## _cr15258 ## Source details **Canonical URL:** [_cr15258](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2015/_cr15258.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2015/_cr15258.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2015/_cr15258.pdf.json) --- ### EXECUTIVE SUMMARY — background and system metrics - Banks, jointly with mortgage companies which they mostly own, accounted for 78 percent of total financial system assets at end–2014. - Mortgage companies grew from 10 percent of total assets at end–2003 to 23 percent at end–2014. - The largest bank—DNB Group ASA (DNB)—accounts for about 35 percent of aggregate banking sector assets; the largest seven banks comprise 75 percent of assets. - Foreign (mostly Nordic) financial conglomerates account for more than 20 percent of aggregate assets. - Aggregate nonperforming loans (NPLs) were 1.3 percent of total loans at end–2014. - Ratio of provisioning to NPLs was 20 percent at end–2014, down from 27 percent at end–2006. - Aggregate return on equity (ROE) for the banking sector was 12.7 percent at end–2014. - Aggregate capital adequacy and Tier 1 ratios at end–2014 stood at 16.5 and 14.5 percent, respectively, up from 11.2 and 8.6 percent at end–2008. - The average CET-1 ratio for the seven largest banks at end–2014 was 12.4 percent, above the 10 percent minimum requirement under the accelerated Basel III schedule in Norway. ### LENDING TRENDS, COMPOSITION, AND FUNDING STRUCTURE - Private sector lending: - Ratio of credit to mainland GDP almost doubled since the mid–1990s. - Household debt: - Increased from about 150 percent of disposable income at end–2003 to about 220 percent at end–2014. - Residential mortgages account for about 90 percent of household lending by banks and mortgage companies. - Corporate credit: - Rose rapidly before the crisis; corporate bond and overseas issuance has recently slowed bank credit growth. - Commercial real estate, particularly in Oslo, accounts for the largest share of corporate loans. - Wholesale funding: - Accounts for about half of banks’ liabilities. - 62 percent of wholesale funding is in foreign currencies and procured via international markets. - Share of covered and senior bonds in FX-denominated funding increased to 34 percent at end–2014 from less than 12 percent at end–2008. - Liquidity buffer challenges: - Difficulty accumulating HQLA in NOK; average LCR in NOK across Norwegian banks is low, below the 60 percent mark currently considered by NB. ### KEY RISKS AND VULNERABILITIES - High private sector leverage and likely significant overvaluation of real estate prices. - Economy’s high dependence on the oil sector makes persistent low oil prices a possible trigger. - Real estate and interest-rate channel: - Almost all household loans are variable-rate, increasing indirect credit risk if interest rates rise. - Banks’ losses are more likely to accumulate in corporate portfolios rather than household portfolios because: (i) households have sound repayment buffers; (ii) full recourse mortgages lead households to prioritize mortgage payments; (iii) LTV regulatory guidance enhances loss absorption. - Oil-price related risks: - Direct exposures to the oil sector account for about 1.5 percent of outstanding corporate loans. - Indirect bank exposures to the oil sector could be as high as 30 percent. - Cross-holdings risk: - Banks’ cross-holdings of covered bonds account for more than half of domestically-issued covered bonds, amplifying liquidity risk under systemic stress. - Liquidity/funding risks: - Reliance on currency swaps to convert FX wholesale funding generates maturity mismatch and rollover risks; most currency swaps have maturities of 3 months or less and the highest turnover maturity is tomorrow next (TN). ### FSAP STRESS TESTING FRAMEWORK AND EXECUTION - Solvency tests: - Three parallel top-down (TD) solvency stress tests by FSA, Norges Bank (NB), and the FSAP team; methodologies differ but use the same macro scenarios. - Bottom-up (BU) tests carried out by individual banks on unconsolidated and consolidated bases (the latter including associated mortgage companies). - Coverage: top 6 banks account for 75 percent of assets unconsolidated and 67 percent consolidated; tests did not cover Kommunalbanken. - Data reference dates: end–September 2014 and end–2014 (projected to end–year where relevant). - Capital assessment based on CET-1 under accelerated Basel III schedule, including conservation buffer, systemic risk buffer, and D-SIB surcharge. - Liquidity tests: - Carried out by the FSAP team on a TD consolidated basis (67 percent of assets including mortgage companies) over a 30–day horizon using recalibrated LCR/NSFR metrics. - Counterbalancing capacity assessed via unencumbered assets at market values net of haircuts. - Scenario horizon: 5 years to end–2019. ### SCENARIOS AND KEY ASSUMPTIONS - Three macro scenarios: baseline and two adverse scenarios (no policy response; with monetary policy easing). - Common adverse calibrations: - Sustained lower nominal oil prices at US$40 per barrel over the entire stress testing horizon. - Increase in money market rates by about 200 bps and wholesale funding spreads by an additional 150 bps (relative to baseline), starting in 2015. - A 40 percent decline in real property prices over five years. - Adverse scenario without policy response: - Real GDP growth declines cumulatively by 6.7 percent over 5 years (3.1 standard deviations from long-term trend). - Level of GDP declines 16.1 percent below the baseline by 2019. - Adverse scenario with monetary policy response: - Monetary easing: 1½ percentage point cut in policy rate in 2015-16. - Real GDP growth declines cumulatively by 4.3 percent over 5 years (2¾ standard deviations). - Level of GDP declines 13.9 percent below the baseline by 2019. - Market risk shock design: - Equities haircut: 26 percent (80th percentile fitted distribution of MSCI Norway annual extrema 1971–2014). - Debt securities: yields assumed to rise by 200 basis points; Norwegian sovereign bond haircut implied at 10.4 percent for a 200 bps yield change. - Selected sovereign yield changes and haircuts (selected rows reproduced exactly): - Norway: Change in yield (bps) 200.0 Stressed yields (in %) 3.4 Haircut (in %) 10.4 - Sweden: 147.4 1.8 7.6 - Netherlands: 137.4 1.6 7.1 - Germany: 137.5 1.5 7.1 - Belgium: 230.9 2.5 12.1 - Italy: 211.4 3.3 11.0 - Spain: 193.7 3.0 10.1 - Greece: 1056.0 17.6 64.4 - Hungary: 328.6 6.6 17.4 - USA: 151.1 3.1 7.8 - Note on percentiles: With the exception of Norway, 95th percentile of the distribution; 80th percentile for Italy, Spain, and Hungary; 80th percentile of the empirical distribution for Greece and Ireland. ### SOLVENCY STRESS TEST RESULTS AND KEY STATISTICS - Starting capitalization (end–2014): - Aggregate CET-1 ratio on a consolidated basis: 12.9 percent. - Aggregate CET-1 ratio on an unconsolidated basis: 12.4 percent. - Baseline projection: - Aggregate CET-1 capital ratio rises gradually to 14.0–16.7 percent by end–2019, from 12.5–13.3 percent at end–2014 (depending on methodology). - Adverse scenarios outcomes: - Under the adverse scenario without policy response, CET-1 falls by between 4.1 and 6.7 percentage points to the 6.3–8.3 percent range (depending on approach). - IMF TD approach: CET-1 falls from 12.9 percent at end–2014 to 6.3 percent at end–2019 under the adverse scenario without policy response. - Aggregate capital shortfall under adverse scenario without policy response: 2.7 percent to 4.6 percent of GDP by 2019 (depending on estimate). - Drivers of CET-1 decline (IMF decomposition): - Increase in loan losses: contributes an average of 1.4 percent a year to CET-1 decline. - Rise of RWAs: contributes 0.8 percent. - Higher funding costs: contributes 0.3 percent. - Loss composition: - Most credit losses stem from corporate exposures; household exposures produce small losses. - FSA and NB TD approaches (historical calibration): household loss rates of 0.2 to 1.1 percent per year; corporate loss rates of 0.7 to 2.7 percent per year. - BU versus TD divergence: - BU tests produce considerably lower capital gaps: CET-1 declines by 0.6 percent (unconsolidated) and 1.4 percent (consolidated) over the stress horizon under BU. - TD peak credit loss rates: 1.5 to 3.6 percent a year; BU peak credit loss rates: 1 percent a year. - Implication: BU may understate credit risks, warranting supervisory discretion (including Pillar 2) and cross-validation. ### FSAP TEAM “RULES OF THUMB” AND MODEL LIMITATIONS - FSAP satellite model for household losses: - Links potential credit losses to deviation of projected 5–year cumulative GDP growth from 20–year trend. - Calibration uses a sensitivity coefficient of 0.4 for adverse scenarios. - Peak loss rate under the most adverse scenario (no policy response): 3.6 percent. - Comparative peak loss rates: FSA 1.5 percent; NB 2.1 percent. - NB macro model projections and limitations: - NB projects corporate problem loans close to 30 percent by end–2019 under adverse scenario without policy response. - NB projects household problem loans of 6.3 percent by 2019 under the same scenario. - Statistical models may understate household-related credit risks due to limited historical variability. - Common modeling assumptions: - Banks’ balance sheets assumed static apart from credit growth; no deleveraging allowed. - New problem loans maintain default status; banks provision fully over 5 years. - Funding structure fixed; banks cannot shift funding or raise new capital; no dividends permitted under stress. - Tests do not capture nonlinear macrofinancial feedbacks or many bank behavioral adjustments. ### LIQUIDITY STRESS TEST RESULTS AND VULNERABILITIES - NOK LCR and gaps: - Aggregate LCR for the banking sector declines from 33 percent in the baseline to 18 percent under a dry-up of unsecured wholesale funding. - NOK liquidity gap grows to NOK306 billion under dry-up of unsecured wholesale funding, from NOK120 billion in the baseline. - Under dry-up of secured wholesale funding (and partial dry-up of unsecured), NOK gap is NOK232 billion. - Aggregate NOK LCR under EU Delegated Act at end–2014: 33 percent, down from 49 percent at end–September 2014. - FX liquidity: - Most banks are above the 100 percent LCR threshold in FX; aggregate FX LCR at 124 percent in complete dry-up of unsecured funding. - FX funding gaps increase to NOK5.3 billion (dry-up unsecured) and NOK3.7 billion (dry-up secured) from NOK3 billion in baseline. - Covered bonds as HQLA: - Qualifying covered bonds represent about 25 percent of HQLAs in NOK and 21 percent in FX as of end–2014. - Recognition of covered bonds improves FX liquidity but raises concerns due to high cross-ownership and potential liquidity contagion. - Overall liquidity vulnerability: - System remains vulnerable to liquidity risks due to scarce NOK liquid buffers and reliance on FX wholesale funding and short-dated currency swaps. ### METHODOLOGICAL DETAILS — FSA AND NB APPROACHES - FSA methodology for corporate PDs: - ADL to project corporate balance sheet risk factors; SEBRA GAM logistic regression to calculate stressed PDs; match PDs to bank exposures to estimate expected losses. - Household PDs proxied from corporate PD changes starting from each bank’s actual default rate at end–2014. - LGD assumptions: baseline LGD 20 percent; corporate LGD in line with Moody's approach (35 percent plus factor increasing with debt-weighted PD); household LGD increases from 25 percent in 2015 to 35 percent in 2017–19 under adverse scenarios. - NB methodology: - ECM models to generate problem loan ratios for corporate and household exposures; one-period change proxies stressed PDs. - Banking balance sheet model projects financial statements and capital adequacy under stress. - Expert-judgment LGDs under stress: 40 percent for corporate loans and 25 percent for household loans. - Endogenous determination of lending and funding rates in macro model. - RWA and regulatory assumptions: - RWA adjustments in line with PDs (IRB or standardized approach). - RWA for new lending: marginal risk weight assumed 40% for households and 80% for corporates. - Norway applies a Basel I floor transitional rule: IRB banks set RWAs at least 80 percent of Basel I RWAs; floor set at 80 percent since 2009 and will continue. - FSA tightened IRB residential mortgage modeling: crises assumed to occur on average once every five years with average PD about 3.5 percent in crisis years; LGD floor of 20 percent; expected increase in average risk weights on residential mortgages to about 20–25 percent from 10–15 percent previously. ### CAUTIONS, LIMITATIONS, AND RECOMMENDATIONS - Stress test caveats: - Subject to data, methodology, and coverage constraints; tests do not include smaller commercial and savings banks, foreign branches, or exposures beyond corporates and households. - Do not capture nonlinear macrofinancial feedbacks or bank behavioral responses; survivorship bias may understate household loss sensitivity. - Policy recommendations and further steps: - Enhance stress testing frameworks: - Consider supplementing frameworks with models based on international experience to address low sensitivities of household default measures. - Cross-validate TD and BU results to address possible underestimation in BU stress tests. - Continue supervisory discretion (including Pillar 2) to ensure sufficiency of capital buffers. - Strengthen liquidity stress testing and management: - Perform stress tests using structure of cash flows at various maturities. - Consider customized LCR versions aligned with banks’ funding profiles. - Monitor and reduce risks related to FX wholesale funding dependency and short-dated currency swap reliance. - Regulatory vigilance on IRB measures: - Maintain Basel I floor and tightened IRB requirements for residential mortgages to mitigate potential underestimation of credit risk amid rapid house price and household debt growth. *Source: EXECUTIVE SUMMARY and selected sections, FSAP Norway (content unit: _cr15258).* ### EXECUTIVE SUMMARY ___________________________________________________________________________ 5 ### EXECUTIVE SUMMARY ### Background and financial system structure - Banks, jointly with mortgage companies which they mostly own, accounted for 78 percent of total financial system assets at end–2014. - Mortgage companies grew from 10 percent of total assets at end–2003 to 23 percent at end–2014. - The largest bank—DNB Group ASA (DNB)—accounts for about 35 percent of aggregate banking sector assets; the largest seven banks comprise 75 percent of assets. - Foreign (mostly Nordic) financial conglomerates account for more than 20 percent of aggregate assets. - Aggregate nonperforming loans (NPLs) were 1.3 percent of total loans at end–2014, generally persistently low (at 1.6 percent or less) in recent years. - The ratio of provisioning to NPLs was 20 percent at end–2014, down from 27 percent at end–2006. - Aggregate return on equity (ROE) for the banking sector was 12.7 percent at end–2014. - Aggregate capital adequacy and Tier 1 ratios at end–2014 stood at 16.5 and 14.5 percent, respectively, up from 11.2 and 8.6 percent at end–2008. - The average CET-1 ratio for the seven largest banks at end–2014 was 12.4 percent, above the 10 percent minimum requirement under the accelerated Basel III schedule in Norway. ### FSAP stress testing framework and exercises - The FSAP stress