## _cr15256

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### EXECUTIVE SUMMARY — Regional and global interconnectedness of Norway’s banks
- Norwegian banks are very dependent on global financial centers as sources of funding and to hedge currency risks; they arrange for a sizeable fraction of their wholesale funding in foreign markets.
- Norwegian banks show strong equity-return links with British, Swedish, and other European banks, which may reflect common risk exposures and ownership links.
- Spillover contributions to Norway’s bank volatility:
  - Rest of the Nordic region: 23 percent of inward spillovers (Denmark, Finland, Sweden contribution sums to 17 percent; Norway’s own variance share is 27 percent, so 17/(100-27)=23 percent).
  - Euro zone (excluding Finland): 48 percent.
  - Other major financial centers (Japan, Switzerland, United Kingdom, United States): 18 percent.
- Granger causality analysis indicates measured spillovers partly reflect causal effects (bank stress in one country leads to more stress in another), not only common factors.
- Cluster analysis of BIS credit links: Norway is part of the global bank credit cluster, not the Nordic cluster.
- Funding channel:
  - Norwegian banking groups have increased reliance on long-term wholesale funding through covered and unsecured bonds issued by associated mortgage credit companies.
  - In September 2014, Norwegian mortgage companies owed about 70 percent of long-term funding to foreign investors.
  - Foreign currency funding at end-2014 corresponded to 60 percent of total funding for Norwegian banks, with similar figures for banks’ long-term funding (Finanstilsynet 2014).
- Covered-bond issuance and hedging:
  - In 2013 a majority of covered bonds was issued in foreign currency (Finanstilsynet 2014).
  - Mortgage companies are mandated to fully hedge the currency risk of foreign issues for the life of the bond, but hedging may be done via currency swaps with parent companies which may not fully transfer the risk.

### Cross-border and cross-sector exposures
- Cross-border structure:
  - Direct provision of credit to nonbanks by foreign branches and subsidiaries is the main driver of Nordic integration.
  - BIS-based network comparisons show total crossborder claims networks are more connected (smaller diameter) than bank-to-bank crossborder claims networks.
- Cross-sectoral exposures:
  - Variance-decomposition analysis indicates Norwegian banks are significantly affected by banks in Sweden, the Euro Area, and the United Kingdom, as well as British real estate companies and European insurers.
  - Norwegian insurers are significantly affected by banks from major financial centers and by foreign insurers.
  - Norwegian real estate sector is highly exposed to the performance of American and British financial firms.
- Domestic oil-related exposures:
  - Direct credit exposures of banks to oil-related sectors (oil and gas production, oil services and engineering, and shipping) are around three percent of total credit.
  - Total direct and indirect exposure of the financial sector to oil-related industries is around 30 percent.
  - Similar exposure patterns hold for insurance and real estate sectors.
- Ownership and investor base:
  - About 40 percent of DNB is foreign-owned; Nordea is more than 2/3 foreign-owned; Danske Bank shows large foreign ownership, particularly from other Nordic countries; Sampo has more than half of known shareholders from outside Finland.
  - Most unsecured and covered bonds issued by Norwegian mortgage companies are foreign-owned (data for 2014Q3).
  - More detailed and complete data are needed on foreign ownership of covered and unsecured bonds issued by banks and mortgage companies to improve funding stress tests.

### Systemic interconnectedness among Norwegian banks
- Interbank connectedness:
  - The Norwegian banking system is becoming more closely interconnected; bank-to-bank exposures are limited but have been growing in recent years.
  - Spillover analysis indicates transmission potential is a function of size: DNB and Nordea exhibit the highest transmission potential.
  - DNB plays a more important interface role between smaller Norwegian banks and larger foreign banks active in the local market.
- Market and balance-sheet network analyses yield similar conclusions; market data are a viable alternative when bilateral balance-sheet data are unavailable.
- Indices of contagion and vulnerability show:
  - Norway’s vulnerability to external funding shocks has been declining.
  - Norway’s vulnerability is below the average for other countries in the Nordic region (Other Nordics = Simple average for Denmark, Finland, and Sweden).
  - These indices measure contagion and vulnerability to shocks originating from BIS reporting countries, do not account for central bank funding, and are not measures of overall financial strength.

