GBN based on cross-border exposures. The reason is that the likelihood of links dying in the network of crossborder exposures is substantially lower than in our network (see Hattori and Suda, 2007).
22
The concept of “private” capital flows refers to changes in foreign assets and liabilities of the recipient domestic
private sector (IMF, 2007, pp. 3).

13unchanged since the year 2000, which suggests that periphery countries have been consolidating
their relative positions in the GBN over the last decade.
B. Country rankings, dynamics, and regional heterogeneity
Two questions arise from the patterns discussed in the previous section. The first is whichcountries and regions have been or currently are the most interconnected in the GBN. The second
is whether the relatively stable shapes of the empirical distributions conceal turbulence ofcountry rankings, which may be possible if countries swap places in terms of connectivity fromone year to another.
We begin by reporting the top ten lenders in terms of node degree and strength (Table 3) for
1980, 1995, and 2007. The most globally connected lenders are Belgium, France, Germany,
Switzerland, and the United Kingdom, with Japan, Switzerland, and the United States joining the
top ranks in terms of absolute and relative volumes (Panel A). The rankings are relatively stable
for lenders, but less so for borrowers, with liquidity appearing to follow different geographicalpatterns (Panel B).23 Before the debt crisis of the early 1980s, LAC countries (Argentina, Brazil,
Chile, Mexico, and Venezuela) were the most central borrowers in the network. In 1995 theygave way to the fast-growing East Asian countries, while the BRICs began their own ascending
path. By the end of the period, the BRICs had become the most interconnected borrowers
alongside emerging Europe (e.g., the Baltics, Poland, Romania, and Ukraine).
To obtain a broader view of ranking dynamics for all counties, we also calculate pair-wise
Spearman rank-correlation coefficients for consecutive years t ,t 1 ( X ) and define a Ranking
Stability Index (RSI) for each network indicator X as the time-average of the Spearmancoefficients, as follows:

RSI ( X ) 

1 T t ,t 1 ( X )T  1 t 2

The RSI, which has the usual properties of a correlation coefficient, is useful in detecting shapepreserving ranking turbulence, which occurs when the empirical distributions of network
indicators do not change but countries swap places in terms of centrality in the network.
The RSIs for degree and strength are depicted in Figure 6. Lender rankings seem more stable
than borrower rankings, becoming less volatile after the debt crisis of the early 1980s andstabilizing at about 0.8. In contrast, borrower rankings based on the number incoming links are
more volatile (0.2 on average over the full period), and even negative in the late 1970s. Thissuggests that the relatively stable empirical distributions of node in-degree (seen in Figure 5)
conceal degree great deal of rankings turbulence. In contrast, rankings based on strength havebecome more stable since the early 1990s, leveling off at about 0.7 during the 2000s. Thispattern suggests that a group of strong borrowers has emerged and is consolidating its place as a23

In constructing borrower rankings we exclude high-income countries. Including them brings to the top ofinterconnectedness countries such as Australia, Finland, Greece, Portugal, and Spain.

14
central destination for cross-border lending in the recent years. Closer inspection of borrower
rankings reveals that this strong group comprises primarily the BRICs and emerging European
countries (Figure 7). Reyes et al. (2008) show that through the 1990s the BRICs have moved
from the periphery into the core of the ITN as well, reaching the top in terms of network
centrality (Reyes et al., 2008). ). Nevertheless, the ITN has relatively less volatile country
rankings than the GBN.
Global averages of network metrics conceal interesting regional heterogeneity (Figure 8). During
the 2000s, the ECA region (including emerging Europe) has been the most integrated region into
the GBN, with the highest level of connectivity (in-degree), average inflows per country (instrength), and clustering (complete triangles with countries in the core). Average and total flows
for EAP display a strong cyclicality, with a peak in cross-border lending to this region occurring
prior to the Japanese crisis in the early 1990s. For other regions, network metrics tell a different
story compared to total flows. The MENA region comes fourth based on total lending, but ranks
higher up in terms of in-degree and binary clustering. Although during the 2000s, SA (driven by
India) has become more interconnected by tapping into a larger pool of lenders, cross-border
lending volumes remain relatively low. Further, total lending to LAC falls markedly in the wakeof the early-1980s debt crisis but surges prior to the 2001 Argentine crisis and the recent
downturn. However, prior to the end of the sample period, the LAC region never attains thedegree of connectivity it enjoyed prior to the debt crisis of the early 1980s. Similarly, EAP
experienced three surges in cross-border lending, but these are relatively muted in network
connectivity terms. In all charts, the downturn in network connectivity and clustering during the
2008–09 crisis stands out as an unusually large perturbation to the network.
C. In-sample dynamics and out-of-sample evolution
We now turn to characterizing the distributional dynamics of the GBN over 1978‒2009. We
have found the fluctuations in network features roughly coincide with previously documented
global waves of private capital flows. To formalize the dates of the waves, we carry out unit root
tests with one and two structural breaks in the mean (Clemente, Montanes, and Reyes, 1998) for
all four indicators (average degree, average strength, network connectivity and network
clustering). The results (Table 4) show that one-break tests identify a break in 2001 or 2003,
while two-break tests find the first mean shift either in 1980 (before the debt crisis) or mid-1990s(before the Asian crisis) and the second one in 2002–03. In what follows we ignore the firstbreak in the early 1980s since our data is insufficient to gauge the build-up of that wave, andrestrict our attention to the behavior of the GBN during the more recent windows 1987–98 and
2002–08 (dated as in IMF, 2007).
These two windows provide an interesting setting for an exercise about out-of-sample dynamics.
Over the past three decades the global financial architecture has been constantly reshaped byfinancial and debt crises, and different regulatory and macroeconomic policy regimes. Here weseek to map the in-sample dynamics of the GBN observed within the two waves into long-term
dynamics. We wish to determine how the global financial architecture would have looked in thelong run if in-sample dynamics were the same as in 1987–98 and 2002–08, respectively. We
proceed in two steps. First, we analyze the within-window transitional dynamics using stochastic

