## 1usaea2020007 ## Source details **Canonical URL:** [1usaea2020007](https://www.imf.org/-/media/files/publications/cr/2020/english/1usaea2020007.pdf) ## Other formats - [Markdown version](/-/media/files/publications/cr/2020/english/1usaea2020007.pdf.md) - [Structured JSON version](/-/media/files/publications/cr/2020/english/1usaea2020007.pdf.json) --- ### Overview and scope - Financial system size and structure: - Assets of the financial system amounted to about US$100 trillion at end-2019 and accounted for 500 percent of GDP. - Equity market capitalization above US$50 trillion (as of 2019). - Pension entitlements assets about US$30 trillion. - Mortgages about US$15 trillion as of 2019. - Mutual funds sector reached 16 percent of the financial sector share as of 2019. - Eight Global Systemically Important Banks (G-SIBs) are incorporated in the U.S. - Banking system assets represent about 22 percent of total financial system assets. - FSAP coverage and data cutoffs: - Data cut-off: Q3 2019, except interconnectedness and liquidity analysis performed in March 2020, and the banking sector solvency stress test relying on Q1 2020 data and June 2020 economic forecasts. - FSAP stress testing coverage: - Banks: 34 largest institutions (33 banks with assets above USD 100 billion and one bank with assets close to 100 billion). - Insurance: 53 large groups and 17 medium-sized and smaller regional insurers (covering more than 70 percent of the life and non-life market by assets). - Mutual funds: about 2,000 largest players (all fixed income and mixed mutual funds covered by Morningstar); sample used in stress tests: 2,743 funds (US$6.398 trillion; 2,743 funds as of end-2019). - Selected money market funds: 208. - Key structural trends: - Financial sector indebtedness fell from 125 percent of GDP at the height of the GFC to about 77 percent currently. - Non-bank financial intermediation has grown; non-banks supply a significant proportion of credit and provide liquidity transformation. - Off-balance sheet commitments (unused credit line commitments) remain material: unused credit line commitments relative to balance sheet size above 40 percent; based on 8-k filings, credit line utilization between March–mid April 2020 had surpassed $200 billion. ### Stress testing scenarios, design, and calibration - Scenario suite and horizon: - One baseline and three adverse sensitivity scenarios (COVID-19 Baseline and three Adverse scenarios); five-year horizon over 2020–2 5. - Baseline follows the June 2020 WEO Update: Unemployment rate peaks at 13½ percent in 2020Q2; GDP remains below 2019Q4 level through end-2022; short-term policy rate at ZLB throughout projection horizon. - Sensitivity scenarios differ by duration of reduced mobility and presence of a second wave (Scenarios 1–3). - Scenario severity benchmarks and historical comparison: - The sharp real GDP contraction in 2020Q2 in the June 2020 WEO Update is equivalent to 12 times the historical standard deviation of quarterly growth since end of WWII; in adverse scenarios equivalent to 17 standard deviations. - Scenario calibration details: - Shocks to GDP derived using accounting-based framework measuring sectoral output losses conditional on duration of containment and recovery intensity. - Employment losses linked proportionately to GDP losses. - Financial variable shocks follow CCAR behavior, adjusted proportionately to FSAP output losses. - Banking sector stress test reference date: Q1 2020; CLASS model with 57 equations; PD/LGD and other satellite models adjusted for COVID-19 specifics. - Appendix scenario example (Sensitivity Scenario 2 quarterly path highlights): - 2020Q2 Real GDP Growth: -66.8 percent (Q-o-Q annualized); Unemployment Rate: 22.8 percent; BBB Corporate Bond Yield: 5.8 percent; Stock Market Index (2019Q4=100): 72.9; House Price Index (2019Q4=100): 88.3. ### Banking sector solvency stress test — key results and drivers - System starting position: - Aggregate CET1 ratio 12 percent as of 2019Q3; leverage ratio (Tier1 capital-to-assets) about 8.7 percent as of 2019:Q3. - LCR ratios above 100 percent on aggregate. - Baseline scenario outcomes (June 2020 WEO baseline): - Industry-wide CET1 ratios decline by 390 basis points on average, reaching their lowest point after 2 years of stress. - Smaller, non-G-SIB banks experience the largest impact. - If recovery follows baseline, impact on CET1 by end of 5-year horizon is 50 basis points. - Declines in outer years mostly driven by decline in net interest income due to compressed margins. - Adverse sensitivity outcomes: - Additional month of containment: median capitalization at lowest point 7.6 percent (190 basis points lower than baseline median). - More severe recession / second wave: systemwide CET1 ratio at lowest point would be 450 basis points lower than baseline and 630 basis points lower than median CET1 at Q1 2020. - All G-SIBs would maintain CET1 ratios above minimum regulatory requirements in modeled scenarios. - Loss drivers and magnitudes: - Banks expected cumulative loss of 4 percent of total assets over the five-year scenario horizon (compare: 1.9 percent cumulative losses in the October 2019 WEO baseline). - Key contributors: credit card-related net charge-offs (losses up to 3 percent of RWAs), C&I loans (losses up to 1.8 percent of RWAs), growth of RWAs due to utilization of credit and funding lines leading to up to 1 p.p. additional CET1 depletion. - Assumed system-wide expected utilization of 20 percent and credit conversion factor of 50 percent. - Capital shortfalls and recapitalization needs: - Adverse Sensitivity Scenario 1: Up to 6 banks (all non-GSIBs) would need additional capital within three years; capital shortfall ~0.5 percent of GDP. - Variant with another quarter of lockdown: up to 8 banks needing capital; shortfall ~0.6 percent of GDP. - Second wave variant: 10 non-GSIB banks failing to meet 4.5 percent CET1 in first three years; capital shortfall ~0.8 percent of GDP. - Shareholder payout sensitivity: maintaining average payouts of 40 percent of net income could raise recapitalization needs by up to 0.2 percentage points of GDP. - Behavioral and modeling adjustments: - Three types of adjustments to CLASS loan-loss satellite models explored: (i) market-data-based PDs; (ii) corporate stress test–guided multiplier approach; (iii) caps on wage and residual non-interest expense growth (annual wage growth cap 1 percent; residual non-interest expense cap 2.6 percent). - Using market-based C&I loss estimates would lead to additional decline in system-wide CET1 ratio by 50 basis points; FSAP used corporate stress test–guided approach for final inputs. - Leverage and supervisory relief: - Most banks maintain leverage ratios above minima; some trading G-SIBs would face difficulty meeting a 6 percent leverage requirement without dividend cuts or asset shrinkage absent temporary relief. - Temporary supervisory relief (excluding some assets from supplementary leverage ratio) helped G-SIBs remain within limits. ### Banking sector liquidity analysis and stress test highlights - Contractual funding gap: - 30-day contractual funding gap excluding retail and operational deposits is close to 40 percent of total assets on average; excluding off-balance sheet commitments, 30-day contractual funding gap ~50 percent of total assets. - Off-balance sheet credit and liquidity facilities constitute another 25 percent of Total Assets. - LCR and HQLA composition: - HQLA heavily Treasury and agency securities; nearly three-quarters of HQLA are highly liquid assets. - Non-GSIBs hold smallest relative amount of HQLA; market trading banks have highest share. - Liquidity stress test scenarios and outcomes: - Gradual increase in utilization of credit and liquidity facilities can produce significant liquidity shortages at some banks; system-wide impact contained for many scenarios. - Full drawdown of contingent lines could deplete CET1 by 20 to 250 basis points depending on drawdown magnitude. - Closure of repo market for non-Treasury collateral: small and short-lived cash flow gaps for several banks; liquidating Treasuries would reduce CET1 by about 9 basis points on average in the illustrated case. - Combined extreme: closure of repo market plus outflow rates on contingent facilities ≥40 percent could render one G-SIB illiquid on 30-day horizon. - Interbank and FHLB implications: - Withdrawal of funding from FHLBs would not lead to significant cashflow gap for the six largest G-SIBs per supervisory-data analysis. ### Corporate sector, leveraged finance, and CLOs - Corporate indebtedness and leverage: - Total business sector debt ~US$16 trillion (75 percent of GDP) at end-2019. - Nonfinancial corporate (NFC) debt at historic peak: 47 percent of GDP. - Total business sector debt rose from 65 percent of GDP in 2012 to almost 75 percent of GDP. - Corporate debt to EBIT ratio about six; interest coverage ratio weakened from nearly seven in 2013–14 to around five. - Leveraged finance: - Outstanding leveraged loans estimated at US$1.1 trillion (about 5 percent of GDP); peak issuance US$650 billion in 2017; issuance US$491 billion in 2019. - Covenant-lite loans accounted for more than half of new leveraged loan issuances in the U.S. for the past four years. - CLOs held roughly US$617 billion of the US$1.1 trillion in leveraged loans at end-2018. - CLO holdings by investor type (end-2018): insurance companies 28 percent, mutual funds 15.5 percent, banks 15 percent. - Off-balance sheet commitments by banks to corporate clients estimated around US$760 billion (FSB estimate); facilities granted to CLO issuers for U.S. banks ~US$28 billion by end-2018. - Corporate sector stress test results: - Sample ~2,000 nonfinancial corporations; total assets US$19 trillion (87 percent of GDP); aggregate indebtedness US$9 trillion. - Under baseline: potential debt-related losses ~US$400 billion (leveraged firms account for over 80 percent). - Under Sensitivity Scenario 3: corporate debt losses could reach US$675 billion (US$465 billion related to leveraged firms). - Systemic transmission: - Large share of corporate debt resides outside banking sector (insurers, mutual funds, pension funds, foreign investors). - Stress in corporate sector would cause significant losses in non-bank financial sector, with potential redemption pressures for open-ended funds. - Estimated mutual fund liquidations following corporate sector stress ~US$97 billion. - Estimated mutual-fund-induced mark-to-market losses: mutual funds liquidation losses ~US$0.9 billion; banks’ mark-to-market losses close to US$10.8 billion (~0.06 percent of total assets), reducing banks’ CET1 by ~0.1 percentage points; insurance sector losses ~1 percent of insurance sector total assets in the one-month market sentiment shock. ### Mutual funds and money market funds — liquidity and market stress testing - Mutual funds (fixed income and mixed) stress testing (sample: 2,743 funds; US$6,398 billion): - Calibration approaches for redemption shocks: - Historical homogeneity: category-level 3 percent ES; resulting redemptions 7 percent of NAV (municipal funds) to more than 15 percent (HY and EM funds). - Historical heterogeneity: fund-specific shocks. - Adverse scenario approach: maps banking adverse scenario to fund redemptions (most funds face levels below 3 percent under this method). - Results: - More than 90 percent of funds (by AUM) would have enough highly liquid assets to meet investors’ redemptions under historical-based shocks; exceptions are HY and loan mutual funds. - HY and loan funds often lack sufficient highly liquid assets and would need to sell less liquid securities (assuming no liquidity tools), potentially triggering fire-sale dynamics. - Variation margin risk: 50 bps rate increase + 1 percent USD depreciation could generate variation margin calls between 3 percent and 10 percent of NAV for some funds; for several funds, variation margins could exceed 50 percent of available cash. - Price impact and liquidation strategies: - Vertical slicing (pro rata) vs waterfall (sell most liquid first) produce different market impacts and first-mover advantages. - Under slicing, price impact in stress: 150–700 basis points across some asset classes; under waterfall, impact muted (less than 200 basis points in stress for most asset classes). - Systemic roles: - Vulnerable categories: EM bond funds, HY, loan funds (large outflows during distress). - Spreader categories: IG corporate bond funds, multi-strategy bond funds; IG corporate bond funds large in size (US$2,427 billion) can propagate stress. - Money Market Funds (MMFs) stress testing (sample: 208; total size US$3,812 billion): - Shock calibration: 100 bps interest rate shock and combined 100 bps interest rate + 100 bps spread shock. - Results: - Under 100 bps interest rate shock, NAV impact average 0.09 percent; max 0.16 percent. - Under combined 100 bps + 100 bps spread shock, prime retail funds max NAV impact 0.27 percent (within CNAV tolerances of 0.5 cent of US$1). - Reverse stress test: interest rates would need to rise >600 bps on average to produce 0.5 percent NAV deviation; for highest-duration MMFs, ~350 bps required. - Observations from March 2020: - Institutional prime MMFs experienced large outflows and strains; sponsor support and Fed interventions stabilized flows by early April 2020. ### Insurance sector solvency stress tests — main findings - Sample and scope: - Stress test sample: 50 insurance groups (21 predominantly life, 22 P&C, 7 health) representing market shares ~45 percent for sample subsets; cut-off date December 31, 2018. - Adverse scenario aggregate impact: - Aggregated reduction in statutory capital: US$226 billion, equal to: - 30.9 percent of the sample’s statutory capital; - 3.9 percent of consolidated balance sheet assets; - 1.1 percent of U.S. GDP. - Sectoral heterogeneity: - Life sector: capital declines US$74.3 billion (−35.7 percent); median decline 32 percent; sector range 14 to >60 percent. - P&C sector: capital declines US$149.8 billion (−31.7 percent); median company loses 19 percent of capital; sample range −5 to −53 percent. - Health sector: capital declines US$1.4 billion (−2.8 percent); median decline 1 percent. - Key channels and sensitivities: - Life sector vulnerable to impairments on shares, non-investment grade debt, and other investment assets (Schedule BA). - Prolonged low interest rates (Low-for-Long) projected to reduce net investment spread (historical 2016–18 average 1.1 percent); by 2021 net investment spread could drop below 1 percent. - Lapse/surrender shock scenarios: - Aggregate liquidations under modeled scenarios: U.S. Treasury bond sales US$23–33 billion; U.S. GSE sales US$13–15 billion; corporate bond sales US$4–11 billion. - A minority (~4–6 companies depending on scenario) would need to liquidate corporate bond portfolios. - Hurricane/catastrophe stress: - Large diversified P&C insurers: 1-in-50-year event leads to 8.1 percent gross reduction in available capital (3.9 percent net after recoverables); 1-in-250-year net ~5.8 percent; 1-in-500-year net ~8.6 percent. - Small, regionally concentrated P&C insurers: 1-in-50-year gross decline 81.4 percent; 1-in-250-year net impact 58.6 percent; 1-in-500-year net impact 164.3 percent; in 1-in-250-year event, ten out of 44 companies record capital shortfalls. - Default of largest banking counterparty: - Median life company would lose 1.2 percent of capital; median P&C and health insurers would lose 0.5 percent of capital. - Accounting and valuation caveat: - Tests based on statutory accounting and NAIC Schedule D; many balance sheet items not marked-to-market under statutory accounting; results differ under fully market-consistent valuation. - Policy implications suggested in text: - NAIC should develop and perform insurance solvency stress tests on a consolidated basis, in line with forthcoming group capital standards. - Further liquidity stress testing for insurers and enhanced public disclosure of market risk and interest rate sensitivities recommended. ### Interconnectedness, contagion, and network analysis - Cross-border and banking system spillovers: - Near a quarter of U.S. banking system consolidated claims held against foreign borrowers. - Claims against borrowers in the U.K. and Japan: about 3 percent of total assets or 35 percent of Tier1 capital of the U.S. banking system (2019:Q3). - Exposure to foreign non-bank financial sectors: 8 percent of U.S. banks’ assets. - Cross-border off-balance sheet exposures: largest by derivatives; unused commitments vis-à-vis foreign borrowers ~5 percent of U.S. banking system assets. - Network contagion scenarios and stylized results: - Severe credit shock + funding shock assumptions: LGD = 1.0; assets liquidated at 50 percent discount; banking systems roll-over 65 percent (share not recovered = 0.35). - Potential inward spillovers into U.S. banking system range between 2–8 percent of initial regulatory capital depending on exposure definition; funding shocks increase inward spillovers by one-third. - Outward spillovers from U.S. banking system produce average impairment of ~10 percent of initial regulatory capital of recipient foreign banking systems; funding channel adds ~10 percent of that impairment. - Sensitivity less severe scenario (LGD = 50 percent; roll-over = 90 percent; haircut = 10 percent) halves potential losses. - Bank-level and market-based contagion: - Bank-level (BHC) inward spillover average due to direct exposures ~1.2 percent of initial regulatory capital for G-SIBs; other BHCs <1 percent on average. - Market-based equity-return spillover analysis (Diebold-Yilmaz metrics) using 2015–19 daily returns (VIX control) shows: - U.S. G-SIBs are strong net transmitters of equity-return spillovers domestically and internationally. - Aggregate relative spillover matrix (rows: from; columns: to) shows notable fractions (example row US GSIBs → US GSIBs 0.25; US GSIBs → US non-GSIBs 0.03; US GSIBs → US Nonbank Financial Sector 0.03; US GSIBs → Foreign GSIBs 0.08; US GSIBs → Foreign non-GSIB banks 0.01; US GSIBs → Foreign Nonbank Financial Sector 0.02). - Solvency–liquidity network model highlights: - Fire-sale price impact estimated via Markov regime-switching PIs using TRACE and other transaction data; stress regime PIs far larger than non-stress. - Example market depth/impact metrics (impact of US$1bn sale in stress / non-stress): - Corp. IG: 3.6 bps / 7.7 bps (normal/stress horizons shown in table). - Corp. HY: 6.5 bps / 22.3 bps. - Leveraged loans: 18.0 bps / 64.0 bps. - UST: 0.1 bps / 0.3 bps. - Network contagion algorithm implements iterative default propagation combining credit losses and funding shortfalls; convergence when no new defaults. ### Key vulnerabilities, second-round effects, and caveats - Migration of risks to non-banks: - Non-bank financial intermediaries hold large shares of corporate bond and leveraged loan exposures (insurance sector ~26 percent of corporate bonds; mutual funds ~17 percent). - Mutual funds and insurers would suffer larger direct losses in corporate debt distress than banks; bank exposure direct is limited but indirect amplification via drawdowns and liquidity provision could be significant. - Nonlinearities and model limitations: - Contagion dynamics are highly nonlinear; price-impact estimates rely on historical absorption capacity and linear PI assumptions. - Stress tests largely top-down, relying on public data and in-house models; differences in granularity exist relative to supervisory CCAR/DFAST. - Tests omit some elements: granular trading book stress, some short-term liquidity horizons, asset encumbrance, full second-round macro feedbacks. - Data gaps and transparency issues: - Public data scarce on ultimate holders of leveraged loans and securitization exposures; greater transparency recommended to strengthen market discipline and systemic risk assessment. ### Policy-relevant implications and recommendations (as presented) - Strengthen capital planning and buffers: - Encourage higher retention of earnings / temporary moratoria on shareholder payouts to preserve CET1 (zero payout could save average 60 b.p. of CET1 by Q2 2022 in simulations). - Ensure non-GSIB banks have adequate capital to support lending during stress. - Liquidity resilience and market functioning: - Ensure all large banks, including Non-GSIBs, have adequate liquidity buffers. - Monitor and address reliance on repo and secured funding markets; preserve market plumbing for Treasury and agency markets. - Non-bank sector monitoring and transparency: - Improve data and transparency on exposures of regulated entities and ultimate holders of leveraged and private loans, CLO tranches, and securitizations. - Expand systemic monitoring of mutual funds’ liquidity and leverage, and of insurers’ consolidated positions. - Insurance sector actions: - NAIC to develop consolidated group-level solvency stress testing and enhance public disclosure of market risk and interest rate sensitivities. - Conduct further work on insurer liquidity stress testing and prepare for low-for-long rates and lapse/surrender risks. - Macroprudential and cross-border considerations: - Monitor cross-border interconnectedness and potential outward spillover channels; consider coordination with foreign authorities on cross-border resilience. - Preserve and enhance tools to address run-like dynamics in non-bank sectors (e.g., liquidity facilities, LMTs, disclosure, and structural reforms). *Source: 1usaea2020007 - EXECUTIVE SUMMARY (IMF FSAP technical note excerpts).