### Resilience to external credit and funding shocks (simulations and findings)
- BIS-data-based analysis suggests vulnerability is similar to peers and has been declining.
- Funding-shock simulations (Espinosa-Vega and Solé (2014) methodology) assume 95 percent of funding from the euro area, the United Kingdom, or the United States is withdrawn; banks must liquidate assets and incur a loss; if losses wipe out all equity, the banking sector is assumed to default.
  - The decreased vulnerability in simulations is attributed to bigger capital buffers and reduced crossborder bank activity.
  - Under the dramatic scenario, the Norwegian banking system would not fail using data for 2014Q1, while it would have failed in 2010Q1.
- Risk remains from overreliance on external sources for bank funding due to the small number of active counterparties in the NOK swap market; a ratings downgrade or strategic business decision could reduce the number of foreign banks willing to serve as counterparties and hurt banks’ access to funding.

### Cross-sector interconnectedness — quantitative highlights
- Variance decomposition and sector links:
  - Norwegian banks are significantly affected by performance of banks in Sweden, the Euro Area, and the United Kingdom.
  - American and British real estate companies and European insurers are important contributors to volatility of Norwegian banks.
- Key statistics on cross-border exposures and investments:
  - Foreign liabilities (including equity) of banks and mortgage companies: 46 percent of total liabilities (December 2014).
  - Insurance and pension fund assets abroad: 34 percent (end-2014).
  - Bank assets abroad: 24 percent (end-2014).
  - Foreign debt liabilities: approximately 60 percent of wholesale bank funding.
  - Share of foreign loans in total bank loans: 22.8 percent.
- Oil-sector spillovers:
  - The oil sector’s contribution to the performance of Norwegian banks could be as high as 30 percent (far exceeding direct credit exposure of around three percent of total credit).
  - A one percentage drop in oil production equity returns causes bank returns to drop by 0.68 percentage points after 12 days; the impact is similar for insurance companies.
  - Oil engineering and services, and marine transportation, have impacts on financial sector returns of similar magnitudes.

### Interbank connectedness — network metrics (December 2011 versus September 2014)
- All listed connectivity measures increased from 2011 to 2014:
  - Diameter: 1.6531 (2011) → 1.3468 (2014).
  - Average Geodesic Distance: 1.0370 (2011) → 0.9877 (2014).
  - Reciprocated Vertex Pair Ratio: 0.4000 (2011) → 0.4375 (2014).
  - Reciprocated Edge Ratio: 0.5714 (2011) → 0.6087 (2014).
  - Graph Density: 0.5833 (2011) → 0.6389 (2014).
- Notes:
  - A lower Diameter or Average Geodesic Distance signifies higher connectivity.
  - Higher Reciprocated Vertex Pair Ratio, Reciprocated Edge Ratio, and Graph Density signify higher connectivity.
  - All metrics except Diameter calculated using NodeXL.

### Policy recommendations
- Enhance data collection and monitoring:
  - Collect bilateral asset exposure data on a regular basis and complement with higher frequency analyses based on market data.
  - Expand monitoring of crossborder and cross-sectoral exposures of the Norwegian financial sector and conduct regional stress tests using market data and, where available, institution-level balance-sheet exposures.
  - Obtain more detailed data on foreign ownership of covered and unsecured bonds to improve funding stress-test quality.
- Strengthen surveillance and mitigate concentration risks:
  - Enhance existing surveillance of interbank lending and exposures.
  - Consider options to dampen cross-ownership of covered bonds in the banking sector, subject to the restrictions imposed by European Union laws and regulations.
- Maintain significant buffers:
  - Maintain significant buffers both economy-wide and at the bank level given strong external links and Norway being mostly on the receiving end of shocks.
- Conduct regional stress tests:
  - Complement domestic stress testing with regional stress test exercises due to important credit and economic links with other Nordic countries.
- Surveillance of interbank lending and covered-bond ownership:
  - Resume regular monitoring of bank-to-bank direct and indirect exposures, including interbank lending in addition to ongoing monitoring of large credit exposures.
  - Contemplate measures to mitigate risks from overlapping ownerships of covered bonds within EU legal and regulatory boundaries, while being aware that remedial measures may carry side-effects.
- Improve data on bond ownership:
  - Obtain more detailed ownership data of unsecured and covered bonds issued by Norwegian financial institutions to improve the quality of funding stress tests.

