Global spillover analysis focused on the Nordics region.

1. NORDIC LINKAGES
EUROPEAN REGIONAL CLUSTER REPORT
BENJAMIN HUSTON, LUIS BRANDAO-MARQUES, MARCO PINON
This presentation and the preliminary results are intended as background for discussions.

2. OUTLINE
 Motivation
 Method
 Balance sheet or market data? Example of Nordic interbank exposures
 Cross-border spillovers (by recipient country and channel)
 Systemic importance of spillover channels
 Inward and outward spillovers to and from the Nordics
 Banking and insurance connectivity
 Sovereign-to-financial and financial-to-nonfinancial
 Domestic spillovers

3. MAIN RESULTS
 Among the Nordics, Sweden and Finland are the most connected and Iceland is the
least connected.
 Strong Nordic connections with North America and Developed Europe.
 Nordic sovereign-to-sovereign, financial-to-financial, and financial-to-nonfinancial
sector connectivity is highly significant.
 Sovereign-to-financial sector connectivity is only moderate: Nordic sovereigns do not
play a systemic role.
 Nordic financial sectors are the most systemically important segments of the
regional Nordic economy, and [REDACTED].

4. MOTIVATION

5. MOTIVATION
 Recent Norway FSAP and coming Sweden and Finland FSAP.
 Nordic regional report (2013).
 Unique confidential dataset of bank-to-bank exposures for the Nordic region
(Denmark, Finland, Norway, and Sweden).
 Denmark, Finland, Norway, and Sweden are all S29 jurisdictions—important to check
how connected they are.

6. METHOD

7. METHOD
 Measure at the firm, sector, and country level: Equity returns;Volatility of equity
returns (in logs) but only for robustness.
 Spillover Analysis (Diebold andYilmaz 2014).
 Order independent: generalized FEVD; order rotation for robustness.
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8. METHOD
 Spillover from i to j is the percent of j’s total inward spillovers that are coming from
i:
 Inward and outward spillover indexes calculated using rolling window (3 years).
 Centrality measure—eigenvalue centrality (see Annex):
 Measures the relative importance or influence of a node within a graph.
 Node i’s score is the i-th entry of the eigenvector associated with highest eigenvalue in the
network’s weighted adjacency matrix (aka spillovers matrix).
 Intuition: a node that is connected to another node with many large connections is more important
than a node that is connected to another node with few small connections.
.
i
ijijij dds

9. BALANCE SHEET OR MARKET DATA?

10. EXAMPLE—NORWAY INTERBANK EXPOSURES
Market data suggests that Swedish banks play the
dominant role in transmitting risks in Norway’s banking
sector.
Note: See Norway stressing testing technical note for additional information.
[REDACTED]

11. NORDIC LINKAGES USING MARKET DATA
Nordic to Nordic channels
Nordic financial sectors are the most systemically
important segments of the regional Nordic
economy
Nordic sovereigns, and Iceland in general, do not
appear to play a significant role in transmitting
systemic risk to the rest of the region
Market data-based analysis suggests links:
• Bank-to-bank lending
• Common exposures
• Cross-ownership links
Note: The figure displays a centrality plot of the Nordic to Nordic spillover network. Node positions are determined using eigenvector centrality scores and a node’s proximity to the center of the network signifies its importance (i.e., closer nodes
are more important). The symbols , , correspond to high, medium, and low levels of systemic risk, respectively, and ring colors are used for solely for visual emphasize. See Diestel (2005) for background information on the graph-theoretic
concept of centrality.
= financial sector
= nonfinancial sector
= sovereign
System importance of spillover channels

12. NORDIC-TO-NORDIC INTERBANK EXPOSURES
[REDACTED]

13. MAKING SENSE OF THE RESULTS: NORDIC BANK LENDING TO OTHER
COUNTRIES
Significant cross-
border lending by
Nordic financial
groups.
Note: See Norway stressing testing technical note for additional information.

