## 1cheea2019009 ## Source details **Canonical URL:** [1cheea2019009](https://www.imf.org/-/media/files/publications/cr/2019/1cheea2019009.pdf) ## Other formats - [Markdown version](/-/media/files/publications/cr/2019/1cheea2019009.pdf.md) - [Structured JSON version](/-/media/files/publications/cr/2019/1cheea2019009.pdf.json) --- ### Banking system structure and recent evolution - Total banking system assets: about 470 percent of nominal GDP in 2018; over 50 percent of total financial sector assets. - Two largest banks (UBS and Credit Suisse) account for about one half of banking assets; both G-SIBs and D-SIBs. - Three other D-SIBs: PostFinance; Raiffeisen; Zürcher Kantonalbank. - Switzerland holds one-quarter of global assets under management. - Aggregate bank balance sheet fell from a 600 percent peak of nominal GDP in 2007 to 470 percent in 2018, mainly due to lower G-SIBs’ foreign assets and liabilities. - G-SIBs’ capital base strengthened to an average 13.1 percent CET1; aggregate Tier 1 capital rose to 17 percent of RWA. - G-SIBs’ cross-jurisdictional activity: 60 percent of assets; intra-financial claims (liabilities) 20 percent (25 percent) of assets in 2018. - Employment: G-SIBs employment fell to 35 percent of total banking system staff from a peak of 50 percent in 2007; overall industry employment contracted by 20 percent relative to pre-crisis. ### FSAP stress-testing exercise: scope, innovations, and data - Scope: - IMF top-down tests cover a sample representing broadly 80 percent of the Swiss banking sector in consolidated assets as of June 2018 (twelve major banks: two G-SIBs, six large domestically focused banks, four largest private banks). - Tests run over a 5-year horizon starting 2019: Q1 and ending 2023: Q4 (SNB TD tests: 2 years for G-SIBs, 5 years for domestic banks). - Innovations and data access: - First-time access to confidential firm-specific supervisory data in Switzerland enabled detailed assessment of mortgage portfolio structure, maturity transformation, complex products, securitized exposures, and underwriting exposures to leveraged loans. - Innovations include quantification of basis risk for cash and derivative positions, inclusion of operational risk, and modeling solvency–liquidity interactions via contingent liquidity-at-risk. - Modular framework: - Solvency stress tests (scenario-based and sensitivity). - Liquidity stress tests: LCR-based and implied cash-flow tests (5-day and 30-day). - Contagion module using bilateral large exposures. - Systemic risk module: market-based CoVaR approach. ### Key stress test findings and solvency impact - Resilience: - Financial sector remains resilient to rising cyclical vulnerabilities but requires close monitoring. - Macroeconomic shocks and market risks deplete capital ratios by 440 basis points at the low point of stress (aggregate). - Heterogeneity: - By 2020 capital depletion: G-SIBs 510 basis points; Private banks 410 basis points; Domestic banks 260 basis points (IMF calculations). - Domestically-oriented banks more resilient than G-SIBs and Private banks due to lower exposure to market, business, and operational risk. - Basel/TBTF hurdle and going-concern metrics: - TBTF2 basic requirement: 12.9 percent (RWA) and 4.5 percent (leverage). - Going-concern requirements for G-SIBs after progression benchmarking: 14.3 percent (RWA) and 5 percent (leverage). - Drivers of CET1 depletion (aggregate contributions): - RWA expansion (credit risk, foreign lending, market volatility): about 190 basis points. - Stressed earnings (commissions, underwriting): 80 basis points. - Operational risk: 60 basis points. - Weak GDP, falling real estate, tighter amortization: 50 basis points. - Market risk (basis risk, hedging ineffectiveness): 40 basis points. - Interest rate risk on net income: 10 basis points. ### Macrofinancial environment and profitability pressures - Macro backdrop: - Swiss GDP growth slows from 3 percent to 1.7 percent by 2023 in baseline. - Scenario horizon: 2019: Q1–2023: Q4. - Policy rates: short-term rate normalizes from -75 basis points to 1.25 percent in baseline; long-term yields rise from -40 basis points to 1.9 percent. - Negative rates and margins: - SNB sight deposit rate reduced to -75 basis points in January 2015. - One quarter of unconsolidated banking system assets, reaching CHF 460 million, are invested in sight deposits at the SNB at -75 basis points (negative rates apply above exemption threshold). - Interest margins narrowed to 1.21 percent in 2017 (about one-third reduction since 2008). - ROA stabilized at 0.3 percent despite negative-rate drag. - Risk-taking and credit gap: - Positive credit gap since 2009; private-sector leverage and banks’ real estate exposure already high—macrofinancial risks require monitoring. ### Mortgage and real estate risk: exposures, model, and results - Mortgage market size and composition: - Domestic mortgage loans in 2017: CHF 975 billion (increase of 2.7 percent); under 30 percent of total assets. - DFBs account for 64 percent of the CHF 980 billion domestic mortgage market in 2018: Q1. - On average 80 percent of mortgage portfolio is residential (50 percent owner-occupied) and 20 percent commercial real estate. - Origination practice: - Typical mortgage up to 80 percent of current market value; at least 10 percent down payment in cash; the other 10 percent may use Pillar 2 or 3 pension benefits. - Interest-rate structure: - Fixed interest rates account for at least 75 percent of total domestic mortgage loans. - Money market (Libor-linked) mortgages ~15 percent; variable mortgages <10 percent. - IMF structural mortgage model (TUI) features: - Tail-event focus; borrower distress defined by inability to service payments and collateral net of costs less than loan. - Stochastic simulation with 10,000 iterations and LGD estimation drawing 2,000 idiosyncratic house-price draws per systematic draw; sale calibrated at s = 2 years. - Outputs: PDs and LGDs by LTV tranche and vintage; projections 2018–20 and 2020–2023. - Stress results and sensitivities: - Adverse scenario: peak-to-trough real estate price decline of 35 percent (system-level headline); Swiss-specific housing scenarios: owner-occupied under 30 percent, investment-led 45 percent, commercial real estate 40 percent (channels section). - Under adverse conditions including snap-back in mortgage rates to 5 percent, average PD rises to 2.7 percent by 2020. - Default rates increase to 4.9 percent if portfolio weighted to recent originations under a 5-year amortization rule. - Default rates increase to 6.2 percent under a 470 basis points increase in mortgage rates. - Sensitivity: a 3.5 percent mortgage default rate yields an aggregate 275 basis points CET1 capital depletion for IMF G-SIBs and DFBs (excluding private banks) under a test with 110 basis points unemployment shock and 25 percent house price correction. - FINMA extreme stress (2018: Q4): - Sample: 18 banks covering ~70 percent of mortgage lending (>CHF 1,000bn). - Extreme scenario: total capital ratios could fall on average by about 400 basis points (median post-stress total capital ratio fall about 400 basis points); over 70 percent of stress losses in investment property portfolio. ### Credit, market, and complex-product stress modelling - Credit modelling: - Asset classes grouped into six portfolios: retail secured by immovable property; financial institutions; corporate specialized lending; corporate other; retail unsecured; sovereign and public institutions. - IRB exposures: PD, LGD, EAD projections applied to bank starting points; LGD structural model for mortgages; multipliers for other portfolios. - STA exposures: starting default rates constructed from multiple approaches; under adverse scenario, one-notch downgrade applied to non-defaulted exposures increasing STA risk-weight density by ~8 percent on average. - Coverage ratio for defaulted exposures floored at IRB LGD rates; estimated coverage ratio under adverse scenario: 65 percent (calibrated to Basel crisis NPL coverage). - Market risk: - Combined instantaneous and multi-year shocks: equity shock −40.0 percent (advanced economies), −30.0 percent (emerging economies); term-premium decompression up to +300 basis points. - Corporate bond ES calibrated with 99 percent threshold, 30-day holding period (EM indices 180 days). - Basis risk quantified by separating cash and derivative positions; full revaluation for G-SIBs; hedges assumed ineffective for DFBs and Private banks when data absent. - Operational risk: - From total RWA CHF 479 billion at end-2017, CHF 11.1 billion cover market risk, ~CHF 30 billion cover unexpected operational losses. - Cumulative capital impact from operational risk over 5-year horizon: ~8 percent of total impact (credit risk 15 percent; market risk 1 percent). ### Interest Rate Risk in the Banking Book (IRRBB) and net interest income - IRRBB findings and approach: - Four-pronged approach: EVE, earnings approach, shocks to repricing structure, bank/product level calibration using FINMA IRR report (eighteen time-buckets). - NIM: about 150 basis points in 2017 for outstanding loans; 121 basis points for outstanding claims including financial claims with 3-month Libor at -75 basis points. - SNB IRRBB estimates: 200-basis point shift → NIM +20 basis points; 400-basis point shock → NIM −20 basis points. - Funding and lending-rate modelling: - Funding: retail funding >70 percent of interest-bearing liabilities in sample; debt securities issued ~20 percent; amounts due to banks/repos ~10 percent. - Liability and asset margin projection: cointegration and FMOLS approaches for deposit and mortgage rates; bank-level endogenous margin adjustments for non-competitive products. - NII shock calculated as NII_ti = Σ_b (Δnim_ti_b * mid_b * gap_ti_b) with repricing-bucket heterogeneity preserved. - Fees and commissions (F&C): - Over one third of operating income generated by F&C; F&C from securities trading and IB account for over three quarters of total commissions. - G-SIBs derive 33 percent of net income from F&C; NII share about 30 percent for G-SIBs. - Under adverse scenario, F&C decreases driven by money market stress, low long-term rates, equity corrections, and higher volatility. ### Liquidity stress testing and liquidity vulnerabilities - Metrics and scenarios: - LCR at about 160–165 percent system-wide in 2018: Q2. - IMF LCR scenarios: Basel III standard; Swiss retail stress (retail run-off up to 20 percent; maturing retail inflows 25 percent; contingent liabilities 20 percent); Swiss wholesale stress (25 percent haircut on Level 2a, up to 60 percent on Level 2b; stressed inflows and high run-off rates). - Cash-flow tests: 5-day consecutive stress and 30-day cumulative stress with haircuts up to 60 percent and call-back rates up to 100 percent. - Results: - Retail scenario: average LCR declines to about 140 percent. - Wholesale scenario: average LCR declines to about 95 percent. - 5-day cash-flow test: no banks illiquid. - 30-day test: 4 banks face marginal liquidity shortages; individual shortfalls small, below 1.5 percent of system total assets. - Currency-specific vulnerabilities: - Some banks vulnerable to USD liquidity shortages; USD LCR for some G-SIBs and Private banks falls below 100 percent. - Banks with USD deficits hold EUR excess and could use CHF/USD and EUR/USD swap markets; Fed USD liquidity assistance expected to U.S. entities of Swiss G-SIBs. - Continued monitoring recommended on USD LCR pressures, asset encumbrance drivers, non-maturing liabilities, intragroup and cross-border flows, contingent flows (rating downgrades, margin calls), and sudden changes in market haircuts on non-HQLA. ### Solvency–liquidity interactions, liquidity-at-risk, and feedback loops - Iterative funding-cost feedback: - Four-stage iterative projection (funding costs → NII → capital → revised funding costs) across quarterly steps 2019: Q1–2023: Q4. - Incorporating feedback adds an average 15 basis points to capital depletion. - Impact of issuer-default and CVA stresses: - Combined issuer default risk, CVA losses from a 200-basis point widening, and replacement cost of lost derivative inflows at a 100-basis point Libor add ~75 basis points CET1 depletion for G-SIBs. - Exploratory liquidity-at-risk: - Bank asset split into four categories (marketable subject to VM; marketable not subject to VM; illiquid subject to VM; illiquid not subject to VM). - Results: only very large shocks would render G-SIBs insolvent and illiquid given counterbalancing capacity and solvency position. ### Network and systemic risk analysis - Interbank contagion: - Network analysis using granular large-exposure data for twelve banks finds low contagion risk through Swiss interbank market relative to capital buffers. - Spillovers greatest between G-SIBs and some Private banks; contagion decreased after Large Exposures regulation implementation. - Even with LGD = 100 percent, no contagion rounds in depicted network. - Market-based contagion (CoVaR): - CoVaR analysis (weekly Oct 2005–Nov 2018) shows delta CoVaR of most systemic bank halved from -15 percent during GFC to -8 percent in 2018. - VaR of financial system returns controlling for macro drivers edged down to about -15 percent of quarterly equity returns in Oct 2018 from -60 percent during GFC. - Conclusion: contribution to systemic risk by listed Swiss banks is contained; risk of spillovers moderate. ### Main policy recommendations (stress testing, data, and supervision) - Data infrastructure (SNB/FINMA) — Timing: MT; Priority: H - Secure more timely, consistent, and granular data with user-friendly IT systems. - Enhance granularity of regular reporting on banks’ securities and investment portfolios (product, accounting category, counterparty, region, remaining maturity breakdowns). - Analytical and reporting enhancements: - Enhance Interest Rate Risk report: include breakdown by residual maturity and more granular Category I/II product and counterparty detail (as per revised reporting from March 2019). Timing: ST; Priority: H. - Report geographical breakdown of material exposures by residence of obligor (IRB, STA) (FINMA). Timing: MT; Priority: M. - Further develop solvency–liquidity interaction analysis and feedback-loop modeling (SNB). Timing: MT; Priority: M. - Increase granularity of stress tests for other D-SIBs to their risk profiles (FINMA/SNB). Timing: MT; Priority: M. - Expand top-down stress test perimeter to large private banks (SNB). Timing: MT; Priority: M. - Supervisory vigilance on liquidity (FINMA): - Monitor non-maturing liabilities, intragroup transactions, cross-border operations, contingent flows from rating downgrades, margin calls, SFTs, and sudden haircut changes on non-HQLA. - Continue monitoring USD LCR pressures and asset encumbrance drivers. ### Key risks (RAM highlights) - 1. Tighter financial conditions and volatile trading business — Overall Level of Concern: High; Relative Likelihood: High. - 2. Severe global recession and low net new money — Overall Level of Concern: Medium; Relative Likelihood: High. - 3. Regional tensions and regulatory pressures — Overall Level of Concern: Medium; Relative Likelihood: High. - 4. Large correction in real estate prices — Overall Level of Concern: Medium; Relative Likelihood: High. *Source: IMF staff compilation from the IMF country report material provided in the content unit.* ### EXECUTIVE SUMMARY ___________________________________________________________________________ 8 ### EXECUTIVE SUMMARY ### Banking system structure and recent evolution - Banking system total assets reached about 470 percent of nominal gross domestic product (GDP) in 2018, representing over 50 percent of total financial sector assets. - The two largest banks (UBS and Credit Suisse)—both global systemically important banks (G-SIBs) and domestic systemically important banks (D-SIBs)—account for about one half of banking assets. - Switzerland has three other D-SIBs and a large private banking industry. - Switzerland holds one-quarter of global assets under management and remains one of the biggest global cross-border financial centers for private banking. - The aggregate balance sheet for all banks in Switzerland fell sharply in 2008–09 from a peak of 600 percent of nominal GDP in 2007, mainly due to lower G-SIBs’ foreign assets and liabilities. - The two G-SIBs have strengthened resilience by exiting non-core activities, shifting focus away from investment banking (IB) to wealth management, settling some litigation charges, and significantly reducing costs. ### FSAP stress testing exercise: scope and innovations - The FSAP stress testing exercise comprises a comprehensive analysis of solvency and liquidity risks in the Swiss banking sector. - Two sets of tests were conducted: - Macroeconomic scenario stress tests to capture the impact of a drastic deterioration in macrofinancial conditions on banks’ solvency. - Hypothetical deteriorations in liquidity and equity markets to gauge impacts on individual entities and interbank contagion risk. - Access to confidential firm-specific supervisory data (a first for Switzerland) enabled assessment of: - Latent risks related to mortgage portfolio structure. - Risks from maturity transformation. - Exposure to complex products including securitized products and underwriting exposures to leverage loans. - Innovations relative to previous FSAPs include: - Quantification of basis risk for cash and derivative positions. - Inclusion of operational risk. - Modeling solvency and liquidity interactions from contingent liquidity-at-risk. ### Key stress test findings - The financial sector remains resilient to rising cyclical vulnerabilities, but close monitoring is necessary. - Macroeconomic shocks and market risks deplete capital ratios by 440 basis points at the low point of stress. - In the specific adverse scenario considered: - Domestically-oriented banks are relatively more resilient than the G-SIBs and Private banks, owing to lower exposure to market risk, business risk, and operational risk. - Comparable, if more favorable, results were obtained by: - The Swiss National Bank (SNB) Top-Down stress test. - The bottom-up (BU) exercise run by the G-SIBs based on an IMF-provided stress scenario. - Liquidity position: - The banking system as a whole has ample liquidity in total currency. - Some banks are vulnerable to a shortage of U.S. dollar (USD) liquidity and to sustained stressful conditions in wholesale markets. - Banks with a USD liquidity deficit have excess in euro (EUR) liquidity and could use CHF/USD and EUR/USD swap markets to tap USD funding. - Network analysis: - The risk of contagion through interbank exposures within the Swiss interbank market is currently low. - Banks’ systemic risk, measured by the delta Conditional Value at Risk (CoVaR) of the most systemic bank, has halved in 2018 relative to the financial crisis. ### Macrofinancial environment and pressures on profitability - The Swiss economy is slowing; global growth forecasts for 2019–20 have been revised down slightly; interest rates are forecasted to remain exceptionally low. - Policy interest rates of major central banks