### Methodology summary
- Market-data spillovers: Diebold and Yilmaz (2014) methodology applied to daily excess returns, logarithm of volatility of equity returns, or expected default frequencies (EDF); X includes control variables (usually VIX or oil prices); lag order chosen by BIC.
- VAR-based spillover identification: Generalized forecast-error variance decomposition using Pesaran and Shin (1998) identifies percent contribution of entity A to the H-step-ahead forecast error variance of entity B; Granger causality tested via Wald tests at the five percent significance level.
- Sectoral spillovers: Baseline model uses daily excess returns of sectoral stock indices; oil contribution metric sums Oil and Gas Production and Oil Equipment and Services’ contributions to the 12-day ahead forecast error variance of bank, insurance, or real estate excess returns divided by the fraction of said returns not explained by their own past values; VIX included as a control.
- Balance-sheet spillovers and funding-shock simulations: Espinosa-Vega and Solé (2014) method applied to BIS crossborder claims and confidential bank-level exposures; inputs include bilateral asset exposures and a measure of capital; parameters include losses given default, percent of funding withdrawn (95 percent in simulations), and losses in asset fire sales (50 percent haircut assumption in simulations).
- Simulation caveats: scenarios are extremely pessimistic and simplistic; no differentiation of haircuts across countries or institutions; simulations assume no policy action and compare resilience over quarters 2010Q1–2014Q1.

### Data sources and samples
- Equity returns: Thomson Reuters Datastream, daily frequency starting in 1993 and ending on 1/30/2015.
  - Volatilities: standard deviation of daily returns over the month.
  - Excess returns: deviations from average total market returns.
- Sector-level analyses: Datastream sectoral indices; countries covered include Denmark, Finland, France, Germany, Norway, Sweden, United Kingdom, and United States.
- Estimated default frequencies: Moody’s CreditEdge+ 1-year ahead EDFs, sampled daily between January 2006 and January 2015.
- Crossborder bank claims: Bank for International Settlements’ Consolidated Bank Statistics, normalized by recipient country GDP (IMF’s International Financial Statistics); sample includes all BIS reporting countries with publicly available total crossborder claims, plus Norway.
- Complementary data: public and confidential BIS claims over banks.
- Capital buffers for network analyses: calculated using Bankscope data for banks likely to be BIS reporting banks; sample selection rules for various jurisdictions described.

*Source: INTERNATIONAL MONETARY FUND, Norway section (excerpt).*

### EXECUTIVE SUMMARY ___________________________________________________________________________ 3

### EXECUTIVE SUMMARY

### Regional and global interconnectedness of Norway’s banks
- Norwegian banks are very dependent on global financial centers as sources of funding and to hedge currency risks; they arrange for a sizeable fraction of their wholesale funding in foreign markets.
- Norwegian banks show strong equity-return links with British, Swedish, and other European banks, which may reflect common risk exposures and ownership links.
- Spillover contributions to Norway’s bank volatility:
  - Rest of the Nordic region: 23 percent of inward spillovers (Denmark, Finland, Sweden contribution sums to 17 percent; Norway’s own variance share is 27 percent, so 17/(100-27)=23 percent).
  - Euro zone (excluding Finland): 48 percent.
  - Other major financial centers (Japan, Switzerland, United Kingdom, United States): 18 percent.
- Granger causality analysis indicates measured spillovers partly reflect causal effects (bank stress in one country leads to more stress in another), not only common factors.
- Cluster analysis of BIS credit links:
  - Norway is part of the global bank credit cluster, not the Nordic cluster, reflecting its role in oil and its banks’ funding models.
- Funding channel:
  - Norwegian banking groups have increased reliance on long-term wholesale funding through covered and unsecured bonds issued by associated mortgage credit companies.
  - In September 2014, Norwegian mortgage companies owed about 70 percent of long-term funding to foreign investors.
  - Foreign currency funding at end-2014 corresponded to 60 percent of total funding for Norwegian banks, with similar figures for banks’ long-term funding (Finanstilsynet 2014).
- Covered-bond issuance and hedging:
  - In 2013 a majority of covered bonds was issued in foreign currency (Finanstilsynet 2014).
  - Mortgage companies are mandated to fully hedge the currency risk of foreign issues for the life of the bond, but hedging may be done via currency swaps with parent companies which may not fully transfer the risk.