testing exercise included comprehensive solvency and liquidity analyses, carried out in close collaboration with the authorities. - Solvency stress tests: - Three parallel top-down (TD) solvency stress tests were run by the Financial Supervisory Authority of Norway (FSA), Norges Bank (NB) and the FSAP team, using different methodologies but the same macroeconomic scenarios. - Bottom-up (BU) tests were carried out by individual banks on an unconsolidated and consolidated basis (the latter accounting for associated mortgage companies), subject to the same scenarios. - Liquidity stress tests: - Carried out by the FSAP team. - Assessed banks’ ability to withstand funding pressures in local and foreign currencies in the context of the Liquidity Coverage Ratio (LCR) under the Basel III and CRD IV liquidity frameworks. - Scenario design: - Scenarios constructed by the FSAP team in cooperation with NB: a baseline scenario and two versions of an adverse scenario. - Adverse scenarios assume considerable negative deviations of economic activity from the baseline path over a five–year risk horizon, with hurdle rates based on the accelerated Basel III implementation schedule adopted by the Norwegian authorities. - The first adverse scenario assumes no policy response; the second adverse scenario assumes monetary policy easing by the authorities. - The baseline scenario is based on IMF staff projections as of December 2014, estimated in part via NB’s macro model. - Adverse scenarios reflect risks from a global slowdown, persistence of oil prices well below current WEO projections, and a sharp real house price correction. ### Solvency stress test results and key statistics - Under the baseline: - Aggregate Common Equity Tier 1 (CET-1) capital ratio rises gradually to 14.0–16.7 percent by end–2019, from 12.5–13.3 percent at end–2014 (depending on methodology). - Under adverse scenarios: - A combination of severe shocks—including persistently low oil prices and a sharp contraction in house prices—could result in an aggregate capital shortfall of up to 4.6 percent of GDP over five years. - BU stress tests produced considerably lower capital gaps than TD exercises. - Interpretation and implications: - Banks’ strong starting point—sizable capital buffers, sound profitability and very low asset impairment levels—supports resilience under the baseline. - The lower gaps from BU exercises could reflect underestimation of credit risks by individual banks, supporting the need for supervisory discretion (including Pillar 2) to ensure sufficiency of existing capital buffers. ### Liquidity stress test results and vulnerabilities - Findings: - The banking system remains vulnerable to liquidity risks, due in part to scarce liquidity buffers in Norwegian krone (NOK). - Covered bond issuance at longer maturities has increased funding stability (especially in foreign currencies) since the 2008 global financial crisis. - Banking groups face liquidity gaps in NOK both under the baseline and in case of potential disruption of secured or unsecured wholesale funding. - Norwegian banking groups are exposed to maturity mismatches and rollover risks because of reliance on currency swaps to convert foreign currency (FX) wholesale funding (mostly of longer maturity) and finance NOK assets. ### Regulatory context and IRB-related measures - Transition to Basel II led to a decline in RWAs for IRB banks and potential inflation of measured capital positions. - Norwegian authorities introduced measures to ensure adequate credit risk measurement at IRB banks: - A “Basel I floor” transitional rule requiring IRB banks to set RWAs to at least 80 percent of RWAs under Basel I; the floor has been set at 80 percent since 2009 and will continue to be applied in Norway in the foreseeable future. - Tightened requirements on banks’ IRB models for estimating residential mortgage risk: - Banks expected to assume that crises on average occur once every five years, with an average PD of about 3.5 percent in crisis years. - LGD estimates subject to a 20 percent floor; model-based LGD calculations expected to be aligned with LGD estimates from a reference model linking LGDs and loan-to-value ratios (LTVs). - The FSA estimates the new PD and LGD requirements should increase average risk weights on residential mortgages to about 20–25 percent, from 10–15 percent previously. - Note: regulatory IRB risk weights may still underestimate potential risks given rapid growth of house prices and household debt in recent years. ### Policy recommendations and further steps - Enhance stress testing frameworks: - Consider supplementing frameworks with models based on international experience, given low sensitivities of household default measures to macroeconomic conditions and limited variability and short data spans of credit risk metrics. - Further cross-validate results from TD and BU exercises to address possible underestimation of risks in BU stress tests. - Continue use of supervisory discretion on the sufficiency of banks’ capital buffers, especially the capital conservation buffer, to ensure resilience. - Strengthen liquidity stress testing and liquidity management: - Consider performing stress tests using the structure of cash flows at various maturities. - Consider performing customized versions of the LCR more closely aligned with banks’ funding profiles. *Source: EXECUTIVE SUMMARY, FSAP Norway (content unit: _cr15258 - EXECUTIVE SUMMARY).* ### 5. Lending to the private sector has increased considerably in recent years, accompanied ### _cr15258 - 5. Lending to the private sector has increased considerably in recent years, accompanied ### Lending trends and composition - Private sector lending has increased considerably faster than income, with the ratio of credit to mainland GDP almost doubling since the mid–1990s. - Household debt: - Increased from about 150 percent of disposable income at end–2003 to about 220 percent at end–2014. - Has been considerably above the OECD average. - Corporate credit: - Rose rapidly, particularly in the years preceding the crisis, but has slowed down recently as corporates have been raising more capital in the bond market and overseas. - The commercial real estate sector—particularly in Oslo, where prices have increased rapidly in recent years—accounts for the largest share of corporate loans. - Household lending composition: - Residential mortgages (typically variable-rate) account for about 90 percent of household lending by banks and mortgage companies. ### Banks’ funding structure - Wholesale funding: - Accounts for about half of banks’ liabilities. - A large proportion (62 percent) is in foreign currencies and is procured via international markets. - Reflects reliance on non-deposit funding that supported rapid growth of household and corporate lending. - Shift since 2008 crisis: - Share of covered and senior bonds in FX-denominated funding increased to 34 percent at end–2014 from less than 12 percent at end–2008. - Similar (albeit smaller) shift in domestic funding toward longer-maturity instruments. - Challenges: - Difficulty accumulating high-quality liquid assets in NOK; average LCR in NOK across Norwegian banks is low, below the 60 percent mark currently considered by NB. ### Key risks and vulnerabilities - High credit risk exposure in case of considerable deterioration in macroeconomic conditions, exacerbated by: - High private sector leverage. - Likely significant overvaluation of real estate prices. - Economy’s high dependence on the oil sector making persistent low oil prices a possible trigger. - Real estate and interest-rate channel: - Banks’ high exposures to the real estate sector make them vulnerable to a sharp increase in