are expected to stay historically low and Swiss policy rates to remain close to their current level. - The SNB’s decision in January 2015 to reduce the interest rate on sight deposits to -75 basis points has put pressure on profitability of domestic banks. - Direct costs from negative rates are limited by the exemption threshold. - Return on other short-term liquid assets in CHF has turned negative. - Interest margins have narrowed, pushing down profitability. - Competition and the decision not to pass-through negative rates to retail customers reduced banks’ interest margin by about one-third since 2008 to 1.21 percent in 2017. - The prolonged period of low interest rates has encouraged risk taking. With a positive credit gap since 2009, and private sector leverage and banks’ real estate exposure already high, macrofinancial risks require careful monitoring. ### Policy recommendations on stress testing and data (Table 1: Main Recommendations on Stress Testing) - Data Infrastructure (SNB/FINMA) — Timing: MT; Priority: H - Secure the availability of more timely, consistent, and granular data, supported by user-friendly IT systems. - Enhance granularity of regular reporting on banks’ securities and investment portfolios (breakdowns by products, accounting categories, counterparty categories, regions, remaining maturities). - Analytical and reporting enhancements - Enhance the Interest Rate Risk report by including a breakdown of Instruments by residual maturity and increasing granularity of Category I and II by product and counterparty as envisaged in revised reporting starting in March 2019 (FINMA/SNB). Timing: ST; Priority: H - Report geographical breakdown of material exposures by residence of the obligor (internal ratings-based (IRB), STA exposures) (FINMA). Timing: MT; Priority: M - Further develop analysis on risk interaction, particularly solvency–liquidity interactions and modeling of feedback loops between banks (SNB). Timing: MT; Priority: M - Increase granularity of stress tests for other D-SIBs commensurate with their specific risk profiles (FINMA/SNB). Timing: MT; Priority: M - Expand perimeter of top-down stress test analysis to large private banks (SNB). Timing: MT; Priority: M ### Supervisory vigilance on liquidity - FINMA is encouraged to remain vigilant on liquidity pressures stemming from: - Non-maturing liabilities. - Intragroup transactions and cross-border operations. - Contingent flows linked to changes in market prices, a downgrade in the bank’s credit rating, margin calls in derivative positions and securities financing transactions (SFTs). - Sudden changes in market haircuts particularly on other than high-quality liquid assets (HQLA). - Continued monitoring of liquidity coverage ratio (LCR) pressures in USD and drivers of asset encumbrance is recommended. *Prepared by Laura Valderrama (Monetary and Capital Markets Department, IMF).* ### 3. The banking system is large and highly concentrated (Figure 1). Banking system total ### 1cheea2019009 - 3. The banking system is large and highly concentrated (Figure 1). Banking system total ### Banking system structure and concentration - Banking system total assets reached about 470 percent of nominal GDP in 2018, representing over 50 percent of total financial sector assets. - The two largest banks (UBS and Credit Suisse)—both G-SIBs—account for about half of banking assets. - Switzerland has significant cantonal banks, Raiffeisen cooperative banks, private banks, and some regional banks. - In addition to the two G-SIBs, the Swiss authorities have designated three other D-SIBs: PostFinance; Raiffeisen; and Zürcher Kantonalbank. - Off-balance sheet items managed by banks are reported by the bankers’ association to total some CHF 6.6 trillion (about 950 percent of GDP). ### Business models and activities - G-SIBs’ strategic focus includes global wealth management, universal banking in Switzerland, competitive investment banking and asset management business. - Cantonal banks’ mandates focus on contribution to economic development of their home region, including provision of mortgage financing. - Raiffeisen banks’ core activities include mortgage and savings business across Swiss regions. - Private banks concentrate on wealth management activities with a global footprint and focus on recurring fee generation from services linked to investment advisory and succession planning. Asset management and technology services are also meaningful profit contributors for some private banks. - Some large cantonal banks are also engaged in corporate banking, wealth management, and select trade-financing operations. - PostFinance is the state-owned financial services arm of Swiss Post. The government has proposed to partially privatize PostFinance and provide it with a full range banking license. ### G-SIBs de-risking and capital strength - The two G-SIBs still represent over 250 percent of GDP despite deleveraging and de-risking. - Aggregate balance sheet for all banks fell sharply in 2008–09 from a 600 percent peak of nominal GDP in 2007 to 470 percent in 2018, mainly attributable to lower G-SIBs’ foreign assets and liabilities. - G-SIBs have shifted focus away from investment banking to wealth management, exited non-core activities, settled some litigation charges, and significantly reduced costs. - G-SIBs’ balance sheet downsized from a peak of over 400 percent of GDP in 2008. - G-SIBs’ capital base strengthened to an average 13.1 percent Common Equity Tier 1 (CET1). ### Global footprint and domestic importance of G-SIBs - Cross-jurisdictional activity of G-SIBs: 60 percent of assets. - Interconnectedness measured by intra-financial claims (liabilities) reached 20 percent (25 percent) of assets in 2018. - Domestic shares of the two G-SIBs: 33 percent share in corporate loans, 27 percent share in the mortgage market (down from 35 percent in 2008), and 33 percent in customer deposits. - Employment by the two G-SIBs dropped to 35 percent of total banking system staff from a peak of 50 percent in 2007. - Overall contraction in banking industry employment relative to pre-crisis: 20 percent. ### Profitability, liquidity, and income composition - A quarter of unconsolidated banking system assets, reaching CHF 460 million, are invested in sight deposits at the SNB at -75 basis points (bps), although negative rates only apply to deposits above the exemption threshold. - Return on assets (ROA) has stabilized at 0.3 percent despite drag from negative rates. - Over one third of operating income is generated by commissions mainly from securities trading and investment banking. - One third of total assets allocated to mortgage loans and over half of customer loans are secured. - Banks have de-risked; G-SIBs have deleveraged by one-third their balance sheet driven by a collapse of bank loans and trading assets; mirrored by a drop in funding from banks and a contraction of customer deposits. ### Mortgage lending and interest-rate structure - Domestic mortgage loans in 2017 stood at CHF 975 billion, an increase of 2.7 percent, representing under 30 percent of total assets. - Growth rates in 2017 by bank category: Raiffeisen banks’ mortgage portfolio grew at 4.4 percent; cantonal banks posted a 3.8 percent growth rate; G-SIBs’ mortgage portfolio remained flat in 2017. - Fixed interest rates account for at least 75 percent of total domestic mortgage loans. - Money market mortgages linked to Libor are estimated to represent about 15 percent of outstanding mortgages. - Redeemable mortgages with adjustable rates (‘variable’ mortgages) are estimated to represent less than 10 percent of outstanding mortgages. ### Risk-weight density and capital ratios - Aggregate risk-weighted assets (RWA) density of the G-SIBs increased from 24 to 29 percent in 2018: Q1, driven by the phase-in of FINMA regulatory multipliers on several credit IRB portfolios mainly linked to real estate, and investment banking (IB) corporate portfolios. - Decrease in the leverage ratio denominator (LRD) was due to a compression of leverage exposures from derivatives, SFTs, and off-balance sheet positions. - For domestically focused commercial banks (DFBs), the risk-weight density reached 41.4 percent in 2018: Q1 with some large banks increasing the STA risk weights on the mortgage lending portfolio particularly for income-producing real estate loans. - Capital ratios: G-SIBs CET1 at 13.1 percent; DFBs CET1 at 16.4 percent. ### Banking system soundness indicators - Aggregate Tier 1 capital has risen to 17 percent of risk-weighted assets. - Asset quality: 0.7 percent of nonperforming loans (NPLs). - Bank liquidity improved with the LCR at about 160 percent. - Banks’ ROA stabilized at 0.3 percent due to improved trading revenues, shift towards wealth management activities, increased cost efficiency, and reduction in provisions. ### Key risk factors (RAM findings) - G-SIB vulnerabilities under stress: corporate defaults, stressed leveraged and securitized products, lack of hedge effectiveness and basis risk, volatile IB sales, low performance fees, and reduced net new money inflows in wealth management. - Domestic banks’ key exposures: sudden and abrupt correction in the Swiss real estate market and sharp snapback in interest rates due to exposure to mortgage business and maturity transformation risk. - Private banks’ key exposures: sensitivity to reduced performance fees in asset and wealth management, market risk in fair-valued portfolios, adverse regulatory changes, competitive pressures, operational risks linked to fraud, and clients/products/business practices. ### Macroeconomic scenarios for stress testing - Two macroeconomic scenarios over five years were developed covering forty countries and variables including GDP, inflation, unemployment, output gap, foreign exchange, asset prices, credit growth, benchmark curves, spreads, commodity prices, and global developments. - Scenario horizon: five years starting 2019: Q1 and ending 2023: Q4. - Baseline scenario: based on October 2018 IMF World Economic Outlook (WEO). - GDP growth in Switzerland slows from 3 percent to 1.7 percent by 2023. - Positive output gap closes gradually. - Inflation remains stable at about 1 percent by end of horizon. - Short-term rate normalizes from -75 basis points to 1.25 percent. - Long-term yields rise from -40 basis points to 1.9 percent. - Adverse scenario: materialization of system-wide risks identified in the RAM; global financial cycle downturn with abrupt repricing of risk premia triggering housing price correction and balance-sheet recession in Switzerland. - Policy rate in Switzerland reaches -275 basis points in 2020: Q4 before normalizing to 30 basis points by 2023. - Scenario calibrated using IMF’s Global Macrofinancial Model (GFM). ### Severity and international spillovers of the adverse scenario - Adverse scenario outcomes: - Recession in Switzerland with GDP growth in negative territory for seven quarters and a peak decline of -4.75 percent in 2019. - Deviation of real GDP from baseline by 7.7 percent in 2020. - A 3.3 standard deviation move in two-year cumulative real GDP growth rate. - A 35 percent peak-to-trough decline in real estate prices. - International component: - Output falls between 5.8 to 8.5 percent below baseline in advanced economies by 2020, and by 4.8 to 5.8 percent in emerging economies. - Overall, world output falls 6.3 percent below baseline by 2020. - Energy and non-energy commodity prices fall over 50 and 30 percent below baseline, respectively. ### Adverse scenario layers and quantitative assumptions (Table 2) - Layer 1: Global capital market disruptions, 2019Q1 – 2019Q4 - Real equity price; Equity risk premium shocks: Advanced economies −40.0 percent; Emerging economies −30.0 percent - Term premium; Duration risk premium shocks: Euro Area periphery +300 basis points; Euro Area core +150 basis points; Other advanced economies +200 basis points; Emerging economies +120 basis points - Layer 2: Global interbank market stress, 2019Q1 – 2019Q4 - Interbank spread; Liquidity risk premium shocks: Advanced economies +100 basis points; Emerging economies +50 basis points - Layer 3: Global housing market downturn, 2019Q1 – 2020Q4 - Real house price; Housing risk premium shocks: Advanced economies −10.0 percent; Emerging economies −5.0 percent - Layer 4: Global credit cycle downturn, 2019Q1 – 2021Q4 - Mortgage loan default rate; Mortgage loan default shocks: Advanced economies +1.0 percentage points; Emerging economies +2.0 percentage points - Corporate loan default rate; Corporate loan default shocks: Advanced economies +2.0 percentage points; Emerging economies +4.0 percentage points - Layer 5: Global confidence losses, 2019Q1 – 2021Q4 - Business investment; Business investment demand shocks −5.0 percent - Layer 6: Interbank market stress in Switzerland, 2019Q1 – 2019Q4 - Interbank spread; Liquidity risk premium shocks +50 basis points - Layer 7: Housing market downturn in Switzerland, 2019Q1 – 2020Q4 - Real house price; Housing risk premium shocks −10.0 percent - Layer 8: Confidence losses in Switzerland, 2019Q1 – 2021Q4 - Business investment; Business investment demand shocks −5.0 percent - Note: All scenario assumptions are expressed as deviations from the April 2018 World Economic Outlook baseline. Those endogenous variable adjustments that peak in 2019Q4/2020Q4/2021Q4 60/40/20 percent dissipate by 2023Q4. *Source: IMF staff compilation from the IMF country report material provided in the content unit.* ### 18. Stress testing has become a cornerstone of post-crisis FSAPs to promote the stability ### 1cheea2019009 - 18. Stress testing has become a cornerstone of post-crisis FSAPs to promote the stability ### Overview and purpose of the stress-testing exercise - Stress testing provides a forward-looking assessment of losses under adverse economic scenarios and complements capital requirements. - The simultaneous stress test of the largest Swiss banks focuses on a large part of the banking system to identify common exposures and risks. - For the 2019 Switzerland FSAP, access to confidential firm-specific supervisory data (a first) enabled assessment of crisis-era risks and latent vulnerabilities. ### Key historical impacts and risk drivers identified - Net income from commission business and services decreased in 2008 by 18.5 percent, and by a further 13.9 percent in 2009. - Commission income from securities trading and investment business fell to CHF 26.0 billion, partly due to falling securities sales. - Litigation charges linked to RMBS, Libor and FX litigation contributed to international Swiss banks’ losses. - Important risk areas requiring granular data: mortgage portfolio, derivative books, interest rate shifts, complex products, hedge effectiveness, and basis risk. ### Data needs and refinements enabled by supervisory data - Mortgage risk assessment requires borrower characteristics (e.g., loan-to-income (LTI)), loan characteristics at origination and over lifetime (e.g., LTV). - Basis risk evaluation requires market risk sensitivities for cash and derivative positions. - IRRBB calculation requires supervisory data on repricing gaps and maturity breakdowns linked to structural balance sheet positions. - Credit risk from Lombard loans, securitized products, and underwriting exposures to leveraged loans requires detailed product-characteristic data. - Assessment of solvency/liquidity interactions was enabled by combining FINMA supervisory data with Liquidity Monitoring Templates (LMT) to examine interactions between credit risk, market risk, and liquidity risk arising from counterparty default. ### Scope, coverage, and timeline of IMF and national tests - IMF top-down (TD) stress tests cover a sample of banks representing broadly 80 percent of the Swiss banking sector in consolidated assets as of June 2018, run at the highest level of consolidation. - Sample: twelve major banks including the two G-SIBs, six large domestically focused banks, and four largest private banks. - IMF tests are carried out over a period of 5 years through 2023, based on mid-year 2018 figures. - SNB TD tests are performed over two years for the G-SIBs and five years for domestic banks. - The DFB sample includes 95 banks which represents about 35 percent and 61 percent of the Swiss banks balance sheet and domestic credit respectively. - SNB conducts a semi-annual stress test on four adverse scenarios covering macrofinancial, financial and, for the two G-SIBs, operational risks; the test covers the two G-SIBs and most DFBs. ### Stress-testing strategy and modular framework - Four-pronged approach: - Solvency stress test: fully-fledged scenario-based test and sensitivity tests; includes macrofinancial and traded risk scenarios. - Liquidity stress tests: LCR-based approach under two additional scenarios by currency; two implied cash-flow tests (5-day consecutive stress and 30-day cumulative stress) using Basel LMT. - Contagion module: interbank (“bilateral exposure channel”) using Swiss banks’ bilateral matrix of large exposures, including intragroup transactions. - Systemic risk module: market-based CoVaR approach (“market contagion channel”) to assess contagion through equity markets and correlated losses. - IMF tests were benchmarked against SNB and FINMA supervisory stress tests; all tests for FSAP purposes were run on the IMF scenario. ### Modeling and methodological features - IMF stress test data infrastructure combined SNB surveys, FINMA Basel III risk surveys, G-SIBs’ supervisory BBA risk profile, and segmentation from banks’ annual reports and Pillar 3 disclosures. - Efforts included building a synthesis tool to aggregate analytical results into consolidated future balance sheets, income statements, and capital ratios. - Risk identification emphasized: - Projecting IRRBB using SNB maturity breakdowns and repricing gaps. - Estimating business risk from commission income, trading, funding cost, and operational risk (litigation, private clients). - Considering risk interactions (e.g., credit–market linkages from Lombard facilities; solvency–liquidity interactions from derivative counterparty risk). - Assessing market risk by risk factor, region, counterparty, and rating class; covering cash and derivative positions separately to evaluate basis risk. - Structural mortgage approach linked stressed losses on owner-occupied and investment portfolios to banks’ LTI and LTV mortgage distribution and affordability shocks from lending-rate snapbacks and housing-price corrections; results comparable to 2018 FINMA pilot enhanced mortgage stress test. ### Credit-risk modeling specifics - Consolidation: tests conducted at the highest level of consolidation including banking associates and foreign subsidiaries; SNB survey data extrapolated to June 2018 for five banking groups. - Geographical coverage included twelve countries: Australia, China, Germany, Ireland, Italy, the Netherlands, Singapore, Switzerland, Turkey, the United Kingdom, the United States, and other advanced and emerging economies. - Balance-sheet dynamics: FSAP scenario assumed constrained dynamic balance sheets by geography, excluding banks’ management