### Cross-border and cross-sector exposures
- Cross-border structure:
  - Direct provision of credit to nonbanks by foreign branches and subsidiaries is the main driver of Nordic integration (a business-driven model), rather than crossborder wholesale interbank funding.
  - BIS-based network comparisons show total crossborder claims networks are more connected (smaller diameter) than bank-to-bank crossborder claims networks.
- Cross-sectoral exposures:
  - Variance-decomposition analysis of equity-return volatility indicates Norwegian banks are significantly affected by banks in Sweden, the Euro Area, and the United Kingdom, as well as British real estate companies and European insurers.
  - Norwegian insurers are significantly affected by banks from major financial centers and by foreign insurers.
  - Norwegian real estate sector is highly exposed to the performance of American and British financial firms.
- Domestic oil-related exposures:
  - Direct credit exposures of banks to oil-related sectors (oil and gas production, oil services and engineering, and shipping) are around three percent of total credit.
  - Total direct and indirect exposure of the financial sector to oil-related industries is around 30 percent.
  - Similar exposure patterns hold for insurance and real estate sectors.
- Ownership and investor base:
  - About 40 percent of DNB is foreign-owned; Nordea is more than 2/3 foreign-owned; Danske Bank shows large foreign ownership, particularly from other Nordic countries; Sampo has more than half of known shareholders from outside Finland.
  - Most unsecured and covered bonds issued by Norwegian mortgage companies are foreign-owned (data for 2014Q3).
  - More detailed and complete data are needed on foreign ownership of covered and unsecured bonds issued by banks and mortgage companies to improve funding stress tests.

### Systemic interconnectedness among Norwegian banks
- Interbank connectedness:
  - The Norwegian banking system is becoming more closely interconnected; bank-to-bank exposures are limited but have been growing in recent years (confidential balance sheet data).
  - Spillover analysis indicates transmission potential is a function of size:
    - DNB and Nordea exhibit the highest transmission potential.
    - DNB plays a more important interface role between smaller Norwegian banks and larger foreign banks active in the local market.
- Market and balance-sheet network analyses yield similar conclusions; market data are a viable alternative when bilateral balance-sheet data are unavailable.
- Indices of contagion and vulnerability (based on Espinosa-Vega and Solé 2014, using BIS reporting countries) show:
  - Norway’s vulnerability to external funding shocks has been declining.
  - Norway’s vulnerability is below the average for other countries in the Nordic region (Other Nordics = Simple average for Denmark, Finland, and Sweden).
  - These indices measure contagion and vulnerability to shocks originating from BIS reporting countries, do not account for central bank funding, and are not measures of overall financial strength.

### Policy recommendations
- Enhance data collection and monitoring:
  - Financial supervisors should collect bilateral asset exposure data on a regular basis and complement with higher frequency analyses based on market data.
  - Authorities are encouraged to expand monitoring of crossborder and cross-sectoral exposures of the Norwegian financial sector and to conduct regional stress tests using market data and, where available, institution-level balance-sheet exposures.
  - Obtain more detailed data on foreign ownership of covered and unsecured bonds to improve funding stress-test quality.
- Strengthen surveillance and mitigate concentration risks:
  - Enhance existing surveillance of interbank lending and exposures.
  - Consider options to dampen cross-ownership of covered bonds in the banking sector, subject to the restrictions imposed by European Union laws and regulations.