interest rates and sustained decline in income. - Almost all household loans are variable-rate, creating indirect credit risk if interest rates rise. - Banks’ losses are more likely to accumulate to corporate portfolios rather than household portfolios because: - (i) Households have sound repayment buffers given high financial asset holdings. - (ii) Full recourse nature of mortgages leads households to prioritize mortgage payments. - (iii) Banks’ loss absorption capacity is enhanced through LTV regulatory guidance. - Oil-price related risks: - Banks’ direct exposures to the oil sector are relatively limited, accounting for about 1.5 percent of outstanding corporate loans. - Indirect bank exposures to the oil sector could be as high as 30 percent. - A persistence of low oil prices could trigger a generalized slowdown, translating into losses for the banking sector. - Macroprudential mitigants: - FSA-recommended upper limits on banks’ LTV ratios and affordability checks on loan underwriting can curb potential bank losses. - Liquidity and funding risks: - Past experience (2008) showed reliance on short-term and foreign currency wholesale funding can produce substantial liquidity risks when liquid asset buffers are insufficient. - Despite a lengthening of funding maturities (covered and senior bonds up to 34 percent of wholesale funding at end–2014 from 7.8 percent at end–2008), banks still face: - Challenges building NOK liquid asset buffers (average LCR in NOK below 60 percent). - Vulnerability with more than 60 percent of wholesale funding in foreign currency (mostly from foreign sources). - Dependency on currency swap markets to convert FX funding into NOK creates potential maturity mismatch and rollover risks because: - Most currency swaps have maturities of 3 months or less and the maturity with the highest turnover is tomorrow next (TN). - Cross-currency basis swaps typically have longer maturities and are used by mortgage companies to fully hedge risks. - Mortgage companies are required to hedge FX risks at the same maturity as the bonds, but this requirement may not be met at the group level if mortgage companies hedge with parent companies that use shorter-term hedges. - Cross-holdings risk: - Banks’ cross-holdings of covered bonds account for more than half of domestically-issued covered bonds. - Cross-holdings can exacerbate liquidity risks under systemic stress as banks may simultaneously sell each other’s covered bonds, negatively affecting market liquidity and issuance. ### Stress testing practices by Norwegian authorities - Authorities’ regular testing: - FSA and NB carry out periodic solvency stress tests; results published in FSA’s Risk Outlook and NB’s Financial Stability Report. - Framework differences: - FSA framework: supervisory unconsolidated data; macro-financial model linking PDs of individual corporates to corporate sector projections under stress; estimated PDs matched to each bank’s exposure to the specific corporate client. - NB framework: commercially-available consolidated institution-specific data; macro-financial model linking aggregate-level problem loan shares of corporate and household exposures to stressed macroeconomic projections. - Coverage: both frameworks incorporate the largest six banks, accounting for 67 percent of total assets on a consolidated basis (NB) and 75 percent on an unconsolidated basis (FSA). - Liquidity stress testing: - FSA carries out liquidity stress tests based on the Basel III / CRD IV LCR. - Framework relies on ongoing reporting of individual bank LCRs over a 30–day horizon, monitoring past cash inflows and outflows, and of maturing assets and liabilities. ### FSAP stress testing framework and execution - Scope and collaboration: - FSAP stress tests evaluated resilience of the banking sector to a wide range of macrofinancial shocks. - Solvency stress tests conducted in close collaboration with Norwegian authorities and included “rule of thumb” estimations by FSAP team in parallel with existing FSA and NB frameworks. - Assessment included solvency stress tests by the top six banks, sensitivity analyses for exchange rate shifts and exposure concentrations, and liquidity stress tests by the FSAP team. - An analysis of cross-sector and cross-border spillover effects was carried out separately. - Methodological approach: - Both top-down (TD) and bottom-up (BU) approaches used. - TD stress tests: three separate but coordinated exercises by FSA, NB, and FSAP team. - BU tests: carried by individual banks using their own internal data and tools. - Data reference dates: tests used data as of end–September 2014 and end–2014; when based on end–September information, balance sheets and income statements were projected to end–year. - Coverage: six largest banks included; accounted for 75 percent of aggregate banking sector assets on an unconsolidated basis (excluding mortgage companies) and 67 percent on a consolidated basis. Tests did not cover Kommunalbanken. - Data sources: IMF and FSA tests used confidential supervisory data as of end–2014; NB relied on publicly-available data as of end–September 2014. - Capital assessment: - Impact on banks’ capital assessed based on Basel III solvency benchmarks. - Capital adequacy evaluated based on the CET-1 capital ratio under the accelerated Basel III schedule followed by Norwegian supervisors, including a conservation buffer, a systemic risk buffer, and a surcharge for domestic systemically important banks (D-SIB). - Capital definition based on Basel III; risk-weighted assets estimated under the IRB method of Basel III under the TD approach of the FSA and the FSAP team, and in line with problem loan shares in the NB approach. - Basel III phase-in in Norway: - Norwegian authorities adopted an accelerated schedule for the phase-in of Basel III capital adequacy requirements. - In the beginning of July 2013, Norwegian banks adopted the requirements of the EU capital framework (CRR/CRD IV). - The minimum required CET-1 ratio was set at 9 percent, including a minimum requirement of 4.5 percent, a (permanent) conservation buffer and a systemic risk buffer. - Note: After the stress testing exercise, the Ministry of Finance increased the countercyclical buffer (CCB) to 1.5 percent, starting June 30, 2016; this increase is not reflected in the stress testing results. ### Solvency stress test scenarios and key assumptions - Scenario design: - Three macroeconomic scenarios: a baseline reflecting anticipated trajectory, and two versions of an adverse macroeconomic scenario. - Adverse scenarios consistent with IMF’s views of global risks; severe but plausible events with considerable negative deviations from baseline. - Scenarios have a risk horizon of 5 years to end–2019. - Baseline reflects IMF staff projections as of December 2014, estimated in part via NB’s macro model. - Two adverse scenarios: - Adverse scenario with no monetary policy response: - Assumes upsurge in global financial market volatility, higher financing costs, strains on fiscal sustainability, slowdown of Norway’s trading partners, prolonged period of significantly lower oil prices, strong downward impact on domestic growth, higher unemployment and a sharp correction in real estate prices. - Under these assumptions, real GDP growth declines cumulatively by 6.7 percent over 5 years (3.1 standard deviations from long-term trend), and the level of GDP declines 16.1 percent below the baseline by 2019. - Adverse scenario with monetary policy response: - Same external shocks as above but presumes monetary easing (a 1½ percentage point cut in the policy rate in 2015-16; without fiscal measures) to offset the effects of the shock, in line with NB’s assumptions. - Under this scenario, real GDP growth declines cumulatively by 4.3 percent over 5 years (a 2¾ standard deviation from long-term trend) and the level of GDP declines 13.9 percent below the baseline. - Common adverse scenario calibration: - Sustained lower nominal oil prices at US$40 per barrel over the entire stress testing horizon. - Increase in money market rates by about 200 bps and wholesale funding spreads by an additional 150 bps (relative to baseline), starting in 2015. - A 40 percent decline in real property prices over five years. - Severity and rationale: - These shocks translate into a decline of 2¾ to 3.1 standard deviations of five–year cumulative real GDP growth rate relative to the baseline—the worst five–year cumulative growth decline in Norway’s recent history. - Scenario severity is justified by Norway’s high structural dependence on oil, build-up of imbalances (high leverage and house prices), and the relative stability of past economic growth which makes large deviations more impactful. - Scenarios are broadly in line (albeit of longer duration) with previous TD stress tests by the FSA and NB. *Source: IMF staff (document text as provided).