## _cr16191 ## Source details **Canonical URL:** [_cr16191](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16191.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16191.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16191.pdf.json) --- ### Executive summary — introduction and scope - Macroprudential stress testing is a central instrument of IMF financial system surveillance and a key component of the FSAP; stress tests focus on credit, market, liquidity, and contagion risk. - This Technical Note assesses Germany’s financial system with emphasis on spillover risk using structural and financial statement analyses, detailed stress tests for banks and insurance companies, and spillover risk analysis. - Coverage and data: - Solvency and liquidity stress tests cover all 1,776 banks operating in Germany. - The insurance sector analysis covers 93 percent of the life insurance sector in terms of the assets. - Solvency assessment uses the EU CRD IV/CRR framework for banks and Solvency II for insurers. - Data sources for banking stress tests included FINREP, COREP, 2015 EBA Transparency exercise, supervisory data from the Bundesbank, and Bankscope; confidential data access was limited to IMF staff working within Deutsche Bundesbank premises. ### Financial system structure and key features - Systemic importance and heterogeneity: - Two global systemically important financial institutions: Deutsche Bank AG and Allianz SE. - One of the largest global central counterparties: Eurex Clearing (interlinked with over 180 clearing members in 17 countries, including 24 G-SIBs). - The Bund is a safe haven and benchmark for fixed income instruments. - System is heterogeneous with a large number of smaller banks and insurance companies and a wide range of business models. - Banking sector evolution and vulnerabilities: - Number of banks has been decreasing steadily; consolidation since the global financial crisis. - Landesbanken have reoriented business models and are less homogenous. - Public ownership remains substantial, albeit declining. - Key vulnerabilities: drop in global demand affecting exporters and credit risk; structural changes in shipping and manufacturing; high interconnectedness of largest banks with European and global markets; low interest rates compress interest income; new regulatory and supervisory frameworks and bail-inable debt create a more restricted operating environment for banks. - Insurance sector pressures: - Ultra-low interest rates seriously affect life insurers and guaranteed-return products. - Capital adequacy ratios for insurers have trended downward. - Solvency II (effective January 1, 2016) places forward-looking pressures on life insurers. - Search-for-yield has led some insurers to invest in riskier assets. ### Macrofinancial scenarios analyzed - Three scenarios: - Global stress scenario: serious recessions in advanced economies, tightening global financial conditions, EM credit cycle downturns, oil prices drop, sharp asset price corrections and strong FX movements. - Return of the EA balance sheet recession: policy uncertainty and delayed reforms trigger higher sovereign yields in highly indebted EA countries, market refinancing costs rise, possible deflationary phase and heightened market volatility (including Brexit risks). - Excessive risk-taking/search-for-yield: prolonged low interest rates induce risky strategies by banks and insurers, lower market liquidity and asset price volatility, drops in deposit funding for some banks. - Negative rates mechanism: - Negative interest rates may accelerate margin compression; impact varies by banks’ ability to reprice loans, deposits and non-deposit liabilities, importance of net interest income, and ability to generate noninterest income. ### Stress testing results — banking sector (aggregate and by size) - Overall resilience: - Most banks are resilient due to substantial capital and liquidity buffers. - Under the baseline, banks on average would sustain current solvency levels; some banks may be challenged as net interest income compresses. - Adverse scenarios and outcomes: - Under adverse scenarios loan losses rise sharply; trading income and sovereign bond valuations decline. - Some larger banks show higher credit risk from transportation (including shipping) and manufacturing exposures. - Smaller banks mainly face lower capacity to generate net interest income and structurally high costs. - Liquidity: - Banks have ample counterbalancing capacity to withstand market and funding liquidity shocks and comply with regulatory standards. - Selected stress-test numeric outcomes: - Global Stress Scenario: CET1 ratio of large banks drops by 2.6 percentage points but remains above 10 percent; aggregate capital shortfalls: EUR 6.0 billion (0.2 percent of annual GDP). Thirty-two banks out of 1,755 in the small- and medium-sized bucket would see CET1 ratios drop below fully-loaded regulatory hurdle rates in 2018. - Euro Area Crisis Scenario: average CET1 ratio for large banks drops by 2.2 percentage points to 12.7 percent in 2018; capital shortfall EUR 4.2 billion (0.1 percent of annual GDP). Smaller banks: aggregate CET1 capital shortfalls around EUR 448 million; 30 small- and medium-sized banks breach regulatory hurdles. - Credit default probabilities could increase by up to 90 percent versus current levels; almost doubling of annual credit impairment needs from a very low level. - Larger banks suffer a 40 percent drop in trading income in adverse scenarios. - Interest rate sensitivity: - Banks’ own projections show profitability expected to decline by around 25 percent by 2019. - Under a persistent low interest rate phase, operating profit could slump by 50 percent on average (static balance sheet assumption). - If interest rates fall by a further 100 basis points: operating profits could decline by 60 percent (dynamic balance sheet assumption) or by 75 percent (static balance sheet assumption). ### Key banking system statistics and indicators (End-2014 or last available year) - Aggregate and mean indicators: - Tier 1 Capital Ratio: 15.1 percent - Total Capital Ratio (CAR): 19.3 percent - Liquid Assets to Total Assets: 13.0 percent - Liquid assets to short-term funding: 19.9 percent - ROAE: 3.2 percent - ROAA: 0.3 percent - Net Interest Margin: 2.27 (percent) - Cost to Income Ratio: 71.8 (percent) - NPL Ratio: 3.54 (percent) - Provisioning Coverage Ratio: 44.1 (percent) - Interbank Ratio (Net interbank lending): 102.6 (percent) - RWA density (RWA to total assets) in December 2014: 31.2 percent - Aggregate leverage ratio (regulatory capital to total assets): 5.6 percent - Bank-type highlights (End-2014 or last available year): - Big banks: Tier 1 14.4; CAR 17.6; Liquid Assets to Total Assets 25.0; ROAE 4.1; NPL Ratio 3.9; Provisioning Coverage 42.5; Interbank Ratio 185.2. - Landesbanken: Tier 1 12.7; CAR 15.6; Liquid Assets to Total Assets 21.6; ROAE 2.5; NPL Ratio 6.7; Provisioning Coverage 31.9; Interbank Ratio 61.2. - Savings banks sector: Tier 1 15.4; CAR 18.2; Liquid Assets to Total Assets 11.4; ROAE 3.3; NPL Ratio 3.3; Provisioning Coverage 47.4; Interbank Ratio 103.3. - Real Estate & Mortgage Banks: Tier 1 15.3; CAR 17.1; Liquid Assets to Total Assets 14.7; ROAE 1.9; NPL Ratio 2.7; Provisioning Coverage 36.5; Interbank Ratio 143.5. - Comparative context: - NPLs in Germany fell from 3.3 percent in 2009 to 2.8 percent in 2014. - European peers’ average NPL ratio rose to 5.5 percent at end-2014. - More than 50 percent of banking sector assets in Germany (AQR sample) have NPL ratios below 5 percent. ### Cross-border interconnections and key statistics - Consolidated foreign claims of German banks on foreign banks, non-bank private sector, and the public sector: about USD $1.7 trillion in 2015Q2 (45 percent of GDP). - Foreign claims on Germany: about USD $1.3 trillion in the first half of 2015 (34 percent of GDP). - Major counterparties for German banks’ cross-border exposures: United States, United Kingdom, France, and Italy. - Major origins of consolidated foreign claims on Germany: Italy, France, the Netherlands, the United States, and the United Kingdom. - Europe and the United States have close linkages with Germany through trade, sovereign holdings, and cross-border exposures via the insurance sector. ### Sovereign risk and liquidity testing findings - Sovereign valuation losses: - Global Stress Scenario: valuation losses of EUR 3.0 billion reduce regulatory capital ratios by one-fourth of a percentage point. - Euro Area Crisis scenario (peripheral yields increase by 200 basis points): sovereign valuation losses of EUR 6.4 billion are less than half a percentage point of CET1 capital, on average. - Sovereign risk index interpretation: index value = 1 implies average risk; > 1 indicates disproportionally higher valuation loss relative to holdings. - Liquidity metrics and coverage: - Cash-flow based top-down liquidity stress tests for around 1,800 banks. - Net Stable Funding Ratio (NSFR) analyzed for the 70 German banks participating in the BIS QIS. - LCR tests show most of the 1,800 banks would withstand market and funding liquidity shocks; almost all banks show ratios above 70 percent and most already have LCR ratios above 100 percent. ### Insurance sector — investment profile, product mix, and size - Life insurers’ asset allocation (largest shares): - German government securities: 25 percent - Mortgage bonds: 21 percent - Bonds of financial institutions: 11 percent - Loans: 12 percent - Real estate: 4 percent - Equity exposure: 6 percent - Total alternative investments: 1 percent - Trends and search-for-yield evidence: - Corporate bond share increased from 4.3 percent in 2011 to 8.2 percent in Q2 2015. - AAA share in fixed income portfolios fell from 48.5 percent to 36.2 percent (2011–2014). - BBB rose from 6.6 percent to 9.9 percent (2011–2014). - Investment in Italian government bonds increased by 5 percent from 2013 to 2014 (from a low base); investment in Spanish government bonds increased by 25 percent from 2013 to 2014 (from a low base). - Asset duration for German life insurers increased from 8.1 years to 10 years (2011 to 2014). - Product mix and guarantees: - Products with guarantees dominate; unit-linked and related products account for less than 10 percent of total liabilities of life insurers. - Premiums from new sales of unit-linked products accounted for about 15 percent of total premium income in the last 5 years. - Average contractual guarantee rate currently stands at 3 percent. - Ministry of Finance: maximum rate for new policies reduced gradually to 0.9 percent by January 2017 (applies only to new policies). - Insurance sector size and supervision: - BaFin supervises 413 insurance companies; most numerous subgroup: 139 small mutual companies. - Total investments of insurers in 2014: EUR 1,569 billion (54 percent of GDP), composed of: Life insurers EUR 911 billion; Health insurers EUR 232 billion; P&C insurers EUR 154 billion; Reinsurers EUR 272 billion. - Number of insurers declined from 460 in 2008 to 413. ### Insurance sector stress testing — Solvency II top-down exercise and results - Scope and methodology: - Top-down stress tests performed for life insurance sector under Solvency II covering 75 life insurers (out of 86) or 93 percent of assets. - Target criterion: Value at Risk with 99.5 percent confidence level for a one-year horizon. - Scenarios: interest rate, equity, spread, property shocks; additional sovereign stress added. - Transitional measures: 16 years, benefit phased out linearly; tests run with and without transitional measures. - Scenario shock specifications (as applied): - Interest rates: shift of risk free yield curve down by 20 percent (long term) to 75 percent (short term). - Equity: a 22 to 49 percent fall in the price of equities. - Spread for corporate bonds and loans: shock levels depending on duration and credit quality (e.g., for a 5-year duration a 4.5 to 37.5 percent haircut). - Property: shocks of 25 percent for both commercial and residential real estate prices. - Sovereign bonds: 100 b.p. higher spreads of peripheries sovereign bonds, 25 b.p. higher spread of core sovereign bonds and 50 b.p. for the U.S., the U.K., and Japan. - Correlation matrix consistent with Solvency II standardized formula applied; correlation recognition estimated to reduce industry-level loss by around 20 percent. - Aggregated outcomes: - With transitional measures: - Weighted average SCR coverage ratios drop from 372 percent to 236 percent after shocks. - No firm would have negative capital after shocks. - 13 out of 75 firms would not be able to maintain a 100 percent SCR coverage ratio after shocks. - Resulting nominal capital shortfalls after shocks would not be material. - Without transitional measures: - Weighted average SCR coverage ratios fall from 126 percent to 48 percent after shocks. - 34 firms (before shocks) and 58 firms (after shocks) would not be able to meet a 100 percent SCR coverage ratio. - Eight firms and 27 firms would have negative capital before and after shocks, respectively. - Total capital shortfall: EUR 12 billion before shocks, and EUR 39 billion after shocks. - Loss Absorption Capacity (LAC) role: - LAC_TP and LAC_DT recognized with capping mechanisms (total LAC_TP ≤ FDB before shocks; total LAC_DT ≤ net deferred tax liabilities before shocks). - LAC_TP improved SCR coverage ratio significantly; without transitional measures interest rate and spread risks materially reduce SCR coverage ratios; LAC mitigates gross loss by more than 50 percent on average. - Non-linearity and sensitivities: - Capital shortfall is non-linear in loss amount; small losses may not increase shortfall but higher losses increase shortfall substantially. - Lapse risk (1-in-200 years’ event) reduces SCR coverage ratio by 17 percentage points; longevity risk reduces it by 7 percentage points. - Firm-level resilience drivers: - Business model, amount of unrealized gains, future discretionary policyholders’ bonuses, and average guaranteed rates explain resilience better than size in many cases. - Large insurers generally more resilient; many small protection-focused firms have high SCR coverage ratios; some medium-size insurers more vulnerable. ### Stress test design, assumptions, and methodological notes - Banking stress tests: - Bottom-up horizon: five years (2015–2019); top-down horizon: three years (2016–2018). - Credit losses modeled via Moody’s KMV 12-month EDFs; PDs and LGDs point-in-time for small/medium firms; large banks’ PDs/LGDs from COREP templates. - Regulatory standards: CRD IV/CRR fully loaded; IAS 39 accounting (HTM not marked to market); AFS prudential filter (60 percent) applied where relevant. - Behavioral assumptions: dividend payout 40 percent conditional on positive net profit; tax rate 30 percent applied; asset allocation invariant in stress (no management actions assumed). - Insurance stress tests: - Solvency II standard formula used; instant shocks and one-in-200 years’ calibration with added sovereign shocks (making exercise conservative). - No management actions assumed (no de-risking). - Key conservatisms and caveats documented: volatility adjustment application differences, loss convexity not taken into account, assumptions on FDB reporting and recognition, potential reporting errors. - Liquidity testing: - LCR and NSFR metrics analyzed; top-down cash-flow approach approximating CRD IV LCR for all 1,800 banks. - NSFR under observation period, aim to become binding by 2018. ### Systemic risk, interconnectedness, and spillovers - Methodologies: - Espinoza-Vega and Sole (2010) network framework using BIS consolidated banking statistics (2015Q1) to consider credit and funding shocks and network propagation across 16 BIS reporting countries. - Diebold and Yilmaz (2014) spillover analysis using daily equity returns to compute to-degree, from-degree, and net-degree measures of spillover and systemic contribution. - Main findings: - Higher degree of outward spillover from the German banking sector than inward spillover. - Germany, France, the U.K., and the U.S. have highest outward spillover degrees. - Failure of all other banking systems could lead to a 5 percent capital loss in Germany (similar to the U.K.); accounting for total exposures, the loss amounts to about 30 percent. - Within Germany, highest interconnectedness between Allianz, Munich Re, Hannover Re, Deutsche Bank, Commerzbank and Aareal Bank; Allianz is the largest contributor to systemic risks among publicly traded German financials. - Deutsche Bank is a major source of outward spillover to publicly listed banks and some insurers and is one of the most important net contributors to systemic risks in the global banking system (followed by HSBC and Credit Suisse). - Commerzbank tends to be a recipient of inward spillover from U.S. and European GSIBs. - Policy implication: Ensure resilience and close supervision of major institutions, particularly Deutsche Bank AG, and monitor linkages between banks and insurers. ### Policy recommendations and capacity building (excerpt) - Improve stress testing methodologies, data quality, and validation analysis to better monitor vulnerabilities and enable prompt action. - Establish a core set of readily-available, consistent data for all types of banks to facilitate financial stability and macroprudential policy analysis. - Ensure surveillance stress testing covers all banks and banking groups, including foreign and market risk exposures, despite the new supervisory framework. - For insurers unable to meet Solvency II requirements, require action plans and intensified supervisory attention. - Develop an effective communication strategy so investors and markets understand published Solvency II ratios; accompany disclosure of complex figures with explanation and credible recovery plans. - BaFin and federal government should review adequacy and sufficiency of insurance guarantee schemes (e.g., Protektor and Medicator); Protektor fund: EUR 897 million accumulated; maximum size could reach EUR 3.4 billion; additional special contributions up to EUR 863 million can be levied; separate private arrangements commit to additional funds up to about 1 percent of net technical provisions (some EUR 9 billion at present). *Source: IMF FSAP Technical Note — EXECUTIVE SUMMARY (content unit _cr16191).* ### EXECUTIVE SUMMARY ______________________________________________________________________________ 4 ### EXECUTIVE SUMMARY ### Introduction and scope - Macroprudential stress testing is a central instrument of IMF financial system surveillance and a key component of the FSAP; stress tests focus on credit, market, liquidity, and contagion risk. - This Technical Note assesses Germany’s financial system with emphasis on spillover risk using structural and financial statement analyses, detailed stress tests for banks and insurance companies, and spillover risk analysis. - Coverage and data: - Solvency and liquidity stress tests cover all 1,776 banks operating in Germany. - The insurance sector analysis covers 93 percent of the life insurance sector in terms of the assets. - Solvency assessment uses the EU CRD IV/CRR framework for banks and Solvency II for insurers. - Data sources for banking stress tests included FINREP, COREP, 2015 EBA Transparency exercise, supervisory data from the Bundesbank, and Bankscope; confidential data access was limited to IMF staff working within Deutsche Bundesbank premises. ### Financial system structure and key features - Systemic importance and heterogeneity: - Germany hosts two global systemically important financial institutions, Deutsche Bank AG and Allianz SE, and one of the largest global central counterparties, Eurex Clearing. - The Bund is a safe haven and benchmark for fixed income instruments. - The system is heterogeneous with a large number of smaller banks and insurance companies and a wide range of business models. - Banking sector evolution: - The banking system has consolidated and restructured since the global financial crisis; the number of banks has been decreasing steadily. - Landesbanken have reoriented business models and are less homogenous than before. - Public ownership in the banking system remains substantial, albeit declining. - Key vulnerabilities: - Drop in global demand affects German exporters and banking credit