15
kernel estimates and a network-wide statistic. We then estimate long-run, limiting distributions
for the network indicators.
Transitional dynamics can be visualized using stochastic kernel estimates, which we present incontour plots. These depict the conditional density estimate for a random variable that governs
the transition of countries from one quantile of a network statistic distribution to anotherquantile. For illustration, we compute the 1-year stochastic kernel estimates for in-degree andrelative in-strength (Figure 9). For the number of incoming links, most of the probability mass isconcentrated around the 45-degree line in the middle range, meaning that countries in the middle
quantiles of the distribution tend to stay in the same quantile from one year to another. In
contrast, countries in the upper quantiles tend to ascend towards higher quantiles, which meansthat once a country establishes borrowing relationships with many lenders, in the next period
chances are that it will borrow from even more lenders. This pattern is clearer still during thesecond wave of capital flows. For relative in-strength a “middle-class” of globallyinterconnected countries seems to be emerging in the second wave.
Another measure of the persistence of distributional dynamics is the empirical M-statistic, which
aggregates country-level probabilities of moving across quantiles (of network statistic X) fromone year to the next. Assuming that the distributional dynamics of our network statistics can beapproximated well by 10 quantiles (or deciles), we construct a Markov transition matrix byestimating 1-step transition probabilities using the maximum likelihood estimator for the
probability of migrating from one decile to another: 24
t 1,tp  nijijt
ni t
where nij t 1,t is the number of countries transitioning from the ith to the jth decile (of each network
metric X) between t–1 and t and ni t is the number of countries in decile i at time t. Since the
network indicators considered present structural breaks which identify distinct waves of capital
flows (1987–98 and 2002–08), we compute our measure of distributional dynamics––the Mstatistic––within each window as the time-average of:M  ,t ( X ) 

1 10 10   pijt
10 i 1 j:| j i|where parameter ɷ=0 corresponds to countries staying in the same quantile and ɷ=1
corresponds to countries moving one quantile up or down.25 The M-statistic is bounded between
0 and 1, with higher values indicating a higher probability that countries stay in the same or
adjacent quantile in consecutive years.

24

Note that we are not assuming time-homogeneity of the transition probabilities.
For ɷ=0, the M statistic is the trace of the 1-step transition matrix(divided by 10 while for ɷ=1, the M statistic isthe average of all the entries in the main diagonal and those lying one entry to the right and one entry to the left ofthe main diagonal.