*Prepared by Mr. Luis Brandao-Marques (MCM).*

### 12.      Norwegian bank’s resilience against external credit and funding shocks seems to have

### 12.      Norwegian bank’s resilience against external credit and funding shocks seems to have improved.

### Resilience to external credit and funding shocks
- An analysis based on confidential BIS data (covering only a part of funding risks) suggests that this vulnerability is similar to that of peer countries and has been declining.
- Simulations of funding shocks coming from global financial centers (the euro area, the United Kingdom, and the United States) show that Norwegian banks are now less exposed to such risks.  
  - The simulations use Espinosa-Vega and Solé’s (2014) methodology and assume that 95 percent of the funding coming from the euro area, the United Kingdom, or the United States, is withdrawn.  
  - In the exercise, banks must liquidate assets to make up for the funding shortfall and incur a loss; if losses wipe out all equity, the banking sector is assumed to default.  
  - The decreased vulnerability to the simulated shocks is attributed to bigger capital buffers and reduced crossborder bank activity.
- Risk remains from overreliance on external sources for bank funding due to the small number of active counterparties in the foreign currency swap market; a ratings downgrade or strategic business decision could reduce the number of foreign banks willing to serve as counterparties in the Norwegian krone (NOK) swap market and hurt banks’ access to funding.

### Cross-sector interconnectedness
- Norwegian banks’ linkages extend beyond the Nordic region and include foreign insurance companies and real estate companies.
- Variance decomposition of equity return volatility indicates Norwegian banks are significantly affected by performance of:
  - banks in Sweden, the Euro Area, and the United Kingdom;
  - American and British real estate companies; and
  - European insurers.
- Key statistics on cross-border exposures and investments:
  - Foreign liabilities (including equity) of banks and mortgage companies were, in December 2014, 46 percent of total liabilities.
  - By end-2014, 34 percent of insurance and pension fund assets were abroad.
  - By end-2014, 24 percent of bank assets were abroad.
  - Foreign debt liabilities were approximately 60 percent of wholesale bank funding (see Norway FSAP Technical Note on “Stress Testing the Banking Sector”).
  - The share of foreign loans in total bank loans is 22.8 percent (see Norway FSAP Technical Note on “Stress Testing the Banking Sector”).
- Norwegian insurers are significantly affected by banks and insurers from major financial centers; the largest contributors to volatility of Norwegian insurance companies are German and British insurers.
- Statistical evidence based on Granger causality analyses suggests many measured spillovers reflect direct causation and could be driven by funding structures.

### Spillovers from oil-related industries to financial and non-financial sectors
- Oil-related industries dominate cross-sectoral spillovers to the Norwegian financial sector.
  - The oil sector’s contribution to the performance of Norwegian banks could be as high as 30 percent, far exceeding direct credit exposure to the oil industry (around three percent of total credit). (This figure does not include credit exposures to foreign firms in the oil and shipping sectors.)
  - A one percentage drop in oil production equity returns causes bank returns to drop by 0.68 percentage points after 12 days; the impact is similar for insurance companies.
  - Oil engineering and services, and marine transportation, have impacts on financial sector returns of similar magnitudes.
  - The large effect of oil activity on banks’ profitability is likely driven by the oil sector’s large spillovers into the rest of the economy in terms of output and employment.

### Systemic interconnectedness among Norwegian banks
- Systemic interconnectedness among Norwegian banks has increased since 2011.
  - Network analysis based on confidential bank-level data indicates Norwegian banks were significantly more connected in September 2014 than at end-2011.
  - The network diameter—which is inversely related to connectedness—decreased by almost 20 percent between end-2011 and September 2014.
  - The connectivity measured is moderate (less than 10 percent of assets), but its continued increase should be monitored.
- Most of the increase in interconnectedness has occurred via increased holdings of covered bonds issued by other banks.
  - Norwegian banks have increased reliance on other banks operating in Norway as a source of long-term funding.
  - The increase in cross-ownership of covered bonds is partly a consequence of expected implementation of the Basel Accord’s liquidity coverage ratio (LCR) and scarcity of high-quality liquid assets (HQLA); bank-issued covered bonds are the most abundant HQLA given Norway’s strong fiscal position.
  - Overlapping claims can amplify the risk of systemic illiquidity events and contribute to bank fragility.
- Market-data analysis of connectedness:
  - DNB and Nordea display the largest spillover potential.
  - The Norwegian banking sector has a two-tier structure: foreign banks operating in Norway, and smaller commercial banks and largest savings banks with limited connections.
  - DNB shows important connections with both groups; Nordea explains around 25 percent of the other large Nordic banks’ probabilities of default (highest outward spillover potential in the Nordic context).
- Comparisons:
  - Network analysis based on market data and that based on confidential balance sheet data yield similar conclusions, making market-data analyses a viable alternative when confidential data are not available.
  - Bilateral balance sheet exposures enable deeper investigation; supervisors would benefit from collecting bilateral asset exposure data regularly and complementing with higher-frequency market-data analyses.