risk. - Structural changes in shipping and manufacturing increase loan risk. - Largest German banks are highly interconnected with European and global markets and are exposed to market volatility, which can substantially affect trading income, balance sheet positions, and share prices. - Low interest rates strain banks exclusively engaged in maturity transformation by compressing interest income, though reduced interest expenses (from favorable market funding and ECB refinancing) have largely offset this to date. - New regulatory and supervisory frameworks, bail-inable debt, and macroprudential tools create a more restricted operating environment for banks. - Insurance sector pressures: - Ultra-low interest rates are a serious issue for life insurers and, over a prolonged period, would seriously challenge business models and guaranteed-return products. - Capital adequacy ratios for insurers have trended downward in recent years. - Solvency II, in effect since January 1, 2016, places forward-looking pressures on life insurers to recognize low-rate impacts on solvency. - A search-for-yield response has led some insurers to invest in riskier assets. ### Macrofinancial scenarios analyzed - Three macrofinancial scenarios were analyzed: - Global stress scenario: serious recessions in advanced economies triggered by tightening global financial conditions and EM credit cycle downturns; oil prices drop; suppressed demand from EMs and oil exporters; German exporters hit; sharp correction of asset prices and strong FX movements affecting unhedged market positions and banks’ trading income. - Return of the EA balance sheet recession: triggered by policy uncertainty, delayed structural reforms, social resistance to austerity, and lower investor sentiment; sovereign yields in highly indebted EA countries increase sharply; market refinancing costs rise for sovereigns and banks; potential deflationary phase in the EA and heightened market volatility (including risks related to possible British exit from the EU). - Excessive risk-taking/search-for-yield: prolonged low interest rates induce banks and insurers to adopt risky search-for-yield strategies; lower market liquidity fuels asset price volatility; banks could see drops in deposit funding and institutional investors may chase higher-yield investments. - Noted mechanism of negative rates: - Current negative interest rates may accelerate margin compression over time as banks have so far proven unwilling or legally unable to pass on negative rates to depositors; impact varies by banks’ ability to reprice loans, deposits and non-deposit liabilities, importance of net interest income, and ability to generate noninterest income. ### Stress testing results — banking sector - Overall resilience: - Most banks are resilient to severe shocks because of substantial capital and liquidity buffers. - Under the baseline scenario, banks on average would sustain current solvency levels, though some banks may become challenged as net interest income continues to compress. - Adverse scenarios: - Under adverse scenarios, loan losses rise sharply; adverse market price movements reduce trading income and the value of sovereign bond holdings. - Some larger banks show higher credit risk, mainly from exposures to transportation (including shipping) and manufacturing. - Smaller banks mainly face lower capacity to generate net interest income and structurally high costs. - Market and sovereign exposures: - Exposure to market risk is generally low, but a search-for-yield has led some banks to invest in riskier sovereign paper (mostly Italian and Spanish), which would cause valuation losses if liquidity had to be mobilized under stress. - Liquidity: - Banks have ample counterbalancing capacity to withstand market and funding liquidity shocks and comply with regulatory standards. ### Stress testing results — insurance sector - Solvency II impacts and transitional measures: - Allowing for transitional measures based on EU law, most life insurers would maintain Solvency II Capital Requirement (SCR) ratios above 100 percent. - Without transitional measures, a majority of life insurers would experience substantial capital shortfalls and would not meet the SCR. - Determinants of resilience: - Business model is a significant determinant of an insurer’s relative resilience. - Tests at legal entity level show individual larger insurers are generally more resilient; many small firms focused on protection-type business have exceptionally high SCR coverage ratios. - Some medium-size insurers are more vulnerable to low interest rates and additional market shocks. - Other important drivers of vulnerability include business mix, amount of unrealized gains, future discretionary policyholders’ bonus, and average guaranteed rates—more important than balance sheet size in many cases. ### Systemic risk and spillovers - Interconnectedness and contributions to systemic risk: - The largest German banks and insurance companies are highly interconnected domestically and globally and exposed to spillover risks. - Network analysis findings: - Allianz SE is the largest contributor to systemic risks among publicly traded German financial corporations. - Deutsche Bank AG is a major source of outward spillover to publicly listed banks in Germany and some insurance companies and is one of the most important net contributors to systemic risks in the global banking system. - These findings underline the importance of ensuring the resilience and stability of major institutions, particularly Deutsche Bank AG. ### Policy recommendations and capacity building - Authorities should improve stress testing methodologies, data quality, and validation analysis to better monitor vulnerabilities and enable prompt action if risks build up. - Establish a core set of readily-available, consistent data for all types of banks, including large ones, to facilitate financial stability and macroprudential policy analysis. - Ensure surveillance stress testing covers all banks and banking groups, including their foreign and market risk exposures, despite the new supervisory framework. - For insurance companies that have difficulties meeting Solvency II requirements, require action plans. - Develop an effective communication strategy so investors and markets understand published Solvency II ratios. *Source: IMF FSAP Technical Note — EXECUTIVE SUMMARY* ### 6. Germany is highly interconnected through trade and financial channels. The total ### _cr16191 - 6. Germany is highly interconnected through trade and financial channels. The total ### Cross-border interconnections and key statistics - Total consolidated claims of German banks on foreign banks, the non-bank private sector, and the public sector stood at about USD $1.7 trillion in 2015Q2 (45 percent of GDP). - Foreign claims on Germany reached about USD $1.3 trillion in the first half of 2015 (34 percent of GDP). - Major counterparties for German banks’ cross-border exposures: United States, United Kingdom, France, and Italy. - Major origins of consolidated foreign claims on Germany: Italy, France, the Netherlands, the United States, and the United Kingdom. - Europe and the United States have close linkages with Germany also through trade, sovereign holdings, and cross-border exposures through the insurance sector. - Germany hosts Eurex Clearing, a CCP interlinked with over 180 clearing members in 17 countries, including 24 globally significant banks (G-SIBs). ### Domestic financial system structure and intermediation - Intermediation is concentrated between households (HHs) and financial institutions; nonfinancial corporates (NFCs) rely less on bank financing and more on intra-segment financing (trade and suppliers’ credits, loans to subsidiaries). - Households are closely interlinked with banks (loans; deposits, bank bonds, and equity holdings) and insurance companies (claims on insurance reserves). - NFC financing by households mainly constitutes payments to corporate pension funds. - Insurance companies, pension funds, and (to a lesser extent) households have increased exposure to investment funds. - Insurance companies and investment funds are expanding claims via debt securities, which have almost doubled since 2008. ### Banking sector composition and consolidation - The German banking sector is dominated by banks, accounting for close to 70 percent of total financial sector assets. - Asset management sector measured by assets under management equals 57 percent of GDP. - The banking sector comprises three main “pillars”: private commercial banks, public savings banks (Landesbanken and savings banks), and cooperative banks. - Since 2010, the number of banks has declined by about 100, with consolidation mainly at local savings and cooperative banks level. - Private commercial banks represented 39 percent of the system by assets in May 2015. - Savings banks and Landesbanken together account for about 27 percent of banking system assets. - Cooperative banks include more than 1,000 institutions, accounting for about 14 percent of banking assets. - Remaining 20 percent of the banking sector comprises mortgage banks, building and loan associations and special purpose banks; mortgage banks’ asset size declined to under five percent of the banking system in 2015. ### Geographical and sectoral loan distribution - Loan exposures to German borrowers increased to 80 percent of banks’ credit portfolio (up +10 percent since 2010). - Foreign exposures constituted 25 percent of the lending portfolio at end-2014 (down 2 percentage points since 2010). - The other notable change: loans to households and non-profit institutions serving households increased by +2.5 percentage points. - Geographical loan distribution (2014Q3): Domestic Exposures 80%, Advanced Economies w/o China 17%, Central and Eastern Europe, CIS 1%, Developing Asia w/China 0%, Other 2%. ### Asset and liability structure, funding - Primary funding sources (aggregate): liabilities to non-banks (deposits); liabilities to the Monetary Financial Institutions (MFIs) sector; securitized debt. - Deposits were the primary source of funding at 42.5 percent in March 2015. - Landesbanken