25

16
Distributional dynamics display little persistence as our empirical M-statistics are relatively low(Table 5), ranging between 0.2 and 0.5 for parameter value ɷ=0 (staying in the same quantile).
For the second window (2002‒08) the M statistics are higher, which is consistent with the
emergence of a group of borrowers that have been consolidating their position in the network.
We also report 95 percent confidence intervals for the M-statistics corresponding to a “random”
network.26 The M-statistics for the GBN always fall to the right of the confidence interval upperbounds for the random network, suggesting that the GBN is more stable than a random network.
Nevertheless, they are lower than the values of 0.8 and 0.9 documented for the trade network(Fagiolo et al., 2010), hinting at in-sample dynamics of financial flows that are more turbulent
than trade links.
Finally, we investigate the implications of these transitional dynamics for the out-of-sample
evolution of the GBN by estimating the ergodic distributions for in-degree, taking the first yearof each window as the base year.27 Figure 10 depicts the long-run stabilizing tendency of the indegree distribution arising from the transitional dynamics observed within each wave of capital
flows (and taking the mid-year as the base). The limiting distribution of country connectivity is
unimodal in the first wave and bimodal in the second wave. A more “diversified” long-run
financial landscape emerges based on second wave transitional dynamics––in which there are
more highly-connected borrowers than previously. This is consistent with the 1990s being
dominated by a single region––East Asia––as the leading destination of cross-border banklending, while the 2000s witnessed the emergence of a number of economies (such as the BRICs
and emerging Europe) as key borrowers of financial capital.28
V. THE GLOBAL BANKING NETWORK DURING FINANCIAL CRISES
We close our exploration of the topological features of the GBN by describing its behavior
before, during, and after financial crises. Cetorelli and Goldberg (2010) have identified crossborder bank linkages as a key transmission channel of the 2007‒08 US subprime crisis toemerging market countries, arguing that domestic loan supply in afflicted economies contracted
due to the collapse of direct cross-border lending by foreign banks and a general weakening ofbalance sheets of both foreign affiliates and domestic banks caused by shortages of interbank andcross-border liquidity. To provide evidence of cross-border lending acting as a potential conduitof the negative shock both inside the core and towards the periphery, we present network
visualizations for 2007Q4 and 2008Q4—just before and after the crisis (Figure 11). There ismarkedly lower intra-core and core-periphery connectivity at end-2008, with links disappearing
as cross-border banking flows dried up (Hoggarth et al., 2009; Milesi-Ferretti and Tille, 2011).

26

The “random” network is obtained by keeping the distribution of flows unchanged and reshuffling the links acrosscountries.
27
The conclusions are qualitatively the same if we consider alternative years as the base years for the ergodic
distributions.
28
How the 2008–09 crisis has affected this trend is an interesting question to explore as more recent data becomes
available.

17
To determine how periods of financial distress correlate with features of the GBN, we plot
network connectivity and clustering against synchronized recession dates (Figure 12). These
include the 1987 stock market crash, the 1991–92 Scandinavian banking crises and 1992–93
ERM crisis, the 1998Q3 LTCM near-collapse, the 2000Q2 Internet bubble collapse, and the
2008Q3 Lehman Brothers bankruptcy. Many links die and network clustering diminishes during
episodes of financial stress. This pattern is even clearer when we move to the regional level toconsider regional network density during the crises of Latin America and East Asia (Figure 13).
How country connectivity in the GBN behaves during financial crises is depicted in Figures 14–
15, where we plot average degree and strength (across countries) around systemic banking andsovereign debt crisis episodes.29 The averages are shown for 5 years before and after the onset ofthe crisis. Without exception, network indicators of borrower connectedness in the GBN fall
during crises, although the decline generally begins before the event.30 The pattern holds up for
lenders despite the paucity of systemic banking crises in the core prior to 2007. For sovereign
debt crises, we only show the evolution of connectedness for borrowers, and document the same
deleterious impact. Borrowers afflicted by debt crises do not attain pre-crisis connectedness
levels within the following five years. When incorporating 2007‒08 crises into the sample andrestricting the window to ‒5/+1 years around the episode (Figure 15), all connectedness
measures for lenders sharply decline one year after the crisis, reflecting the unusually large
impact of the 2008–09 episode on the GBN.
We formalize the analysis by estimating a simple panel specification that traces the evolution of
network measures around crisis dates. Specifically we regress country-level indicators of
network centrality (degree, strength, and relative strength) on a set of dummies for crisis andpost-crisis years while controlling for country fixed effects. The results (Table 6) indicate thatbanking and debt crises are associated with reduced borrower access to capital markets asmeasured by network degree (number of links) and strength (total flows). Cross-border inflows
to the afflicted country decrease by $24 million at the onset of the crisis, and $30 million on
average over the subsequent five years (columns 1‒3). On the lending side, the F tests of joint
significance of coefficient estimates on the lags cannot reject a nil effect of banking crises onlender connectedness (columns 4‒6), which may be explained by the lack of variation in thesample. The negative effects of sovereign debt crises are also evident for borrowers (columns
7‒9), but are smaller in magnitude. These results complement the literature on access to capital
markets in the aftermath of debt crises (e.g., Ozler, 1993; Arteta and Hale, 2008; Fuentes andSaravia, 2009). Furthermore, by looking beyond lending volumes, they represent a deeper
characterization of access to global financial markets in the aftermath of crises.

29
30

See Laeven and Valencia (2008, 2010) for definitions.
The analysis of network measures’ capacity to act as leading indicators of crises is left for future research.