* ### 20. The NB’s macroeconomic model was used to map the evolution of various ### _cr15258 - 20. The NB’s macroeconomic model was used to map the evolution of various ### Macroeconomic mapping and macrofinancial risk factors - The NB’s macroeconomic model was used to map the evolution of macrofinancial risk factors under each macroeconomic scenario. - Macrofinancial risk factors include inflation, the unemployment rate, the policy interest rate, credit growth, house prices, deposit and lending rates, and exchange rates. - The factors were estimated via the NB’s macroeconomic model. ### Market risks: assumptions and shock design - All market risk shocks were assumed to occur instantaneously and remain permanent, without price recovery over the stress testing horizon. - Market risks evaluated: potential valuation losses on sovereign bonds, covered bonds, corporate bonds and equities. - Shocks affect all securities in trading and available-for-sale (AFS) accounts and persist through the entire stress testing horizon. - Shock magnitudes were determined from empirical or fitted distributions of past price dynamics for each asset class. - Equities: haircut determined at the 80th percentile of the fitted distribution of the quarterly extrema of daily year-on-year MSCI Norway price changes between 1971 and 2014, accounting for a haircut of 26 percent. - Debt securities: yields assumed to rise by 200 basis points; yields on foreign sovereign bonds increasing based on an extreme value distribution fitted to past yield changes. ### Sovereign bond loss assumptions and outcomes - Losses on sovereign bond holdings estimated assuming loss of “safe haven” status for Norway and higher market expectations of default risk in other countries. - Norwegian sovereign bond yields under stress: - Assumed to increase by 200 bps across maturities in 2015. - Corresponding haircut of 10.4 percent (Table 3). - Sovereign yields assumed to edge up in the baseline in line with forward rates. - Jointly, yields rise to 4 percent by end–2019, a cumulative increase of about 270 basis points by end–2019. - Yields on banks’ holdings of other countries’ sovereign debt are assumed to increase in 2015 and remain elevated; haircuts estimated from historical distribution of daily year-on-year changes in 5–year government bond yields between 2006 and 2014. - Table 3 (selected rows reproduced exactly as in source): - Change in yield (bps) Stressed yields (in %)Haircut (in %) - Norway200.03.410.4 - Sweden147.41.87.6 - Netherlands137.41.67.1 - Germany137.51.57.1 - Denmark142.71.67.4 - Finland132.91.56.8 - France141.01.77.3 - Belgium230.92.512.1 - Austria142.01.67.3 - Italy211.43.311.0 - Spain193.73.010.1 - Greece1056.017.664.4 - Ireland142.82.07.4 - Czech Republic136.61.67.0 - Hungary328.66.617.4 - Poland183.13.99.4 - UK131.62.86.7 - USA151.13.17.8 - Note: With the exception of Norway, 95th percentile of the distribution; 80th percentile for Italy, Spain, and Hungary; 80th percentile of the empirical distribution for Greece and Ireland, as the extreme value distribution not a good fit. ### Sensitivity tests (BU basis) - Sensitivity tests estimate the effect of one single factor at a time; shocks materialize instantaneously with immediate negative impact on capital. - Banks could not use profits or make behavioral adjustments to counteract shocks. - FX shock: - Assessed impact of extreme NOK depreciation against major currencies on net open positions. - For each currency, shock magnitude set at two times the maximum shift of the annualized FX volatility from its long-term level (corresponding to the 2008–11 financial crisis). - Banks estimated impact on both P&L and the RWAs of the trading book in 2015 (at 100 percent of the calibrated shock) and 2016 (at 50 percent of the calibrated shock). - Credit concentration risk: - Banks estimated potential losses assuming defaults of their largest 1, 2, 5, and 10 exposures, per definition in the IMF’s Financial Stability Indicators (FSIs). - Banks estimated direct impact on capital assuming an LGD of 45 percent. ### Credit risk models — underlying assumptions (common to TD and BU solvency tests) - Banks’ balance sheets assumed static, apart from credit growth (largely in line with nominal GDP growth); banks cannot deleverage over the stress testing horizon. - New problem loans assumed to maintain default status, with banks provisioning fully any losses over the entire 5–year stress testing horizon. - Banks’ funding structure assumed fixed; banks cannot shift funding sources in response to changes in relative funding costs. - Stress tests do not account for potential risk-mitigating managerial actions; credit portfolio composition is assumed fixed and banks cannot raise capital over the stress testing horizon. - Modeling of shock transmission does not account for the impact of new policy measures, except for the monetary policy response in the second adverse scenario; automatic fiscal stabilizers are not accounted for in the stress testing framework. ### FSA (Finanstilsynet) methodology - Multi-model approach to estimate impact of credit risk on banks’ capital adequacy for exposures to non-financial corporates. - Steps: 1. Project key balance sheet risk factors of non-financial corporates based on stressed macro variables using an autoregressive distributed lag model (ADL). 2. Use a generalized additive model (GAM) logistic regression (SEBRA model) to calculate stressed PDs for individual non-financial corporates. 