### Policy recommendations
- Maintain significant buffers:
  - Norway has strong financial and trade links with the rest of the world but is mostly on the receiving end of shocks; strong justification exists for maintaining significant buffers both economy-wide and at the bank level.
- Conduct regional stress tests:
  - Authorities should consider complementing domestic stress testing with regional stress test exercises due to important credit and economic links with other Nordic countries.
- Enhance monitoring of crossborder exposures:
  - The Norges Bank and the Finanstilsynet should expand regular monitoring of crossborder bank-to-bank direct and indirect exposures beyond the large-exposures framework.
  - Data collection and analysis should go beyond credit exposures and emphasize the role of nonbanks.
  - Regulators and supervisors should have sufficient resources to collect necessary data, including from commercial sources.
- Surveillance of interbank lending and covered-bond ownership:
  - Authorities are encouraged to enhance surveillance of interbank lending and exposures and consider options to dampen cross-ownership of covered bonds in the banking sector, subject to European Union laws and regulations.
  - Authorities should resume regular monitoring of bank-to-bank direct and indirect exposures, including interbank lending in addition to ongoing monitoring of large credit exposures.
  - Because the increase in connectedness is related to overlapping ownerships of covered bonds brought about in part by LCR, authorities should contemplate measures to mitigate associated risks within EU legal and regulatory boundaries, while being aware that remedial measures may carry side-effects.
- Improve data on bond ownership:
  - Authorities would benefit from having more detailed ownership data of unsecured and covered bonds issued by Norwegian financial institutions.
  - More detailed and complete data are needed on foreign ownership of covered and unsecured bonds issued by banks and mortgage companies to improve the quality of funding stress tests.

### Methodology (summary)
- Spillover measurement methods draw from:
  - Diebold and Yilmaz (2014) for market-data analyses; and
  - Espinosa-Vega and Solé (2014) for balance-sheet data exercises.
- For market-data analyses, a VAR is estimated where Y can be equity excess returns, logarithm of volatility of equity returns, or expected default frequencies (EDF). X is a vector of control variables (usually the VIX or oil prices). The lag order is chosen by the Bayesian Information Criterion (BIC).

*Source: INTERNATIONAL MONETARY FUND, Norway section (excerpt).*

### 3.      The VAR model above is used to build a generalized forecast-error variance

### _cr15256 - 3.      The VAR model above is used to build a generalized forecast-error variance

### Methodology: VAR-based spillover identification
- Generalized forecast-error variance decomposition using Pesaran and Shin’s (1998) methodology is used to identify uncorrelated structural shocks to returns or probabilities of default.
- Spillover measures: percent contribution of entity A to the H-step-ahead forecast error variance of entity B; entities can be banks, sectors, or countries.
- Advantage: does not require any assumption on the order of the variables (relative to Cholesky ordering or more structural approaches).
- Complementary test: Granger causality using the same VAR model. A Wald test is performed on each entity where the null is that lagged returns (or volatility of returns or probabilities of default) of entity A do not help explain returns of entity B. Rejection at the five percent significance level implies a direct connection from A to B beyond common-factor exposures.

### Sectoral spillovers to banks and financial sector
- Approach: baseline model with Diebold and Yilmaz’s (2014) methodology using daily excess returns of sectoral stock indices instead of monthly volatilities.
- Sectoral indices used: Oil and Gas Production, Oil Equipment and Services, Marine Transportation, Banks, Insurance, Real Estate, Food Products, Consumer Services, Technology, Construction and Materials, and Utilities.
- Oil contribution metric: sum of Oil and Gas Production and Oil Equipment and Services’ contribution to the 12-day ahead forecast error variance of bank, insurance, or real estate excess returns, divided by the fraction of said returns not explained by their own past values.
- VIX included as a control.

### Sweden comparison
- Sweden exercise uses country-relevant sectoral indices by market value: Banks, Industrial Goods and Services, Industrial Engineering, Industrial Machines, Consumer Services, Specialty Finance, Consumer Goods, Construction and Materials, Real Estate, Health Care, and Retail.
- Model includes VIX and oil prices as additional controls to allow a fair comparison with Norway.