and savings banks account for about 30 percent of the unsecured wholesale funding in the banking system. ### Solvency, liquidity, profitability, and asset quality (selected indicators) - Average (arithmetic mean) banking-system indicators (End-2014 or last available year): - Tier 1 Capital Ratio: 15.1 percent - Total Capital Ratio (CAR): 19.3 percent - Liquid Assets to Total Assets: 13.0 percent - Liquid assets to short-term funding: 19.9 percent - ROAE: 3.2 percent - ROAA: 0.3 percent - Net Interest Margin: 2.27 (percent) - Cost to Income Ratio: 71.8 (percent) - NPL Ratio: 3.54 (percent) - Provisioning Coverage Ratio: 44.1 (percent) - Interbank Ratio (Net interbank lending): 102.6 (percent) - Bank-type highlights (End-2014 or last available year): - Big banks: Tier 1 14.4; CAR 17.6; Liquid Assets to Total Assets 25.0; ROAE 4.1; NPL Ratio 3.9; Provisioning Coverage 42.5; Interbank Ratio 185.2. - Landesbanken: Tier 1 12.7; CAR 15.6; Liquid Assets to Total Assets 21.6; ROAE 2.5; NPL Ratio 6.7; Provisioning Coverage 31.9; Interbank Ratio 61.2. - Savings banks sector: Tier 1 15.4; CAR 18.2; Liquid Assets to Total Assets 11.4; ROAE 3.3; NPL Ratio 3.3; Provisioning Coverage 47.4; Interbank Ratio 103.3. - Real Estate & Mortgage Banks: Tier 1 15.3; CAR 17.1; Liquid Assets to Total Assets 14.7; ROAE 1.9; NPL Ratio 2.7; Provisioning Coverage 36.5; Interbank Ratio 143.5. - Profitability remains thin: ROAE of 3.2 percent and ROAA of 0.3 percent; profits lowest in real estate and mortgage banks and highest in cooperative and commercial banks. - Net interest accounts for about 100 percent of operating income of mortgage banks, and about 80 percent for Landesbanken, savings, and cooperative banks. - Asset quality is generally sound but weaker in Landesbanken: NPL ratio at 6.7 percent and provisioning coverage at 32 percent. - German banks’ average RWA density (RWA to total assets) stood at 31.2 percent in December 2014. - Aggregate leverage ratio (regulatory capital to total assets) stands at 5.6 percent. ### Comparative context - NPLs in Germany fell from 3.3 percent in 2009 to 2.8 percent in 2014. - European peers’ average NPL ratio rose to 5.5 percent at end-2014. - More than 50 percent of banking sector assets in Germany (AQR sample) have NPL ratios below 5 percent—a more favorable position than in most of the euro area peers. - Compared with G-7 and European peers, German banks display low profitability, relatively good asset quality, and reasonably large capital and liquidity buffers, while RWA density is relatively low which may understate leverage-based risks. *Source: _cr16191 — IMF staff summary.* ### 23. German life insurers invest conservatively. The largest shares of life insurers’ investments ### _cr16191 - 23. German life insurers invest conservatively. The largest shares of life insurers’ investments ### Investment profile of German life insurers - Largest shares of life insurers’ investments are: - German government securities: 25 percent - Mortgage bonds: 21 percent - Bonds of financial institutions: 11 percent - Exposures to riskier asset classes: - Equity exposure: 6 percent - Total alternative investments: 1 percent - Other exposures: - Loans: 12 percent - Real estate: 4 percent - Non-life insurers (property and casualty (P&C) and reinsurers) have a similarly conservative allocation. ### Evidence of search for yield and changing credit quality - Corporate bond share increased from 4.3 percent in 2011 to 8.2 percent in Q2 2015. - Average ratings in fixed income portfolios have declined, including through rating downgrades without active allocation changes. - Securities rating composition shifted between 2011 and 2014: - AAA fell from 48.5 percent to 36.2 percent - BBB rose from 6.6 percent to 9.9 percent - Geographic diversification trends: - Investment in Italian government bonds increased by 5 percent from 2013 to 2014 (from a low base). - Investment in Spanish government bonds increased by 25 percent from 2013 to 2014 (from a low base). - Exposures to the German Bund fell slightly. - Asset duration for German life insurers increased from 8.1 years to 10 years (2011 to 2014), reducing duration gap but increasing credit risk for longer-duration lower-grade holdings. ### Product mix and liability characteristics - Market composition: - Products with guarantees (such as participating products) dominate; unit-linked and related products account for less than 10 percent of total liabilities of life insurers. - Premiums from new sales of unit-linked products accounted for about 15 percent of total premium income in the last 5 years. - Non-life insurance remains focused on traditional lines (motor, property, liability). - Less traditional business lines (e.g., credit and surety insurance) represent less than 1 percent of total premium income. - Guarantees and liability duration: - EIOPA finds the average rate for existing guaranteed products by German insurers is one of the highest among European countries. - Duration of liabilities is relatively long, requiring coverage of guaranteed costs for substantially longer periods. ### Impact of low interest rates and insurer responses - Regulatory maximum guarantee rate: - Ministry of Finance sets a maximum rate for new policies, reduced gradually in accordance with market rates to 0.9 percent by January 2017. - The maximum rate applies only to new policies; guarantee rates for existing policies remain unchanged at origination. - Average contractual guarantee rate currently stands at 3 percent. - Investment returns have declined faster than guarantee rates, prompting insurers to build additional premium reserves. - Insurer adjustments: - Reduction of guaranteed rates in new business. - Reduction of policyholders’ profit participation. - Increased reliance on specialized investment funds to augment investment income. - Reinvestment returns reported by some large groups are considerably lower than industry-average investment income. ### Regulatory reforms (Life Insurance Reform Act, 2014) - Key measures included: - Reduction of the maximum interest rate for new insurance contracts from 1.75 percent to 1.25 percent as of January 1, 2015. - Restriction on shareholder’s bonus payments and limitation of policyholders’ participation in valuation reserves of fixed-income securities only if valuation reserves exceed amounts needed to safeguard continuing policyholders. - Flexibility for insurers to offset investment losses with gains from insurance risk assumptions and other income when determining policyholder profit participation. - Complementary measures: - Increase in minimum allocation of policyholders’ bonus attributed to insurance risk from 75 percent to 90 percent. - Reduction in planned participation of policyholders at maturity noted as particularly significant; market participants report mixed to overall net beneficial impact on long-term financial soundness. ### New product development and emerging risks - Larger firms are introducing non-traditional products (unit-linked, alternative savings products with lower guarantees), but: - Mostly offered by large and diversified groups; medium and small insurers less active. - Majority of new products still include guarantees; pure unit-linked production appears limited. - Given long liability durations, changes in new product mix will take years to affect industry risk characteristics. - New products may introduce non-traditional risks (market volatility, liquidity) depending on product design and hedging. ### Insurance sector size and supervision - BaFin supervises 413 insurance companies; most numerous subgroup: 139 small mutual companies. - Total investments of insurers in 2014: EUR 1,569 billion (54 percent of GDP), composed of: - Life insurers: EUR 911 billion - Health insurers: EUR 232 billion - P&C insurers: EUR 154 billion - Reinsurers: EUR 272 billion - Number of insurers declined from 460 in 2008 to 413. ### Interplay with banking sector stress-testing and macro scenarios - Stress-testing of banking sector considered three macroeconomic scenarios: - Baseline: October WEO forecasts. - Global Stress Scenario (Adverse 1): serious global recession with tightening global financial conditions, credit cycle downturns in emerging economies, deleveraging in China, private domestic demand contractions. - Euro Area Crisis Scenario (Adverse 2): balance sheet recession in euro area, dry-up of secondary market liquidity, renewed financial stress in periphery; periphery yields rise by 100 basis points more during 2016 and core yields rise by 50 basis points less; fiscal tightening in periphery raises primary fiscal balance ratio by 2 percentage points during 2016 and 2017. - Sectoral loan-loss concentration: - Transportation sector (including shipping) accounts for about one-third of banks’ loan loss impairments despite relatively small exposure. - Manufacturing also contributes relatively high losses compared to outstanding amounts, affected by reduced global demand. ### Banking sector resilience under scenarios (key stress-test outcomes) - Baseline: banking system broadly stable; CET1 ratios around 15 percent on average; solvency improves under baseline. - Adverse scenarios impact: - Credit default probabilities could increase by up to 90 percent versus current levels. - Almost doubling of annual credit impairment needs from a very low level. - Larger banks suffer a 40 percent drop in trading income; smaller banks less affected by trading and FX exposures. - Global Stress Scenario: - CET1 ratio of large banks drops by 2.6 percentage points but remains above 10 percent. - Aggregate capital shortfalls: EUR 6.0 billion (0.2 percent of annual GDP). - Smaller banks group: temporary CET1 drop of around 0.3 percentage point; total CET1 shortfall around EUR 0.5 billion. - Thirty-two banks out of 1,755 in the small- and medium-sized bucket would see CET1 ratios drop below fully-loaded regulatory hurdle rates in 2018. - Euro Area Crisis Scenario: - Average CET1 ratio for large banks drops by 2.2 percentage points, to 12.7 percent in 2018; capital shortfall EUR 4.2 billion (0.1 percent of annual GDP). - Smaller banks group: drop of around 0.2 percentage point in the first year; aggregate CET1 capital shortfalls around EUR 448 million; 30 small- and medium-sized banks breach regulatory hurdles. - Low interest rate sensitivity for small- and medium-sized credit institutions: - Banks’ own interest rate projections show profitability expected to decline by around 25 percent by 2019. - Under a persistent low interest rate phase, operating profit could slump by 50 percent on average (static balance sheet assumption). - If interest rates fall by a further 100 basis points: - Operating profits could decline by 60 percent (dynamic balance sheet assumption) or by 75 percent (static balance sheet assumption). *Italic: IMF staff summary based exclusively on the supplied content of the source PDF chapter.