3. Estimate banks’ expected losses by matching PDs for each corporate to banks’ exposures to that corporate. - Household PDs proxied via changes in corporate PDs from SEBRA; FSA estimated household stressed PDs starting from each bank’s actual default rate at end–2014 and updating annually based on estimated corporate PD changes. - LGD assumptions: - For non-financial corporates: in line with Moody's approach—equivalent to 35 percent plus a factor that increases with each bank's debt-weighted PD; LGDs increase to between 35 and 53 percent under adverse conditions depending on scenario and year. - For households: LGDs assumed to increase from 25 percent in 2015 to 35 percent in 2017–19 in adverse scenarios. - Baseline LGD set at 20 percent for both corporate and household exposures. ### Norges Bank (NB) methodology - Two-step approach: model problem loan ratios for aggregate banking system as a function of macroeconomic risk factors, separately for corporate and household exposures. - ECM models obtain projected problem loan ratios under stress; one-period change in estimated ratios used as proxy for stressed PDs. - Banking balance sheet model projects banks’ financial statements and estimates capital adequacy under stress. - For FSAP stress tests: - No investor return or capital targets assumed; banks precluded from distributing dividends under stress. - Lending and funding rates determined endogenously by the macro model. - Loss given problem loans (proxy for LGDs under stress): expert judgment at 40 percent for corporate loans and 25 percent for household loans. - Data: quarterly consolidated institution-specific data and credit risk data for aggregate banking sector. ### NB model projections and limitations - NB model projects substantial deterioration in corporate credit quality under stress: - Corporate problem loans increase to close to 30 percent by end–2019 under the adverse scenario without policy response, well above the 18 percent peak after Norway’s banking crisis of the early 1990s. - Household credit deterioration is more muted in the NB model: - Household problem loans increase to only 6.3 percent by 2019 under the adverse scenario without policy response, versus a historical peak of about 12 percent after the banking crisis. - The limited rise in household problem loans suggests statistical models may understate household-related credit risks when macroeconomic deterioration is considerably more severe than past precedents. ### FSAP team’s “rules of thumb” (satellite model) - Applied because of low sensitivity of household-related bank losses in statistical models and prolonged period of benign household credit quality. - Link potential credit losses to severity and duration of strain measured by shocks to cumulative real GDP growth. - Calibration: - Cumulative losses over the stress horizon determined by deviation of projected 5–year cumulative GDP growth from its long-term (20–year) trend. - Sensitivity coefficient of 0.4 for the adverse scenarios (corresponding to evolution of credit losses at times of crises). - Evolution of credit losses across years mapped to trajectory of GDP growth; bank losses peak in 2017, the trough year for GDP growth. - LGDs determined based on empirical relationship (presented in source). - Resulting estimated loss rates: - In most adverse scenario (without policy response), “rule of thumb” peaks loss rate at 3.6 percent. - By comparison, maximum loss rates are 1.5 percent (FSA) and 2.1 percent (NB). ### Results and capital position - Banks’ capitalization viewed as strong with relatively high capital buffers. - Regulatory minimum requirement (floor) on RWAs ensures capitalization more closely reflects internal risks. - Capitalization at initial point (end–2014): - Aggregate CET-1 ratio at 12.9 percent on a consolidated basis. - Aggregate CET-1 ratio at 12.4 percent on an unconsolidated basis. - Under the stress test’s baseline scenario (absence of macroeconomic shocks), banks in aggregate expected to be able to meet the new requirements, though differences exist across banks. *Sources: Statistics Norway; NAV; Thomson Reuters; Eiendom Norge; FINN.no; Eiendomsverdi; Dagens Næringsliv; OPAK; Norges Bank; and IMF staff.* ### 35. However, in case of deterioration of macroeconomic conditions, as assumed in the ### _cr15258 - 35. However, in case of deterioration of macroeconomic conditions, as assumed in the ### Solvency stress tests: adverse scenarios and drivers - Under the adverse scenario without policy response, the CET-1 ratio would fall by between 4.1 and 6.7 percentage points to the 6.3–8.3 percent range (depending on the specific approach). - The IMF’s TD approach produces the largest losses and decline in capitalization: CET-1 falls from 12.9 percent at end–2014 to 6.3 percent at end–2019 under the adverse scenario without policy response. - Reasons for variation across approaches: - IMF approach uses parameters based on global experience with severe crises (larger losses). - Norwegian authorities (FSA and NB) use historical risk parameters on banks’ credit exposures (PDs and problem loans), yielding more moderate results given Norway’s long history of low credit risks. - Contributions to the decline in CET-1 (IMF decomposition): - Increase in loan losses: contributing by an average of 1.4 percent a year to the decline of the CET-1 ratio under the scenario with no policy response. - Rise of RWAs: contributing by 0.8 percent. - Higher funding costs: contributing by 0.3 percent. - Capital shortfall under the adverse scenario without policy response: 2.7 percent to 4.6 percent of GDP by 2019 (depending on the estimate). ### Loss composition: corporate vs household exposures - Most credit losses stem from corporate exposures; household exposures produce small losses. - Under FSA’s and NB’s TD approaches (calibrated on Norway’s historical experience): - Household loss rates: 0.2 to 1.1 percent per year. - Corporate loss rates: 0.7 to 2.7 percent per year. - Drivers of low household loss magnitudes: - Full recourse nature of mortgage loans. - High welfare support. - Constraints in modeling severe stress in environments with limited past credit-quality variability (survivorship bias). - Oil sector exposure: - Banks’ direct losses from the oil sector are small due to limited lending activity. - Indirect credit losses from oil-related corporate exposures are likely significantly higher, but quantification is hampered by identification issues. ### Bottom-up (BU) versus top-down (TD) stress test results and conservatism - BU stress tests show much milder adverse effects on capitalization than TD: - CET-1 declines by 0.6 percent (unconsolidated) and 1.4 percent (consolidated) over the stress horizon under BU. - TD approaches show a 4.1—6.7 percentage point drop. - Main driver of discrepancy: lower assumed credit loss rates in BU: - BU peak credit loss rates: 1 percent a year. - TD peak credit loss rates: 1.5 to 3.6 percent a year. - Implication: Banks should consider introducing more conservative assumptions and calibration of risk parameters (e.g., reliance on expert judgment for large corporate exposures, survivorship bias in household loss estimates). - Additional BU finding: solvency risks tend to be lower on a consolidated basis because banks transfer high-quality household loans to owned mortgage companies. ### Sensitivity analyses: concentration and FX risks - Credit concentration: BU simulations of defaults of banks’ largest borrowers (up to 10 largest) applied to end–2014 positions and assumed to materialize immediately show banks can absorb defaults of their largest clients: - CET-1 ratios remain above the regulatory minimum even after default of the 10 largest borrowers (with only one smaller bank needing additional capital). - FX shocks: impact is negligible: - Large foreign exchange shocks set at two times the maximum shift of the annualized FX volatility from its long-term level have minimal effect because banks’ FX open positions are very small and well below position limits. ### Liquidity stress tests: framework and calibration - Framework: - Liquidity stress tests assessed resilience to sizable sudden funding outflows and access to stable funding, based on recalibrated Basel III metrics (LCR and NSFR). - Tests completed on a TD basis, independently from solvency analysis, using data as of end–2014 and end–September 2014. - Analyses carried out on a consolidated basis (67 percent of aggregate banking sector assets), accounting for bank-owned mortgage companies. - Metrics: - LCR (30–day stressed scenario): numerator = HQLA holdings; denominator = potential net cash outflows (subject to a 75 percent cap on cash inflows); LCR < 100 percent indicates a funding shortfall. - Norwegian banks face a shortage of HQLAs in