actions; credit growth determined by EAD-weighted nominal GDP growth across jurisdictions. - EAD shock calibration used the maximum increase in off-balance-sheet exposures to EAD ratio for corporates reported in Pillar 3 disclosures. - Asset-class treatment: Basel asset classes grouped into six portfolios: retail secured by immovable property; financial institutions; corporate specialized lending; corporate other; retail unsecured; and sovereign and public institutions. - Loss computation: loan losses computed over non-defaulted exposures under reporting accounting standards; Basel exposure data broken down by obligor grade; IRB non-defaulted PDs and LGDs derived by removing obligor grade PD=1 exposures; expected and unexpected losses calculated for non-defaulted exposures. - Accounting regimes: for the sample, only one bank publishes consolidated financial statements in accordance with IFRS; expected impact of IFRS 9 included in 2017 financial statement showing a low expected impact with no material effect on capital ratios. One bank reports under U.S. GAAP with expected credit loss approach scheduled in 2020; remaining banks report under local GAAP using incurred loss approach. - Modeling approaches by asset class: - Structural approach for mortgage lending risk. - Econometric approach for financial institutions; corporate specialized lending; corporate other; retail unsecured. - Merton-based approach for sovereign and public institution exposures. - Parameter generation: two-step process - Top-down default risk and LGD projections by asset class and geography based on the stress scenario. - Application of top-down paths to banks’ starting points to derive bank-specific parameters (IRB: regulatory PDs and LGDs; STA: adjustments in provisions and rating migrations). ### Assessment criteria and calibration - Hurdle rates include TBTF2 going-concern provisions under baseline and Basel III minimum requirements under adverse scenarios. - TBTF2 basic requirement: 12.9 percent (risk-weighted assets) and 4.5 percent (leverage ratio). - Going-concern requirements for G-SIBs after progression benchmarking: 14.3 percent (risk-weighted assets) and 5 percent (leverage ratio). - For non-D-SIBs, Basel minimum capital requirements were applied under the adverse scenario; Basel requirements augmented by the CCyB and the CCB buffer under baseline conditions. - Comparison to FINMA and IMF mortgage stress parameters: FINMA includes demographic effects and a sharp rise in lending rates of 500 bps; IMF approach includes tighter amortization from LTV rises on second mortgages and a lending-rate increase of 300 bps. ### Limitations, caveats, and interpretation guidance - Building a consistent granular database required substantial data gathering and spreadsheet modeling in the absence of a dedicated IT stress-testing infrastructure. - Matching and reconciliation of multiple data sources is complex; some risk parameters projected using proxy variables due to data constraints (e.g., fixed income portfolio duration proxied by remaining maturity of outstanding traded bonds). - PD estimates for selected portfolios were projected drawing on expected default frequency (EDFs), which display greater volatility than regulatory PDs. - Stress-test scenarios are calibrated using historical data to identify extreme ‘tail events’ based on historical distributions, while crises often involve unanticipated shocks and unexpected interrelationships beyond historical precedent. - IMF stress test results should be interpreted with caution given these data and methodological constraints. *IMF staff summary of FSAP stress-testing chapter* ### 40. The mortgage and real estate markets are a key source of risk for financial stability on ### 40. The mortgage and real estate markets are a key source of risk for financial stability on ### Housing market imbalances and affordability - Estimated accumulated growth rate of real estate prices over 2000: Q1–2018: Q3: - 150 percent in the investment-led segment. - 75 percent for owner-occupied property. - Affordability risk: share of new mortgages where imputed costs would exceed one-third of income at an interest rate of 5 percent is about 50 percent. ### Bank exposures and mortgage portfolio structure - Share of mortgage loans in Swiss banks’ lending portfolios (excluding amounts due to banks and SFTs): - About three quarters for Swiss banks overall. - 95 percent for Raiffeisen and regional and savings banks. - 87 percent for cantonal banks. - 62 percent for the Swiss legal entities of the G-SIBs (consolidated level: 40 percent of total customer loans for G-SIBs). - 40 percent for other banks, including private banks. - DFBs account for 64 percent of the CHF 980 billion domestic mortgage market in Switzerland in 2018: Q1. - On average, share of residential mortgage loans is about 80 percent of mortgage portfolio (of which 50 percent for owner-occupied property) and 20 percent for commercial real estate. - Typical mortgage origination practice: - Mortgage up to 80 percent of current market value; requires a down payment of 20 percent. - At least 10 percent must be put down in cash; the other 10 percent can be arranged using Pillar 2 or 3 pension fund retirement benefits. ### Modeling approach and assumptions - IMF model builds on TUI (Tool for Unobserved-event Investigation). - Focuses on tail risk events with little information available. - Key behavioral and macroeconomic drivers: demographic idiosyncratic events, changes in house prices, unemployment rate, mortgage interest rates. - Borrower behavior assumption: borrowers will continue to service a loan if they can afford scheduled interest and principal payments, even with negative equity. - Default definition: default occurs if both conditions are satisfied: - Borrower is in distress (unable to repay debt on time due to financial difficulties). - Net value of collateral, after disposable costs, is less than value of the loan (borrower cannot sell collateral to service loan). - Swiss regulatory/self-regulation rules captured: - Debt service ratio includes amortization schedule and interest payments. - Mandatory amortization (second mortgage) to two-thirds of collateral at origination over a 15-year period (post-2014). Before 2014, amortization to two-thirds within 20 years is the rule. - Banks can request a margin call if collateral value after a housing correction is insufficient to meet self-regulation rules, tightening the debt servicing ratio. - Affordability under stress depends on mortgage vintage and initial loan terms: initial interest rate, housing price, LTV ratio, mortgage tenor, and self-regulation rules at origination. ### Model calibration, mechanics, and validation - Calibration: - Calibrated on banks’ total mortgage portfolio; housing price path reflects composition across residential (owner-occupied and investment-led) and commercial sectors. - Calibrated initially on data from New Zealand and supported by estimation to replicate observed loan loss rates in Switzerland in the early 1990s. - Borrower distress probability (PSS) specified with factors: - Demographic event D impacting affordability if debt service ratio exceeds threshold. - Tightening of income gearing γ_t ΔDSR linked to interest path i_t and Point-in-time LTV depending on outstanding loan L_t and real estate price P_t. - Decrease in borrower income proxied by starting unemployment u_t and nonlinear function of shock to unemployment Δu_t with sensitivity parameter β_3 increasing with DSR_γ_t. - Default condition modeled as combination of borrower capacity to service loan (PSS) and the collateral net of transaction costs being lower than loan value, with loan value including outstanding principal L (amortized at annual rate ρ over t periods) and present value of future interest payments discounted at market rate i_{t+j}. - Stochastic simulation: - Default rate generated using draws from stochastic house price index linked to scenario mean. - Repeated 10,000 times to generate iterations. - LGD mechanics: - Sale assumed at time t+s with s calibrated at 2 years. - Sale proceeds net of transaction costs discounted at a rate reflecting foreclosed asset risk premium linked to scenario spreads. - For each systematic draw, model generates 2,000 draws for individual house prices (idiosyncratic risk); average of the 2,000 losses is LGD for the risk bucket. - LGD formula (as presented): s ttstt csrPCLtLGD)1/(11 + + − − − + − = + δρ - Outputs: - PDs and LGDs calculated by risk bucket defined by LTV tranche and vintage. - Initial distribution of outstanding mortgages across risk buckets sourced from BBA templates for large banks and applied to remaining banks. - Projects cumulative forecasts of PDs and LGDs by bank over 2018–20 and 2020–2023; generates annualized rates by dividing accumulated losses over number of years. - Model validation procedure: - Two stages: stress testing under baseline and adverse conditions; backtesting by comparing observed outcomes in relevant historical periods against model expected outcomes. ### Backtesting, historical replication, and caveats - Simulated scenarios: - ‘Benign’ conditions: baseline scenario with gradual improvement. - ‘Good time’: replicates improvement observed in Switzerland in 1999. - ‘Bad time’: reproduces early 1990s Swiss housing mortgage default experience. - Caveat: no historical data on structure of banks’ mortgage portfolio by LTV/LTI bucket; assumption that historical distribution replicates current distribution. - Replication results: - Under current ‘benign’ conditions, expected loss rate of the portfolio reaches 0.13 percent. - Baseline scenario assumptions: unemployment rate improves by 10 basis points to 2.6 percent; mortgage rates increase gradually from 1.5 percent to 2.8 percent over two years; house prices remain stable. - 0.13 percent expected loss rate compares against latest observed default rate 0.014 percent. - ‘Good time’ (late 1990s recovery): - Observed loan loss rate halved over 1997–99 from 1.32 percent to 0.61 percent. - Model inputs: unemployment decreased by 250 basis points to 2.7 percent; interest rates contracted by 50 basis points to 3.9 percent; housing prices remained stable at about -0.3 percent. - Applying model LGD to observed loan losses in 1999 yields default rate of 1.30 percent; model simulated default rate is 1.28 percent. - Early 1990s crisis replication: - Historical loss rates: doubled in 1991 from 0.53 percent in 1989 to 1.03 percent. - Model inputs fed with 1989 conditions and simulated 1991 recession: 50 basis points increase in unemployment to 1.1 percent; 180 basis points increase in mortgage rates to 7.8 percent; sharp fall in real estate of 20 percent (this housing price decline materialized through 1996). - Using model LGD on observed loan losses in 1991 delivers observed default rate of 1.90 percent, matching model projected 1.91 percent. - Caveats highlighted: - Limited data from actual tail events and structural changes (e.g., post-2010 allowance to use Pillar 2 to cover part of down payment) make statistical estimation of mortgage loan loss events unreliable. - Statistical approaches are hindered by current benign cycle: observed default rates about 0.15 percent in last three years, unsuitable to deal with far tail events. - Caveats relate to assumptions over time to repricing of outstanding mortgages, construction of vintages using inflow/outflow data, and assumptions on LTV/LTI distribution of historical vintages. - Model strengths: transparency, precision, flexibility; outperforms statistical analysis for tail events. ### Scenario results and sensitivities - Adverse scenario features: - Rises in mortgage lending rates from 1.5 to 5 percent on back of widening credit risk premia. - Peak-to-trough average decline in real estate prices of 32 percent with differential impact across mortgage objects. - Key projections and sensitivities: - Under adverse conditions including a snap back in mortgage rates to 5 percent, average PD rises to 2.7 percent by 2020. - Default rates: - Increase to 4.9 percent if portfolio is more heavily weighted to more recent originations under a 5-year amortization rule. - Increase to 6.2 percent under a 470 basis points increase in mortgage rates. - Results are highly sensitive to: - Shocks to mortgage rates and credit risk premia. - Margin calls triggered by Point-in-Time LTV exceeding two-thirds of outstanding loan value and inability to amortize over remaining tenor. - Vintage distribution (higher distress for portfolios heavily weighted to more recent originations). - Average annualized projections and sensitivity tests illustrated: - PD base, EL base, PD adv 2020, EL adv 2020, PD adv 2023, EL adv 2023 shown in modeled outputs (figures reported in source text). ### An econometric approach for other credit exposures - Default risk in retail unsecured and corporate exposures proxied by average one-year EDF from Moody’s Analytics. - Portfolios by geography used: corporate group; construction and real estate development group; financials group; consumer nondurables and services group. - Mapped to Basel credit portfolios: corporate and SME exposures; specialized lending exposures; exposures to financial institutions; retail unsecured. - Data and estimation: - Econometric analysis using quarterly data over 2002: Q2 through 2018: Q4. - Bank-specific PDs generated by attaching top-down parameter paths to bank starting points with locational and portfolio perspective. - Logit transformation applied to address truncated distribution of default rates and ensure projected rate within 0–1 bound. - Drivers used to estimate credit risk: - Local variables: real GDP growth, a GDP-based recession indicator, inflation, unemployment, output gap, credit growth, equity prices, housing prices, FX rates, 3-month T-bill, 10-year yield, 3-month libor rate, slope of yield curve, real long-term rate, swap rate, spread of 10-year yield over swap rate; local drivers forecasted for twelve geographies in lending portfolio breakdown. - Regional variables: euro area and emerging markets GDP growth and inflation, euro swap curve developments, 3-month euro repo rates, 3-month euro Libor, European corporate spreads, Emerging market bond index spreads, Eurostoxx 50, sovereign debt spread of Italian 10-year over Bund. - Global financial market variables: world GDP growth, commodity price developments (fuel, non-fuel), changes in 3-month U.S. dollar Libor, TED spread, changes in U.S. IG and HY corporate bond spreads, volatility index (VIX), U.S. equity prices. - Econometric strategy rationale: - Preferred over bank panel regression because bank credit risk parameters are not segmented by geography in Basel templates or SNB survey data. *Source: IMF staff estimates and IMF country report text.* ### 64. Given the uncertainty associated to tail risk predictions, a robust econometric ### 1cheea2019009 - 64. Given the uncertainty associated to tail risk predictions, a robust econometric ### Econometric framework and estimation details - A robust econometric framework combined multiple approaches: - Time series econometric technique using Newey-West HAC-robust standard errors to obtain consistent estimators when the error term is heteroskedastic, autocorrelated, or both, once the regressors are proved stationary. - Quantile regression approach dividing the distribution in quartiles (four segments); robustness checked using a distribution based on deciles (ten segments) to explore cliff-effects far in the tail. - Estimation period: 2002: Q2 through 2018: Q4. - Dependent variable: Moody’s 1-year average EDF for the corporate portfolio (“1-Yr EDF 9 Average”) in Switzerland. - Forecast window: 2019: Q1–2023: Q4 under each scenario. - Sample sizes reported for regressions: Observations 66 and 59 (full and truncated samples for out-of-sample forecasting). - Model selection criteria: goodness of fit, forecasting performance in-sample (2002: Q–2018: Q4) and out-of-sample (2016: Q4–2018: Q4), and expert judgment based on 2008–09 crisis behavior. ### Quantile regression specification and key findings - Quantile empirical specification for the PD level of portfolio j in geography i (notation preserved): - ji t jii t ji t uZPD , , 1 , ,    - where i t Z 1  is the set of lagged determinants including country, regional, and global factors, and  denotes the quantile of the conditional distribution. - Quantile approach benchmarks econometric model against severe credit risk in upper quantiles of the default rate distribution. - Quantile results: - Credit risk projections more severe using estimated coefficients from upper quantiles of the EDF distribution. - Under the baseline scenario, corporate EDF for Switzerland increases from 0.9 percent (observed in 2018) to 1.6 percent (50th percentile) and to 2.6 percent (90th percentile). - Under the adverse scenario, EDF increases to 2.8 percent (30th percentile) and to a peak of 7.4 percent in 2020 under the 80th percentile. - Impact of EDF determinants is non-linear across quantiles with wide confidence intervals, suggesting cliff effects from worsening underlying risk factors. - Figure 8 (described): EDF projections show increasing severity across upper quantiles for both baseline and adverse projections. ### Main EDF determinants (Switzerland) from Newey-West approach - Credit risk in domestic non-financial corporate exposures increases with: - Deflationary pressures (low inflation) - Widening output gap (negative output gap) - Tightening of financial conditions - Specific contributors to rising default rates cited: - Low inflation, negative output gap, a dummy capturing negative economic growth. - Tightening financial conditions reflecting low equity prices, low fuel prices, rises in the T-bill, a steepening of the yield curve, and sovereign stress in the Euro Area. - Determinants differ across geographies: - United Kingdom: a rise in inflation, lower credit growth, a slowdown in global growth, and a widening in money market spreads are key drivers for U.K. firms. - United States: a flattening of the yield curve, a rise in long-term real interest rates, and a slowdown in emerging markets contribute to corporate distress. ### Sovereign exposures and Merton-based PD extraction - FSAP team constructed sovereign exposures database by mapping SNB surveys, Basel data, and banks’ annual reports; used SNB ‘Debt Securities’ template to estimate country shares of fixed income securities and applied country breakdown to IRB and STA sovereign and non-financial institutions’ exposures reported in Basel templates. - Under the adverse scenario, the slope of the yield curve increases for most bond curves due to term premium. - Structural Merton-based model used to extract PD estimates from sovereign spreads projected under each scenario: - Assumes difference between a risk-free security and a risky security is the put option on asset value defined as expected loss under stressed conditions (i.e., PD*LGD). - Using credit spreads S_i,tT, residual maturity (T-t), and assuming LGD=45 percent, the implied risk-neutral default rate is computed as: -  , , 1exp i tT S i tT i t Tt PD LGD    - Impairments on sovereign exposures calculated on claims not measured at fair value through profit and loss; newly created adjustments to provisions for default risk projected for available-for-sale, loans and receivables, and held-to-maturity investments. Fair-value held-for-trading positions subjected to market shocks per market risk modelling. ### Stressing complex credit products and Lombard lending - Leveraged loan default risk estimated using G-SIBs’ BBA templates; leveraged loan exposures include syndicated underwriting temporary exposures (syndicated loans, bridge loans, equity bridges). - IMF applied stressed PD and LGD parameters consistent with credit risk paths for high-yield corporate indices on banks’ reported leveraged loan exposures to estimate credit losses. - Structured credit products included: securitizations (ABS, MBS) and structured credit derivatives. Impairments computed on carrying value for accrual positions and on net exposures for fair-value positions; underlying pools’ credit and prepayment models stressed under IMF scenario using banks’ stylized price shocks from BBA templates. - Lombard lending: default risk from Lombard lending on uncovered exposures included. Size of uncovered exposures determined by applying IMF market shock scenario; stress loss computed by applying stressed PDs from retail exposures to net exposures after scenario shocks to market risk factors. ### Impact of credit risk on regulatory capital and IRB modelling - Impact depends on regulatory booking approach: - IRB exposures: credit risk evolves with EaD, PD, and LGD. - STA exposures: credit risk reflected in higher provisions and higher risk weights from deterioration in creditworthiness (higher LTVs, migration to substandard categories, rise in past due loans). - At end-2018, four main IRB banks in Swiss system: two G-SIBs (A-IRB banks booking about 95 percent of credit exposures under IRB) and two large cantonal banks (F-IRB with conservative implementation, RWA levels close to standardized values). For cantonal banks, considerable sovereign exposures are cash holdings at SNB. - Expected losses estimated as: - j ti j ti j ti j ti EADLGDPDEL ,,, * *  where i denotes the bank, j the asset class, and t time. - PD projections: multiplier approach applied on paths generated by IMF credit risk models; applied on bank-level starting regulatory parameters using exposure-weighted geographic breakdown. - LGD parameters: - Structural model for mortgage risk used for housing-related segments. - For other portfolios, multipliers calibrated by ECB staff for Euro Area FSAP and insights from the global financial crisis; LGD shifts calibrated by geography and portfolio. - LGD post-credit risk mitigation expressed as bank-specific j ti LGD , using aggregate LGD shifts j t LGD. - EAD projection driven by balance sheet assumptions, structural FX risk, and triggered credit lines and guarantees. EAD evolution formula preserved as in source: -   jjj t j FXiti jj tititititi UCLLPDFXfgEADEAD 1,,1,1,, 11 ,,   where ti g , is growth rate of IRB portfolio, j FXi f , is fraction of foreign currency loans, t FX is shock to foreign currency, (j ti PD 1, 1   ) non-defaulted portfolio, j ti L ,  is shock to triggered credit lines and guarantees, and j ti UCL 1, is undrawn guarantees. - Capital requirements computed using Basel III formula for IRB exposures: - RWAs computed after applying scaling factor of 1.06 to credit RWAs. - Multiplier of 1.25 applied to the correlation parameter of all exposures to large regulated financial institutions and to all unregulated financial institutions. *Italic: IMF staff summary extracted from 1cheea2019009 content unit.* ### 84. An adjustment factor was applied on projected RWAs to account for idiosyncratic ### 1cheea2019009 - 84. An adjustment factor was applied on projected RWAs to account for idiosyncratic ### Credit risk modelling for STA exposures - Four approaches were used to construct the starting point of default rates: - Compute the ratio of the stock of exposures in default over the amount of total exposures drawing on Basel templates. - Calculate the share of impaired loans and non-performing loans over total receivables in the credit portfolio. - Estimate the ratio of newly created adjustments to provisions for credit risk charged to income statement over gross loans, to exclude the impact of write-offs and recoveries. - Use the 1-year expected default rate reported for IRB exposures for IRB banks and that of peer banks for STA banks. - Given benign cyclical conditions in the Swiss economy, the FSAP team used the forward-looking IRB default rate in peer banks’ portfolios as a starting point for STA exposures. - Empirical starting-point observations: - The average NPL ratio in 2017 for the sample banks reached 0.5 percent for customer loans and 0.3 percent for mortgage loans. - The amount of newly created provisions on the gross carrying amount of loans was even lower. - Rationale for using IRB default rate: - IRB default rate is a forward-looking measure reporting a 1-year flow and does not require additional assumptions on write-off rates and recoveries over the stress test horizon. ### Provisions, regulatory treatment, and coverage assumptions - Basel III treatment distinctions: - General provisions (GP): forward-looking concept allowed, added to Tier II capital; GP are subtracted from CET1 but added to Tier II. - Specific provisions (SP): provisions ascribed to identified deterioration of particular assets; excluded from both regulatory capital and from RWAs for credit risk. - Basel Committee consideration: - Options include removing the GP/SP distinction and introducing regulatory expected losses under SA. - Coverage ratio assumptions for defaulted exposures: - The coverage ratio for defaulted exposures was floored at the LGD rate reported for the same portfolio under the IRB approach. - Under the adverse scenario, a 65 percent estimated coverage ratio was applied, subject to the calibration constraint described in the source. - Footnote: the 40 percent floor applies to certain exposure classes (claims on central government, central banks, regional governments, public sector entities, multilateral development banks, and international organizations). - The 65 percent ratio was calibrated using Basel findings on NPL coverage ratios during the global financial crisis. ### Projection mechanics for defaults, provisions, and RWAs - Forecasting defaults and provisions: - Flow of exposures in default = default rate × amount of performing exposures projected each period. - General and specific credit risk adjustments projected by multiplying the flow of new impairments by stressed LGD by geography and portfolio. - Flow of new impairments projected using the same econometric approach applied to IRB exposures. - Stock of performing exposures driven by: growth of the loan book, structural FX risk in foreign exposures, triggered credit lines and guarantees, and the flow of new impairments. - Regulatory capital requirements computation steps: - For defaulted portfolio: compute risk weight of exposures in default (excluding exposures at 0 percent risk weight) for each portfolio using Basel templates. - Risk weight of exposures in default set at an average 100 percent, following Basel III which specified a 100 percent risk weight should be applied on past-due loans when specific provisions are no less than 20 percent of the outstanding amount of the loan. The risk weight on the new flow of exposures in default was computed as max(, 100%) per the source. - For non-defaulted portfolio: assume one-notch downgrade of the underlying exposure under the adverse scenario. The initial risk-weight density reported by banks for non-defaulted exposures in Basel templates was mapped to the external rating by portfolio laid out by Basel, and one-notch downgrade was added to calculate the projected risk-weight density. - Note: This amounts to an average increase in the risk weight density for STA exposures of about 8 percent. - Four components driving shifts to RWAs in STA portfolio: - Motion of RWAs from flow of provisions, portfolio growth, and FX effects. - Increase in risk weights from triggered off-balance sheet credit lines and guarantees. - Increase in risk density from transition of loans from performing to nonperforming. - Change in risk density from the transition matrix estimated for performing exposures. - Adjustment factor on loan loss impairment projections: - To avoid a structural break and address IMF upward bias from forward-looking approach relative to Swiss GAAP incurred loss accounting, an adjustment factor was computed as the ratio between the projected loan loss impairment charges at the starting point and banks’ reported new inflows of provisions. This limited the contribution of credit losses to banks’ capital shortfall under stress. ### Market risk modelling approach - Scope: - Covers all fair value positions: financial assets held for trading (HFT), financial assets designated at fair value through profit and loss, and available-for-sale financial assets (AFS) which impact regulatory capital through OCI. - Excludes amortized cost positions held in a hedge-accounting relationship and hedge accounting derivatives. - Changes in CVA and CCR excluded from scenario-based analysis; stressed CVA assessed as a separate sensitivity test linked to solvency and liquidity interactions. - Shock calibration and timing: - Combined instantaneous and multi-year shocks. - Debt securities (repricing risk, credit spread risk): multi-year shock in line with the 5-year scenario; losses absorbed the same year the shock hits. - Other market-risk-sensitive products (equity risk, FX risk, commodity risk): instantaneous shock; losses recognized in the first year of stress. - IMF applies a multi-year approach for interest rate shocks and assesses basis risks. - Fair valuation impact computed separately on cash and derivative instruments for G-SIBs to assess basis risk. - For DFBs and private banks, hedging instruments for interest rate risk and credit shocks are assumed ineffective under stress. - Traded risk losses partially reversed as asset prices recover after a first-year sharp correction. - Market risk factors and generation: - Factors: risk free curves, libor rates, sovereign and corporate credit spreads, equity prices, exchange rates, commodity prices. - Paths for sovereign spreads, libor rates, equity prices, exchange rates, and commodity prices generated by the macrofinancial scenario. - Swap curves and corporate credit spreads required scenario expansion; swap curve path generated by modeling a cointegration equation on 3-month libor and sovereign yields estimated using FMOLS. - Corporate instrument shocks: - Calibrated by index and credit rating class using an expected shortfall (ES) approach. - ES computation: - Daily yield to maturity (YTM) computed over 2000–2018. - 99 percent threshold. - 30-day holding period for the calculation of the market shock. - Holding period for EM indices set to 180 days. - Revenue treatment: - Revenue and cost changes in investment banking business included in commission income. - Client revenues of trading assets projected as commission income by applying shocks to the effective implied rate at the starting date using amounts reported in Basel templates. - Client revenues reported under gains on financial assets and liabilities held for trading were excluded when not differentiable from valuation impact. ### Selected scenario calibrations and observed market figures - Equity prices: fall by an average rate of 40 percent across major equity indexes. - Commodity prices: replicate the oil prices’ plunge observed during the global financial crisis. - Swiss sovereign yields (Oct 2018): -64 bps (1-year), -34 bps (5-year), +15 bps (10-year). - Swap rates on Swiss francs: average daily spreads of 5-year sovereign yields over swap rates at -31 bps over 2000-2018. - Basis risk observations: - Financial crisis example: daily spread between the 5-year sovereign yield and the swap rate hovered about -31 bps in 2000–2018, tripled to over -100 bps in October 2008. - Holding periods: - Corporate bond curve risk: assumed 30-day holding period. - Swap-referenced positions: assumed 10-day holding period. - CCAR reference: - For BBB corporate bonds, the relative shock to the bond position and the underlying derivative indexed by the Credit Default Swap Index-Investment Grade (on the run) was estimated at a scaling factor of 2. - Expected shortfall calibration specifics: - 99 percent threshold, 30-day holding period (2000–2018 YTM series); EM indices holding period = 180 days. ### Data granularity and portfolio booking - Data sources: SNB BS surveys, G-SIBs’ BBA reporting, banks’ annual reports and Pillar 3 disclosures. - Fair value positions identified by issuer, regulatory book, and counterparty. - Securities portfolio evolution (notional values grow according to FX and growth terms per the model equation in the source); no portfolio rebalancing or liquidation allowed during the stress horizon. - Sovereign exposures booking (asset-weighted): - About 50 percent under HTM portfolios. - About 40 percent in trading and fair value portfolios. - About 10 percent in AFS portfolios. - Large dispersion across banks: G-SIBs primarily HFT, DFBs in HTM, private banks either in AFS or HTM. - Sovereign exposures predominantly to the United States, United Kingdom, Switzerland, and other foreign issuers. - G-SIBs market-risk assessment: - Used BBA templates with delta sensitivities by major index/counterparty, long vs. short positions, cash vs. derivative breakdown, and fair value impact to ‘severe event’ and ‘extreme event’ prescribed shocks. - FINMA reporting suggests G-SIBs rely on delta hedging; small net delta for a 1-bp move suggests banks are, on aggregate, well hedged under normal conditions. - Basis risk assessed via differentiated shocks across cash and derivative positions informed by historical crises, market liquidity, and CCAR scenarios. *Source: IMF staff estimates and IMF calculations as presented in the selected content unit.* ### 108. The P&L fair valuation impact resulted from applying the corresponding shock to the ### 1cheea2019009 - 108. The P&L fair valuation impact resulted from applying the corresponding shock to the ### Market Risk Approach for DFBs and Private Banks - P&L fair valuation impact: - Resulted from applying the corresponding shock to the cash and derivative position separately. - BBA data includes the net delta on cash and derivative positions reported by credit rating (IG, HY), counterparty (sovereign, public entities, financial, corporate), and region (Switzerland, North America, Asia...). - Differentiated shocks calibrated in the IMF scenario were applied to the fair value of the position to compute P&L impact. - Information reported by banks on prescribed shocks was used to account for non-linear effects in the size of the shock. - Valuation on debt securities: - Disaggregated into repricing risk and credit spread risk. - Impact on P&L and capital is recognized over the stress test horizon. - Banks assumed to maintain the same portfolio structure throughout the stress testing horizon. - Repricing risk (interest rate sensitivity): - Measured by effective maturity proxied by duration; duration approximated by residual maturity at the cut-off date. - Full valuation approach change in risk-free bond price: - ΔP_i_t = - (B_i_t · D_i · Δr_i_t) / (1 + r_i_t) (presented in the source as the relationship between modified duration, carrying value, and shock to the risk-free rate) - Risk-free curve proxied by the Swiss bond curve. - Term premium decompression and absence of flight-to-quality steepen the Swiss bond curve with the 10-year rate rising by 100 bps in 2020: Q4 relative to 2018: Q2. - Polynomial interpolation applied to span term structure between the 3-month and the 10-year rate generated in the macroeconomic scenario. - Credit spread risk: - Depends on re-emergence of sovereign stress and monetary policy space. - For sovereign distress and at the zero-lower bound, credit spreads widen significantly relative to the Swiss bond curve; for countries with monetary policy space, expansionary response pivots the yield curve downwards mitigating short-end pricing impact (e.g., China, United States). - Credit spread impact on fair value using modified duration: - ΔP_j_t = - (B_j_t · D_j · Δcs_j_t) / (1 + r_i_t + cs_j_t) (presented in source linking modified duration, carrying value, risk-free rate, credit spread, and shock) - Duration effects: - HFT portfolio duration typically low; larger in AFS portfolio, exacerbating valuation impact in some cases. - Long-duration assets hit by larger shocks when curve pivots downwards and steepens. - Less monetary policy space (e.g., euro area) limits downward shift, increasing impact of term premium decompression and sovereign stress. - Equities held with trading intent: - Fair value impact floored using the 2018 EBA methodology: - ΔEquity_j_t = 1.5 · Equity_long_j_t - 0.05 · Equity_short_j_t (expressed in source as % and with VaR scaling factor upper bound of 1.5) - VaR scaling factor set to upper bound of 1.5. - Hedges: - Assumed ineffective for DFBs and Private banks due to data limitations (no market risk sensitivities available). - IMF approach considered conservative under the assumption that Private banks hedge positions effectively. ### Operational Risk - Significance: - Operational risk is a major source of risk for Swiss banks, particularly for G-SIBs and private banks. - From a total RWA of CHF 479 billion at end-2017: - CHF 11.1 billion cover market risk. - About CHF 30 billion cover unexpected losses in operational risk. - Nature and drivers: - In turmoil, operational loss events (fraud, processing errors, lawsuits) may become more likely and/or more severe. - Operational risk often idiosyncratic but can have delayed relationships to macroeconomic deterioration (e.g., legal losses). - Scenario assumptions enter projections via frequency of loss events or loss severity distribution drawing on far tail percentiles of historical realized loss data or estimated losses per event. - Main operational risk categories: - Internal fraud, external fraud, execution/delivery/process management, clients/products/business practices (CPBP). - Most losses expected under adverse conditions relate to CPBP and, to a lower extent, internal fraud. - CPBP losses linked to potential delays in execution of Lombard loans under stress, risks in enforcing collateral (emerging markets), litigation from underwriting/trading, and mis-selling of complex products amid new conduct requirements. - Litigation and provisions: - Some major open litigation items remain despite closure of many legacy items. - Estimation challenges for potential loss amounts and timing add uncertainty. - Potential shortfall of provisions partly compensated by conservative regulatory capital calibration for operational risks. - Institutions face additional FINMA-imposed institution-specific buffers for litigation and reputational risks. - Bottom-up projections linked to the