Nordic linkages, European Regional Cluster Report (redacted version)
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Transcript of Nordic linkages, European Regional Cluster Report (redacted version)
NORDIC LINKAGESEUROPEAN REGIONAL CLUSTER REPORTBENJAMIN HUSTON, LUIS BRANDAO-MARQUES, MARCO PINON
This presentation and the preliminary results are intended as background for discussions.
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
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].
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.
METHOD
Measure at the firm, sector, and country level: Equity returns; Volatility of equity returns (in logs) but only for robustness.
Spillover Analysis (Diebold and Yilmaz 2014).
Order independent: generalized FEVD; order rotation for robustness.
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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.
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ijijij dds
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]
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
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.
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.
DECOMPOSITION OF SPILLOVERS TO THE 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.
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).
TOP SPILLOVERS TO THE 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).
TOP SPILLOVERS TO THE 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
TOP SPILLOVERS TO THE 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.
MAKING SENSE OF THE RESULTS: NORWAY GOVERNMENT PENSION FUND GLOBAL – COUNTRY/CURRENCY EXPOSURES BY ASSET CLASS
TOP SPILLOVERS TO THE 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
TOP SPILLOVERS TO THE 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
SUMMARY OF NORDIC EXPOSURE TO INWARD SPILLOVERS
Emerging Economies
Sweden
Finland
Denmark
NorwayIceland
1 2
3
45
2
1
5
4
3
1
2
3
2
1 3
4Global 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.
TOP SPILLOVERS FROM THE 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
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 = energyM = metalsG = goldF = foodHF = hedge fundsRE = real estateUSD = US FX ratea = Asiae = Europela = Latin Americama = Middle East and Africana = North America
= equities
= bonds
= global factors
= Nordics= developed economy= emerging economy= global factor
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).
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).
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
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
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.
REFERENCES
Csardi G, and Nepusz T, 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, Graph Theory (3rd ed.), Berlin, New York: Springer-Verlag, ISBN 978-3-540-26183-4.
Klößner, S. and Wagner, S., 2014b, “International spillovers of policy uncertainty.” Economics Letters 124, no. 3: 508–512.
Pesaran, H. Hashem, and Yongcheol Shin, 1998, "Generalized impulse response analysis in linear multivariate models." Economics Letters 58, no. 1 (1998): 17-29.
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.
DATA DESCRIPTION: CROSS-BORDER SPILLOVERSAlias Financial Index Name Spillover Group
IS OMX Iceland All Share NordicsSE Datastream Sweden Equities NordicsDK Datastream Denmark Equities NordicsFI Datastream Finland Equities Nordics
NO Datastream Norway Equities NordicsEmerging Europe MSCI Emerging Markets Europe Emerging Economies
Latin America MSCI Emerging Markets Latin America Emerging EconomiesEmerging Asia MSCI Emerging Markets Asia Emerging Economies
MENA Standard and Poor's / IFCI Middle East and Africa Emerging EconomiesDeveloped Europe FTSE Developed Europe Developed Economies
Developed Asia FTSE Developed Asia Pacific Developed EconomiesNorth America FTSE World North America Developed Economies
Emerging Europe JP Morgan GBI-Emerging Markets Europe Emerging EconomiesLatin America JP Morgan GBI-Emerging Markets Latin America Emerging EconomiesEmerging Asia JP Morgan GBI-Emerging Markets Asia Emerging Economies
MENA Citigroup World BIG Overall Africa Middle East Emerging EconomiesDeveloped Europe Citigroup World BIG Overall West Europe Developed Economies
Developed Asia Barclays Asia Pacific Bond Aggregate Developed EconomiesNorth America Citigroup World BIG Overall North America Developed Economies
Energy S&P GSCI Four Energy Commodities Spot Global FactorsMetals S&P GSCI Industrial Metals Spot Global FactorsGold S&P GSCI Precious Metal Spot Global FactorsFood S&P GSCI Agriculture Spot Global Factors
Real Estate MSCI World Real Estate Global FactorsHedge Funds HFRX Global Hedge Fund Index Global Factors
USD FX JP Morgan US Real Effective Exchange Rate (Trade-Weighted) Index Global FactorsUS Policy US Economic Policy Uncertainty Index Global Factors
Macro Risk Citi World Short Term Macro Risk Index Global FactorsMacro Risk Citi World Long Term Macro Risk Index Global FactorsMacro Risk Citigroup Economic Surprise Index, China Global FactorsMacro Risk Citigroup Economic Surprise Index, Eurozone Global FactorsMacro Risk Citigroup Economic Surprise Index, USA Global Factors
-- CBOE SPX Volatility VIX Controls-- CBOE Skew Index Controls-- Merryl Lynch Move 3 Months Bond Volatility Controls-- Global Ex-Ante Equity Risk Premium Controls