* ### 38. In-depth sovereign risk analysis shows a mixed picture across banks. Valuation losses ### In-depth sovereign risk analysis shows a mixed picture across banks. Valuation losses ### Sovereign risk: findings and stress-test results - Valuation losses from sovereign exposures tend to be rather low overall because banks usually keep more risky securities in the held-to-maturity portfolio, which is not being marked to market. - Duration differs considerably across accounting portfolios and banks; banks with higher sovereign risk index values hold longer-term or riskier paper, or try to generate profit from market movements in yields. - Table 2 sample aggregate: Total 100% 577,497.12 (portfolio breakdown reported in source table). - Stress-scenario outcomes: - Global Stress Scenario: valuation losses of EUR 3.0 billion reduce regulatory capital ratios by one-fourth of a percentage point. - Euro Area Crisis scenario (peripheral yields increase by 200 basis points): sovereign valuation losses of EUR 6.4 billion are less than half a percentage point of CET1 capital, on average. - Sovereign risk index definition and interpretation: - Index compares a bank’s share in total valuation losses (VL) with its share in total sovereign exposures (Exp). - If index value = 1, valuation loss corresponds to the total sovereign exposure held by the bank (average risk). - Index value > 1 indicates valuation loss is disproportionally higher than holdings, implying relatively more risk (and vice versa). - Index values are determined by (i) the issuer’s risk as expressed by the sovereign yield and its volatility over time, (ii) average maturity of the bonds in the portfolio, and (iii) the bank’s accounting of that exposure (HTM, AFS, FVO, HFT). - Caveat noted in source: existing accounting and regulatory standards foresee that the held-to-maturity portfolio is not priced at current market values, while the available-for-sale prudential filter on sovereign exposures is being phased out. If banks must mobilize liquidity under stressed conditions and sell securities in the banking book, losses would increase. ### Drivers of cross-bank divergence - Different portfolio duration and exposure composition across banks explains divergence in individual results. - Banks with higher sovereign risk index values: - hold longer-term paper, or - try to generate profit from higher-yield instruments. - The analysis includes the largest twenty German banks’ gross and net long exposures with portfolio composition and respective average duration (as presented in source Table 2). ### Liquidity risk: coverage and metrics - Scope and methods: - Cash-flow based top-down liquidity stress tests performed for all banks operating in Germany (around 1,800 institutions). - Net Stable Funding Ratio (NSFR) analyzed for the 70 German banks participating in the BIS Quantitative Impact Study (QIS). - Compliance with regulatory liquidity standards: - Tests based on the Liquidity Coverage Ratio (LCR) show most of the 1,800 banks would be able to withstand market and funding liquidity shocks. - Almost all banks show ratios above 70 percent, and most banks already today have LCR ratios above 100 percent, with foreign banks showing the lowest dispersion. - Trends: - During recent months, banks have been increasing both LCR and NSFR, and larger banks appear to be managing their ratios more efficiently. - Observable improvement in ratios since 2011 and reduced variation across banks’ LCRs over time. - Regulatory timeline: - NSFR is currently under an observation period, with the aim of becoming a binding standard by 2018. ### Insurance sector stress testing: scope and approach - Scope of exercise: - Top-down stress tests performed for the life insurance sector under Solvency II to quantify impact of prolonged low interest rates. - Scenarios cover major market shocks: lower interest rates, widening of sovereign and corporate credit spreads, shocks to equity and property markets; sensitivity analysis for longevity and lapse risk. - Exercise covers 93 percent of the life insurance sector’s assets. - Stress test applied to individual legal entities in the life insurance sector: covers 75 life insurers (out of 86) or 93 percent of the assets of all life insurers subject to Solvency II. - Metrics and transitional arrangements: - Results are stated on the SCR ratio, with and without transitional measures. - Transitional measures: 16 years, with the benefit phased out linearly; stress test conducted with 2 hurdle rates: coverage ratios of 100 percent of the new SCR, with and without transitional measures. - Key industry indicators (source data): - Average ROEs in the last 3 years: 6.6 percent for life, 4.0 percent for P&C, and 8.3 percent for reinsurers. - Average Solvency I ratios at end-2014: 163 percent for life insurers, 312 percent for P&C, and 885 percent for reinsurers. - About 60 percent of insurance sector assets are held by the life sector. - Future Discretionary Benefits (FDB) in liabilities: EUR 136 billion at end-2014. - Net deferred tax liabilities: close to EUR 10 billion. - Since the beginning of 2015, German life insurers have increased their capital resources by EUR 2.1 billion, including through issuance of subordinated debt. - Main risk drivers and modeling challenges: - Market risk dominant for life insurers, representing about 70 percent of total risk; spread risk is most significant, followed by interest rate, equity, and property risk. - Lower interest rates increase asset values but raise liabilities more due to a negative duration gap. - Traditional insurance features policyholder participation in returns, allowing insurers to reduce future profit participation in response to adverse shocks—this loss absorption capacity is central to the analysis. - Data, methodology, and implementation constraints: - Most stress-testing exercises by authorities historically based on Solvency I; 2016 FSAP uses Solvency II and applies an SCR-based hurdle rate. - Complexities of Solvency II implementation in Germany prevented bottom-up stress tests for the 2016 FSAP exercise. - BaFin lacks legal powers to share confidential supervisory data of individual insurers with the IMF; official Solvency II industry reporting was not available at the time of the FSAP; latest limited Solvency II data available were as of end-2014. - To address data-sharing constraints, the FSAP team and authorities developed a single spreadsheet methodology; all computations were performed by BaFin and validated by the FSAP team using publicly available and aggregated industry-wide data. - End-2014 data judged to be still representative; long-term interest rate particularly low at end-2014; measures taken by insurers since end-2014 to improve SCRs were not considered in the stress test, adding conservativeness. - Loss Absorption Capacities (LAC) treatment and safeguards: - LAC_TP (in Technical Provisions) and LAC_DT (in Deferred Taxes) recognized, but capping mechanisms introduced so total LAC_TP does not exceed FDB before shocks and total LAC_DT does not exceed net deferred tax liabilities before shocks. - Box summary: EIOPA 2014 findings—LAC_TP and LAC_DT reduced gross capital requirements by 23 percent and 8 percent respectively (EIOPA core sample context). - Caution: while legally possible to reduce policyholders’ profit participation, such reductions may have reputational effects, lower new sales, and increase lapse risk—these feedbacks can adversely affect future profitability and capital under Solvency II. *Source: IMF staff calculations and analysis as presented in the provided content unit.* ### 57. Given the risk associated with the industry’s reliance on Solvency II transitional ### _cr16191 - 57. Given the risk associated with the industry’s reliance on Solvency II transitional ### Stress test design and transitional arrangements - The stress test is conducted with and without taking into account the impact of Solvency II transitional arrangements. - Based on EU-law, the transitional measures: - allow insurers, on BaFin’s approval, to mitigate material Solvency II impacts arising from lower interest rates (the transitional period is 16 years, with the benefit phased out linearly). - require insurers using the arrangements to disclose the Solvency II figures with and without application of these measures. - Risk observation: insurers relying on transitional arrangements to meet SCR may be less resilient to general market turmoil or a stress affecting an individual company. ### Scenarios and calibration - Target criterion: Value at Risk with 99.5 percent confidence level for a one-year time horizon (a one in 200 years’ event). - Major risks calibrated with the same scenarios used to calibrate Solvency II: interest rate risk, equity risk, spread risk, property risk. - A sovereign stress scenario is added, same as the global stress test scenario used in the banking stress test. - Scenario shock specifications: - Interest rates: a shift of the risk free yield curve down by 20 percent (long term) to 75 percent (short term). - Equity: a 22 to 49 percent fall in the price of equities. - Spread for corporate bonds and loans: shock levels depending on duration and credit quality, e.g., for a 5-year duration a 4.5 to 37.5 percent haircut. - Property: shocks of 25 percent for both commercial and residential real estate prices. - Sovereign bonds: 100 b.p. higher spreads of peripheries sovereign