* ### EXECUTIVE SUMMARY __________________________________________________________________________ 9 ### EXECUTIVE SUMMARY ### Introduction - Document sections listed: INTRODUCTION (page 13), including: - A. Objective - B. Stress Testing Work Done by the Authorities - C. Risk Analysis and Stress Testing under the U.S. FSAP Program ### Financial system: risks and vulnerabilities - Main subsections: - A. Financial System Structure and Performance (page 16) - B. Resilience and Vulnerabilities of Borrowers (page 32) - C. Leveraged Finance: Leveraged Loans and CLOs (page 37) - Related figures referenced include Figures 1–11 and Figures 13–15 covering system frameworks, sector structure, linkages, and borrowing. ### Stress testing scenarios - Section headings: - A. Scope (page 39) - B. Scenario Narrative and Calibration (page 40) - C. Risks Related to High-impact Events and their Transmission Channels (page 42) - Figure 12: Stress Test Scenarios ### Corporate sector stress tests - Section heading: CORPORATE SECTOR STRESS TESTS (page 43) - Figure 14: Corporate Stress Test Results - Figure 15: Loss Estimation ### Banking sector stress tests - Section headings and components: - BANKING SECTOR STRESS TESTS (page 46) - A. Solvency (page 46) - B. Banking Sector Liquidity Risk Analysis and Stress Tests (page 64) - C. Banking Sector Interconnectedness (page 74) - Multiple figures referenced: 16–31 and 34–35 covering solvency results, leverage ratios, liquidity measures, funding, contagion, and spillovers. ### Liquidity stress testing for U.S. mutual funds - Section headings: - LIQUIDITY STRESS TESTING FOR U.S. MUTUAL FUNDS (page 82) - A. Objective and Scope (page 82) - D. Methodology (page 83) - E. Results (page 86) - Figures and tables referenced: Figure 36, Figure 37, Figure 38, Table 1, Appendices IV and XI–XIV relevant to mutual funds. ### Market risk stress testing for money market funds (MMFs) - Section headings: - MARKET RISK STRESS TESTING FOR MONEY MARKET FUNDS (page 93) - A. Objective and Scope (page 93) - B. Methodology and Results (page 94) - Figures and tables: Figure 41, Table 2 ### The insurance solvency stress tests - Section headings and items: - THE INSURANCE SOLVENCY STRESS TESTS (page 96) - A. Objective (page 96) - B. Valuation and Capital Standard (page 97) - C. Sample (page 97) - D. Stress Test: Adverse Scenario (page 98) - E. Stress Test: Modeling Assumptions and Output (page 99) - F. Stress Test: Results (page 102) - G. Sensitivity Analysis (page 104) - Figures and boxes: Figure 42, Figure 43, Figures 44–49, Box 2 "COVID-19 Impact on the Insurance Industry" - Table 3: Market Risk Parameters; Table 4: Sample of Regionally Concentrated P&C Insurers - Appendices XV and III referenced for insurance stress-testing matrices and sample selection. ### Systemic risk, interconnectedness, and contagion analysis - Section headings: - SYSTEMIC RISK, INERCONNECTEDNESS, AND CONTAGION ANALYSIS (page 114) - A. Scope (page 114) - B. Contagion Between Banks, Non-banks, and Nonfinancial Corporates (page 115) - C. Complementary Market-Based Contagion Analysis (page 120) - Figures referenced: 29–35, 52–55 covering network contagion analyses, cross-border spillovers, and market-based interconnectedness. ### Appendices, figures, and tables inventory - Appendices I–XVII listed, including focused matrices and models: - I–IV: Stress testing matrices (banking, interconnectedness, insurance, mutual funds) - V–VII: Grouping of Banks; Risk Assessment Matrix; Structure of the U.S. Financial System - VIII–XVII: Scenario details, econometric estimation, contribution to RWAs, fund data, methodology, contagion algorithms, network models - Figures enumerated: 1–55 (selected figures cover frameworks, results, vulnerability analyses, and network schematics) - Tables enumerated: Table 1–4 (mutual funds, MMF results, market risk parameters, P&C sample) ### Glossary and abbreviations - Extensive glossary with acronyms preserved exactly as listed, including (excerpt): - ABS, AE, AFS, AR, BBB, BHCs, BIS, BNY Mellon, BPs, CAR, CB, CBC, CBOE, CCB, CCP, CET1, CLASS, CLO, CMO, CoVaR, CRD IV, CRR, CUSIP, C&I, DFAST/CCAR, DSGE, EaD, EA, ECB, EDFs, EM, ES, ETFs, FC I, Fed, FFIEC, FI, FINRA, FRB, FR-Y, FSAP, GAAP, GAS, GDP, GFC, GFM, GSE, G-SIB, G-SIFI, GVD, HTM, HY, HQLA, ICI, ICPF, IHC, IMF, IRB, IT, LCR, LEI, LGD, LMTs, MASS, MBS, MF, MMF, NAIC, NAV, NPL, N-PORT, Non-GSIB, OFR, OTC, PD, PI, PiT, PPML, PPNR, P&C, RBC, RCR, RWAs, SEC, SFTs, SNL, SSM, ST, STA, SVAR, TBA, TD, TRACE, Top-Down, TTC, U.S., UST, U.K., VA, VAR, VIX, WEO ### Document structure and navigation - Main table of contents spans sections from INTRODUCTION through CONCLUSIONS (page 124) and REFERENCES (page 194). - Boxes highlighted include: - Box 1: Maximum Allowable Leverage under the Absolute VaR Approach (page 89) - Box 2: COVID-19 Impact on the Insurance Industry (page 100) - Box 3: CLO Tranches, the Pricing of Risk, and Implications for Financial Institutions (page 119) *Source: 1usaea2020007 - EXECUTIVE SUMMARY* ### EXECUTIVE SUMMARY ### EXECUTIVE SUMMARY ### Overview and scope - The U.S. financial system is very large, well-diversified, and home to numerous financial institutions which are significant at a global scale. - Assets of the financial system amounted to about US$100 trillion at end-2019 and accounted for 500 percent of GDP. - Eight Global Systemically Important Banks (G-SIBs) are incorporated in the U.S. - Banking system assets represent only about 22 percent of total financial system assets. - The systemic risk assessment (including stress testing) focuses on banks, mutual and money market funds, insurance companies as well as cross-institutional and cross-sectoral linkages and exposures. ### Timing, data cutoffs, and pandemic context - The FSAP was conducted and this note was largely written prior to the pandemic onset and did not assess the impact of the shock and effectiveness of policy measures to mitigate that impact. - The data cut-off point for this note was Q3 2019, except for: - the interconnectedness and liquidity analysis performed in March 2020, and - the banking sector solvency stress test that relies on Q1 2020 data and June 2020 economic forecasts. - Baseline economic growth projections were significantly revised downward in the April 2020 WEO and subsequently in the June 2020 WEO Update. - The U.S. authorities implemented urgent measures to address health concerns and to safeguard economic and financial stability. ### Structural features and trends - The financial sector’s indebtedness fell from 125 percent of GDP at the height of the Global Financial Crisis (GFC) to about 77 percent currently. - Financial intermediation and concomitant risks increasingly shifted to non-bank financial institutions (non-banks). - Non-bank intermediaries supply a significant proportion of credit and provide liquidity transformation; funding provided by non-banks is growing faster than funding provided by depositary institutions. - Non-bank intermediaries often depend on banks for liquidity and short-term funding. ### Corporate and household indebtedness and vulnerabilities - Total business sector debt stood at about US$16 trillion (75 percent of GDP) at the end of 2019, with corporate sector debt (comprising corporate bond debt and bank loans) accounting for about two thirds. - ‘Leveraged finance’ rose to about 7 percent of total business sector debt (issuance of syndicated loans or non-investment grade bonds by highly-leveraged companies and related structured products, such as Collateralized Loan Obligations (CLOs)). - The leveraged finance segment has seen a rise in issuance with less covenant protections. - Total household debt declined from close to 100 percent of GDP at the onset of the crisis to about 75 percent currently; mortgage debt reduction was widespread across income groups and new mortgage loans accrued largely to relatively high quality borrowers. - Rising unemployment and faltering income due to the COVID-19 outbreak and oil price shock will put pressure on household debt servicing capacity, particularly among workers in leisure, hospitality, transportation services. ### Interconnectedness, exposures, and spillovers - Banks provide significant short-term funding to non-bank financial institutions, households and corporates. - Unused credit lines and other funding commitments provided by banks constitute about 15 percent of intra-financial system exposures. - The U.S. banking system’s average capital impairment due to their exposure to foreign banking systems could be as low as 2 percent of regulatory capital. - The U.S. banking system has substantial interconnections with global financial markets including foreign banks. - Outward spillover risk is mitigated by large banks’ capital and liquidity buffers. ### Banking system buffers and stress test results - Banks entered the COVID-19 outbreak with substantial capital and liquidity buffers and ability to expand balance sheets to support the real sector. - The systemwide Common Equity Tier 1 capital ratio (CET1) before the COVID-19 crisis was 12 percent on average and liquidity (LCR) ratios above 100 percent. - In the baseline scenario (following the June 2020 WEO update): - industry-wide CET1 ratios decline by 390 basis points on average, reaching their lowest point after 2 years of stress. - Smaller, non-G-SIB banks experience the largest impact. - If the recovery is as fast as projected in the baseline scenario, the impact on CET1 ratios by the end of the 5-year horizon would be 50 basis points. - The declines in the systemwide CET1 ratio in the outer years are mostly driven by a decline in net interest income due to compressed margins. - In the adverse sensitivity scenario (which assumes an additional month of containment measures): - median capitalization at the lowest point of the horizon is 7.6 percent, which is lower than the CET1 ratio under the baseline by 190 basis points. - In the case of a more severe recession (such as a second wave of infection and subsequent containment measures): - the impact on the systemwide CET1 ratio at its lowest point would be 450 basis points compared to the baseline and 630 basis points compared to the median CET1 ratio at Q1 2020. - Nevertheless, all G-SIBs would maintain CET1 ratios above the minimum regulatory requirements. ### Bank-to-bank and bank-to-nonbank contagion findings - Illiquidity or default of a G-SIB would affect other banks via counterparty losses, liquidity shocks and asset fire-sales. - A joint IMF-FRB analysis indicates these effects are relatively small within the group of 6 G-SIBs. - Vulnerabilities in smaller banks could increase as they ramp-up risk taking and reduce liquidity buffers in the context of recent regulatory relief; such banks may struggle to provide liquidity to customers in market shocks. ### Corporate sector stress and non-bank financial institutions - A large proportion of corporate sector debt resides outside of the banking sector, including in insurance companies, mutual funds, pension funds, and foreign investors. - Over half of leveraged loans outstanding are owned by CLOs, which are in turn held by a wide range of investors, with banks mainly holding the AAA-rated tranches. - Stress in the corporate sector would result in significant losses in the non-bank financial sector, especially holders of equity tranches, resulting in some funding redemption pressures for open-ended funds; the fire-sales channel would be contained by the contractual structure of CLOs. - Marked-to-market losses would be contained; the impact on banks would be moderate because of their limited direct exposure, though credit line drawdowns by corporates and non-bank financial institutions could increase. ### Mutual funds and money market funds stress testing results - Mutual funds stress tests indicate that most funds would be able to withstand severe redemption shocks, but high yield and loan mutual funds would face significant shortfalls. - More than 90 percent of funds (measured by assets under management) would have enough highly liquid assets to meet investors’ redemption. - Funds exposed to high yield and leveraged loans would need to sell less liquid securities in their portfolio (assuming they do not use any liquidity risk management tools), potentially giving rise to fire sale dynamics. - Funds with large exposures to derivatives could face liquidity demands related to variation margins; sensitivity analysis shows potential variation margin calls could be higher than their liquid assets, increasing the potential risk of forced sales. ### Insurance sector stress testing results - The stress test covered more than 70 percent of the life and non-life market by assets and included smaller, regionally concentrated non-life firms. - A materialization of the adverse scenario would have a substantial balance sheet impact, especially in the life sector, stemming from impairments on shares, non-investment grade debt, and other investment assets. - Current buffers and the valuation and solvency regime would prevent major disruptions, but persistently low interest rates are expected to further erode profitability of life insurers. - A large interest rate hike, triggering a mass lapse and large cash outflows from the life sector, would affect insurers heterogeneously: some insurers would need to liquidate only small amounts of Treasury bonds, but a few would have to liquidate larger amounts of assets, including potentially less liquid corporate bonds. ### Climate-related and natural disaster risk - Climate related risks would have a relatively contained impact on the financial system in the near term, but some companies and segments, like insurers and the municipal bond market, would be affected more. - A very severe hurricane (expected to occur every 250 years) would have a major impact on companies and households in affected regions, but large and diversified non-life insurers would have enough capital to pay out compensations; several smaller and more regionally concentrated insurers would face capital shortfalls. - Further impacts may come from insurers leaving affected regions, a deterioration of income of affected municipalities and a negative impact on the municipal bond market. ### Transparency, data gaps, and systemic risk assessment - The migration of activities from well-regulated, public and transparent financial institutions (such as banks) towards more opaque, private and unsupervised entities creates challenges for identification and assessment of systemic risks. - Public data is scarce on exposures of regulated financial institutions as well as the ultimate risk holders of leveraged loans and associated securitization vehicles. - Greater transparency about those exposures would strengthen market discipline and allow for constant assessment of systemic risk. - System-wide interconnectedness stress tests confirm that some corporate bond mutual funds and insurers may have considerable direct losses in a corporate debt distress scenario; banks would experience smaller losses because of their limited exposure, except for potentially higher credit line drawdowns. ### Stress testing coverage and methodology highlights - The FSAP risk analysis conducted stress tests of: - banks: 34 largest institutions (33 banks with assets above USD 100 billion and one bank with assets close to 100 billion), - insurance companies: 53 large groups and 17 medium-sized and smaller regional insurers (covering more than 70 percent of the life and non-life market by assets), - mutual funds: about 2,000 largest players (all fixed income and mixed mutual funds covered by Morningstar), - selected money market funds: 208. - The FSAP stress tests are largely top-down, mostly relying on public and non-confidential data and scenarios generated by in-house models; they differ in data granularity and calibration from the Federal Reserve’s CCAR/DFAST supervisory stress tests. - Interconnectedness and systemic risk analysis link stand-alone stress tests; common exposures and simulated asset fire-sales provide system-wide stressed loss estimates. - Scenarios include global and regional financial market stress (shocks to term and risk premiums and resulting asset price corrections) and a major slowdown of economic activity. - The approach covers solvency (banks, insurance companies), liquidity (banks, mutual, money market funds) and solvency-liquidity feedback analysis (fire-sales across the financial sector). *Source: 1usaea2020007 - EXECUTIVE SUMMARY* ### 9. Like in other FSAPs in large advanced economies, the IMF scenario design is based on ### 1usaea2020007 - 9. Like in other FSAPs in large advanced economies, the IMF scenario design is based on ### Scenario design and severity benchmarks - FSAP scenario design uses multiple approaches and severity benchmarks. - Baseline and scenario inputs: - WEO baseline projections, which included assumptions about duration and severity of COVID-19 crisis. - Inference about severity from 2019 DFAST/CCAR scenarios. - Historical benchmark: 2008–2009 Global Financial Crisis (GFC). - COVID-19 scenario suite: - COVID-19 Baseline and three Adverse scenarios based on duration of containment measures and a second wave of contagion. ### Analytical framework and model coverage - Risk analysis divided into multiple blocks (Figure 1): solvency of banks and insurers; limited liquidity analysis of banks and mutual and money market funds; sectoral risk assessment; scenario design; interconnectedness; systemic risk models developed by FSAP team. - Solvency of banks: - Balance-sheet regulatory approach based on exposures (domestic/foreign). - Forecast of pre-loss income, credit and market losses, changes in RWAs based on refined CLASS model as well as internal models. - Forecast of balance sheet and income statement items: 57 equations based on the refined CLASS model. Three or five year ST horizon. - Top-down: 34 banks (8 G-SIBs; 11 subsidiaries of foreign banks and 16 other domestic banks). - Sensitivity analyses (e.g., Real estate price risk). - Risks from common exposures (fire-sales of assets). - Liquidity analysis: - Mutual funds: fund liquidity—redemption shocks; emphasis on US fixed income and mixed funds. - Bank liquidity: limited analysis using public data (LCR disclosure reports). - Sample for bank liquidity: 33 banks: 8 G-SIBs; 9 subsidiaries of foreign banks and 16 other domestic banks. - Solvency of insurers: - Balance-sheet regulatory approach based on exposures. - Alignment with macrofinancial scenarios used for the banking stress test. - Sensitivity analyses (e.g., interest rate shocks, default of largest banking counterparties). - Coverage: 70 insurance groups. - Cross-sectoral and interconnectedness analysis: - Flow of funds among the different econ sectors: financials, households, corporates, public sector, foreign entities. - Exposure based analysis: US interbank, cross-border (incl. aggregated cross-border exposure analysis). - Market-data based interconnectedness analysis (domestic and foreign linkages). - Money market funds: fund market risk—interest and credit spread shocks. ### Data, cooperation, and limitations - FSAP team used various public and commercial data sources and cooperated with the FRB on banking sector interconnectedness and liquidity analysis. - Confidential supervisory data were used for insurance stress testing and for liquidity risks for six G-SIBs (the FRB performed that test using supervisory data). - Limitations noted: - Unable to conduct a granular stress test of trading books (portfolio hedges, short positions, potential changes in banks’ balance sheets). - Liquidity stress tests of banks based on public 30-day Liquidity Coverage Ratio (LCR) disclosure templates and omit other time horizons (1 day, 5 days, or 3 months). - Structural liquidity risk analysis did not elaborate on Asset Encumbrance because such data were not available. - Some banking sector interconnectedness analyses undertaken in close collaboration with FRB staff to preserve confidentiality of underlying input data; analysis remains IMF work product and does not reflect views of U.S. regulators. ### Financial system size, structure, and global role - Overall size and composition: - Financial system surpassed US$100 trillion in 2019 and amounts to about five times the U.S. nominal GDP. - Mutual funds sector reached 16 percent of the financial sector share as of 2019. - Equity market capitalization above US$50 trillion (as of 2019). - Pension entitlements assets about US$30 trillion. - Mortgages (home, multi-family residential, commercial, and farm) about US$15 trillion as of 2019. - Cross-border and dollar role: - Average daily trading volume of U.S. Treasury securities grew by 10 percent year-over-year in 2019, reaching nearly US$600 billion per day on average. - Portfolio investment position of foreign residents on the U.S. above US$20 trillion in 2019. - U.S. residents’ portfolio investment position in the rest of the world over US$12 trillion. - Share of