IMF scenario used by FSAP: - Cumulative capital impact from operational risk over the 5-year horizon accounts approximately for 8 percent of total impact. - By comparison, credit risk accounts for 15 percent and market risk for 1 percent. ### Interest Rate Risk in the Banking Book — Earnings Approach - Context and recent performance: - Net interest margin (nim) on outstanding loans hovered about 150 bps in 2017. - Reached 121 bps for outstanding claims, including financial claims, with 3-month libor rates at -75 basis points and 5-year sovereign benchmark rates at -52 basis points. - NII represents about 70 percent of total earnings for DFBs. - Risks and dynamics: - Maturity transformation exposure: assets locked at longer rates than liabilities (retail deposits). - Shift from variable-rate to long-term fixed-rate mortgages increases interest rate risk. - Government benchmark curve flattened since 2008 from 150 bps in the 5-year to 3-month Treasury spread to the current 50 bps. - SNB IRRBB findings referenced: - Moderate increase in interest rates would benefit banks on average; large upward shocks would strongly negative affect banks. - SNB estimates: - Following a 200-bps interest rate shift, banks’ net interest margin would increase by about 20 bps. - In the event of a 400-bps shock, banks’ interest rate margin would fall by about 20 bps from current low levels. - FSAP IRRBB four-pronged approach: - Economic value approach: measures valuation effect on fair value of assets (EVE) using market risk approach; only marked-to-market assets in the banking book (through OCI) re-valued. - Earnings approach: measures impact of interest rate scenarios on banks’ future net interest income over a 5-year horizon; shocks calibrated at bank/product level. - Scenario components: commercial margin and intermediation margin; includes systematic risk factor aligned with scenario and idiosyncratic component linked to banks’ historical rates behavior. - Application: shocks applied to repricing structure of assets and liabilities using Interest Rate Risk report as of June 2018; impact measured separately for stock of loans/liabilities and for flow of new lending/funding instruments. ### Structure of Assets and Liabilities - Loan composition (sample of banks in stress test): - Mortgage loans: about 40 percent of total loans. - Amounts due from banks: about 18 percent. - Lombard loans: 16 percent of outstanding claims. - Other customer loans: 13 percent. - Securities financing transactions: 13 percent. - Bank-type differences: - G-SIBs: amounts due to banks 24 percent of loans. - DFBs: mortgage loans 82 percent of loans. - Private banks: Lombard loans 62 percent of loans. - Funding composition: - Swiss banks fund mainly with retail deposits. - In the stress testing sample: - Retail funding represents over 70 percent of interest-bearing liabilities. - Amounts due to banks and repos: about 10 percent. - Debt securities issued: 20 percent. - Private banks: retail deposits about 85 percent of interest-bearing liabilities. - Behavioral and market sensitivities: - Liquidity reserves at the SNB and retail funding have low market sensitivity to money market stress but are subject to behavioral uncertainty. - Cash balances at the SNB are motivated by ultra-low rates and may shift if rates rise. - Decoupling of rates paid to depositors from market rates increases uncertainty if rates rise; potential shift from sight deposits to term deposits could occur. ### Modelling Strategy - Dynamic elements captured: - The FSAP IRRBB assessment modeled changes in commercial margins, liability margins, and pass-through effects. - Non-maturing deposits modeled as overnight deposits. - The FSAP did not change maturity profile at renewal nor modeled customer shifts between deposit types, consistent with constant-portfolio assumption. - Contrast with SNB approach, which incorporates dynamic customer shifting patterns and banks’ implied duration targets. *Source: 1cheea2019009 - 108.* ### 134. The FSAP team calculated interest income and expense at the portfolio level using a ### 1cheea2019009 - 134. The FSAP team calculated interest income and expense at the portfolio level using a ### Methodology for projecting interest income and expense - Twofold approach: - For competitive loan products and funding instruments, rates were projected at the aggregate level using shocks to benchmark rates and assuming no changes in margins. - For non-competitive loan products and credit sensitive liabilities, rates were projected at the bank level using shocks to systematic and idiosyncratic risk factors, with bank margins determined endogenously. - Rationale: - Addresses heterogeneity in funding market conditions across banks and concerns over policy rate pass-through to lending rates. ### Data sources and portfolio mapping - Primary data: - FINMA’s interest rate risk (IRR) report containing all banking book positions by repricing maturities with highly granular time bands (eighteen time-buckets, ranging from up to 1 day to over 15 years). - SNB’s maturity structure survey used to deepen portfolio composition and enable margin projection. - IRR reporting categories referenced: - Category I: positions with determined repricing maturities (e.g., fixed-rate loans, or medium-term notes). - Category II: positions with undetermined repricing maturities (i.e., sight claims, claims against customers, variable mortgage loans, sight liabilities, and customer deposits callable). - Other categories include: Category III: positions with no repricing maturity (e.g., trading portfolio assets and liabilities); Category IV: own funds; and, Category V: non-linear derivatives. - Mapping and limitations: - The IRR report is based on cash-flows (nominal value and interest payments); IMF analysis is based on carrying amounts — mapping to SNB’s maturity structure survey enabled splitting carrying amounts by time to maturity including on sight, cancellable, and remaining maturity buckets (<1m, 1-3m, 3m-1y, 1-5y, and >5y). - IRR does not report separately positions with remaining contractual maturity for fixed-rate and floating-rate instruments; this distinction is required to project margins when fixed rate instruments are re-issued over the stress test horizon. - Additional inputs: - Income statement information from banks’ annual reports and Dealogic to project bank-specific margins. - Dealogic data used to map cost on deposits and other liabilities to interest spread over benchmark at issuance for debt instruments. ### Funding costs modeling - Key insight: - Effective funding costs vary across banks due to differences in funding patterns and liquidity management; liability margin also depends on proximity to the zero lower bound. - Construction of effective funding cost measure by instrument: - Amounts due to banks and funding from repo transactions linked to 3-month libor rates by currency. Liability breakdown by instrument and currency (CHF, USD, EUR, other) available in SNB balance sheet surveys. - Rate on customer deposits is a function of deposit type (sight deposits, saving deposits, time deposits) and evolves with benchmark rates (libor rates, government yields). - Debt securities repriced according to the swap rate (at time to repricing bands) and the spread over benchmark at issuance (at re-issuance intervals) which is bank and instrument specific. Debt securities include cash bonds, bond issues, and central mortgage institution loans. - Modeling customer deposit rates: - Interest rates on customer deposits projected using a cointegration approach. - Findings: most retail funding is in the form of savings deposits and bank savings rates comove with benchmark rates. - Projections estimated using a cointegration equation of savings rates (by currency), 3-month libor (by currency) and sovereign yields (by currency) using a FMOLS approach. - Rationale for using spreads at issuance for debt instruments: - Captures banks’ ALM response to stress and introduces credit sensitivity on maturing debt. - Spreads at issuance represent actual cost banks face when raising wholesale funding and incorporate bank solvency risk as capital weakens under stress. - Common proxies (5-year CDS spreads, secondary market spread for 5-year senior unsecured bonds) are less representative under stress because banks refrain from unsecured issuance when market spreads widen. ### Determinants of debt spreads at issuance (econometric strategy and findings) - Econometric approaches: - Panel-based regression using fixed effects (also run by bank type: Big and private banks vs. DFBs). - Quantile regression approach to assess drivers at different percentiles of the funding cost distribution. - Model specification (basic form): - Funding cost of instrument j for bank i is regressed on macro variables (growth, inflation, unemployment, recession indicator), financial variables (libor, swap rates, spreads over swap), and regional/global factors (growth in the EA, ted spread, VIX), with lags to capture broad changes. - Key empirical results: - Negative comovement between policy rates and debt spreads; coefficient of SNB’s policy rate on debt spreads is negative particularly for Big and private banks. - Debt spreads are highly sensitive to economic conditions with evidence of non-linear effects during recessions, to money market spreads, and to equity prices, indicating contagion across financial markets. - Debt spreads on bank issuance are less sensitive to idiosyncratic risk; pooled regression coefficient is negative but not statistically significant — possibly due to limited issuance by some Swiss banks and issuance of Swiss covered bonds. - Quantile regression results: the adverse effects of money market stress and equity market sell-offs are more severe at higher percentiles; money market spreads and declines in equity prices have economically and statistically larger coefficients at higher quartiles, pointing to amplification mechanisms across markets. - Estimation details noted: - Estimation period: 2002: Q2 through 2018: Q4. - Dependent variable: Debt spreads over benchmark rates from Dealogic. - Regressors include: g (growth rate), pol (policy rate – 3-m target libor), VIX (CBOE index), libor_usd_tbill (spread of the 3-month libor USD over the U.S. T-bill rate), g_ea (growth rate in the euro area), stress_g#cL.g (a dummy variable taking the lagged GDP rate when it’s negative), equity (equity price growth), equity_sq (equity squared), tier 1 (tier 1 capital), and tier1_sq (tier 1 squared). L. denotes the lag operator. - Note on dynamic panel estimators: - Dynamic panel data estimators were not used because T (48 observations) is large and N (29 firms) is small; fixed effects expected to forecast better than Arellano-Bond in this application. ### Lending rates modeling - Portfolio segmentation and mapping to lending rates: - Five portfolio segments: Amounts due from banks, securities financing transactions, Lombard loans, mortgage loans, and ‘other customer’ loans. - Mappings: - Amounts due from banks and securities financing transactions linked to libor developments by currency. - Lombard loans linked to 3- through 12-month libor in USD. - Mortgage loans projected using the 5-year fixed rate mortgage rate as a benchmark. - ‘Other customer loans’ (consumer loans, leasing, corporate loans, loans to SMEs) projected using an econometric approach allowing time-varying bank-level asset margins; assumes customer loans are less price sensitive than mortgage loans and the market is less competitive. - Mortgage rates projection: - Projected using a cointegration approach: 5-year fixed rate mortgages, 3-month libor, and sovereign yields, using a FMOLS. - High degree of comovement in mortgage rates across banks conditional on tenor. - Mortgage vintage analysis: - Estimated vintage composition of outstanding mortgages (prior to 2014, 2015, 2016, 2017, and 2018) used to assess NII impact from renewing older vintages issued at higher rates at lower projected rates over the stress horizon. - Modeling ‘Other customer loans’ rates: - Rates projected conditional on scenario and bank-specific drivers; bank-specific regressions allow controlling for banks’ behavior under stress rather than system-level mark-ups. - Relationship specified with autoregressive term, controls for macro state, bank funding variables, and a coefficient estimating banks’ propensity to increase lending rates to restore capital ratios (coefficient 4 estimates this propensity). - Empirical strategy: - Panel-based regressions with fixed effects and quantile regressions. - Estimation period: 2002: Q2 through 2018: Q4. - Dependent variable in reported regressions: Implicit lending rates on customer loans using Fitch data. - Regressors include: g (growth rate), lt (10-year sovereign rate), spread_IT (10-year Italian sovereign rate over swap rate), libor_usd_tbill, g_ea, libor usd, fx (change in CHF over USD), int_mor (5-year fixed rate mortgage rate), stress_g#cL.g (dummy), tier 1. - Key findings: - Lending rates on ‘other customer loans’ are driven by money market spreads, libor rates, and banks’ capital position. - Econometric results provide some evidence of pass-through effects from funding costs to lending rates in customer loans. - Banks with a weaker capital position tend to price loans at higher rates, suggesting managerial strategic targets to restore capital. - Table 6 note: - For higher quartiles, the coefficient of sovereign spreads on funding cost is more significant, pointing at the importance of the sovereign-bank nexus. ### Net interest income (NII) impact calculation - NII shock calculation: - The impact of interest rate shocks on banks’ net interest income is calculated as: NII_ti = Σ_b (Δnim_ti_b * mid_b * gap_ti_b) - where gap_ti_b is the gap of bank i in bucket b and time t, mid_b is the mid-point in bucket b, and Δnim_ti_b is the net interest margin shock for bank i at time t. - Repricing frequency heterogeneity: - Shocks differ across repricing gaps because instruments reprice at different frequencies: - On liabilities: retail deposits reprice at high frequency, debt instruments reprice at longer frequency. - On assets: Lombard loans reprice at 3–12-month frequency, mortgage loans reprice at longer schedules depending on mortgage type and tenor. ### Fees and commissions (F&C) - Contribution to operating income: - Over one third of Swiss banks’ operating income is generated by fees and commissions (F&C). - F&C from securities trading and investment banking account for over three quarters of total commissions. - Commission income from lending activities contributes over 10 percent, and other services account for the remaining 10 percent. - Bank-type differences: - G-SIBs generate higher net income from F&C (33 percent) than from interest related activities, including loans and financial investments (30 percent). - The share of F&C is even higher for Private banks due to private banking activities in asset and wealth management. - Additional notes on income shares: - The share of NII relative to results from ordinary activities stands at about 70 percent for cantonal, regional, and Raiffeisen banks. - The share of F&C reaches 40 percent for G-SIBs in 2017 with the share rising during periods of financial distress. *Source: IMF staff calculations and the FSAP analysis provided in the referenced content.* ### 158. At the same time, there are concerns over the stability of F&Cs going forward on the ### Fees and Commissions (F&C) — Stability and Stress Testing Results ### Drivers and Risks - Structural changes in the wealth management industry and a potential market correction raise concerns over the stability of F&C income. - Competitive pressures: - Banks expanded asset management activities and sales of collective investment fund shares. - Emergence of standalone asset managers and advisors (including multifamily offices) pressures banks’ capacity to sustain commission income. - Income diversification may not improve stability due to sensitivity of performance fees to financial asset returns. - Stability of F&C hinges on resilience of F&C income to adverse financial market developments. ### Regulatory and market pressures - Implementation of MiFID II / MiFIR in the EU and future Swiss tighter conduct requirements may reduce commission rates and trading margins and affect timing of fee recognition. - December 2017 equivalence determinations allowed EU-domiciled institutions to continue executing on Swiss trading venues; MiFID II introduction expected to result in a reduction in commission rates and trading margins. ### Composition of F&C and macro linkages - F&C composition: - Investment banking: underwriting fees and mergers and acquisitions. - Wealth management: brokerage fees, investment fund fees, portfolio management and advisory fees. - Wealth management fees split into volume- and transactions-based fees, advisory fees, and discretionary investment fees. - Macroeconomic and market links: - Decrease in business/client activity and market volume from significant market volatility is expected to adversely affect transaction fees, commissions, and margins, particularly in wealth management and IB operations. ### Empirical model and predictors - Dynamic panel data model estimated over a pool of banks using system GMM (Arellano and Bond) to address endogeneity and dynamic panel bias. - Model notation: i t fc is the share of fees and commissions to total assets for bank i at time t and the vector of explanatory variables; model includes lagged dependent variable and explanatory variables. - Estimation period: 2002: Q2 through 2018: Q4. - Dependent variable: implied net fee and commission income to total assets using Fitch data. - Predictors include: lagged F&C over assets; contemporaneous and lagged GDP growth; the inflation rate; house price growth; stock market returns; first difference of short- and long-term rates; equity volatility; government spread over swap; TED spread; and other financial-condition variables. ### Key empirical findings on F&C determinants - F&C decrease with: - Money market stress. - Low long-term rates. - Equity correction. - Rising volatility. - Evidence in estimated coefficients: - Positive coefficient on short-term rate difference. - Positive estimate for changes to U.S. equity price. - Positive coefficient on long-term rate difference. - Negative coefficient on the VIX. - Implication: under adverse scenario assumptions, F&C income decreases for most banks. ### Other P&L items and dividend policy - Other P&L items projected using panel fixed-effects estimation with Fitch, SNB, and FINMA data; main variables included: - Interest income from trading assets and financial investments. - Other operating income. - Non-interest expenses including administrative expenses, depreciation, negative goodwill and other operating expenses. - Tax expenses over net profits. - One-off adjustments are not taken into account (treated as non-recurrent; P&L impact = zero under baseline and adverse conditions). One-off events list includes profit and loss from non-current assets, discontinued operations, disposal groups, and extraordinary