### Spillover analysis using balance sheet data
- Methodology: Espinosa-Vega and Solé’s (2014) method applied to crossborder bank claims (BIS data) and to confidential bank-level exposures within Norway.
- Required inputs: bilateral asset exposures and a measure of capital.
- Parameters: losses given default, percent of funding withdrawn, and losses in asset fire sales as a percentage of the initial value of assets.

### Simulation of a funding shock
- Scenarios: three possible sources—euro area, the United Kingdom, and the United States.
- Assumption: 95 percent of the funding coming from either country or region is withdrawn.
- Asset liquidation assumption: banks must liquidate assets and incur a loss of 50 percent (haircut).
- Failure rule: if loss exhausts banks’ (or banking system’s) capital base, the bank (or banks) fails.
- Caveats: scenarios are extremely pessimistic and simplistic; no differentiation of haircuts across countries or institutions.
- Temporal scope: simulation conducted at each quarter between 2010Q1 and 2014Q1 and assumes no policy action is taken—only compares resilience, not crisis likelihood.
- Result summary: under this dramatic scenario, the Norwegian banking system would not fail using data for 2014Q1, while it would have failed in 2010Q1.
- Drivers of increased resilience: bigger capital buffers and reduced crossborder bank activity.

### Interbank connectedness (Norwegian banking sector)
- Confidential interbank exposure data cover all significant banks operating in Norway for December 2011 and September 2014.
- Objective: assess whether Norwegian banking system is more connected in 2014 than in 2011.
- Several network connectivity measures were calculated; all metrics confirmed increased connectedness from 2011 to 2014.

- Appendix Table 1. Connectivity of the Norwegian Banking Sector — key metrics (all measures of connectivity increased from 2011 to 2014):
  - Year 2011:
    - Diameter: 1.6531
    - Average Geodesic Distance: 1.0370
    - Reciprocated Vertex Pair Ratio: 0.4000
    - Reciprocated Edge Ratio: 0.5714
    - Graph Density: 0.5833
  - Year 2014:
    - Diameter: 1.3468
    - Average Geodesic Distance: 0.9877
    - Reciprocated Vertex Pair Ratio: 0.4375
    - Reciprocated Edge Ratio: 0.6087
    - Graph Density: 0.6389
  - Notes:
    - A lower value of Diameter or Average Geodesic Distance signifies higher connectivity.
    - A higher value of Reciprocated Vertex Pair Ratio, Reciprocated Edge Ratio, and Graph Density signifies higher connectivity.
    - All metrics except Diameter calculated using NodeXL.

### Data sources and samples
- Equity returns: Thomson Reuters Datastream, sampled at the daily frequency starting in 1993 and ending on 1/30/2015.
  - Volatilities: standard deviation of daily returns over the month.
  - Excess returns: deviations from average total market returns.
- Sector-level analyses use Datastream’s sectoral indices when available.
  - Countries covered: Denmark, Finland, France, Germany, Norway, Sweden, United Kingdom, and United States.
  - Number of constituents varies by country and over time; as of January 2015, constituents per index ranged from 74 for the United States real estate index to one for Finnish bank and insurance indices, and the Danish real estate index.
- Estimated default frequencies: Moody’s CreditEdge+ 1-year ahead EDFs, sampled daily between January 2006 and January 2015.
- Crossborder bank claims: Bank for International Settlements’ Consolidated Bank Statistics, normalized by recipient country GDP (from IMF’s International Financial Statistics) when used in network charts.
  - Sample: all BIS reporting countries with publicly available total crossborder claims, plus Norway.
- Complementary data: public and confidential BIS claims over banks.
- Capital buffers for network analyses: calculated using Bankscope data for banks likely to be BIS reporting banks.
  - For Australia, Canada, European Union, Japan, Switzerland, and the United States, possible sample of BIS reporting banks drawn from BIS’s Regulatory Consistency Assessment Program (RCAP).
  - For all other countries, commercial banks with at least 10 billion U.S. dollars in assets are selected.

*Source: _cr15256 - 3.      The VAR model above is used to build a generalized forecast-error variance*

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