dollar-denominated cross-border claims about 50 percent of global banks’ total cross-border claims in 2019. - Foreign-owned banks maintain dollar-denominated cross-border claims over US$12 trillion, amounting to about 10 percent of their cross-border claims on average (October 2019 GFSR Chapter 5). ### Banking sector: structure, asset and funding composition, and capitalization - System structure: - Number of banks reduced to less than 5,000 from over 10,000 at the end of the last millennium. - System dominated by 34 large bank/intermediate holding companies (BHCs/IHCs) with consolidated assets over US$100 billion each, accounting for nearly 80 percent of U.S. banking system assets. - Eight globally systemically important banks (G-SIBs) hold more than 50 percent of banking system assets. - The list of 34 includes 12 large intermediate holding companies (IHC); four IHCs classified as large and complex and supervised under LISCC with the eight G-SIBs. - Asset growth and composition: - Assets of the largest BHCs in the stress testing sample have increased by 23 percent since the 2015 U.S. FSAP. - Largest contributing factor for asset growth: net loans and leases (10 percent contribution). - Net loans and leases comprise 40 percent of total assets. - Highly liquid federal funds assets and reverse repos account nearly 12 percent of total assets. - Cash and balances with central banks declined from 12 percent during the previous FSAP to 8 percent. - Loan composition and trends: - Loans outstanding increased by 26 percent since the last FSAP. - Real estate loans: 35 percent of total loans, contributed 5 percent towards total loan growth. - Commercial and industrial (C&I) loans: 24 percent of total loans, contributed 8 percent to overall loan growth. - Credit card-related loans: 11 percent of total loan portfolio. - Automobile loans and other consumer loan categories: 10 percent of the loan portfolio. - Some C&I loan subcategories (e.g., leverage loans) experienced decline in covenants. - Funding composition: - Domestic deposits account for 52 percent of total liabilities. - Other borrowed funds account for 15 percent of total assets. - Federal funds and repos account for nearly 9 percent of total liabilities and contributed nearly 3 percent to growth in total liabilities. - Capitalization and leverage: - CET1 ratio reached 12 percent at the aggregate level as of 2019Q3. - Leverage ratio (Tier1 capital-to-assets) reached about 8.7 percent as of 2019:Q3. - CET1 increase attributed mostly to steady growth in retained earnings amidst higher RWAs. - Significant disparities in CET1 ratios: most IHCs record significantly higher CET1 ratios compared to domestic banks in the sample. - Off-balance sheet exposures and liquidity commitments: - Off-balance sheet exposures relative to balance sheet size declined by a factor of about three since 2009, mainly due to lower derivatives positions. - Total gross off-balance sheet activities accounted for about 110 percent of total assets in 2019. - Credit derivatives purchased and sold declined from over 100 percent to low double-digit levels since the crisis. - Unused credit line commitments relative to balance sheet size remain above 40 percent and are the largest off-balance sheet exposure category. - Based on 8-k filings, credit line utilization between March-mid April 2020 had surpassed $200 billion. - Asset quality and profitability: - NPLs-to-gross loan ratio for delinquencies longer than 90 days fell below 1 percent in 2019 for the 35 largest BHCs. - Past due loans of 30 to 89 days show similar trends. - Reserve-coverage-ratio (allowances for reserves over the stock of NPLs) has been steadily increasing. - NPL ratios higher for residential real estate loans relative to other categories, but significantly improved since the crisis. - NPLs on consumer loans show a slight uptick in recent years. - Banks maintain robust profitability; reduction in corporate tax rate in late 2017 increased ROE of large banks by about 4 percentage points on average. - Significant dispersion in profitability across banks; foreign banks on average record lower profitability ratios. ### Mutual and money market funds - Role and exposures: - Fixed income and mixed funds play a significant role in credit intermediation and liquidity transformation. - Mutual funds hold more than 15 percent of all U.S. corporate bonds outstanding and about 10 percent of leveraged loans. - Liquidity mismatch risks: - All mutual funds offer daily redemptions; some funds are significantly invested in less liquid securities and may face forced asset sales under heavy redemptions. - Derivatives use can create liquidity demands via mark-to-market losses and variation margins. - Regulatory requirements: - Funds must establish a written liquidity risk management program designed to assess, manage, and periodically review liquidity risk under reasonably foreseeable stressed conditions and generally maintain a minimum amount of highly liquid assets. - Limit purchases of illiquid assets to 15 percent of the fund’s net assets. - Leverage and derivatives: - Mutual funds may borrow from banks up to 50 percent of NAV (maximum balance sheet leverage of 1.5x). - Derivatives can create synthetic leverage beyond balance sheet borrowing. - SEC (2019) statistics: - About 60 percent of mutual funds do not use derivatives. - 20 percent of mutual funds have adjusted notional amounts above 10 percent of NAV. - About 14 percent of mutual funds have gross exposures above 50 percent of their NAV. - Commercial data on fixed income and FX derivatives indicate some mutual funds report gross leverage more than four times the NAV. ### Insurance sector (summary) - Asset allocation and characteristics (stress test sample): - 42 percent of life insurance sector assets are held in segregated accounts; in the general account, corporate bonds dominate. - Bond maturities are longest in life insurance with 38 percent of bonds having a maturity of 10 years or more. - More than 90 percent of corporate bonds in each sector have an investment grade rating (NAIC 1 and 2). - Stress testing: - Insurance stress testing was based on confidential supervisory data. *Source: IMF staff; content reflects discussions, presentations and consultations with the authorities, private sector and data sources available to the FSAP team.* ### 27. The U.S. life and health insurance sector is exposed to significant interest rate risks, ### 27. The U.S. life and health insurance sector is exposed to significant interest rate risks ### Interest rate risk in life and health insurance - U.S. life insurers actively underwrite long-term annuity products with liability durations exceeding asset durations. - The risk-based capital framework includes (mostly factor-based) capital charges associated with asset-liability mismatches. - The recent less restrictive stance of the U.S. monetary policy could have a negative impact on life and health insurers with large duration gaps. - If long-term interest rates stay low for long, the accounting and solvency positions of life and health insurers would deteriorate gradually but significantly over time. ### Asset allocation, liquidity, and market risk sharing - Market risks in life insurance are to a large extent shifted to policyholders in segregated accounts and are rather diversified in the general account. - Segregated accounts represent 42 percent of the life sector’s balance sheet. - The general account composition: - Corporate bonds: 30 percent. - Sovereign bonds: 9 percent. - Equity investments: play a minor role in the general account. - Life insurers’ liquidity and less liquid asset exposure: - Life insurers allocate 8 percent of total assets to mortgages. - Life insurers are important buyers of CLOs. - Non-life sector asset allocation appears more biased towards equity exposures, but this is mainly driven by very few large outliers. - Health insurance is mostly a cash-flow business; investments are typically very liquid and less risky. ### Interconnectedness landscape and common exposures - Financial system resilience depends on individual institutions’ health and interplay of vulnerabilities through direct and indirect exposures within and between segments. - Sectors are indirectly interconnected through exposures to common asset classes, such as corporate bonds, equities, agency and treasury securities markets; exposures to common asset classes amplify risk transmission via marked-to-market losses. - Cross-sectoral exposures (Flow of funds data as of 2018:Q4; panel 3 illustration as of 2019:Q3) highlight: - A relatively large share of financial assets of the domestic financial sector is held by household and corporate sectors, while the largest asset share is held within the financial sector. - Households’ financial assets issued by the financial sector is the largest linkage between any two sectors. - Within the financial sector, non-banks have large exposures to households (about two-thirds of residential real estate loans are held by non-bank entities). - Among the 25 largest mortgage originators and servicers, non-banks currently originate 51 percent of mortgages and service 47 percent (FSOC annual report, 2019). - The non-bank financial sector plays a pivotal role in corporate sector financing, maintaining larger exposure levels vis-à-vis the domestic corporate sector. ### Sectoral asset holdings and potential amplification channels - Insurance and mutual fund exposures to corporate bonds (data as of year-end 2018): - Insurance sector held nearly 26 percent of corporate bonds. - Mutual funds held nearly 17 percent of corporate bonds. - Equity exposures: - Mutual funds had over 60 percent of assets invested in equities. - Pension funds had over 20 percent of assets invested in equities. - Money market funds: Agencies and U.S. Treasury securities combined consist of half of money market funds’ balance sheet assets. - Direct intra-sectoral exposure patterns: - Insurance sector has exposure to mutual funds. - Mutual funds have exposure to banks through providing short-term funding. - Such cross-exposures may amplify transmission of spillovers through asset fire sales. ### Banks’ domestic links and liquidity distribution - Banks continue to play a key role in distributing liquidity, offering payment services, liquidity support, other credit lines, hedging services to banks and non-bank financial institutions. - G-SIBs are key players with the largest assets and liabilities positions against the financial system. - Banks’ domestic intra-financial system exposures (data as of 2019Q3): - Composition of intra-financial system assets and liabilities