bonds, 25 b.p. higher spread of core sovereign bonds and 50 b.p. for the U.S., the U.K., and Japan. - Correlation recognition: - A correlation matrix is used to calculate overall capital impact to make the scenario more plausible. - Solvency II allows two steps of correlation recognition, namely within market risk and among overall risks. - The FSAP exercise applied the correlation matrix used in the Solvency II standardized formula within market risk to be broadly consistent with a 1-in-200 years event. - Note: The impact of the application of correlation matrix is estimated around 20 percent reduction of the loss in the entire industry level; the impact would be different and depending on the risk characteristics of each firm. - Methodological note: top down stress tests required critical assumptions; a conservative approach was taken when assumptions were needed. Detailed descriptions of those assumptions are provided at the end section of the Stress Test Matrix (STeM). ### Stress test results — aggregated and firm-level outcomes - With transitional measures: - Life insurers maintain SCR coverage ratios above 100 percent even after the shocks. - Weighted average of SCR coverage ratios drops from 372 percent to 236 percent. - No firm would have negative capital after the shocks. - 13 out of 75 firms would not be able to maintain a 100 percent SCR coverage ratio after the shocks. - Resulting nominal capital shortfalls after the shocks would not be material. - Without transitional measures: - Weighted average SCR coverage ratios would fall from 126 percent to 48 percent. - 34 firms (58 firms) would not be able to meet a 100 percent SCR coverage ratio before (after) the shocks. - Eight firms and 27 firms would have negative capital before and after the shocks, respectively. - The total capital shortfall would be EUR 12 billion before shocks, and EUR 39 billion after shocks. - Role of loss absorption capacity (LAC_TP): - LAC_TP improved the SCR coverage ratio significantly. - Without transitional measures, interest rate and spread risks have material negative impact on SCR coverage ratios; the gross loss causes the overall coverage ratio to turn negative. - Loss absorption capacity from traditional insurance mitigates the gross loss by more than 50 percent, bringing the average above zero. - Non-linearity of capital shortfall: - Capital shortfall is a non-linear function of loss amount. - Five different combinations of shocks were applied for the relationship between loss amount and capital shortfall without transitional measures: 1. interest rate shock only, 2. interest rate shock + equity shock, 3. interest rate shock + equity shock + property shock, 4. interest rate shock + equity shock + property shock + credit spread shock, 5. interest rate shock + equity shock + property shock + credit spread shock + sovereign shock. - The capital shortfall does not increase for relatively small losses, but increases substantially for higher losses; the more severe the stress scenario, the less loss absorption capacity is available. - Underwriting risks: - Largest underwriting risks are lapse and longevity risk. - Separate sensitivity analysis for a 1-in-200 years’ event: - Lapse risk causes the SCR coverage ratio to drop by 17 percentage points. - Longevity risk shaves off 7 percentage points. - Results suggest German insurers are generally resilient to liability side shocks. ### Business model, firm size, and drivers of resilience - Business model is a significant determinant of an insurer’s relative resilience. - Large, medium, and small insurer SCR ratios (without transitional measures): - Large (8 largest firms): - Before stress: 155% (Weighted) (142% Un-weighted) - After stress: 73% (Weighted) (52% Un-weighted) - Medium (25 firms): - Before stress: 90% (Weighted) (120% Un-weighted) - After stress: 17% (Weighted) (31% Un-weighted) - Small (42 firms): - Before stress: 115% (Weighted) (187% Un-weighted) - After stress: 41% (Weighted) (115% Un-weighted) - Observations: - Individual large insurance companies generally appear more resilient than companies of other sizes. - Smaller insurers show relatively high loss absorption capacity and many focus on protection-type business where profitability is less affected by the low interest rate environment. - Some medium-size insurers are more sensitive to low interest rates and other market risk factors. - Further analysis shows business mix, the amount of unrealized gains, future discretionary policyholders’ bonuses, and average guaranteed rates seem to be better explanatory factors than size. - Firms under intensified supervisory attention: - Most firms identified as vulnerable were already under intensive supervision by BaFin, on BaFin’s watch-list, subject to enhanced reporting and more frequent on-site inspections. - Common features of weaker firms: higher average guaranteed rates, higher share of traditional products, lower level and trend of profitability, lower recognition of Future Discretionary Benefits (FDB), and smaller hidden reserves (unrealized gains on the asset side). ### Systemic risk analysis — methodologies and findings - Methodologies: - Espinoza-Vega and Sole (2010) methodology applied to examine cross-border bank exposures using BIS consolidated Banking Statistics; considers both credit and funding shocks and network propagation; sample consists of 16 BIS reporting countries with the highest banking sector exposure to Germany. - Diebold and Yilmaz (2014) methodology with daily equity returns data to examine contagion between publicly traded banks and insurance companies in Germany, and spillover among Deutsche Bank, Commerzbank, and GSIBs. - Network analysis findings: - Higher degree of outward spillover from the German banking sector than inward spillover. - Germany, France, the U.K., and the U.S. have the highest degree of outward spillover as measured by the average percentage of capital loss of other banking systems due to a banking sector shock in the source country. - Failure of all other banking systems could lead to a 5 percent capital loss in Germany (similar to the U.K.); if accounting for total exposures, the loss amounts to about 30 percent, relatively low by international comparison. - Interconnectedness among German banks and insurers: - The largest German banks and insurance companies are highly interconnected. - Highest degree of interconnectedness between Allianz, Munich Re, Hannover Re, Deutsche Bank, Commerzbank and Aareal Bank, with Allianz being the largest contributor to systemic risks among the publicly traded German financials. - Deutsche Bank and Commerzbank are sources of outward spillovers to most other publicly listed banks and insurers in Germany. - Finding implies linkages between German banks and insurers should be closely monitored. - Global systemic contributions: - Deutsche Bank is a major source of systemic risk in the global financial system. - Deutsche Bank appears to be the most important net contributor to systemic risks in the global banking system, followed by HSBC and Credit Suisse. - U.S. banks such as JP Morgan, Goldman Sachs, and Bank of America also contribute positively to systemic risks. - Asian banks tend to be net recipients of systemic risks despite relative large asset size. - Commerzbank, while important in Germany, does not appear to be a main contributor to systemic risks globally. - Deutsche Bank is a key source of outward spillovers to other G-SIBs as measured by bilateral linkages. - Regional clusters exist: European banks highly interconnected with each other; similar patterns among American banks and some Asian banks. - Commerzbank tends to be a recipient of inward spillover from U.S. and European GSIBs, with exceptions for some smaller GSIBs such as State Street or Standard Chartered. - The relative importance of Deutsche Bank underscores the importance of risk management and intense supervision of G-SIBs and close monitoring of their cross-border exposures. ### Policy recommendations (excerpted) - Authorities would benefit from establishing a readily available, consistent set of data for financial stability analysis. - While a wealth of data is collected by authorities, condensing and integrating different sources and definitions can be challenging and time-consuming. - The process could be simplified by having a consistent set of automatically updated key data that allows performing various stability analyses without first collecting and condensing all necessary raw data. *Sources: BaFin; Authorities and IMF Staff Calculation; IMF Staff Calculations using Espinoza-Vega and Sole (2010) and Diebold and Yilmaz (2014) approaches as presented in the source content.