18
VI. CONCLUSIONSThe potential usefulness of network techniques in analyzing systemic risk has taken center-stagein academic and policy debates in the aftermath of the 2008‒09 global crisis. Nevertheless, little
is known about the structural properties and time-evolution of the network of cross-country
financial linkages, which are key to understanding how the global financial system reacts toshocks, and whether and where systemic risk may emerge. In this paper we have analyzed theproperties of the global cross-border bank lending market (for 184 countries over 1978–2009)
using network metrics of interconnectedness such as centrality, connectivity, and clustering. We
have also sought to determine how geographical linkages changes around financial crises.
We found that the global banking network is relatively unstable; we have identified structural
breaks in network connectivity and centrality, and documented volatile interconnectedness
rankings for countries, especially borrowers. In the 2002–08 wave of global capital flows, theBRIC countries and high-growth Europe emerge and consolidate their position as highlyinterconnected borrowers. Network density expands and contracts, following the cycle of capital
flows. Furthermore, country centrality falls at the onset and in the aftermath of banking andsovereign debt crises. While the global banking network appeared more stable in the second halfof the 2000s than in earlier periods, the 2008–09 global financial crisis stands out as an unusuallylarge perturbation.
A number of questions emerge from our analysis. While it has been established that cross-borderbank lending is a key channel of transmission of financial crises, how the topology of financial
networks relate to the emergence of systemic risk remains underexplored. How do the propertiesof the different networks–– banking, FDI, trade, and remittances––compare and how docountries’ degrees of connectedness interact in different webs of relationships? What is theempirical relationship between connectedness and the way in which shocks get amplified ordiffused? These and related questions remain interesting avenues to explore in future work.

19
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23
APPENDIX
Figure 1. Cross-border financial flows to advanced economies
Advanced economies

5000(2009 US$, bn.)
4000

3000

2000

Total bank lending, net
Portfolio investment, net
Direct investment, net

1000

0

-1000

‐2000
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Figure 2. Cross-border financial flows to emerging markets and developing economies
500

Emerging markets and developing economies(2009 US$, bn.)

400

300

Total bank lending, net
Portfolio investment, net
Direct investment, net

200

100

0

-100

-200
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Source: Authors’ calculations based on World Economic Outlook (December 2010) and BIS locational banking
statistics.
Note: Total bank lending flows (net) are from BIS reporting countries (with with complete data over 1978Q4–
2009Q3) and are calculated by summing up flows across borrowers in each country group. Note that there is somedegree of overlap between bank lending flows and portfolio investment, with cross-border investments by banks indebt securities showing up in both variables.

Figure 3. Network view of cross-border banking, 1980 and 2007
Panel A. Core-periphery
1980

2007

25
Panel B. Core-core
1980

2007

Source: Authors’ calculations using BIS locational banking statistics (yearly).
Note: The countries represent nodes, while the links between countries represent cross-border bank loans. Thicker and darker colored links indicate larger flows.
In Panel B arrows indicate the direction of the flows. When bi-directional flows occur, the connecting links is split into two, each half-link reflecting themagnitude of one flow (hence may have a different color).

Table 1. Summary statistics of network indicators
Obs.

Mean

St. Dev.

Min

Max

Measures of country centrality
Full network
In-degree
20,903In-strength
20,903
Relative in-strength
20,903

4.4
26.7
0.1

3.2
122
0.3

0
0
0

15
5907
5

Out-degree
Out-strength
Relative out-strength

1,904
1,904
1,904

48.1
293
9.9

17.4
446
8.4

0
0
0

149
6646
55

Core-periphery
In-degree
In-strength
Relative in-strength

20,903
20,903
20,903

3.7
7.3
0.1

3.2
30.9
0.3

0
0
0

15
859
4

Out-degree
Out-strength
Relative out-strength

1,904
1,904
1,904

40.3
80.5
8.9

16.7
122.5
7.7

0
0
0

135
1156
51

Core-core
Degree
1,905
7.8
2.5
0
Strength
1,905
212.2
355.2
0
Relative strength
1,905
1.0
0.9
0
Measures of network density
Full network
Connectivity
31
0.33
0.05
0.26
Binary clustering
31
0.11
0.05
0.04
Weighted clustering
31
0.37
0.06
0.25
Core-periphery
Connectivity
31
0.36
0.05
0.28
Binary clustering
31
0.11
0.05
0.04
Weighted clustering
31
0.27
0.05
0.19
Core-core
Connectivity
31
0.66
0.08
0.46
Binary clustering
31
0.13
0.07
0.01
Weighted clustering
31
0.57
0.12
0.23
Source: Authors’ calculations using BIS locational banking statistics (quarterly).
Notes: Node strength is expressed in 2009 US$ million.