14. MAKING SENSE OF THE RESULTS: NORDIC BANK-TO-BANK OWNERSHIP
LINKAGES
60%
16%
7%
3%
2%
2%
2%
8%
Norway
United States
United Kingdom
China
Other Nordic
Saudi Arabia
Canada
Other
DNB Ownership by Country
(Percent of total capital as of end of 2014)
31%
24%6%
5%
5%
2%
2%
4%
22%Sweden
Finland
United States
Norway
Denmark
Saudi Arabia
United Kingdom
Other
Unknown
Nordea AB Ownership by Country
(Percent of total capital as of end of 2014)
48%
13%
6%
5%
4%
2%
4%
18%
Denmark
Sweden
UK
Cayman Isl.
United States
Norway
Other
Unknown
Danske Bank Ownership by Country
(Percent of total capital as of end of 2014)
26%
15%
3%
2%2%2%7%
43%
Finland
United States
Sweden
Norway
Germany
United Kingdom
Other
Unknown
Sampo Plc Ownership by Country
(Percent ot total capital as of end of 2014)
Significant cross-
ownership links
among Nordic
financial groups.

15. CROSS-BORDER SPILLOVERS

16. DECOMPOSITION OF SPILLOVERSTOTHE NORDICS
Equity and commodity prices are the major transmission channels of spillovers to the Nordics
Note: See Klößner and Wagner (2014b) for an assessment of US economic policy uncertainty on cross-border spillovers.

17. SPILLOVER CHANNEL DYNAMICS
• The largest share of
spillovers to the
Nordics has historically
come from developed
economies.
• Recently, spillovers
from emerging
economies have
become more
important.
Left: Spillover time-series were calculated using 3-year rolling windows. Right: colors denote low (white), moderate (pink), and high (red) levels of spillover connectivity. Connectivity is a bilateral measure which is defined as
the sum of total spillovers shared between two given sectors (Diebold, Yilmaz; 2014).

18. TOP SPILLOVERSTOTHE NORDICS*
Non-Nordic to Nordic
Global Factor to NordicNordic to Nordic
Sweden, Finland, and
Denmark are responsible
for the largest share of
Nordic-to-Nordic
spillovers
Norway is the Nordic
most affected by global
factors, such as commodity
prices and the USD FX
rate
The largest spillovers to
the Nordics originate in
Developed Europe,
followed by Emerging
Europe and North America
* Spillovers were estimated using daily market data which spanned the period of January 2002 to August 2015. In the figures above, arrow size corresponds to a rank ordering of spillover magnitude. The top, middle, and bottom third of
spillovers within a given figure are denoted by large, medium, and small-sized arrows, respectively. Numbers displayed next to arrows denote the percentage of given recipient ‘s total spillovers that is attributable to a given spillover
originator. For further information on spillovers from a network perspective, see Diebold and Yilmaz (2014).

19. TOP SPILLOVERSTOTHE NORDICS, BY RECIPIENT COUNTRY
FinlandSweden
Sweden, Finland, and Denmark have the greatest exposure to many of the same spillovers:
global real estate, hedge funds, Developed Europe, and each other.
Denmark

20. TOP SPILLOVERSTOTHE NORDICS, BY RECIPIENT COUNTRY
IcelandNorway
Norway and Iceland are the least exposed to other Nordics. Both countries share large
common exposures to Latin America and global real estate.

21. MAKING SENSE OFTHE RESULTS: NORWAY GOVERNMENT PENSION FUND
GLOBAL – COUNTRY/CURRENCY EXPOSURES BY ASSET CLASS

22. MAKING SENSE OF THE RESULTS: STATOIL COUNTRY EXPOSURES

23. TOP SPILLOVERSTOTHE NORDICS, BY CHANNEL
Energy pricesEmerging market
asset prices
Developed market
asset prices
Among developed economies,
Developed Europe is the source
of the largest inward spillovers to
the Nordics
Among emerging economies,
spillovers from Emerging Europe
are the greatest in magnitude
The influence of energy prices on
Norway alone is greater than the
cumulative influence of energy prices
on all other Nordics

24. TOP SPILLOVERSTOTHE NORDICS, BY CHANNEL
USD FX appreciation Global real estate Active asset management
Changes in the USD FX rate have
had the greatest effect on Norway
Iceland is the country most affected
by global real estate prices, followed
by Sweden and Finland
The trading activities of hedge
funds have had the largest impact
on Norway and Denmark