* ### 76. Despite newly organized supervisory responsibilities, the Bundesbank should continue ### _cr16191 - 76. Despite newly organized supervisory responsibilities, the Bundesbank should continue ### Top-down stress testing for banks - Recommendation: Despite newly organized supervisory responsibilities, the Bundesbank should continue performing and developing top-down stress tests for large banks, as well as for medium-sized and small banks. - Rationale: - Supervisors need a clear understanding of categories of banks, their interaction, and their combined impact on the German and European economy. - Perimeter of concurrent stress tests should be expanded to cover and assess in more detail foreign loan exposures, sovereign and off balance sheet exposures, including hedges. - Foreign credit exposures are substantial in larger banks and limited for smaller banks; these assets should be covered and meaningful risk parameters collected so exposures can be stress tested appropriately. ### Methodology improvements and integration - Findings: - Top-down stress testing frameworks have been improving but still need further development. - Recommendations: - Model balance sheets and unexpected losses more dynamically, noting that the share of IRB portfolios is relatively large. - Design better integration of liquidity and solvency risk analysis rather than keeping them largely separate. - Strengthen interconnectedness analysis, including inward and outward cross-border spillover effects. ### Solvency II implementation and insurer supervision (BaFin) - Recommendation: BaFin should require action plans where companies face difficulties in meeting Solvency II requirements. - Findings and guidance: - Given uncertainty over financial health of insurers on Solvency II measures and market focus on new requirements, where companies rely on transitional measures they must have robust and credible plans for meeting full requirements, including under stress conditions during the long transitional period and by the end of the period. - BaFin should take action to restrict business or withdraw approval of transitional measures where necessary. - Authorities are encouraged to develop a communications strategy to the public and policyholders to mitigate risk. - Transparency over Solvency II figures without transitional measures is appropriate. - Disclosure of complex figures without supplemental explanation could damage market confidence; disclosures should be accompanied by well written explanation and credible recovery plans. - Authorities and industry should fulfill accountability (such as through speeches and publication describing actions taken in general terms) so the public keep reasonable assurance on firms and the industry. ### Insurance sector safety net and medium-term resilience - Recommendation: Authorities are encouraged to analyze the sufficiency and flexibility of the safety net of the insurance sector in the medium term. - Findings: - The FSAP stress test identified potential capital shortfalls that may be more than an individual firm’s capacity to meet. - The German life insurance sector has insurance guarantee schemes (such as Protektor), but there is uncertainty about effectiveness of transferability of complex portfolios, including derivatives and reinsurance transactions. - Guidance: - Encourage best efforts by individual firms to improve recovery and resolution planning. - An adequate and flexible safety net could help the industry from disruptive reputational failure and further deterioration of capital positions in worst cases. - Authorities are encouraged to satisfy themselves that the current safety net is sufficient even in a plausible market wide stress situation and improve the safety net as necessary. ### Insurance guarantee schemes — Annex I highlights (key figures and features) - Protektor (life insurance) and Medicator (private health insurance) are voluntary industry-based schemes. - Protektor financing and capacity: - Protektor has accumulated a fund of EUR 897 million. - The maximum size of the fund could reach to EUR 3.4 billion. - Fund accumulation is based on 1 per mille of the net technical provisions of all members. - Additional special contributions up to EUR 863 million can be levied, and beyond that BaFin is required to use its powers to impose a 5 percent reduction of liabilities. - Under separate private arrangements, German life insurers have committed to provide additional funds up to a further 1 percent of net technical provisions (some EUR 9 billion at present). - Limitations: - Role of German guarantee schemes is strictly limited to run off insurance contracts and policy transfer; they do not generally provide compensation up to a limited amount or capital to facilitate transfer as in other countries. - Uncertainty in transferring derivative and reinsurance transactions in a failed insurer with material such transactions. - Recommendation: BaFin and the federal government should review adequacy and sufficiency of the insurance guarantee scheme given increasing complexity of life insurers’ business models and hedging/reinsurance activities. ### Contagion, interconnectedness, and spillover analysis (Annex II) - Two complementary approaches assessed contagion risks and interconnectedness: - Network Analysis Framework (Espinoza-Vega and Sole, 2010) using BIS consolidated Banking Statistics. - Credit shock: a loss given default rate of 100 percent is assumed. - Funding shock: fraction of lost funding not replaceable assumed to be 35 percent (65 percent rollover); haircut in the fire sale assumed to be 50 percent. - Sample: 16 BIS reporting countries (Australia, Austria, Belgium, Canada, Finland, France, Germany, Italy, Japan, Korea, the Netherlands, Spain, Sweden, Switzerland, the United Kingdom and the United States). - Cross-border banking exposure data on ultimate risk basis; Tier 1 regulatory data from IMF’s FSI Statistics. - Analysis based on 2015Q1 data. - Failure defined as losses larger than total Tier 1 capital; simulations consider reporting banks’ exposure to foreign banks and total exposure of the banking sector. - Spillover Analysis with Market Data (Diebold and Yilmaz, 2014) using daily equity returns. - VAR model and Generalized Variance Decomposition (Pesaran and Shin, 1998) used. - Two sets of simulations: interconnectedness between publicly traded banks and insurers in Germany; spillover among Deutsche Bank, Commerzbank and GSIBs. - Sample periods: - German bank-insurer analysis: July 16, 2015 to February 23, 2016. - GSIB analysis: October 11, 2007 to February 26, 2016. - Measures: - From-degree measure captures inward spillover (exposures to systemic shocks), analogous to Marginal Expected Shortfalls (MES). - To-degree measure captures outward spillover (contributions to systemic events), analogous to Delta CoVaR. - Net-degree measure = to-degree minus from-degree describes relative contribution to systemic risk. - Results based on rolling window estimations; relative importance of each institution’s net-degree broadly stable and robust over time. - Select visual findings presented (net contributors to systemic risks in the German financial sector and top ten net contributors among GSIBs). ### Stress Test Matrix (STEM) for the banking sector — key assumptions and scope (Annex III excerpts) - Institutional perimeter: - Bottom-up by banks: Around 1,600 institutions; 1,776 institutions operating in Germany. - Top-down by Bundesbank and FSAP Team: Nearly 100 percent of total banking sector assets. - Market share example: 28 percent of total banking sector assets (context implies a category, preserved as stated). - Data and baseline date: - Bottom-up: Balance sheet, income statement, and portfolio data as of December 2014; for small and medium banks December 2014 used; regulatory information as of June 2015. - Top-down: Publicly available data and reporting data; cut-off date December 2015 for large banks. - Coverage: Consolidated and unconsolidated depending on type; full coverage of sovereign exposures for large banks. - Channels and methodology: - Bottom-up: Banks’ own models. - Top-down: Detailed balance sheet stress test covering key on- and off-balance sheet exposures; for large banks group/holding level including domestic and foreign exposures. - For small and medium-sized banks certain market risk exposures (including sovereign paper) and foreign exposures were excluded due to incomplete reporting; foreign exposures constitute only around 1 percent of small and medium-sized banks’ total assets. - Sovereign risk: net direct sovereign exposures published by EBA used; haircuts on sovereign holdings estimated separately by accounting portfolio and duration; instantaneous and permanent shock assumed with valuation loss realized in first year and no recovery in yields. - Credit losses modeled via macroeconomic credit risk models using Moody’s KMV 12-month expected default frequencies (EDFs). - Market risk shocks included in macroeconomic scenarios or applied separately; house price shock assumes over three years a 10 percent reduction in real estate values vis-à-vis the starting point, affecting mortgage exposures via higher loss rates (stressed LGD). - Stress test horizon: - Bottom-up: Five-year horizon: 2015–2019. - Top-down: Three-year horizon: 2016–2018. *International Monetary Fund — excerpt from the Germany FSAP chapter* ### 3. Tail shocks ### 3. Tail shocks ### Scenario analysis (banking sector) - Constrained BU tests include common shocks for all scenarios: - Increase in PDs between 60 and 155 percent - Haircut on collateral of 10% and 20% - Widening of credit spreads on trading book exposures - Scenario 1: - Banks estimate performance under a scenario that assumes a continuation of the current low interest rate environment (based on banks’ own expectations) - Performance is then estimated (dynamic balance sheet assumption) - Scenario 2: - Banks estimate performance under a constrained scenario, where the yield curve shape and its level are fixed at December 2014 - No behavioral response - Scenario 3: - -100 basis points parallel shift (drop) in yield curve as of December 2014, with behavioral response (dynamic balance sheet) - Additional scenarios and references: - “Baseline Scenario” was the IMF October 2015 World Economic Outlook - “Global Stress Scenario” features: - a serious recession, triggered by a tightening of global financial conditions, accompanied by credit cycle downturns in emerging economies - realization of financial stability risks delays or stalls monetary normalization in the systemic advanced economies, including an abrupt decompression of asset risk premia relative to the baseline - secondary market liquidity drops in all of the systemic advanced economies as financial risk taking unwinds - credit cycle downturn in emerging economies, accompanied by a disorderly deleveraging in China, and suppressed economic risk-taking worldwide - substantial drop in private domestic demand induced by negative investment and consumption demand shocks, representing a loss in confidence by nonfinancial corporates and households, which raise their saving rates and delay expenditures - “Euro Area Crisis Scenario” features: - a return of the balance sheet recession experienced in 2011–2013, induced by a collapse of financial risk taking, a complete dry-up of secondary market liquidity throughout the euro area, and renewed financial stress in the euro area periphery - divergence of long-term government bond yields between the periphery, where they rise by 100 basis points more during 2016, and the core, where they rise by 50 basis points less - a