reveal significant linkages through deposits and OTC derivatives positions. - About one-half of the liabilities position comes from non-bank deposits, while OTC derivatives suggest large positions are held against non-banks. - On aggregate, about 15 percent of the total intra-financial exposures consists of unused credit lines. - The amount of unused credit lines obtained from the financial system is only about one-tenth of the amount extended, suggesting extended credit lines are mostly against the non-bank financial sector. - A rapid draw-down in off-balance-sheet commitments during stress episodes increases potential for contagion. ### Cross-border interconnectedness of the U.S. banking sector - Nearly a quarter of the U.S. banking system’s consolidated claims are held against foreign borrowers. - Concentrated exposures by residence of ultimate borrowers (data as of 2019:Q3): - Claims against borrowers in the United Kingdom: about 3 percent of total assets or 35 percent of Tier1 capital of the U.S. banking system. - Claims against borrowers in Japan: about 3 percent of total assets or 35 percent of Tier1 capital of the U.S. banking system. - Claims on Germany, France, and Canada: above 1 percent of U.S. banks’ assets. - Foreign claims of the eight U.S. G-SIBs (as of 2019:Q3): - United Kingdom and Japan: about 4 percent of assets or nearly 50 percent of Tier1 capital of the eight G-SIBs. - Claims on Germany, France, and Canada: 1–3 percent of the eight G-SIBs’ assets. - Sectoral cross-border exposures: - U.S. banks’ claims vis-à-vis foreign non-bank financial sectors: 8 percent of U.S. banks’ assets. - Exposure to foreign banking systems: 4 percent of U.S. banks’ assets at aggregate level. - Among foreign banking systems, the U.K. and Japanese banking systems are the largest counterparties. - Cross-border off-balance sheet exposures: - Derivatives make up the largest share. - Unused commitments vis-à-vis foreign borrowers amount to 5 percent of the U.S. banking system assets. - Largest off-balance sheet exposures by counterparty country: U.K. (5 percent), France (4 percent), Germany (3 percent), Japan (2 percent) of U.S. banking system assets. - Bank-level patterns: - Some G-SIBs have large liabilities to the domestic financial system while maintaining large cross-border claims. - Some non-G-SIBs including intermediate holding companies (IHCs) have relatively sizable cross-exposures. - Banks’ exposures vis-à-vis foreign banking systems mostly concentrated in the U.K., Japan, Germany, and Canada. - A large proportion of IHCs’ claims are concentrated in their parent banking systems. - Cross-border funding from top counterparty banking systems (data as of 2019Q2): - Funding coming from the Japanese banking system is notably larger than other counterparties, followed by Canada. - Unconsolidated nationality-based exposures reveal large liabilities to the U.K. banking system and to the German banking system to a lesser extent; these are relevant for deposits held for FX transactions and settlement purposes. ### Resilience and vulnerabilities of borrowers — households - Households have continued to reduce overall indebtedness over the past decade driven by a large fall in housing-related leverage. - Residential mortgage debt has decreased substantially since the Global Financial Crisis (GFC) across all income groups. - Mortgage loan performance has been robust (with low delinquency rates, particularly for newly originated mortgages). - Debt servicing costs have been falling and maturities extended as households refinanced their mortgages benefiting from low interest rates. - Outstanding mortgages exceed US$10 trillion (roughly 50 percent of GDP, or two-thirds of total household debt); a deterioration in credit quality could adversely impact financial stability. *Source: IMF staff analysis (extract provided).* ### 40. Certain segments of consumer credit are rising rapidly. Although still relatively small, ### 1usaea2020007 - 40. Certain segments of consumer credit are rising rapidly. Although still relatively small, ### Consumer credit: student loans and auto loans - Student loans and some segments of consumer credit debt (auto loans) are on the rise. - A large part of that growth accrues to households with prime credit scores. - Most student loans are issued under government programs; risks to financial institutions remain limited. - Delinquencies on student loans remain high, underscoring pre-existing debt repayment pressures among certain households, most notably younger cohorts. - The relatively small share of subprime debt in overall consumer debt is a relatively positive development. - Rising vulnerabilities in these sub-segments of household credit warrant close monitoring to ensure they do not reach systemic proportions. ### Household vulnerabilities and COVID-19 implications - Households entered the current crisis with lower indebtedness levels than at the onset of the global financial crisis. - House price deviations from fundamentals were significantly smaller, evidenced by lower house price-to-income and house price-to-rent ratios. - The COVID-19 outbreak and related containment measures are expected to have a significant impact on employment and income. - If economic strains owing to long-lasting hysteresis effects and behavioral changes related to social-distancing norms lead to subdued demand, significant output and employment losses in hard-hit sectors (e.g., entertainment, hospitality, transportation services) could severely impact households' ability to service their debt, particularly at the lower end of the income distribution. - High-end service sectors, such as financial, legal, IT, and other professional & business services have experienced relatively small losses following the COVID-19 outbreak, in terms of both output and employment. ### Housing market and government role - The Government-Sponsored Enterprises (GSEs)―most notably, Freddie Mac and Fannie Mae―currently own or guarantee about half of all mortgages. - In the early 1980s, the GSEs owned or guaranteed about 8 percent of outstanding single-family mortgage debt; that share grew to 25 percent by the end of that decade and to 44 percent in the early 2000s, close to current levels. - The share of outstanding multifamily debt owned or guaranteed by the GSEs grew from 25 percent at the onset of the global financial crisis to about 40 percent currently. - Outstanding MBS guaranteed by the GSEs and Ginnie Mae exceed US$8 trillion, representing about 85 percent of that market. - The market for MBS guaranteed by these entities is one of the most liquid fixed income markets worldwide, with average daily trade volumes exceeding US$200 billion. - Any reform to the housing finance market could have significant financial depth and stability implications. ### Non-bank mortgage lenders - The share of mortgages originated by non-depository mortgage companies has increased in recent years. - Non-bank mortgage lenders’ activities include origination, loan servicing, and securitization of MBS. - Their operations rely heavily on short-term credit lines for mortgage origination, which are subject to liquidity risks in periods of stress. - Risks from limited capital buffers and servicing-advance obligations (e.g., tax and insurance payments, making mortgage payments to MBS investors when borrowers default) create additional liquidity pressures. - Given their growing market share and complex interlinkages with the broader financial system, non-bank mortgage lenders could be a source of risk. ### Nonfinancial corporate sector indebtedness and resilience - Debt of nonfinancial corporates (NFCs) in the United States is at a historic peak: 47 percent of GDP, surpassing the 2009 peak of 44 percent. - Total debt of the business sector (including noncorporate firms) rose from 65 percent of GDP in 2012 to almost 75 percent of GDP. - The largest share (about two-thirds) of nonfinancial corporate debt is in the form of corporate bonds and commercial paper, amounting to about US$6.5 trillion. - The amount of outstanding commercial real estate (CRE) loans exceeds US$2 trillion and has been growing in line with the economy. - The ratio of corporate debt to EBIT has recently shot up to about six. - The interest coverage ratio (EBIT to net interest expense) has weakened from nearly seven in 2013–14 to around five today, comparable to levels seen during the global financial crisis. ### Leveraged finance: leveraged loans and CLOs - Peak annual issuance of leveraged loans in the United States was US$650 billion in 2017; issuance volume was US$491 billion in 2019. - The United States accounts for over 80 percent of global leveraged loan issuances. - Outstanding leveraged loans are currently estimated at US$1.1 trillion (about 5 percent of GDP). - The leveraged loan market has been growing at an average annual growth rate of nearly 15 percent since dipping to US$497 billion in 2010. - Some broader measures put the leveraged loan market size at US$2.2 trillion when including smaller, less liquid loans and bank-held loans. - Covenant-lite loans have accounted for more than half of new leveraged loan issuances in the U.S. market for the past four years. - CLOs held roughly US$617 billion of the US$1.1 trillion in outstanding leveraged loans at end-2018. - At end-2018, CLO holdings by investor type were: insurance companies 28 percent, mutual funds 15.5 percent, and banks 15 percent. - Banks are exposed to the leveraged loan market directly (originations retained, revolving credit lines) and indirectly (warehouse credit lines to CLO arrangers, holdings of AAA CLO tranches). - Off-balance sheet commitments by banks to corporate clients are estimated at around US$760 billion (FSB estimate); facilities granted to CLO issuers for U.S. banks were around US$28 billion by end-2018. - Stress in leveraged loans and CLOs during a macroeconomic downturn could trigger asset sales, mark-to-market losses, and contagion through indirect channels affecting other financial system segments. ### Ongoing crisis impact and sectoral vulnerabilities - The ongoing crisis will put significant stress and amplify existing vulnerabilities in an already leveraged corporate sector. - The rise in leverage in risky credit markets—including leveraged loans, high yield and private debt—combined with weakening underwriting standards could see segments of the U.S. nonfinancial corporate sector underperform under stress. - Corporate short-term liquidity needs are large, but most are concentrated in investment-grade companies whose debt markets are supported by recently introduced Fed liquidity facilities, including “fallen angels”. - With the potential of a protracted economic slowdown and behavioral changes induced by social-distancing norms, long-term sustainability of certain business models could be challenged; solvency risks could materialize leading to large credit risk losses. - Sectors appearing most vulnerable include energy, entertainment and leisure services, retail, and durable-goods manufacturers. ### Stress testing scenarios: scope and design - The FSAP team developed sensitivity test-based scenarios incorporating corporate sector risks and followed the Risk Assessment Matrix (RAM) (Appendix VI). - The RAM considers existing vulnerabilities and salient risks, particularly from the COVID-19 outbreak. - The FSAP stress test scenarios cover a five-year ahead horizon over the period 2020–2 5. - The FSAP used one baseline and three separate sensitivity scenarios for stress testing. - Shocks to GDP were derived using a simple accounting-based framework measuring sectoral output losses subject to duration of containment measures and intensity of recovery after re-opening. - Employment losses and the unemployment rate path are proportionately linked to scenario severity in terms of GDP losses. - Short-term interest rates assume the policy rate remains at the effective zero lower bound (ZLB) throughout the projection horizon in all scenarios. - Longer-dated Treasury bill rates are derived from short-term interest rates and a term premium component which varies according to scenario severity. - Other macro-financial variables remained similar to those observed during the Global Financial Crisis. - The baseline scenario is based on the June 2020 WEO Update projections; three additional scenarios are used as sensitivity analysis. - These scenarios complement the Federal Reserve’s severely adverse CCAR/DFAST scenario, which has a three-year horizon. *International Monetary Fund — excerpt from the United States FSAP technical note (sections 40–55).* ### 56. The scenarios were applied consistently to test the resilience across all types of financial ### 1usaea2020007 - 56. The scenarios were applied consistently to test the resilience across all types of financial ### Scenario Narrative and Calibration - FSAP baseline scenario follows the June 2020 WEO Update projections: - Sharp contraction mainly in 2020Q2 owing to containment measures. - Unemployment rate peaks at 13½ percent in 2020Q2. - GDP remains below its pre-crisis (i.e., 2019Q4) level through end-2022. - Asset prices face a short-term fall, then recover to previous levels within a two-year horizon. - Three alternative sensitivity scenarios (differing lengths of reduced de facto mobility): - Sensitivity Scenario 1: - Reduced mobility lasts the entire second quarter of 2020. - Output in 2020Q2 is 75 percent of pre-shock output (i.e., output loss of 25 percent non-annualized). - Slower recovery than baseline; larger hysteresis losses. - Financial asset price falls adjusted proportionately to output losses. - Sensitivity Scenario 2: - Containment and reduced mobility extend for an additional quarter (both 2020Q2 and 2020Q3 at 75 percent of pre-crisis level). - Larger and longer-lasting economic “scarring” than Scenario 1. - Sensitivity Scenario 3: - Initially follows Sensitivity Scenario 1, plus another wave and new containment in 2021Q1. - Produces a “W-shape” in output; more persistent losses and slower recovery. - Historical context and calibration: - The sharp real GDP contraction in 2020Q2 in the June 2020 WEO Update is equivalent to 12 times the historical standard deviation of quarterly growth since end of WWII. - The same number is equivalent to 17 standard deviations in the adverse scenarios. - FSAP financial variable shocks follow CCAR behavior, adjusted proportionately to FSAP output losses (large shocks to corporate risk premia, stock market prices, and real estate prices). ### Risks Related to High-impact Events and Transmission Channels - Climate and weather-related risks considered: - Increased frequency of extreme weather events raises probability of severe damage to businesses, households, and municipalities in affected areas. - Implications for the insurance sector through idiosyncratic weather-related catastrophes (e.g., hurricanes, flooding). - Structural risk factors noted (beyond FSAP assessment window): - Shift in technology and decline in carbon-intense production/transportation could pose structural risks but are unlikely to materialize rapidly within the FSAP time frame. - Transmission observations: - Weather-related severe events can impact financial condition of private corporates, public utilities, and municipalities in affected areas. ### Corporate Sector Stress Tests (Scope and Method) - Sample and data: - Sample of about 2,000 nonfinancial corporations. - Total assets amounting to US$19 trillion (87 percent of GDP). - Aggregate indebtedness of US$9 trillion. - Balance sheet and profit-and-loss data sourced from Capital IQ. - Methodology: - Project firms’ net income and debt servicing capacity under macro scenarios. - Firms considered in “distress” when cumulated net losses exceed reported capital (negative equity). - Liquidity needs gauged by comparing beginning cash and cash equivalents to amortizations, maturing debt, and other net cash inflows. - Revenues modeled as function of real GDP growth; certain industries also depend on oil price developments. - Interest expenditures modeled using existing debt profile and share of re-priceable debt, applying stress scenario interest rates. ### Corporate Stress Test Results (Key Findings) - Leveraged firms (debt-to-EBITDA ratio higher than 5) are especially vulnerable: - Represent roughly three quarters of all firms with negative equity under baseline projections. - Despite small size of leveraged loan market, leveraged firms account for over 80 percent of the US$400 billion in potential debt-related losses under baseline. - Scenario outcomes: - Under a more severe scenario (FSAP Sensitivity Scenario 3), losses from corporate debt holdings could reach US$675 billion (US$465 billion related to leveraged firms). - Banking system exposure: - Banks have limited direct exposure to these corporate products; banks hold slightly less than 20 percent of outstanding corporate bonds and loans, representing roughly one-tenth of their own assets. - Indirect exposures and market dislocations combined with liquidity shortages could amplify stress. ### Banking Sector Stress Tests — Solvency (Scope, Methodology, and Drivers) - Scope: - Top-down solvency and liquidity stress tests cover the 34 largest U.S. bank holding companies (BHCs). - These 34 BHCs account for about 86 percent of BHC assets and 75 percent of total banking system assets. - Reference date for banking data: Q1 2020. - Methodology: - TD stress test builds on the CLASS (Capital and Loss Assessment under Stress Scenarios) model and uses FRB and publicly available data (e.g., FR-Y 9C, FR-Y 15). - Additional robustness checks: accounting (write-offs) vs. market-implied PDs (EDFs). - Sensitivity analyses performed for low interest rate environment, high shareholder payout ratios, Fintech competition, and credit growth assumptions. - Bank balance sheet and income statement components projected using multiple panel regression models calibrated on quarterly data from 1991 to 2020:Q1. - Forecasted ratios used to calculate net income, balance sheet, and capital ratios through the stress horizon (i.e., 2025:Q1). - Primary loss drivers identified: - Unemployment, house prices, corporate credit spreads, and interest rates explain the largest part of variation in accounting-based losses (charge-offs, recoveries) and PPNR. - Unemployment and housing prices drive losses on household loans; GDP, corporate spreads, and interest rates drive C&I loan losses. - Without adjustments, projected loss rates (except credit card loans) would be slightly lower than those observed during the GFC due to lower interest rates and risk premiums relative to 2008–09. - COVID-19-specific model adjustments required: - Satellite models calibrated on pre-COVID historical data underestimated risks for certain exposure classes (corporate loans, commercial real estate) and overestimated some PPNR expense components. - Specific sectors disproportionately affected: small businesses, travel, hotels, office rent, and retail trade. - Banks may mitigate expense pressures (e.g., lower fines, reduced capital and other expenditure, minimize shareholder payouts). - CLASS model adjustments were applied to account for these COVID-19-era specifics. *Source: https://www.imf.org/-/media/files/publications/cr/2020/english/1usaea2020007.pdf* ### 68. Three types of adjustments to CLASS loan loss satellite models were explored: (i) based ### 1usaea2020007 - 68. Three types of adjustments to CLASS loan loss satellite models were explored: (i) based ### Adjustments explored to CLASS loan loss satellite models - Three types of adjustments were explored: - (i) based on market data; - (ii) a separate corporate risk stress test and COVID-19 market intelligence-based adjustments; - (iii) adjustments for salaries growth as well as growth of non-interest expenses (excluding wages). ### Market-data-based PD approach and implementation - A market data-based alternative to accounting based (write-offs) loan loss specifications was performed to check robustness and compare results of corporate credit risk. - The PD proxy for exposures to tradeable securities were extracted from bond yields using a Merton-based approach. - Data source: public returns (FR-9 Y) on the breakdown of financial assets to back out banks’ exposures to various types of securities. - PDs were extracted from spreads projected in the scenario using a reduced-form structural model. - Assumption: LGD = 45 percent. - Implied risk-neutral PD formula (as provided in the source): - 푃푃퐷퐷 푡푡,푇푇 푖푖 = 1 − 푒푒푒푒푒푒 −푆푆 푡푡,푇푇 푖푖 ⋅ (푇푇 − 푡푡) ) 퐿퐿퐿퐿퐷퐷 푡푡 푖푖 Notes from footnotes in source: - The market-data test cited was performed before COVID-19 shock with cut-off date for the data as of end of 2019. - An increase in sovereign issuer risk is reflected in higher loan loss impairment charges on HTM and AFS portfolios. - The structural approach assumes that the difference between a risk-free security and a risky security is the put option on the value of the assets which includes the loss induced by the stressed PD and LGD of the bond. ### Linking bank exposures and stressed PDs; outcomes - Banks’ exposures to different sectors of commercial and industrial (C&I) loans were linked with market data-based PDs for these sectors; manufacturing and construction