25. SUMMARY OF NORDIC EXPOSURETO INWARD SPILLOVERS
Emerging Economies
Sweden
Finland
Denmark
NorwayIceland
1 2
3
4
5
2
1
5
4
3
1
2
3
2
1 3
4
Global Factors
Advanced Economies
Other Nordics
Among the Nordics,
Sweden and Finland are the
most exposed to spillovers.
Iceland is the least
exposed.
Sweden’s greatest
exposures are to emerging
economies and to other
Nordics
Finland’s greatest
vulnerabilities are to
developed and emerging
economies
Note: Greater graph area corresponds to greater inward spillover vulnerability. The numbers 1 through 5 are rank orderings and illustrate a given country’s vulnerability to a given spillover source, where ranks of ‘1’ and ‘5’
denote the highest and lowest levels of vulnerability, respectively. Some rank ordering labels are omitted to enhance figure readability.
Norway has the greatest
exposure to global factors
and one of the smallest to
the Nordic region.

26. TOP SPILLOVERS FROMTHE NORDICS
Nordic to Non-Nordic Nordic to Global Factors
Spillovers originating in the Nordics have the greatest
impact on Developed Europe
The Nordics also exert a notable influence on energy
and metal commodity prices

27. SYSTEM IMPORTANCE OF SPILLOVER CHANNELS
Cross-border channels
Sweden and Norway are the most systemically
important Nordic countries.
Developed Europe and North America are the most
systemically important transmitters of spillovers in the
developed world, and Emerging Europe and the Middle
East and Africa are the most systemic transmitters
among emerging market economies.
The USD FX rate, global real estate, and energy and
metal commodities crisis are the most systemically
important global factors.
Equities universally dominant bonds in terms of systemic
risk transmission.
E = energy
M = metals
G = gold
F = food
HF = hedge funds
RE = real estate
USD = US FX rate
a = Asia
e = Europe
la = Latin America
ma = Middle East and Africa
na = North America
= equities
= bonds
= global factors
= Nordics
= developed economy
= emerging economy
= global factor

28. BANKING AND INSURANCE CONNECTIVITY
Bank to bank connectivity Insurer to insurer connectivity
Nordic banks are highly connected with each other and with
banks in North America and Developed Europe
With the exception of Sweden, the connectivity of
Nordic insurers is analogous to that of Nordic banks
Note: colors denote low (white), moderate (pink), and high (red) levels of spillover connectivity. Connectivity is a bilateral measure which is defined as the sum of total spillovers shared between two given sectors (Diebold, Yilmaz; 2014).

29. Financial to nonfinancial sector connectivitySovereign to financial sector connectivity
NORDIC-TO-NORDIC CONNECTIVITY
Nordic sovereign-to-sovereign and financial-to-financial sector
connectivity is significant, but sovereign-to-financial sector
connectivity is present only at moderate levels
Levels of Nordic financial-to-nonfinancial connectivity
are also highly significant
Left: red lines denote sovereign-to-sovereign connections. Right: green lines denote nonfinancial-to-nonfinancial sector connections. Both: blue line denote financial-to-financial sector connections and black lines denote connections
between different types of sectors. Connectivity is a bilateral measure which is defined as the sum of total spillovers shared between two given sectors (Diebold, Yilmaz; 2014).

30. DOMESTIC CROSS-SECTOR SPILLOVERS

31. DOMESTIC CONNECTIVITY: SWEDEN AND FINLAND
Note: colors denote low (white), moderate (pink), and high (red) levels of spillover connectivity. Connectivity is a bilateral measure which is defined as the sum of total spillovers shared between two given sectors (Diebold, Yilmaz; 2014).
Sweden exhibits high levels of domestic connectivity
between banks, shadow banks, and major nonfinancial
sectors, but banking and insurance connectivity is low.
Bank-to-sovereign connectivity is low.
Finland shows high connectivity between banks and
insurers and moderate bank-to-sovereign connectivity.
Finnish nonfinancial sectors are highly connected.
Sweden Finland

32. DOMESTIC CONNECTIVITY: DENMARK AND NORWAY
Similar to Finland, Denmark exhibits high bank-to-insurer
and moderate bank-to-sovereign connectivity. Unlike
other Nordics, Denmark insurers are also highly
connected to domestic nonfinancial sectors.
Norway also exhibits high bank-to-insurer and moderate
bank-to-sovereign connectivity. Norwegian non-financial
sectors, in particular those engaged in the export of energy,
industrial, and food commodities, are highly connected.
Denmark Norway

33. TAKEAWAYS
 Nordic countries are systemically important as a whole.
 For the IMF: Nordic-centric surveillance makes sense.
 For authorities: Cross-border surveillance and regional cooperation
(including stress testing) is important but not enough.
 For the IMF and country authorities:When credit exposure data are not
available, market data may be good substitutes.