14
6012
6

0.47
0.29
0.49
0.51
0.28
0.38
0.79
0.27
0.81

27
Figure 4. Trends in network indicators, 1978–2009
Average node degree
70

Average node strength12

out-degree60

in-degree (right-axis)
10250

Poly. (out-degree)
50

50

300

out-strength
in-strength (right-axis)
Poly. (out-strength)
Poly. (in-strength (right-axis))

Poly. (in-degree (right-axis))
8

45
40
35

200
30

40
6

150

4

100

25

30

20
20

15

2

10

10
50
5

0

0

1978q1 1981q1 1984q1 1987q1 1990q1 1993q1 1996q1 1999q1 2002q1 2005q1 2008q1

Network connectivity

Network clustering
0.5

1.0

Core-core
0.9

0

0

1978q1 1981q1 1984q1 1987q1 1990q1 1993q1 1996q1 1999q1 2002q1 2005q1 2008q1

Core-periphery

0.5

Full
0.8

0.4

0.7

0.4

0.6

0.3

0.5

0.3

0.4

0.2

0.3

0.2

0.2

0.1

0.1

0.1

0.0

Binary (core-periphery)
Weighted (core-periphery)
Binary (core-core)

0.0
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Source: Authors’ calculations using BIS locational banking statistics (quarterly and annual).
Notes: Results are shown for the core-periphery network. In the upper panels the smooth curves are nonparametriclocal polynomial smoothed estimates. In all panels we superimpose the dates of the two global waves of capital
flows discussed in the text: 1987–98 and 2002–08 (as dated in IMF, 2007).

28
Figure 5. Empirical distributions of network indicators
in-degree
0 .02 .04 .06 .08 .1

0 .005 .01 .015 .02

out-degree

0

50

100

1980

150

0

5

2007

10

1980

20

2007

relative in-strength

0

0

20

40

60

80

.02 .04 .06 .08

relative out-strength

15

0

10
1980

20

30
2007

40

0

.1

.2
1980

.3

.4

2007

Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes: Results are shown for the core-periphery network. The kernel density estimates use the Epanechnikov kernel
and optimal bandwidth (Silverman, 1986).

29
Table 2. Kolmogorov-Smirnov tests
Lenders

Borrowers

Out-degree

In-degree
1980
0
0
25

1990

1990

1980s
1990s
2000s

1980
0
22
63

Relativeout-strength
1980s
1990s
2000s

1980
0
0
38

1990

1980s
1990s
2000s

11
50

2000
1980s
1990s
38 2000s

Out-strength

1980
11
100
100

1990

2000

0
75

0

1980
56
100
63

1990

2000

22
50

50

1980
11
100
100

1990

2000

0
75

0

In-strength

0
50

0
0

2000
1980s
1990s
25 2000s
Relative2000 out-strength
1980s
1990s
0 2000s

Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes: The table reports the proportion of years that display “statistically different” empirical distribution of the
network indicators in each decade compared to the year indicated as column head. For instance, the figure 25 in theout-degree panel says that in 25 percent of years (i.e., 2 years) through the 2000s, the empirical distribution of outdegree was different than that in 1980. The statistical closeness of empirical distributions is assessed using
Kolmogorov-Smirnov tests at the 5 percent level of significance.

30
Table 3. Country rankings by degree of interconnectedness
Panel A

1995

2007

UK

Switzerland

Switzerland

FranceGermany

Belgium

Netherlands

1980
Out-degree

out-strength

Borrowers
1980

1995

Argentina

Indonesia

BrazilUK

Venezuela

China

China

FranceEgypt

ThailandRussian Fed.

In-degree

2007

US

FranceGermany

Chile

Philippines

India

Luxembourg

UK

BelgiumBrazil

India

PolandGermany

Luxembourg

Luxembourg

Colombia

Malaysia

Chile

Austria

Belgium

Netherlands

MexicoIran

UkraineNetherlands

Austria

Denmark

Ecuador

Chile

LatviaCanadaUS

Austria

Nigeria

Argentina

Lithuania

Italy

Italy

Japan

Syria

Pakistan

PanamaOut-strength UK

Relative

Panel B

Lenders

Japan

UK

US

UK

FranceMexicoThailandRussian Fed.

BrazilBrazilChina

FranceUS

Japan

Germany

US

Argentina

Indonesia

BrazilJapan

Venezuela

PanamaPolandIn-strength

Belgium

FranceGermany

Chile

China

India

Luxembourg

Luxembourg

Austria

RomaniaSouth Africa

TurkeyGermany

Netherlands

Netherlands

Philippines

TurkeyRomania

CanadaBelgium

Belgium

PanamaChile

UkraineNetherlands

Austria

Luxembourg

PolandArgentina

PanamaAustria

Italy

Switzerland

Egypt

India

MexicoUK

UK

UK

Relative

MexicoThailandRussian Fed.

in-strength

FranceFranceFranceBrazilBrazilRomaniaUS

Germany

Germany

Argentina

Indonesia

PolandBelgium

US

Switzerland

PolandChina

BrazilGermany

Switzerland

US

Venezuela

Russian Fed. China

Luxembourg

Japan

Austria

Romania

Argentina

Netherlands

Netherlands

Japan

PanamaTurkeyUkraineJapan

Belgium

Netherlands

Nigeria

Chile

Lithuania

TurkeyAustria

Austria

Belgium

AlgeriaSouth Africa

India

CanadaLuxembourg

Canada

Chile

Iran

LatviaSource: Authors’ calculations using BIS locational banking statistics (annual).
Notes: Panel A refers to our sample of BIS reporting countries only. Panel B refers to non BIS-reporting, middleincome countries.