profit. - Dividend payout behavior linked to profits and bank capital ratio with constraints: - Floor set at 30 percent (positive profits). - Capital conservation buffer (CCB) schedule for CET1. - Statistical analysis on historical dividend payments. - If profits exceeded 50 percent of income at the cut-off date, dividend payout ratio set at 50 percent. - If banks were making losses, zero-dividend payout assumed. - Assumed dividend payout rule noted elsewhere: on average 50 percent of net profits. ### SNB Top-Down (TD) stress-testing approaches - SNB conducted TD stress test of the two G-SIBs and the DFBs using SNB framework, run at June 2018 reference date on IMF scenarios; results benchmarked against IMF and SNB calibrated scenarios. - SNB uses separate approaches for DFBs and G-SIBs. - DFBs TD stress test: - Uses SNB survey data and FINMA’s Interest Rate Risk report with a 5-year horizon (longer than 2-year horizon for G-SIBs). - Mortgage risk modeled structurally; calibrated to 1991–1995 observed loss rate; projected loss rates scaled by region, collateral type, and rank. - Non-mortgage credit risk assessed via hybrid structural and regression models; total non-mortgage domestic exposures for DFBs ~ CHF 100 billion; foreign exposures ~ CHF 30 billion; mortgage exposures ~ CHF 650 billion in 2017. - IRRBB modeled dynamically separating assets and liabilities, accounting for asset margins, customer behavior, position renewals, margins split (asset, liability, structural), hedging strategies, and evolution of sight deposit accounts at the SNB. - Business and market risk for DFBs assessed via regression analysis and expert judgment; results checked and, if necessary, adjusted with prudent bias. Business expenses kept constant; RWAs kept constant given standardized approach usage. - G-SIBs TD approach: - Credit risk: regression-based model with credit rating migration, separate domestic and foreign exposures; foreign loss rates projected econometrically; scenario stress factor (SSF) translates projected loss rates into relative changes from long-term average loss linked to average credit rating; includes credit transition model. - Market risk: calculations based on standardized risk and exposures modules in banks’ BBA reporting templates; full revaluation of standardized shocks; IMF scenarios used to interpolate/extrapolate standard shock results; equity and credit basis risks considered. - Other risk factors: combination of BBA data and SNB econometric modeling; structural approach for major income and expense components; granular F&C projection by AuM composition; operational risk and time-varying RWAs included. ### G-SIBs Bottom-Up (BU) stress testing - Two G-SIBs ran BU tests on IMF scenario over 5-year and 2-year horizons; IMF scenario extended to include traded risk component calibrated using historical granular market risk data. - Banks expanded scenarios adding volatility shocks on interest rates, FX, equity, commodities; commodity shocks by product; price shocks for credit structured products by product category, instrument type, original rating; stressed collateral values for Lombard loans. - Banks used internal approaches with highly granular loan/security-level calculations covering business, market, credit, operational risk, share-based personnel expenses, pension impact, and regulatory impacts (fundamental review of the trading book and Basel IV elements including output floors and IRB/STA revisions). ### Solvency results (baseline and adverse scenarios) - Baseline projections: - Slight decrease in capitalization ratios partly due to low interest rates. - Weighted-average 70 basis point decrease in banks’ CET1 ratio by 2020 with differences across banks. - Domestically-oriented banks face greater profitability challenges (more affected by negative liability margin). - IMF adverse scenario: - Minimum capital requirements met in aggregate; a few banks breach capital buffers. - Macroeconomic shocks and market risks deplete capital ratios by 440 basis points at the low point of stress (aggregate), with wide variation across banks. - Capital depletion for the sample represents about 3 percent of projected nominal GDP in 2022. - Under the severe scenario, all banks would meet minimum capital requirements with one bank breaching its CCB and posting partial recovery by the end of the 5-year horizon. - Tier 1 leverage ratios soften by about 70 basis points by the end of the horizon. - Profit and loss impact driven by market price shocks on fair value portfolios, and significant stress in commission income and net interest income. - Heterogeneity in impact by bank type: - By 2020, capital depletion reaches 510bps for G-SIBs, 410bps for Private banks, and 260bps for Domestic banks according to IMF calculations. - G-SIBs more impacted by larger trading books, risk-weight expansion on IRB exposures, business risk from commission income, and higher operational risk. - Private banks more affected than DFBs by stressed earnings related to asset and wealth management, market risk on fair value portfolios, and operational risk tied to reputation and earnings capacity. - DFBs comparatively more impacted by credit risk losses. - IMF TD results broadly in line with SNB TD results over the common sample; differences mainly attributable to higher contribution of RWAs to capital depletion in the IMF test. *Source: IMF staff calculations and accompanying analysis in the provided content.* ### 181. Sensitivity tests on mortgage default risk point at a large capital impact from the ### 181–196. Sensitivity tests on mortgage default risk and liquidity stress testing ### Mortgage default risk: IMF adverse scenario and sensitivity tests - IMF adverse scenario: - Real estate price correction: 25 percent over two years. - Rise in 10-year mortgage lending rates from 1.5 to 3.0 percent. - Additional sensitivity outcomes: - Default rates increase exponentially with the size of the shock (Figure 16). - Under the scenario-based real estate path, an increase in lending rates to 6.0 percent raises mortgage default rates to 3.5 percent. - A 3.5 percent mortgage default rate leads to an aggregate 275 basis points CET1 capital depletion for the IMF G-SIBs and DFBs (excluding private banks). - Test assumptions and notes: - Test is based on an unemployment shock of 110 basis points, a house price correction of 25 percent, and a wide range of interest rate shocks. - Projections use conservative assumptions related to: the distribution of historical vintages by LTV and LTI similar to the matrix observed in June 2018; the repricing tenor of outstanding mortgages; and banks’ activation of margin calls from the widening in Point-in-time LTV ratios due to a real estate price correction required to satisfy the amortization of the second mortgage over the remaining maturity of the mortgage. ### FINMA real estate stress test: extreme scenario results (2018: Q4) - Scope and sample: - FINMA mortgage stress test covering 18 banks, including SIBs and other DFBs. - Sample covered approximately 70 percent of total Swiss mortgage lending currently standing at over CHF 1,000bn. - Extreme scenario headline: - Total capital ratios could fall on average by about 400 bps (median post-stress total capital ratio would fall by about 400 bps). - Key insights from the 2018 stress test: - On average, banks’ risk profile deteriorated compared to tests between 2012 and 2017, leading to higher loss ratios for the same negative scenarios. - Risk shift: Over 70 percent of the stress losses would incur in the investment property portfolio, although this only accounts for 29 percent of total mortgage lending in the sample. - About half of the banks would fall below their capital thresholds, in some cases by a wide margin, and would therefore have to raise additional capital. - FINMA key scenario assumptions since 2012 (used for calibration): - 30-40 percent real estate price correction for the owner-occupied segment (44-54 percent for owner-occupied luxury segment). - 35-45 percent for both the investment-led segment and commercial real estate. - Hike in lending rates to 7.0 percent. ### Drivers of capital depletion under the IMF adverse scenario (cumulative impacts) - Aggregate CET1 ratio depletion contributors (aggregate shortfall drivers and magnitudes): - RWA expansion driven by heightened credit risk, projected paths for aggregate lending in foreign jurisdictions, and higher market volatility: lowers CET1 ratios by about 190 basis points across banks. - Stressed earnings (decline in commission income from portfolio management/advisory services and underwriting, partly offset by higher brokerage fees): CET1 shortfall of 80 basis points. - Operational risk (clients, products, business practices, execution, delivery, process management, market regulatory reforms): capital erosion of 60 basis points. - Weaker GDP growth, falling real estate prices, and tightened amortization schedules increasing credit risk: reduces aggregate CET1 ratio by 50 basis points relative to June-2018. (Decline in policy rates in the scenario mitigates some recession impact.) - Market risk (basis risk in corporate debt, FX, and equity markets; ineffectiveness of hedges in sovereign securities): CET1 capital depletion of 40 basis points. - Interest rate risk on net income: CET1 ratio declines by 10 basis points (reflects large stock of customer deposits, hedging, repricing structure of longer-tenor debt instruments, and banks’ ability to partly pass on funding cost to loans). ### Additional sensitivity tests (scenario-based analysis) - Further decompression of risk premia: - A 200 basis points widening of credit spreads over the swap curve lowers the post-stress aggregate CET1 ratio by 70 basis points (excluding hedges); G-SIBs and Private banks relatively more affected. - Credit rating downgrade: - A one notch-credit downgrade on standardized exposures erodes bank capital ratios by 90 basis points. Domestically-oriented banks are impacted more severely due to higher reliance on external ratings. ### Policy and supervisory considerations for stress testing frameworks - SNB stress testing enhancements suggested: - Increase data granularity in supervisory returns to better leverage banks’ regular supervisory reporting into stress testing. - Support consistency of data sources when aggregating data within supervisory stress tests or across multiple stress tests. - Deepen analysis of risk interactions, particularly between solvency and liquidity risks and modeling of feedback loops between banks. - Swiss authorities should consider including major private banks in stress testing frameworks: - The possible failure of a major private bank would expose the Swiss banking system to significant reputational risk and could spread to other Swiss banks through wealth management activities and potential contagion in funding markets. ### Liquidity stress testing: scope, scenarios, and results - Scope and methodologies: - Analysis included Basel III liquidity metrics, LCR by currency, contractual maturity mismatch by time band, availability of unencumbered assets by currency (broader than HQLA), and potential liquidity generation via monetization of securities with market haircuts. - LCR-based tests used LCR banks’ submissions and FINMA liquidity monitoring template across eighteen maturity buckets. Solvency sample used for LCR-based tests; cash-flow based test covered twenty-two firms. - No NSFR-based test conducted (NSFR not introduced / not binding for most banks). - IMF LCR scenarios: - Basel III LCR 2013 standard scenario. - Swiss retail stress scenario: run-off rates up to 20 percent for retail deposits; inflow rates of 25 percent for maturing retail inflows; stressed contingent liability risk at 20 percent of undrawn but committed credit and liquidity facilities. - Swiss wholesale stress scenario: captures global financial crisis–like conditions—25 percent haircut on Level 2a assets and up to 60 percent on Level 2b assets; stressed inflow rate of 25 percent for maturing corporate inflows and 40 percent for financial institution flows; 50 percent run-off for operational deposits, 100 percent for corporate deposits, 50 percent for secured funding backed by non-Level 1–2a assets; 100 percent drawdown of liquidity facilities. - IMF cash-flow based tests: - Simulated gradual outflow over five consecutive days and over a 30-day horizon. - 30-day test assumptions: haircuts up to 60 percent for securities and bank loans mobilized in repo backed by non-general government collateral; no issuance of new unsecured funding and freeze of securitization markets; callback rates of 100 percent on expected cash inflows related to credit extension without liquid collateral and no offsetting cash inflows from renewed wholesale lending; cash outflows up to 75 percent of maturing and non-maturity funding without liquid collateral and no renewal of term retail and wholesale deposits. - Liquidity results and vulnerabilities: - Average LCR across all banks stood at 165 percent in 2018: Q2, supported by an ample stock of high-quality liquid assets; G-SIBs and Private banks posted comparatively higher ratios. - LCR in USD for G-SIBs and Private banks is more volatile and falls below 100 percent for some banks; overall nominal shortage for Private banks is very small. - Under IMF stressed scenarios, USD liquidity falls further, particularly for private banks, due to USD-denominated net new money in asset management flows, Lombard loans benchmarked to USD libor, and USD-denominated client sight deposits with higher run-off risk. - G-SIBs’ US-centric activities and FX activity in USD drive potential USD liquidity shortages; intragroup centrally controlled funding model requires U.S. entities to have adequate liquidity risk tools and adhere to local liquidity rules. - Swiss banks have excess EUR and CHF liquidity and potential access to CHF/USD and EUR/USD swap markets; Fed expected to provide USD liquidity assistance to U.S. entities of Swiss G-SIBs. - Swiss banks remain vulnerable to turmoil in global money markets that can spill into FX and currency swap markets; recommendation that banks ensure currency-specific mismatches are managed effectively and FINMA monitor major currency-specific liquidity requirements closely. - LCR scenario outcomes: - Retail scenario: average LCR declines to about 140 percent. - Wholesale scenario: average LCR declines to about 95 percent. - LCR sensitivity higher to a wholesale event than to a retail event, reflecting liquidity risk from high value deposits and contingent liabilities. - Cash flow-based stress outcomes: - Under 5 days of cumulative stress, no banks turn illiquid. - In the 30-day test, 4 banks face marginal liquidity shortages; individual shortfalls are small, below 1.5 percent of system’s total assets. - Structural improvements and context: - General resilience to liquidity shocks has increased amid excess liquidity at the SNB. - Swiss banks hold significant liquid assets as sight deposits at the SNB, partly due to SNB foreign exchange transactions against Swiss francs. - Banks have changed liability composition and counterbalancing capacity (CBC) structure, but notable differences remain across banks in funding structures, asset encumbrance, and CBC composition. *Source: IMF staff estimates; FINMA. Note: All figures, scenario assumptions, and percentages are reported exactly as presented in the source content.* ### 197. The continued monitoring of FINMA Liquidity Monitoring Metrics by currency helps ### The continued monitoring of FINMA Liquidity Monitoring Metrics by currency helps ### Continued monitoring of FINMA Liquidity Monitoring Metrics - The data collection introduced by FINMA enhances the monitoring and supervision of contingent liquidity risks including those associated with sudden changes to market haircuts on unencumbered assets, non-maturing liabilities, the cross-border flow of collateral, securities funding transactions, currency swaps and other derivative flows, and contingent liabilities from credit and liquidity facilities. - Potential risks requiring continued vigilance: - Liquidity trapping in foreign jurisdictions. - Ring-fencing of intragroup liquidity. ### Cash-flow based stress test results (summary) - A few banks become illiquid under the 30-day Cash-Flow test, but they are small relative to system-wide liquid assets and aggregate total assets. - Figures are expressed in thousands of CHF (source: IMF Staff Estimates and FINMA). - The 30-day Cash-Flow test output shows the number of banks that become illiquid after 30 days of cumulative stress (column “Survival”, row “No”), and their size relative to system assets (column “Net cash short shortfall”) using FINMA contractual maturity mismatch data. - Stressed assumptions on contractual inflows, contractual outflows, and available unencumbered assets are shown in Jobst, Lian Ong, and Schmieder (2017), “Macroprudential Liquidity Stress Testing in FSAPs for Systemically Important Financial Assets,” IMF Working Paper 17/102. ### Solvency and liquidity interaction — overview - Regulatory practice treats solvency and liquidity risks as if they were unrelated; risks are measured and required against separately. - The 2008–09 crisis illustrated key interactions: asset fire sales, credit rating downgrades pressuring funding costs, loss of market funding, and liquidity risk from securitization pipeline back-ups. - Supervisors are developing stress testing models that incorporate liquidity–solvency interplay and systemic risk feedback effects (BIS Working Paper 29 (2016) overview). - Channels by which stressed capital ratios feed into liquidity stress include: - Reduced inflows from maturing credit claims and maturing securities. - Larger haircuts on counterbalancing capacity following deterioration in credit rating of issuers. - Higher run-off rates of outflows of credit-sensitive funding following a downgrade in the bank’s credit rating. - Empirical calibration challenges: banks’ behavioral responses (e.g., deleveraging), unconventional monetary policy activation (asset purchase programs), and broadening of central bank collateral frameworks. ### Quantitative approach to solvency–liquidity–contagion interaction - The section quantifies interactions using a three-pronged approach: - Feedback loops between solvency risk and funding costs. - Impact of financial institutions’ distress on solvency risk. - Exploratory analysis of liquidity-at-risk. A. Feedback loops between solvency risk and funding costs - Projection process covered quarterly from 2019: Q1 through 2023: Q4. - Iterative four-stage projection at each time step: - Stage 1 — Initial Projection of Funding Costs: map liability structure with interest rate path for each funding instrument; interest paid on debt at issuance is conditional on bank’s initial capital ratio. - Stage 2 — Initial Projection of Interest Income: net interest income projected econometrically at bank level; asset margins allowed to vary by bank for “other customer loans”; loan rates on claims against financial institutions, Lombard loans, and mortgage loans follow benchmark rates at