34. REFERENCES
Csardi G, and NepuszT, 2006,“The igraph software package for complex network research.” InterJournal, Complex
Systems 1695, http://igraph.org.
Diebold, Francis X., and KamilYılmaz, 2014, "On the network topology of variance decompositions: Measuring the
connectedness of financial firms." Journal of Econometrics 182, no. 1: 119-134.
Diestel, Reinhard, 2005, GraphTheory (3rd ed.), Berlin, NewYork: Springer-Verlag, ISBN 978-3-540-26183-4.
Klößner, S. andWagner, S., 2014b,“International spillovers of policy uncertainty.” Economics Letters 124, no. 3: 508–512.
Pesaran, H. Hashem, andYongcheol Shin, 1998, "Generalized impulse response analysis in linear multivariate
models." Economics Letters 58, no. 1 (1998): 17-29.

35. ANNEX

36. NETWORK METHODOLOGY
1
The value of is not necessarily unique and is numerically estimated using the Power Iteration algorithm.
Let be a network with associated sets of vertices and edges and . The eigenvector
centrality score, , of a given vertex ∊ is defined as
1
,
∊
where
 is a vertex which shares an edge with ,
 , is the entry in , the adjacency matrix of , which corresponds to the vertices and ,
 and the eigenvalue is a constant which is satisfies the relationship .1
Higher eigenvector centrality scores signify greater network importance. When is a financial
network, eigenvector centrality is interpreted as a proxy for systemic risk.

37. DATA DESCRIPTION: CROSS-BORDER SPILLOVERS
Alias Financial Index Name Spillover Group
IS OMX Iceland All Share Nordics
SE Datastream Sweden Equities Nordics
DK Datastream Denmark Equities Nordics
FI Datastream Finland Equities Nordics
NO Datastream Norway Equities Nordics
Emerging Europe MSCI Emerging Markets Europe Emerging Economies
Latin America MSCI Emerging Markets Latin America Emerging Economies
Emerging Asia MSCI Emerging Markets Asia Emerging Economies
MENA Standard and Poor's / IFCI Middle East and Africa Emerging Economies
Developed Europe FTSE Developed Europe Developed Economies
Developed Asia FTSE Developed Asia Pacific Developed Economies
North America FTSE World North America Developed Economies
Emerging Europe JP Morgan GBI-Emerging Markets Europe Emerging Economies
Latin America JP Morgan GBI-Emerging Markets Latin America Emerging Economies
Emerging Asia JP Morgan GBI-Emerging Markets Asia Emerging Economies
MENA Citigroup World BIG Overall Africa Middle East Emerging Economies
Developed Europe Citigroup World BIG Overall West Europe Developed Economies
Developed Asia Barclays Asia Pacific Bond Aggregate Developed Economies
North America Citigroup World BIG Overall North America Developed Economies
Energy S&P GSCI Four Energy Commodities Spot Global Factors
Metals S&P GSCI Industrial Metals Spot Global Factors
Gold S&P GSCI Precious Metal Spot Global Factors
Food S&P GSCI Agriculture Spot Global Factors
Real Estate MSCI World Real Estate Global Factors
Hedge Funds HFRX Global Hedge Fund Index Global Factors
USD FX JP Morgan US Real Effective Exchange Rate (Trade-Weighted) Index Global Factors
US Policy US Economic Policy Uncertainty Index Global Factors
Macro Risk Citi World Short Term Macro Risk Index Global Factors
Macro Risk Citi World Long Term Macro Risk Index Global Factors
Macro Risk Citigroup Economic Surprise Index, China Global Factors
Macro Risk Citigroup Economic Surprise Index, Eurozone Global Factors
Macro Risk Citigroup Economic Surprise Index, USA Global Factors
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