31
Figure 6. Ranking stability indices
Lenders

Borrowers

1.0

1.0

0.9
0.8

0.8
0.6

0.7
0.6

0.4

0.5
0.2

0.4
0.3

0.0

0.2
Out-degree
Out-strength
Relative out-strength

0.1

In-degree-0.2

In-strength
Relative in-strength
-0.4

0.0

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes. The results are based on the sub-sample of borrowers present throughout the sample period.

Figure 7. Average rankings for the BRICs and emerging Europe
The BRICs
80

Emerging Europe
50

In-degree70

In-strength
Relative in-strength

45
40

60

35
50

30

40

25
20

30

15
20

10
10

5

0

In-degree
In-strength
Relative in-strength01995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes. The countries grouped under ‘emerging Europe’ comprise Latvia, Lithuania, Poland, Romania, and Ukraine.
In both panels we superimpose the dates of the second global wave of capital flows discussed in the text: 2002–08.

32
Figure 8. Regional heterogeneity: degree, strength, and clustering
Average in-degree

Average in-strength
250

12
EAP

ECA

LAC

MENA

SA

800

2009 US$ million

SSA

EAP
700

LAC
10

MENA

200

SA

600

SSA
8

ECA (right-axis)

500

150

400

6
100

300

4
200
50

2
100

0

0

0
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Binary clustering

Total flows

0.25

500

EAP

ECA

LAC

MENA

SSA

SA

0.20

450
400

0.15

0.10

0.05

0.00
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 20082009 US$ billion

5000

EAP
LAC
MENA
SA
SSA
ECA (right-axis)

4500
4000

350

3500

300

3000

250

2500

200

2000

150

1500

100

1000

50

500

0

0
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes: Results for North America/Oceania are omitted from all charts for visual tractability. In the bottom rightpanel, the figures for 2009 are upto Q3 inclusive.

33
Table 4. Unit root tests for empirical moments of network indicators
2 breaks

1 breakp-value

Out-degree
In-degree
Out-strength
In-strength

2001
2003
2002
2003

0.001
0.044
0.000
0.000

1994
1980
1993
1980

2003
2003
2002
2003

Connectivity
Binary clustering
Weighted clustering

2003
2003
2004

0.034
0.016
0.142

1996
1996
1996

2003
2003
2002

p-value p-value
first
second
break
break
0.002
0.006
0.042
0.003
0.017
0.000
0.411
0.000
0.679
0.679
0.121

0.010
0.010
0.031

Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes: The results pertain to the core-periphery network. Years in boldface identify the structural breaks that are
statistically significant at the 5 percent level.

Table 5. Empirical M-statistics
First window: 1987‒98
Node degree
Node strength
Relative node strength
Second window: 2002‒08
Node degree

 0

 1

0.2134(0.0872, 0.1185)
0.3583(0.0836, 0.1159)
0.3539(0.0845,0.1159)

0.5335(0.2534, 0.2991)
0.7204(0.2561, 0.3065)
0.7460(0.2569,0.3026)

 0

 1

0.2536
0.5996(0.0774, 0.1177)(0.2496, 0.3150)
Node strength
0.43630.8270(0.0780, 0.1200)(0.2486, 0.3117)
Relative node strength
0.45560.8449(0.0780, 0.1223)(0.2478, 0.3117)
Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes: 95 percent confidence intervals for the M statistics for a “random” network are shown in parentheses (seetext for explanations).

34
Figure 9. Stochastic kernel density estimatesFirst window: 1987‒98

Second window: 2002‒08
Panel A. In-degree
15

14

0.03

0.03

0.025

0.025

12

10
0.02

8
6

0.015

4

0.01

0.02
Year t+1

Year t+1

10

0.015
5
0.01

2
0.005

0.005

0
0

5

0

10

0

Year t5

10

15Year tPanel B. Relative in-strength
3

0.22

3

0.22

0.2
2.5

0.2

2.5

0.18

0.18

0.16
0.14
0.12
1.5

0.1
0.08

1

0.06
0.04

0.5

0.16

2
Year t+1

Year t+1

2

0.14
0.12

1.5

0.1
1

0.08
0.06

0.5

0.04

0.02
0

0

0.5

1

1.5
Year t

2

2.5

3

0.02
0

0

0.5

1

1.5
Year t2

2.5

Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes: The results are based on the sub-sample of countries present through the sample period.