current spreads. - Stage 3 — Initial Projection of Capital: projected net interest income and other risk projections translate into revenues, costs, balance sheet positions, and CET1 capital at t+1. - Stage 4 — Iterative Process: projected CET1 capital informs next period’s funding cost and interest income projections; repeat until convergence in funding costs. - Accounting for feedback loops in funding costs adds an average 15 bps to capital depletion. - Rationale for moderate impact on average bank: low credit risk sensitivity of debt at issuance (partly due to share of central mortgage institution loans), limited reliance on wholesale funding by Swiss banks, and relative resilience of Swiss capital ratios to stress. B. Impact of financial institutions’ distress on solvency risk - Model explores solvency and liquidity impact from issuer default risk related to credit default events on underlying names to which banks’ positions refer. - Issuers’ long exposures correspond to “jump to recovery” measures reported in BBA templates; defaults increase credit risk losses and lower inflow rates of maturing inflows. - Sensitivity test assumptions: - Issuer default risk generates lower inflows from currency swaps and other derivative inflows. - Test assumes 10 percent of lost inflows from counterparty default for derivatives inflows in the overnight-to-two-week maturity window. - To meet liquidity pressures, banks replace lost inflows by interbank borrowing. - On the back of financial institutions’ distress, liquidity dries up on money markets, credit spreads widen, and libor rates increase to 100 bps, adding funding cost pressures to the borrowing counterparty. - P&L impact from stressed CVA: consider CVA impact from a 200-bps widening of credit spreads for investment grade financial institutions; calculations are net of CVA hedges but without dynamic hedging consideration. - Results from combined interactions: - Issuer default risk from long exposures to financial institutions on a jump to recovery measure, compounded with CVA losses on OTC derivatives from a 200bps widening of credit spreads on financial firms, and the replacement cost of 10 percent of lost contractual cash-inflows from derivative transactions (overnight through 2 week) at a 100bps libor rate, would lead to a CET1 capital depletion of 75 bps for the G-SIBs. C. Exploratory analysis of liquidity-at-risk - LCR standardization improved measurement across banks but does not capture contingent liquidity risks well. - For large Swiss banks, contingent liquidity risks mainly arise from: - Changes in the value of posted collateral securing derivative and SFTs. - Valuation changes on derivatives and other transactions. - LCR assumptions: - Prescribed 20 percent of the value of non-Level 1 posted collateral to account for valuation changes on pledged collateral. - Look-back approach to calibrate outflows from market valuation changes in derivatives: outflow needs calculated by identifying the largest absolute net 30-day collateral flow realized during the preceding 24 months (currently low realized outflows due to compressed volatility). - Exploratory liquidity-at-risk setup: - Bank assets split into four categories: - Category I: marketable assets subject to variation margin (VM). - Category II: marketable assets not subject to VM. - Category III: illiquid assets subject to VM. - Category IV: illiquid assets not subject to VM. - Liquidity risks arise from market valuation changes on Category I and III. - Solvency shocks affect categories via credit risk losses (Category IV) and market risk losses (Categories I–III) generated by the same solvency scenario. - Bank pecking order to meet liquidity needs: - Repo available unencumbered marketable assets at repo rate R_t, constrained by initial counterbalancing capacity. - Borrow in interbank market at interbank rate L_t; access linked to bank’s credit rating. If solvency shock triggers a rating downgrade (from sudden reduction in leverage ratio), the bank can no longer access the interbank market. - Liquidate non-marketable assets at discount rate D_t if pressures persist; discount linked to credit quality of underlying portfolio. - If liquidity still insufficient, the bank turns illiquid. - Results: impact of solvency–liquidity interactions on G-SIBs is contained. - The test identifies combinations of shocks to marketable assets subject to VM (x axis) and illiquid assets not subject to VM (y axis) that render the bank solvent/liquid, insolvent but liquid, or insolvent and illiquid. - Only very large shocks would put banks under severe solvency and liquidity stress, owing to large counterbalancing capacity and strong solvency position. ### Systemic risk analysis — scope and methods - Scope: confined to the banking system, focused on contagion risk through Swiss interbank market including intragroup domestic exposures; contagion through equity markets assessed using CoVaR methodology. - Limitations noted: other advanced stress testing approaches capturing financial–macro feedback and contagion to non-banks rely on dynamic balance sheets and are not assumption free; further research needed. - Channels for shock transmission assessed: - Bilateral Exposures: large exposures to a distressed firm can cause cascading defaults. - Market Contagion: equity losses from revised investor expectations about banks with similar business models in same macro environment. - Contagion exercises: - Sample for interbank contagion: twelve major Swiss banks using ARIS large exposures data. - Scenarios: credit shock simulation (one credit counterparty defaults at a time) and funding shock simulation (default of a funding counterparty induces liquidity shortfall). - Network analysis based on granular supervisory interbank exposures evaluates systemic implications beyond bilateral data. ### Network analysis — findings - Contagion depends on interbank claim structure: incomplete and two-tiered structures are more fragile; money center failures more disruptive. - Model-based simulation (Espinoza-Vega and Sole (2010) approach) using granular supervisory data finds: - Risk of contagion through interbank exposures within the Swiss interbank market is currently low relative to banks’ capital buffers. - Network displays modest interbank connectedness relative to capitalization. - Spillovers are greatest between the G-SIBs and some Private banks. - Spillovers have decreased due to implementation of Large Exposures regulation, minimizing exceptions. - Data constraints: ARIS data included positions to Swiss counterparties in the sample (twelve banks); lack of access to foreign exposure data precluded cross-border contagion assessment using bank position data. - Even under extreme assumption of LGD = 100 percent of original exposures, there is no potential for contagion rounds in the depicted network. ### Systemic risk analysis — CoVaR and market-based contagion - CoVaR methodology evaluates propagation of individual bank stress through equity markets; contagion can arise if one bank’s failure signals distress for similarly modeled banks. - Analysis uses weekly data from October 2005 through November 2018. - Market-perception findings: - VaR of financial system returns controlling for macrofinancial drivers edged down to about -15 percent of quarterly equity returns in October 2018 from a peak of -60 percent during the GFC. - The CoVaR coefficient related to individual bank return dynamics is always significant and improves tail forecasting of the financial system portfolio. - Sample and results: - Sample included the 6 listed Swiss banks in the 12-bank solvency sample. - Average delta CoVaR of the most systemic bank reached -15 percent of weekly equity returns during the GFC; it edged down to -8 percent during 2018. - Scatter plots indicate weak correlation between individual VaR (x-axis) and Delta CoVaR (y-axis) in 2018. - Overall conclusion: the contribution to systemic risk of the listed Swiss banks is contained; the risk of spillovers between listed Swiss banking institutions appears moderate. *Source: IMF staff and FINMA (excerpts from the Switzerland FSAP chapter).* ### Appendix I. Risk Assessment Matrix (RAM) ### Appendix I. Risk Assessment Matrix (RAM) ### Major Risks (RAM entries 1–4) - 1. Tighter financial conditions and volatile trading business. - Description: Volatility rises, and term premia decompresses, triggering an abrupt tightening of financial conditions. This generates a generalized sell-off in stock markets, and the reemergence of sovereign stress in the Euro Area. Despite low policy rates, the sustained rise in risk premia leads to raising funding costs for banks bringing a sharp increase in lending rates. Higher debt service stresses leveraged firms, stretched households, and vulnerable sovereigns. Trading business suffers as volatile markets push clients to seek shelters in cash hurting transaction-driven capital market revenue and lowering performance fees. - Overall Level of Concern: High - Relative Likelihood: High - 2. Severe global recession and low net new money. - Description: A hard Brexit without a deal in place, a U.S. trade war against Europe or China, concerns over public debt sustainability in Europe, or a credit cycle downturn in emerging markets undermine global medium-term growth. Switzerland economy is severely impacted as it remains exposed to global fragile economic conditions and the potential misallocation of investments in an ultra-low-yield environment. Financial stress from confidence shocks trigger a sharp fall in asset prices, followed by a pronounced credit crunch. Swiss banks face additional external risks (particularly in emerging markets), such as sudden inability to exchange currency, risk of capital controls and restrictions to capital transfers, political risk, or inability to enforce the right to sell collateral due to legal prescriptions. Net new money outflows surge hurting the wealth and asset management industry. - Overall Level of Concern: Medium - Relative Likelihood: High - 3. Regional tensions and regulatory pressures. - Description: A significant deterioration of bilateral relations with the European Union (EU) impacts Switzerland’s externally-oriented economy eroding competitiveness and weighing on economic growth in Switzerland. Marked changes in tax and regulatory requirements impact the wealth-management industry's putting pressure on Swiss banks’ business models and feeding adverse feedback loops. Litigation reserves prove insufficient and operational risks inherent in private banking business models increases. - Overall Level of Concern: Medium - Relative Likelihood: High - 4. Large correction in real estate prices. - Description: A sharp reversal of historically high house prices in Switzerland poses material credit risks, due to domestic banks' high exposure to real estate markets. The correction in residential and Commercial Real Estate prices is concentrated in regions which have experienced more rapid price increases. Falls in asset prices widen LTV ratios, trigger margin calls, and increase borrowers’ amortization schedules. Affordability risk surges and default events spread to the nonbank financial industry exposed to real estate. - Overall Level of Concern: Medium - Relative Likelihood: High ### Appendix II & III — EDF Projections for Non-Financial Corporates (United Kingdom and United States) - General notes (applies to both appendices): - Sources: Moody’s KMV, WEO, Bloomberg, and IMF estimates. - Estimation period: 2002: Q2 through 2018: Q4. - Dependent variable: Moody’s 1-year average EDF for the corporate portfolio (i.e., “1-Yr EDF 9 Average”). - Models: Three specifications for each corporate portfolio for the full sample period (66 observations) and a truncated sample (59 observations) to conduct out-of-sample forecasting. - Lag notation: L denotes 1 lag, L2 denotes 2 lags and L3 denotes 3 lags. - Purpose: Models used for forecasting rather than structural identification; forecasting ability is the relevant performance metric. - Regressors (UK model): inf, gap, tb, ltr, slope, credit, equity, g_w, g_em, spread_IT, libor_usd_tbill, stress_g#cL.g (dummy taking lagged GDP rate when negative). - Regressors (US model): inf, gap, tb, ltr, slope, equity, g_m, g_oil, spread_IT, libor_usd_tbill, stress_g#cL.g (dummy taking lagged GDP rate when negative). - Selected coefficient features preserved exactly as reported (examples across specifications): - UK: L.inf 0.119** (std. err. 0.044); L.gap -0.104** (0.044); L.libor_usd_tbill 0.298*** (0.062); Constant -3.287*** (0.177). - UK: Observations 66 66 66 59 59 59 (as reported per column grouping). - US: L.inf 0.068** (0.030); L.gap -0.085* (0.045); L.ltr 0.074* (0.038); L.libor_usd_tbill 0.185*** (0.050); Constant -2.545*** (0.208). - US: Observations 63 62 61 56 55 54 (as reported per column grouping). - Statistical notation preserved: Standard errors in parentheses; ***, **, * denote p<0.01, p<0.05, p<0.1 respectively. ### Appendix IV — Overview of the CoVaR Methodology - Definition: - The CoVaR is defined as the maximum expected loss in the banking system for a given confidence level and time horizon, conditional on the maximum expected loss of an individual bank at a specific confidence level and time horizon. - The (1%−λ) CoVaR of system j given the (1%−λ) VaR of bank i, denoted | ji t CoVaR λ , is defined as the λ quantile of the conditional loss function: - Pr( Φ( Xj t ) ≤ Φ( CoVaR | Xi t ) ) (as presented in the source). - System and individual returns notation: jt X and it X denote system and individual bank returns. - ΔCoVaR: - A bank’s individual contribution to systemic risk approximated by Δ CoVaR: - Δλ,ijt = CoVaR j|i,t,λ − CoVaR j|VaR,t,λ (presentation preserved as in source: |,, , j i t t t CoVaR CoVaR VaR λ λ λ Δ = −). - Captures how much risk bank i adds to overall systemic risk when it reaches its VaR. - Banking system construction for conditioning events: - For each conditioning event, construct a different banking system excluding the bank in distress; the banking system returns given bank i’s distress are the weighted average returns of the remaining banks: - Sij,t = Σ_{j≠i} ω_{j,t} X_{j,t}, with ω_{j,t} = W_{j,t} / Σ_{j≠i} W_{j,t}, where j t X refers to returns of the jth bank and j t W is the book value of total assets. - Symmetric and asymmetric specifications: - Symmetric approximation: X_{S,i,t−λ}' = β' Z_{t−λ} + δ_{λ,i} X_{i,t−λ} + u_{t,λ} (as presented). - Asymmetric specification to capture reinforcing effects and left-tail behavior: - Uses indicator splits for X_{i,t−λ} < 0 versus ≥ 0, estimating separate δ parameters for negative and non-negative conditioning values (presentation preserved as in source). - Final contribution formula (econometric linkage): - (Δ̂%50)_{i,t} = δ̂_{λ,i} (VaR_{i,t} − VaR_{i,t,50%}) (source presentation: ( )%50 ˆ , , t i t ii i t VaR VaR CoVaR − = Δ λ δ). ### Appendix V — Calibration of IMF LCR-based Liquidity Test - Tests and definitions: - 5-day implied cash flow (ICF) test: cumulative inflow and outflow over 5 consecutive days. - 30-day implied cash flow (ICF) test: non-cumulative. - Liquid financial assets categories with haircuts/ranges preserved: - 5-day: (i) cash and cash balances with central banks [haircut: 0 percent], (ii) securities and bank loans eligible at major central banks [0-15], (iii) securities and bank loans which can be mobilized in repo transactions (or another type of lending against financial collateral) [5-30], and (iv) marketable securities [10-35]. - 30-day: (i) cash and cash balances with central banks [0], (ii) securities and bank loans eligible at major central banks [0-20], (iii) securities and bank loans which can be mobilized in repo transactions (or another type of lending against financial collateral) [10-60], and (iv) marketable securities [20-70]. - Cash inflows (preserved parameters): - 5-day cumulative inflows: expected credit extension without liquid assets (call-back rate: 20 percent per day); maturing transactions with liquid securities and bank loans [20]; derivatives net contractual cash flows [20]; potential inflows from committed/uncommitted credit lines to related and third parties [5/3]. - 30-day non-cumulative inflows: credit call-back rate 100 percent; maturing reverse repo and securities borrowing transactions [100]; derivatives net contractual cash flows [100]; potential inflows from committed/uncommitted credit lines [23/12]. - Cash outflows (preserved parameters): - 5-day cumulative outflows: maturing and non-maturity funding without liquid assets (discount factor: 5 percent per day), excluding sovereign/public sector/central bank clients [0]; expected outflows related to transactions with liquid securities and bank loans [20]; maturing outflows to related parties [20]; committed/uncommitted contingent claims [5]. - 30-day non-cumulative outflows: maturing and non-maturity funding without liquid assets [10-75] excluding sovereign/public sector/central bank clients [0]; expected outflows related to transactions with liquid securities and bank loans [100]; maturing outflows to related parties [100]; committed/uncommitted contingent claims [23]. - Test interpretation and assumptions: - A ratio lower than 100 percent implies a liquidity shortage if the stress scenario materializes at the reporting date. - Only unencumbered liquid assets included; new unsecured financing and securitization impossible within horizon; no offsetting new secured/unsecured wholesale lending; full renewal of secured retail lending; central bank eligible collateral monetizable at appropriate haircuts; repo markets open at appropriate haircuts; fire-sale possible at appropriate haircuts; no rehypothecation funding; limited unsecured support from related/third parties; no renewal of term retail and wholesale deposits; full convertibility between currencies (within one week). ### Appendix VI — Calibration of IMF Cash-Flow-based Liquidity Test & Banking Sector Solvency Test (Perimeter and Data) - Institutional perimeter (Top-Down by SNB; Top-Down by FSAP Team; Bottom-Up by Banks): - Institutions included: - SNB: The two G-SIBs, and all domestically focused banks (DFB). DFBs include banks whose domestic credit exposure amounts to at least 50 percent of their total balance sheet. - FSAP Team: Twelve major banks, including the two G-SIBs, six domestically focused banks, and four private banks. Criteria: firms’ balance sheet; firms’ share in the domestic market; and firms’ reputational risk. - Banks (bottom-up): The two G-SIBs. - Market share covered: - SNB: About 90 percent of the domestic credit positions. - FSAP Team: About 80 percent of banking system total assets. - Banks: Over 50 percent of banking system total assets. - Data and effective date: - Effective date: June 2018 for SNB and FSAP Team; data sources include SNB survey data; FINMA regulatory data (including interest-rate risk report templates); Building Block Analysis data; supervisory data complemented by public sources (bank Pillar 3 disclosures; Bloomberg; Dealogic; Haver Analytics; Moody’s KMV; Fitch). Scope of consolidation: Consolidated group basis. - Bottom-up banks: Effective date June 2018; data: banks’ managerial and proprietary data at the portfolio level. - Stress testing process (high-level): - SNB: Conducted stress tests for G-SIBs using internal models and Building Block Analysis (BBA) module; used structural model for mortgage loans, hybrid approaches for other loans, simulation of interest-rate bearing cash-flows accounting for bank and client behavior, regression and expert judgment for market/business risk, and constant RWA. - FSAP Team: Conducted top-down macroprudential stress test using in-house models; for IRB exposures calibrated separate credit risk model for 5 Basel asset classes in Switzerland and 10 foreign geographies; projected stressed NPL ratios, stressed coverage ratios, and stressed transition matrices; fair valuation impact on fair value debt instruments projected for 40 country issuers; results benchmarked against top-down projections. - Banks: Performed bottom-up calculations based on the FSAP scenarios. ### Appendix VII — Banking Sector Stress Testing Matrix (STeM) - Framework comparison (Solvency Test): - Domains covered: Top-Down by SNB, Top-Down by FSAP Team, Bottom-Up by Banks. - Top-Down SNB: SNB used internal models and BBA; DFB stress tests based on SNB banking statistics and structural model for mortgages, hybrid/regression approaches for other loans, simulation of interest-rate cash-flows, and constant RWA. - Top-Down FSAP Team: Used in-house macroprudential models with defined country coverage and asset-class calibrations; benchmarked results. - Bottom-Up Banks: Performed bank-level calculations per FSAP scenarios using managerial/proprietary data. *Source: 1cheea2019009 - Appendix I. Risk Assessment Matrix (RAM).