NOK. - NB has (on a preliminary basis) suggested a regulatory NOK LCR of 60 percent. - NSFR (one-year stable funding): final specification still subject to deliberation; expected introduction beginning of 2018. ### Liquidity scenarios tested - Three scenarios: baseline (identical to standard LCR) and two adverse scenarios reflecting extremely severe market liquidity stress: - Adverse scenario 1: complete dry-up of unsecured wholesale funding (motivated by 2008 crisis experience). - Adverse scenario 2: complete dry-up of secured wholesale funding, partial dry-up of unsecured wholesale funding (including corporate deposits), and strong outflows of committed credit and liquidity facilities (complements scenario 1; reflects possible loss of investor confidence in Norwegian covered bonds). - Assumptions (impacting net cash outflows and counterbalancing capacity): amortization/renewal rates, asset value declines, callback rates on contingent claims and liabilities; cash inflow rates and haircuts identical to those in the LCR. ### Liquidity test results: NOK and FX outcomes - Aggregate NOK LCR and gaps: - Aggregate LCR for the banking sector declines from 33 percent in the baseline to 18 percent under a dry-up of unsecured wholesale funding. - NOK liquidity gap grows to NOK306 billion under dry-up of unsecured wholesale funding, from NOK120 billion in the baseline. - Under the secured wholesale funding dry-up (second adverse scenario), the NOK gap is NOK232 billion. - Aggregate NOK LCR under EU Delegated Act at end–2014: 33 percent, down from 49 percent at end–September 2014. - FX liquidity: - Most banks are above the 100 percent LCR threshold under Basel III in FX. - Aggregate FX LCR at 124 percent in case of complete dry-up of unsecured funding (most adverse scenario). - FX funding gaps: increase to NOK5.3 billion (dry-up of unsecured wholesale funding) and NOK3.7 billion (dry-up of secured wholesale funding) from NOK3 billion in the baseline. - Effect of broader recognition of covered bonds as HQLAs (EU Delegated Act, October 2014): - Norwegian banks’ qualifying covered bonds represent about 25 percent of HQLAs in NOK and 21 percent in FX as of end–2014. - Recognition of covered bonds improves FX liquidity positions but LCRs deteriorated in NOK terms. - Increased reliance on covered bonds as HQLAs raises concerns about liquidity risks due to high cross-ownership of these instruments. - Additional note: banks hold additional assets (mostly covered and corporate bonds, including own issuances) that do not meet EBA requirements but may be eligible for NB’s standing facilities; FSAP team could not verify eligibility. Even under lenient haircuts (in line with Level 2b assets), liquidity conditions remain broadly similar. *Source: IMF staff estimates and excerpts from the provided FSAP material.* ### 45. Stress tests should always be interpreted with caution, as they are subject to data, ### _cr15258 - 45. Stress tests should always be interpreted with caution, as they are subject to data, ### Caution and limitations of FSAP stress tests - Stress tests are subject to data, methodology and coverage constraints. - Data limitations: - FSAP stress tests are based on market and supervisory data that are collected (and hence, reflect risks) at a specific point in time, and have not been subject to an independent data validation. - Data coverage constraints: - Tests do not include certain segments of the banking system (e.g., smaller commercial and savings banks, and foreign bank branches), as well as credit exposures to sectors other than corporates and households. - Ability to account for feedback loops: - FSAP solvency tests do not capture nonlinear macrofinancial feedback channels. - In Norway, amplification of credit risks via some channels (for example, a downward spiral in housing prices) are mitigated by structural features such as high household wealth, and ample and long-lasting unemployment benefits. - In the absence of models able to capture such effects, it is not feasible to quantify the possible downward bias in credit risk estimates. - Tests do not account for the impact of banks’ response to financial distress, such as a change in their business mix. ### Areas for enhancing authorities' stress test frameworks - Augmenting household credit risk measurement: - Low sensitivities of household default measures to changes in macroeconomic conditions suggest supplementing existing measures with measures based on international experience. - Limited variability and short data spans of household problem loans and PDs account for weak statistical relationships between household credit risk measures and macroeconomic conditions. - Even under very extreme macroeconomic scenarios—including an assumed 40 percent drop in real housing prices—credit risks related to household exposures are perhaps underestimated. - The “rule of thumb” applied by the FSAP stress testing team is noted as only one possible approach. - Incorporating market risks in solvency stress tests: - Authorities intend to expand their TD solvency modules to account for market risks, related to bond, equity and (possibly) derivatives holdings. - Cross-validating BU and TD stress test results: - Norwegian banks appear well-positioned to meet regulatory capital requirements under baseline projections. - Even under severe shocks, high potential losses in the FSAP stress tests appear manageable. - Possible underestimation of risks in BU stress tests warrants further cross-validation of the TD and BU exercises and justifies continued proactive steps to ensure banks maintain sound capital buffers. - The FSA’s use of additional Pillar 2 buffers has been valuable. - Enhancing liquidity stress testing framework: - Authorities monitor banking sector liquidity via onsite supervision, ad hoc requests, and reporting tools including the LCR, the NSFR, past cash inflows and outflows, and tenor buckets of maturing assets and liabilities. - Banks’ liquidity situations are followed more closely, via more frequent reporting, where there are indications of higher funding market volatility. - Further steps to monitor and reduce potential risks related to banks’ wholesale funding exposures could include: - Performing liquidity stress tests using the structure of cash flows at various maturities. - Performing customized versions of the LCR more closely aligned with banks’ funding profiles. - Adoption of such approaches would require time, particularly given changing banks’ reporting requirements, but would facilitate a more anticipatory approach to identifying potential liquidity difficulties. ### Risk Assessment Matrix — key risks, likelihoods, and expected impacts - Note on RAM interpretation: - The RAM shows events that could materially alter the baseline path (the scenario most likely to materialize in the view of IMF staff). - Relative likelihoods: “low” indicates a probability below 10 percent, “medium” between 10 and 30 percent, and “high” between 30 and 50 percent. - High / Medium: Protracted period of slower growth in key advanced and emerging economies - Sources: - Euro Area and Japan: Weak demand and persistently low inflation from failure to fully address crisis legacies and appropriately calibrate macro policies. - Emerging markets: Maturing of the cycle, misallocation of investment, and incomplete structural reforms leading to prolonged slower growth. - Expected impact: High - Protracted slower global growth would weaken non-oil exports and contribute further to lower oil prices, resulting in economic slowdown and higher unemployment due to a fall in exports and less oil investments. - Lower asset prices would impact negatively GPFG's rate of return. - A surge in global