3

35
Figure 10. Ergodic distributions for node degree
First window

Second window

0.018

0.018
1992
ergodic

0.016
0.014

0.014

0.012

0.012

0.01

0.01

0.008

0.008

0.006

0.006

0.004

0.004

0.002

0.002

0
-2

0

2

4

6

8

10

12

14

2005
ergodic

0.016

16

0
-2

0

2

4

6

8

10

12

14

16

Source: Authors’ calculations using BIS locational banking statistics (annual).
Note: The ergodic distributions are computed based on transitional dynamics in each window, after restricting thesample to the countries present in the dataset throughout the full period. The base year is 1992 for the first window
and 2005 for the second window.

Figure 11. Global banking network before and during the 2008-09 crisis
Panel A. Core-periphery
2007Q4

2008Q4

37

Panel B. Core-core
2007Q4

2008Q4

Source: Authors’ calculations using BIS locational banking statistics (quarterly).
Notes: The countries represent nodes, while the links between countries represent cross-border bank loans. Thicker and darker colored links indicate larger
flows. In Panel B arrows indicate the direction of the flows. When bidirectional flows occur, the connecting links is split into two, each half-link reflecting themagnitude of one flow (hence may have a different color).

Figure 12. Network density during financial crises
Connectivity

Binary clustering
1.0

1.0

Crises

Crises0.9Core-periphery

0.9Core-periphery

Core-core
0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

Core-core

0.2

0.2

0.1

0.1

0.0

0.0

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes: Highlighted areas represent synchronized recession dates (IMF, 2007), namely the 1987Q3 stock market
crash, 1991–92 Scandinavian banking crises, 1992 ERM crisis, 1998Q3 LTCM near-collapse, 2000Q2 Internet
bubble collapse, and 2008Q3 Lehman Brothers bankruptcy.

Figure 13. Regional clustering for LAC and EAP regions during financial crises
Panel A. LAC region

Panel B. EAP region

1.0

1.0

Crises0.9

Crises0.9

Clustering
0.8

Clustering
0.8

Weighted clustering

Weighted clustering

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0.0

0.0

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

Source: Authors’ calculations using BIS locational banking statistics (annual).
Notes: For LAC we superimpose dates for the onset of systemic banking crises for Argentina, Brazil, andMexico. For EAP we superimpose dates for the onset of systemic banking crises for Japan, Indonesia, Korea(Republic of), Malaysia, Philippines, Thailand, Vietnam, and Mongolia. Crisis dates have been taken from
Laeven and Valencia (2010).

39
Figure 14. Interconnectedness before and after financial crises
A. Systemic banking crises

Sovereign debt crises
In-degree

-5

-4

-3

-2

-1
0
1
Time 0=crisis
In-Degree

2

3

4

5

0

0

2

2

Average
4
6

Average
4
6

8

8

10

10

Degree

-5

-4

-3

-2

Out-Degree-101Time 0=crisis

2

3

4

5

2

3

4

5

In-strength

0

0

10

10

Average
20
30

Average
20
30

40

40

50

50

Strength

-5

-4

-3

-2

-1
0
1
Time 0=crisis
In-Strength

2

3

4

5

-5

-4

-3

-2

-1
0
1
Time 0=crisis

Out-Strength

Source: Authors’ calculations using BIS locational banking statistics (annual) and Laeven and Valencia (2010)
database for systemic banking and sovereign debt crises dates.
Notes: Results are based on the full network. The left panels include systemic banking and sovereign debt crisesthat occurred between 1985 and 2003 and are at least 10 years apart, which allows for a 5-year non-overlapping
window around them. (Countries with two crises within 10 years are dropped, but the results are robust to
retaining either crisis.)

40
Figure 15. Interconnectedness before and after systemic banking crises (including 2007‒08
episodes)–5/+1 years around crises
Strength

2

50

4

100

Average
150
200

Average
6
8

250

10

300

12

Degree

-5

-4

-3

-2

-1
0
1
Time 0=crisis
In-Degree

2

3

4

5

-5

-4

-3

Out-Degree

-2

-1
0
1
Time 0=crisis
In-Strength2

Out-Strength

Source: Authors’ calculations using BIS locational banking statistics (annual) and Laeven and Valencia (2008)
database for systemic banking crises.
Notes: Results are based on the full network. Data for 2009 is upto Q3 inclusive.