pro-cyclical expenditure-based fiscal consolidation reaction in the Euro Area periphery raising the primary fiscal balance ratio by 2 percentage points during 2016 and 2017 - a massive selloff in stock markets due to generally lower risk appetite, and substantial investor sentiment shocks - Country-specific calibration (Germany): - House prices decline by 10 percent over three years vis-à-vis the starting point - Over two (three) years, the scenario constitutes a shock to real annual GDP growth equaling 3.8 standard deviations (3.2 standard deviations) in one specification and 3.5 standard deviations (3.0 standard deviations) in another specification - Sensitivity analysis: - Scenario 3 (alternative): +200 basis point parallel shift (increase) as of December 2014 yield curve under the static balance sheet assumption - Scenario 4: -100 basis points parallel shift (drop) in yield curve as of December 2014, without behavioral response (i.e., static) ### Risks, behavioral adjustments, and buffers (banking) - Risks/factors assessed: - Interest rate risk, credit risk, asset price risk - Market risk (FX risk, equity price risk, house price risk, interest rate risk, incl. sovereign risk) - Behavioral adjustments: - Conditional on test and scenario - Constant balance sheet assumptions, with full replacement of defaulted exposures - Risk weighted assets (RWAs) are kept constant for STA banks and stressed for IRB banks in adverse scenarios, following Chapter 3 of the EU CRR for the IRB banks - Dividend payout assumed at 40 percent conditional on positive net profit - A 30 percent tax rate is applied to remaining net profit; post tax net profit is calculated towards capital - Invariant asset allocation: no change in business models, lending standards, or investment pattern in response to shocks (over three years) ### Regulatory and market-based standards and parameter calibration (banking) - Calibration of risk parameters: - Either internal parameters or determined by Bundesbank and BaFin (in constrained bottom-up tests) - For small and medium firms: point-in-time PDs (Moody’s KMV Expected default frequencies), and point-in-time LGDs, estimated from the borrowers statistics - For large banks: point-in-time PDs and LGDs are taken from COREP, with the exposure adjusted downwards to account for performing exposures and those to the non-financial private sector only - PDs are taken from COREP template 8.2, excluding defaulted exposures - Regulatory/Accounting and Market-Based Standards: - National regulation and accounting (GAAP) - CRD IV / CRR fully loaded levels for CET1, Tier 1, and Total Capital, including Capital Conservation Buffer (CCB) and G-SIB and O-SII buffers - Capital shortfalls were measured for CET1 - IAS 39 accounting standards (no mark-to-market for held-to-maturity portfolio) - For sovereign exposures accounted in the available-for-sale portfolio, the AFS Prudential Filter (60 percent) was applied - Fair value option and held-for-trading sovereign exposures ### Reporting format for results (banking) - Output presentation: - Evolution and distribution of operating profit - Evolution of capital ratios - Aggregate results according to type and size of banks - Impact of different result drivers, including profit components, losses due to realization of different risk factors - Capital shortfall as sum of individual shortfalls; in euro and in percent of nominal annual GDP - Number of banks and corresponding percentage of assets below regulatory minimum ### Tail shocks (liquidity risk) - Institutional perimeter: - Bottom-up: 44 German banks participating in Basel Quantitative Impact Study (QIS) - Top-down: All 1,800 banks operating in Germany - Market share covered: - Bottom-up: More than 90 percent of total banking sector assets and liabilities - Top-down: 100 percent of total banking sector assets and liabilities - Data and baseline date: - Basel QIS data for German banks participating in the study - Results for 2011Q2 to 2015Q2, in 6-month intervals - Supervisory and regulatory reporting data as of June and December 2015 - Channels of risk propagation / Methodology: - Bottom-up: LCRs and NSFRs as calculated by the banks - Basel III Liquidity Coverage Ratio (LCR) - Basel III Net Stable Funding Ratio (NSFR) - Bank run and dry up of wholesale funding markets, taking into account haircuts to liquid assets - Top-down: cash-flow-based, short-term liquidity stress test approximating banks’ CRD IV Liquidity Coverage Ratio (LCR) using supervisory and regulatory reporting data - Risks and buffers: - Risks: Funding liquidity risk; Market liquidity risk; Medium-term maturity mismatch analysis - Buffers: Counterbalancing capacity after application of market liquidity shocks and stressed liquidity inflows; Assessment of available and required stable funding across maturity buckets; Stressed available and required stable funding (NSFR) - Central bank facilities considered - Tail shocks — shocks and references: - For LCR, see: BCBS (2013), The Liquidity Coverage Ratio and liquidity risk monitoring tools, Basel, January 2013 - For NSFR, see: BCBS (2014), Basel III: The Net Stable Funding Ratio – Consultative Document, Basel, April 2014 - Regulation (EU) No. 575/2013 of the European Parliament and the Council on prudential requirements for credit institutions and investment firms - CRD IV/ CRR liquidity standards - Regulatory standards and presentation: - Basel III liquidity standards for LCR and NSFR - Liquidity ratios, disaggregated by type and size of bank - Counterbalancing capacity - Whisker plots for different groups of banks: total; small and medium-sized banks; foreign banks; savings banks; and cooperative banks - Reporting output: - Liquidity ratios, disaggregated by type and size of bank - Counterbalancing capacity - Box plots with whiskers at fifth and ninety-fifth percentile, and weighted average separately for Group 1 and Group 2 banks - Coverage: All 1,800 banks operating in Germany ### Tail shocks (insurance sector) - Institutional perimeter: - Institutions included: German life insurance companies - Market share: 93 percent of life insurance companies’ assets - Data and baseline date: QRT as of the end of 2014, comprehensive life survey 2015, EIOPA stress test 2014, local GAAP-accounting, BaFin sovereign survey basis - Methodology and horizon: - Solvency II Standard Formula - Stress test horizon: Instant shocks - One in 200 years event (Solvency II parameters) — shocks: - A shift of the risk free yield curve down by 20 percent (LT) to 75 percent (ST) - A 22 to 49 percent fall in the price of equities - A 4.5 to 37.5 percent haircut of corporate bonds (with 5 year maturity) - 100 b.p. higher spreads of peripheries sovereign bonds, 25 b.p. higher spread of core sovereign bonds and 50 b.p. for US, UK and Japan - Shocks of 25 percent for both commercial and residential real estate prices - Sensitivity analysis (insurance): - One in 200 years event using Solvency II parameters as a basis, including: - Increased lapse rate - Decreased mortality rate relative to latest observed actuarial data - Risks and behavioral adjustments: - Risks/factors assessed: Interest rate, equity, property, credit risks - Behavioral adjustments: No management actions after the stress scenario assumed - Regulatory and accounting standards: - Solvency II own funds and SCR with and without transitional measures - Reporting format for results: - Dispersion of solvency ratios: average with 25 and 75 percentile of distribution, with and without transitional arrangements, with additional segmentation information of large, medium and small insurers - Capital shortfall to reach 100% SCR coverage ratio, system-wide - Key assumptions and conservatisms: - Four market scenarios are calibrated as one in 200 years event; sovereign shocks are added on top, making the exercise more conservative than one in 200 years event in terms of calibration of the Solvency II market risk standard formula - No management actions are considered, such as de-risking - Some insurers did not apply the volatility adjustment at the end of 2014; spread widening in the stress scenario would result in a higher volatility adjustment, which would have an offsetting effect on own funds as the risk free rate used for valuing technical provisions would be increased - Loss amount from sovereign shocks are applied without taking into account the convexity - Some companies didn't provide FDB figures and assumed FDB equal to LAC_TP - Some companies have reporting errors which result in underestimation of SCR reduction after the shocks - Other assumptions with unknown or positive impact: - LAC_DT could be higher or lower depending on the magnitude of the stress and the extent that DTA is recoverable - Reduction of policyholders’ profit participation does not have any negative impact on the future profitability of the existing policies - LAC_TP and LAC_DT after the shocks are recognized in the same way as before the shocks as long as FDB and net DTL are remaining ### Interconnectedness and contagion analysis - Approaches used: - Espinoza-Vega and Sole (2010) methodology: - Examine cross-border banking sector exposures using the BIS consolidated banking statistics (2015 Q1) and regulatory capital data from FSI - Positions include aggregated bilateral banking and total exposures (bank, non-bank private sector and public) - Consider both initial credit and funding shocks to the banking sector - Diebold and Yilmaz (2014) methodology: - Analysis 1: Bank and insurance linkages within Germany - Examine spillover risks among publicly listed German bank and insurance companies - Use daily equity returns data from 16 July 2015 to 23 February 2016 for publicly listed German banks and insurers - Interconnectedness measure is derived from the variance decomposition of the VAR - Analysis 2: Interlinkages among Deutsche Bank, Commerzbank and GSIBs - Examine the spillover risks among Deutsche Bank, Commerzbank and other GSIBs - Use daily equity returns data from 11 October 2007 to 26 February 2016 for systemically important international banks *Source: _cr16191 - 3. Tail shocks* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16191.pdf_