* ### 2. Channels of risk propagation ### 2. Channels of risk propagation ### Methodology - Coverage: credit risk, market risk, IRR, funding risk, F&C risk, operational risk. - Structural credit risk models: - Explicitly account for non-linear responses to shocks; benchmarked against banks’ practices. - Mortgage risk models incorporate affordability risk linked to the interest rate path and projected income among clients. - Average loss rates match the empirical loss rate over the 1991–1995 period, and are scaled according to regions, collateral types, and ranks. - Non-mortgage domestic credit: stress on unsecured loans simulated using bankruptcy rates for corporate since the 1980s; loss rates scaled depending on loan type (bank, covered loan, off balance sheet). - Interest rate risk: granular and dynamic top-down simulation with explicit modeling of dynamic elements including banks’ margins and client behavior (migration of product categories and durations at renewal). - Top-Down stress test included a detailed stress test of the Swiss mortgage book by LTV/LTI density and estimated vintage, using a structural Merton-based TUI model: - TUI: structural model of the residential housing loan default process; designed to calculate economic capital where empirical evidence of tail-event losses is slight or non-existent. - Model inventory and estimation: - Over 50 credit risk models and 150 econometric specifications estimated for PDs based on multivariate VAR, PCA, quantile-based regressions, panel regressions, and a structural Merton-based approach for LGDs on mortgage loans. - Combination of banks’ own models and pre-defined benchmarks set in the Building Block Analysis (BBA). - Constrained assumptions based on FSAP stress testing methodology (e.g., balance sheet dynamics, pass-through of funding costs to margins, dividend payout ratios). - G-SIB-specific treatment: - Idiosyncratic funding risks evaluated based on sensitivities included in the BBA reporting. - Market risk based on granular market risk sensitivities on cash and derivative positions. - Business risk estimated using a structural approach differentiating major income and expense components according to BBA data. - DFBs (Domestically-focused Banks) treatment: - Market risk via regression approaches; business risk via regression complemented by expert judgment. - Operational risk scenario impact determined based on banks’ risk-based capital requirements for operational risks. - Business risk modeling: shocks to net interest income, trading income, fees & commissions modeled separately using projections of client flows linked to projected trading volume and assets under management. - Solvency and funding cost interactions included: - Bank funding costs linked to system-wide stress in money markets, bank-specific capital positions, and contagion pressures from peer banks. - Lending rates linked to shocks to government yields and bank funding costs. - Interest rate risk applied on granular repricing and maturing banking book positions simulated to target banks’ initial duration gap over the stress testing horizon. - Market risk assessment included fair valuation impact on fair value debt instruments in 40 countries; equity risk and foreign exchange risk modeled separately linked to major indexes and currency pairs, allowing for basis risk. ### Tail shocks — Scenario analysis - Baseline: based on the October WEO forecast. - Adverse scenario calibration: - Uses the IMF’s Global Macrofinancial Model and auxiliary models to estimate stressed paths for investment-led residential and commercial real estate prices. - Calibration follows a layer-of-shocks approach featuring exogenous shocks and the economy’s endogenous structural response. - Global shock assumptions: - Global equity risk premium decompression reduces real equity prices by 40.0 percent in advanced economies and by 30.0 percent in emerging economies. - Global term premium decompression raises long term government bond yields by: - 300 basis points in the Euro Area periphery, - 150 basis points in the Euro Area core, - 200 basis points in other advanced economies, - 120 basis points in emerging economies. - Interbank market stress raises interbank spreads by 100 basis points in advanced economies and by 50 basis points in emerging economies. - Global housing risk premium decompression reduces real house prices by 10.0 percent in advanced economies and by 5.0 percent in emerging economies. - Switzerland-specific peak-to-trough real estate declines: - 40 percent in commercial real estate, - 45 percent in investment-led residential real estate, - under 30 percent in owner-occupied real estate. - Macro outcomes in Switzerland by 2020 under adverse scenario: - Output falls 7.7 percent below baseline by 2020. - Consumption falls 8.7 percent. - Investment falls 20.1 percent. - Consumption price inflation falls 2.5 percentage points below baseline by 2020. - Unemployment rate rises by 2 percentage points. - Nominal policy interest rate is cut 2.3 percentage points below baseline by 2020. - Additional shock: idiosyncratic and system-wide funding risk shock triggered by dislocation of money markets and linked to banks’ capital ratios under stress. - Scenario severity: constitutes a 3.3 standard deviation move in two-year cumulative real GDP growth rate by 2020, calculated over 1990–2018. ### Sensitivity analysis - Additional shocks considered: - Shocks to credit spreads in fixed income instruments. - Impact of a risk-based risk-weight add-on on STA exposures. - Shocks to the interest rate swap curve, bond yields, and LIBOR rates, impacting affordability risk in mortgages. - Counterparty risk in derivative markets. ### Risks and buffers — Positions and risk factors assessed - Credit risk: - Estimated according to the Swiss implementation of the Basel III framework. - Includes: (i) lending risk from exposures to sovereign, public entities, financial institutions, corporates, and other; (ii) mortgage-related lending; (iii) Lombard lending; and (iv) leveraged loans including syndicated loans, bridge loans, and equity bridges. - Positions include term loans, revolving credit facilities, and loan commitments. - Counterparty credit risk from combined credit and market risk stress on OTC products and securities financing transactions (SFTs) is included. - Issuer default risk related to credit default events on underlying issuers and structured credit risk on financial asset securitization (e.g., ABS, MBS, or ABCP), and structured credit derivatives is covered. - Sovereign risk: mark-to-market valuation of securities (from shocks to interest rates and credit spreads) in trading book and AFS/FVO linked to macro scenario. - Market risk other than sovereign risk: shocks to interest rates, credit spreads, exchange rates, commodities, and equity prices. - Profits and income modeling: - Interest income declines by the amount of lost income from defaulted loans. - Interest income from non-defaulting loans estimated according to satellite models. - Interest expenses increase due to rising funding costs linked to the macroeconomic scenario with empirically estimated pass-through, and add-on funding stress from a market event with no pass-through to lending rates. - Trading income evolves with projected client flows and market prices. - Net fee and commission income evolves with projected off-balance sheet customer assets, including deposits and holdings of securities and precious metals from customers without securities dealers; assets under discretionary asset management agreement; and fiduciary investments. ### Behavioral adjustments and dynamic balance sheets - Credit supply effects are disallowed to calibrate credit risk projections. - Balance sheets evolve with key macroeconomic aggregates adjusting for credit demand effects. - Exposure at default under stress from off-balance sheet exposures increases about 5–10 percent on average, reflecting higher use of undrawn credit and liquidity facilities. - Conservative assumption: all facilities assumed contractually irrevocable (“committed”) to extend funds in the future. - Maturing assets are replaced by exposures of the same type and risk. - Dividends: - Linked to banks’ net profits. - Under positive profits, the dividend payout floor is set at 30 percent. Otherwise, no dividend payout is assumed. - Effective tax rate evolves with the macro scenario. - Losses are recognized in the same year that a shock hits. - If banks’ capital ratio falls below regulatory minimum during the stress test horizon, no prompt corrective action is assumed. ### Calibration of risk parameters and regulatory standards - Mortgage risk: default risk parameters matched to empirical loss rate over the 1991–1995 period, scaled according to regions, collateral types, and ranks. - Non-mortgage risk: credit risk simulates stress on unsecured loans using bankruptcy rates for corporate since the 1980s; loss rates scaled depending on loan type (bank, covered loan, off balance sheet). - Parameter definitions: - PiT PDs and LGDs for expected losses (P&L impact) and stressed regulatory PDs and LGDs on non-defaulted exposures for regulatory capital requirements. - Estimated non-defaulted PDs by Basel asset class and geography. - Estimated non-defaulted LGDs calculated post-credit risk mitigation by Basel asset class and geography. - Banks’ internal models project PiT stressed losses covering: lending risk (mortgage related lending, Lombard lending, leveraged loans), interest rate risk in the banking book, equity risk, foreign exchange risk, commodity risk, credit spread risk, lending risk, counterparty credit risk, issuer default risk, structured credit risk, business risk, funding risk, and operational risk. Banks’ regulatory models project TTC capital requirements for credit risk, market risk, and operational risk. - Parameter calibration specifics: - IRB exposures: shifts to PDs informed by shocks to credit risk losses, Moody’s EDFs rates, and banks’ estimated PDs calculated in historical stressed episodes. - Shocks to LGDs projected using a Merton-based approach for mortgage exposures, shocks to unemployment for retail unsecured exposures, and shocks to GDP for corporate exposures. - STA exposures: inflows into NPL categories based on a panel regression estimated on supervisory data, including risk migration for performing exposures, and stressed coverage ratios. - Capital definition: phased-in Swiss implementation of Basel III. - Non-material impact from transitional arrangements for capital components that are no longer eligible for additional Tier 1 and Tier 2 capital components. - TBTF regime (going concern requirements for five D-SIBs): - Basic requirement for risk-weighted assets is 12.9 percent. - Leverage ratio requirement is 4.5 percent. - Swiss sectoral counter cyclical buffer: set at 2 percent of risk-weighted positions secured by residential property situated in Switzerland. ### Reporting format for results (solvency tests) - Outputs presented: - Evolution of CET1, Tier 1, CAR, and leverage ratio for the aggregate banking system and by bank type (G-SIBs, Domestically-focused Banks, Private Banks). - Contribution of key drivers to aggregate net profits and aggregate CET1 capital ratios. - Number of banks and share of total assets below hurdle rates. - Capital shortfall in terms of nominal GDP. --- ### Liquidity Stress Testing Matrix ### Institutional perimeter and data - Institutions: Twelve major banks including the 2 G-SIBs, 6 domestically focused banks, and 4 private banks. - Market share: About 80 percent of banking system total assets. - Data and base date: Regulatory data based on Basel III standardized liquidity monitoring tools as of June 30, 2018. ### Channels of risk propagation — Methodology - Shocks feed into four Basel III monitoring-tool metrics: - Liquidity coverage ratio (LCR) in CHF (requirement), and in significant currencies (EUR, GBP, JPY, USD) (monitoring metric). - Contractual maturity mismatch analysis (monitoring metric). - Concentration of funding (monitoring metric). - Available unencumbered assets (monitoring metric). ### Risks and buffers - Risks: Funding risk, rollover risk, market liquidity risk, liquidity risk related to margin requirements mainly related to the overall level of collateral posted for derivative positions. - Buffers: HQLA securities assessed at market values net of haircut on a security-by-security basis. ### Tail shocks — Size of the shock - Range of adverse scenarios: - LCR Scenario under standard assumptions calibrated by BCBS. - LCR “Switzerland retail stress” scenario (calibrated to replicate peak stress observed in relevant comparator jurisdictions during the global financial crisis). - LCR “Switzerland wholesale stress” scenario (replicates liquidity stress observed during the global financial crisis characterized by: a freeze of wholesale funding; liquidity risk from margin calls related to secured funding, derivatives and foreign currency funding; and disruptions in the FX swap market with rollover of secured funding backed by other than Level 1 and Level 2A assets of up to 0 percent). - Implied cash flow 5-day and 30-day tests using contractual cash-flow data on the maturity ladder and unencumbered assets in the counterbalancing capacity. - Assumptions include haircuts of up to 60 percent for securities and bank loans that can be mobilized in repos, no issuance of new unsecured funding and freeze of securitization markets, call-back rates of up to 100 percent, and cash outflows of up to 75 percent. ### Regulatory standards and reporting - Regulatory standards: Basel III full implementation for the LCR ratio for total currency and CHF at 100 percent. Counterbalancing capacity above net cash outflows under stress scenario. - Reporting format outputs: - Changes in average liquidity position and counterbalancing capacity for each scenario. - Distribution of banks’ liquidity position for each scenario. - Number of banks with counterbalancing capacity below net cash outflows. - Banks’ post-shock net liquidity position. - Liquidity shortfall in terms of banking system total liabilities. --- ### Interconnectedness Matrix — Network Analysis ### Institutional perimeter and data - Institutions: Twelve major banks including the 2 G-SIBs, 6 domestically focused banks, and 4 private banks. - Market share: About 80 percent of banking system total assets. - Data and base date: Regulatory data based on large exposure template as of June 30, 2018. ### Channels of risk propagation — Methodology - Network analysis using Furfine algorithm and Espinosa-Sole tool. - Captures cascading effects from individual defaults through counterparty exposures and liquidity shortages from individual defaults through concentration of funding. ### Approach and buffers - Linkages with solvency and liquidity stress tests: - Transmission of credit shocks linked to solvency stress test results to project capital depletion endogenously. - Transmission of funding shocks linked to liquidity stress test results by allowing banks to draw down their liquid buffers to replace funding from defaulting funding counterparties. - Buffers: Tier 1 capital and counterbalancing capacity. ### Tail shocks, sensitivity tests, and reporting - Tail shocks: Individual bank defaults; stressed capital position in line with solvency stress test results. - Sensitivity factors: Performance of collateral (for secured exposures), loss given default (for unsecured exposures), substitutability of funding (for funding shock). - Reporting format outputs: - Failed capital in percent of total capital. - Number of induced failures and contagion rounds. - Absolute hazard rate. - Vulnerability level to counterparty defaults. --- ### Interconnectedness Matrix — CoVaR Analysis ### Institutional perimeter and data - Institutions: All listed Swiss banks. - Market share: About 70 percent of Swiss banking system assets. - Data: Weekly frequency over October 2005 through November 2018. - Bank-level data includes equity prices. - Swiss financial state variables include: VSMI, liquidity spread (difference between the 3m SNB repo rate and the 3m CHF benchmark curve), change in CHF 3m rate, change in the slope of the Swiss government yield curve, change in corporate credit spread of 10y Swiss BBA bond, change in Swiss equity market index; a global/European crisis dummy. ### Channels of risk propagation — Methodology - CoVaR methodology based on Adrian and Brunnermeier (2016), and Valderrama et al (2012, 2015). - Characterization of co-dependence using a quantile approach. - Characterization of individual VaR dynamics using a GARCH (1,1) process on conditionally demeaned returns. ### Tail shock, robustness, and reporting - Tail shock size: 5 percent quantile of the conditional loss distribution. - Robustness checks: - Symmetric/asymmetric specification in the co-dependence structure of tail returns. - CoVaR analysis applied to banks’ equity returns and implied asset returns. - Analysis of the 99th percentile of the loss distribution. - Reporting format outputs: - Evolution of systemic risk in Switzerland. - Determinants of tail banking system returns. - Individual contribution to systemic risk in the Swiss financial system; the European financial system; and the Global financial system. *Source: 1cheea2019009 - 2. Channels of risk propagation (PDF chapter).* --- _Source: https://www.imf.org/-/media/files/publications/cr/2019/1cheea2019009.pdf_