Table 6. Interconnectedness during financial crises: Regression estimates
Systemic banking crises[1]

Year of crisis
1 year later2 years later
3 years later
4 years later5 years later
F test joint significance:
test statistic and p-value
Obs.
Number of countries
R-squared[4]

Sovereign debt crises

[2]

[3]

[5]

In-degree

In-strength

Relative instrength-0.75***(0.26)-1.66***(0.26)-1.19***(0.25)-0.86***(0.22)-0.67***(0.22)-0.45*(0.25)-24.25*(12.88)-26.91**(12.37)-39.32**(15.54)-36.23**(17.97)-27.29**(10.78)-21.84*(13.26)-0.02*(0.01)-0.02***(0.01)-0.03***(0.01)-0.02**(0.01)-0.00(0.01)-0.01***(0.00)-0.11(0.10)-0.16(0.21)-0.32*(0.19)-0.32*(0.19)-0.03(0.18)
0.12(0.17)

3.38(12.47)-12.95(8.33)-27.80*(16.42)-15.39(12.13)-20.64(12.87)-22.43*(13.57)

14.86
0.00
4398
86
0.63

3.11
0.01
4398
86
0.49

4.13
0.00
4398
86
0.79

1.36
0.24
4398
86
0.94

1.43
0.21
4398
86
0.54

[6]

[8]

[9]

In-degree

In-strength

Relative instrength-0.02(0.06)
0.01(0.04)-0.12(0.07)-0.08(0.06)-0.04(0.07)
0.00(0.06)-0.23(0.45)-0.90**(0.37)-1.36***(0.33)-0.81**(0.34)-0.68*(0.37)-0.67*(0.38)-5.22(3.78)-4.72(4.15)-9.30***(3.50)-7.97***(2.87)-7.84***(2.78)-5.45***(1.89)

0.02*(0.01)
0.01*(0.01)-0.00(0.00)-0.01*(0.00)-0.00(0.00)
0.01(0.01)

0.96
0.44
4398
86
0.90

5.82
0.00
4398
41
0.63

3.30
0.01
4398
41
0.49

0.92
0.47
4398
41
0.79

Relative outOut-degree Out-strength
strength[7]3Source: Authors’ estimations using BIS locational banking statistics (annual).
Notes: Robust standard errors in parentheses (*** p<0.01; ** p<0.05; * p<0.1). The F-test of joint significanceis for all coefficient estimates on lagged variables.

4

5

41
List of countries used in the analysis
Core (Sample of BIS
reporting countries)
Austria
Belgium
CanadaDenmark
FranceGermany
Ireland
Italy
Japan
Luxembourg
Netherlands
SwedenSwitzerland
United Kingdom
United States

1/

Periphery (Other countries)2/

Afghanistan
Czech Republic
KuwaitParaguay
TurkeyAlbania
Côte d’Ivoire
Kyrgyz Republic
Peru
Turkmenistan
Algeria
Djibouti
Laos
Philippines
UgandaAngolaDominica
LatviaPolandUkraineArgentina
Dominican Republic Lebanon
Portugal
United Arab Emirates
Armenia
EcuadorLesotho
Qatar
Uruguay
Aruba
Egypt
Liberia
Romania
Uzbekistan
Australia
El Salvador
Libya
RussiaVanuatu
Azerbaijan
Equatorial Guinea
Lithuania
RwandaVenezuela
BahamasEritrea
Macau SAR
Samoa
VietnamBahrainEstonia
Macedonia, FYR
Sao Tomé and PrincipYemen
Bangladesh
Ethiopia
Madagascar
Saudi Arabia
ZambiaBarbados
Fiji
MalawiSenegalZimbabwe
BelarusFinland
Malaysia
SerbiaBelizeGabon
Maldives
Seychelles
Benin
GambiaMali
Sierra Leone
BhutanGeorgia
Malta
Singapore
Bolivia
Ghana
Mauritania
Slovakia
Bosnia and Herzegovina Greece
Mauritius
Slovenia
Botswana
GrenadaMexicoSolomon Islands
BrazilGuatemalaMicronesia
Somalia
BruneiGuineaMoldova
South Africa
Bulgaria
Guinea-Bissau
Mongolia
South Korea
Burkina Faso
GuyanaMontenegro
Spain
BurundiHaiti
MoroccoSri Lanka
Cambodia
Honduras
Mozambique
St. Lucia
Cameroon
Hong Kong, SAR
MyanmarSt. Vincent
Cape Verde
Hungary
Namibia
Sudan
Central African Republic Iceland
Nepal
Surinam
Chad
India
Netherlands Antilles Swaziland
Chile
Indonesia
New Zealand
Syria
China
Iran
Nicaragua
Tajikistan
Colombia
Iraq
Niger
Tanzania
Comoros Islands
IsraelNigeria
ThailandCongo, Rep.
Jamaica
NorwayTimor Leste
Congo, DRC
JordanOman
Togo
Costa Rica
Kazakhstan
Pakistan
Tonga
Croatia
Kenya
PanamaTrinidad and Tobago
CyprusKiribati
Papua New Guinea Tunisia

1/ Includes BIS reporting countries with complete data from 1978Q4 onwards.
2/ Includes all countries for which the sample BIS reporting countries provide data.