financial volatility (Likelihood: High) - Description: - Prices of risky assets drop abruptly; investors move to safe assets; rise in actual and expected volatility; global growth negatively impacted. - Expected impact: Medium - Renewed stress in global wholesale funding markets would lead to liquidity strains for Norwegian banks that rely on FX wholesale funding. - The large presence of foreign-owned banks increases spillover risks. - Impaired global demand would lead to economic slowdown due to fall in exports, less oil investments, and impaired consumer confidence. - Protracted low energy prices (Likelihood: Medium) - Description: Persistently low energy prices triggered by supply factors reversing only gradually, and weaker global demand. - Expected impact: High/ Medium - Persistently low oil prices would weaken growth directly via reduction in oil-related demand for mainland goods and services, and indirectly via reduction in demand for housing due to confidence effects or reversal of immigrant inflows. - A significant drop in house prices (Likelihood: Medium) - Description: - Norway has the highest house price-to-rent ratio relative to its historical average among OECD economies; risk of significant overvaluation. - Expected impact: High - A fall in house prices would dampen private consumption and reduce residential investment. - High household debt level may cause a sharp contraction in household consumption and retail sales, leading to potential rise in default rates and higher solvency risks for banks. ### Stress Test Matrices — solvency and liquidity design and assumptions - Solvency (overview of BU and TD exercises) - Institutions included: - Top 6 commercial and savings banks: DNB Bank, Nordea Bank Norge, SpareBank 1 SR-Bank, SpareBank 1 SMN, SpareBank 1 Nord-Norge, and Sparebanken Vest. - Market share covered: - 75 percent of banking sector assets (excluding mortgage companies; unconsolidated basis). - 67 percent of banking sector assets (including mortgage companies; consolidated basis). - Data and baseline date: - Banks’ internal data as of December 2014. - Bank-by-bank supervisory data as of December 2014. - Bank-by-bank commercial data and aggregate data as of September 2014. - Consolidation: - Consolidated and unconsolidated basis (2 tests); unconsolidated basis; consolidated basis (including mortgage finance companies). - Methodology: - Banks’ internal risk management framework; Guidance from FSAP team; Finanstilsynet models; Norges Bank’s models; Global "rule of thumb" approach and balance sheet solvency framework. - Stress test horizon: - 5 years. - Exposure coverage: - Credit risks related to: (i) aggregate exposures; (ii) sectoral exposures (e.g., corporate, mortgages, other household lending; other financial institutions; FX loans); (v) exposures to industries; and (vi) exposures to up to 10 largest obligors. - Credit risk-sensitive (corporate and household loans) and market risk-sensitive exposures (equity and debt). - Shocks — Scenarios: - Baseline: IMF staff macroeconomic projections as of December 2014, estimated via NB’s macro model. - Upsurge in global financial volatility and a considerable slowdown of global growth (w/out policy reaction): - A permanent rise in domestic and global spreads (money market spreads: up by about 200 basis points; wholesale funding spreads: up by additional 150 basis points (over stress testing horizon; relative to baseline)); a slowdown of the world economy; sustained drop of oil prices (to $40) over stress-testing horizon, starting in 2015; a real house price decline of 40 percent over 5 years; a cumulative 5–year decline of 6.7 percent in real Mainland GDP (16.1 ppt cumulative drop relative to baseline). - Upsurge in global financial volatility and a considerable slowdown of global growth (w/ policy reaction): - Identical scenario, but allowing for monetary policy easing (policy rate down by 1½ ppts in 2015-16); a cumulative 5–year decline of 4.3 percent in real Mainland GDP (13.9 ppt cumulative drop relative to baseline). - Sensitivity analysis (examples in BU by banks): - Exchange rate depreciation: shock set at two times the maximum shift of the annualized FX volatility from its long-term level. - Credit concentration risk: default of the largest one, three, five and ten exposures. - Behavioral adjustments and constraints: - Balance sheets assumed to be static, except for credit growth based on macro model (no deleveraging allowed). - Corresponding funding increases in line with projections from macro model. - No changes in structural business models and managerial decisions allowed. - Dividend payout ratio assumed to be zero under stress. - Income composition assumed to remain constant. - Asset disposals not permitted (apart from credit growth projection). - No raising of new capital allowed. - RWA adjustments and assumptions: - RWA assumed to be adjusted in line with PDs (IRB approach or standardized approach, depending on bank). - Basel IRB approach; through-the-cycle (TTC) PDs estimated as updated on long-term average PiT PDs. - RWA for new lending: marginal risk weight assumed to be 40% for lending to households and 80% for lending to corporates. Norway's transitional rule of the Basel I floor is taken into account. - RWA for operational risk estimated at 15 percent of (Net interest income + Net commission income + (net) Other income) * 12.5. - RWA for market risk is set as a fixed share of the holdings of financial instruments (at fair value). - Regulatory standards: - RWA per Basel 2.5 and III. - Hurdle rates for regulatory capital (CET-1) based on Norway's schedule (accelerated Basel III schedule). - Reporting format: - Post-shock solvency ratios and losses by type of exposure. - Distribution of capital ratios across the banking system; aggregated basis. - Liquidity (top-down by the IMF) - Institutions included: - Top 6 commercial and savings banks: DNB Bank, Nordea Bank Norge, SpareBank 1 SR-Bank, SpareBank 1 SMN, SpareBank 1 Nord-Norge, and Sparebanken Vest. - Market share: - 67 percent of banking sector assets (including mortgage companies; consolidated basis). - Baseline date: - September 2014 and December 2014. - Consolidation: - Consolidated basis. - Risks covered: - Systemic funding and market liquidity risks (withdrawal and market freeze; uniform shocks across banks; independent of solvency tests). - Buffers: - Counterbalancing capacity assessed via unencumbered assets at market values net of haircuts (by type of securities). - Test horizon: - 30 days. - Methodology: - LCR / NSFR. - Analysis assumes wholesale funding difficulties and deposit withdrawals (funding risk), and fire sales of assets (market liquidity risk) to meet liquidity constraints. Asset-specific haircuts are assumed. - Shocks: - Magnitude of shocks in line with severe liquidity difficulties experienced by banks globally after the Lehman bankruptcy (first scenario) and IMF analysis of past liquidity episodes (second scenario). Both scenarios are more severe than the historical experience in Norway. - Dry-up of unsecured wholesale funding: Inability to rollover maturing unsecured wholesale funding. - Dry-up of secured wholesale funding: Inability to rollover maturing secured wholesale funding; deposit runs and withdrawal of contingent liabilities. - Regulatory standards: - LCR ratios; liquidity gaps; NSFR ratios. - Reporting format: - Distribution of banks with LCR under 100 percent (FX liquidity) and 60 percent (domestic liquidity). - Liquidity shortfall (in absolute terms), both FX and LCR. *Source: _cr15258 - 45. Stress tests should always be interpreted with caution, as they are subject to data,* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2015/_cr15258.pdf_