receive most of the loans. - Regressions were constructed to obtain stressed market-data-implied PDs; results yielded shocks to PDs similar to those observed during the GFC. - Flow of loan loss provisions = stressed PDs × LGDs × bank-by-bank exposures. - Using market-based C&I loss estimates led to an additional decline in the system-wide CET1 ratio by 50 basis points. - Given model calibration to past crises (dot-com 2000–2001 and GFC 2008–2010), the FSAP team did not use market data-based PDs for final loss inputs, and instead utilized approach (ii). ### C&I losses guided by corporate stress test (multiplier approach) - Historic data on C&I loan losses may not reflect structural risk from rapid growth of leveraged loans with few covenants. - FSAP used output from the corporate stress test (ST) exercise to guide C&I losses via a multiplier approach: - Multiplier = relative increase of C&I compared to the base quarter (Q1 2020); - Multiplier applied to each bank’s C&I loan portfolio to derive flow of provisions. - Potential losses from the C&I loan category relative to the recent historic peak (GFC) by scenario: - Baseline scenario: loss rate reaches 11.2 percent and is 4 times higher; - Adverse sensitivity 1 scenario: losses reach 14.9 percent and are 5 times higher; - Adverse sensitivity 2 scenario: maximum losses reach 18.1 percent (6 times higher); - Adverse sensitivity scenario 3: maximum losses reach 15 percent (5 times higher). Footnote context on scenarios: - Scenario 2 is more severe than Scenario 3 in terms of GDP losses in 2020 (it entails two quarters of reduced mobility, compared to only one quarter in Scenario 3); however Scenario 3 is more severe in 2021 (the second wave of infection triggers a quarter of reduced mobility in Q1 2021, which does not affect 2020). Accordingly, corporate sector expected losses are higher in Scenario 2 in 2020 (distress rate of 13 percent compared to 11.1 percent in Scenario 3), but corporate losses are higher in Scenario 3 in 2021 (4.8 percent compared to 1.3percent in Scenario 2). Cumulatively (over 2020-25), Scenario 3 entails larger losses than Scenario 2. ### Commercial real estate (CRE) losses and market guidance - Market data guided estimates for potential losses from CRE portfolios because historic loss experience is unlikely to capture the unprecedented crisis impact. - CRE loan portfolios were hit hard by containment measures with expected losses far exceeding observed loss rates during previous cyclical downturns. - CRE loan losses were projected by market sources to lead up to a 3-fold increase in losses exceeding those observed in 2008–9. ### Non-interest expenses and wage growth adjustments - Non-interest expense data shows high historic volatility, adding uncertainty to stress-test estimations. - Largest components of non-interest expenses: wages and residual items (operating losses, M&A, restructuring costs, fines). - Many residual items are discretionary and weakly dependent on macro variables (except wages). - To avoid overestimation of these items under stress, the FSAP team introduced upper bounds lower than historic average growth rates (reference period 2015–2019): - Annual wage growth would not exceed 1 percent in all scenarios; - Residual non-interest expenses would not exceed 2.6 percent in all scenarios. - These caps omit potential large discretionary expense items due to litigation or operational risk events that are not determined by macro scenarios. ### Hurdle rates and capital definition - Definition of eligible capital includes CET1, Tier 1, and total CAR. - Hurdle rate components: - CET1 regulatory minima: 4.5 percent; - Capital Conservation Buffer (CCB) fully loaded level applicable in 2019: 2.5 percent (allowed to be depleted in the adverse scenario); - Bank-specific G-SIB surcharge buffer included. - Tier 1 leverage ratio (U.S. implementation of Basel principles): - 4 percent for all banks in the sample, except for G-SIBs with more than US$700 billion of assets, for which 6 percent minima applies. ### Baseline scenario results and key metrics - Banking system entered COVID-19 crisis with solid capital buffers; future depletion subject to uncertainty (duration/intensity of containment measures). - Stress testing incorporated multiple uncertainties: duration of containment, shareholder payouts, dynamics of non-interest expenses, credit portfolio growth; did not fully estimate impact of fiscal measures like temporary postponement of loan repayments. - Capital depletion drivers: - Immediate increase in credit losses, especially exposures to credit cards (losses of up to 3 percent of risk-weighted assets (RWAs)); - C&I loans (losses of up to 1.8 percent of RWAs); - Growth of RWAs due to utilization of credit and funding lines leading to additional CET1 depletion of up to 1 p.p. - Assumed system-wide expected utilization of 20 percent and a credit conversion factor of 50 percent. - Banks expected cumulative loss of 4 percent of total assets over the five-year scenario horizon (compare: 1.9 percent cumulative losses in the October 2019 WEO baseline). - Net interest income offsets majority of losses but declining margins reduce PPNR: - Cumulative five-year gross interest income goes down from 15.5 to 12.2 percent of total assets (when compared to the October 2019 WEO baseline). - This leads to a 360 b.p. annual capital uplift from net interest income compared to 420 b.p. before the COVID outbreak. - Shareholder payout sensitivity: - If shareholder payouts remain at an average level of 40 percent of net income, up to 4 banks (none of them G-SIBs) would need additional capital to meet the minimum 4.5 CET1 requirement within the three-year horizon. - Recapitalization amounts would be 0.4 percent of GDP. - No G-SIB would fall below minimum requirement within the five-year horizon. - One additional non-GSIB bank would fall below minimum, leading to a total of five banks requiring additional capital; recapitalization needs would be around 0.8 percent of GDP. - If shareholder payouts are zero for the stress test horizon, four non-GSIBs would need additional capital with recapitalization needs falling to 0.6 percent of GDP over the five-year horizon. - Box plot presentation note: Baseline assumes shareholder payouts continue at an average of 40 percent. ### Leverage ratio outcomes - Most banks would maintain leverage ratios above minimum requirement. - Some trading banks designated as G-SIBs would face a challenge in maintaining a 6 percent leverage ratio without reducing dividend payout ratio or asset growth. - Temporary supervisory relief (excluding some assets from supplementary leverage ratio calculation) allowed G-SIBs to remain within regulatory limits. - Foreign and some non-GSIBs (not subject to supplementary leverage ratio) would need additional capital to remain within minimum Tier 1 leverage ratio of 4 percent. ### Fintech competition sensitivity - Potential impact of fintech competition is relatively small system-wide, highest for smaller non-GSIB banks. - Industry estimates: - Banks spend on IT about 15 percent of their total expenses on average, with average spending growth rate increase of up to 4 percent annually. - Without IT investments, smaller banks may lose up to 14.5 percent of potential revenue from payment processing. - Sensitivity analysis assumptions: banks either increase IT investments or risk losing potential revenue. - Applying IT spending and revenue loss projections from industry surveys and rating reports (pre-COVID-19) results in an average 10-basis point decline in CET1 ratio. - An increase in IT expenses leads to larger impact on CET1 than the loss of payment revenue in the sample. - Analysis conducted using Q3 2019 data (pre-COVID-19); COVID-19 may accelerate digitalization and branch closures, affecting outcomes. *Source: https://www.imf.org/-/media/files/publications/cr/2020/english/1usaea2020007.pdf* ### 80. Assuming a more prolonged economic disruption leads to a further depletion of banks’ ### 1usaea2020007 - 80. Assuming a more prolonged economic disruption leads to a further depletion of banks’ ### Solvency stress testing results (Adverse Sensitivity Scenario 1) - Scenario focuses on credit losses from extension of containment measures; C&I loans and consumer loans (credit cards) constitute the bulk of credit risk related losses. - Up to 6 banks (all non-GSIBs) would need additional capital within the three-year period under Adverse Sensitivity Scenario 1. - Overall capital shortfall against the 4.5 percent CET1 minimum in Adverse Sensitivity Scenario 1 is about 0.5 percent of GDP. - Expanding the stress test horizon to five years would require recapitalization of only one additional bank (non-GSIB). - Systemically important banks (G-SIBs) have high enough capital buffers to withstand the simulated shocks. - Decomposition of PPNR items shows profitability remains high during the severe downturn; most of the decline in CET1 is due to non-interest expenses (such as salaries). ### Sensitivity analysis and alternative adverse scenarios - An additional quarter of lockdown reduces system-wide CET1 ratio on average by an additional 90 b.p (Q2 2022). - Up to 8 banks (non-GSIBs) would need additional capital in this variant. - Overall capital shortfall against the 4.5 percent CET1 minimum in this variant is about 0.6 percent of GDP. - In case of a second wave of infections with reactivation of full containment measures: - CET1 declines compared to the baseline by additional 450 b.p. in the third year. - Prolonged containment and a second wave would lead to 10 non-GSIB banks failing to meet minimum CET1 within the first three years. - Capital shortfall against the 4.5 percent CET1 minimum amounts to about 0.8 percent of GDP. - Recapitalization needs would be on average 0.1 p.p. lower in scenarios that assume shareholder payouts are zero during the simulated crisis period. ### Bank-type vulnerabilities and leverage dynamics - More resilient: banks with high share of retail funding, diversified asset portfolios, and high initial CET1 buffers (notably G-SIBs and trading banks). - More vulnerable: Non-GSIBs and some foreign-owned banks with considerable exposure to C&I loans, lower capital buffers, and large shareholder payout ratios. - Non-GSIB exposures: unsecured lending to households (credit cards), secured loans (residential mortgages), C&I loans, commercial real estate lending. - Leverage under stress evolves with capitalization: - Some G-SIBs would need additional capital to maintain required minimum leverage ratios of 6 percent (without temporary relief) within the first two years. - Foreign and non-GSIB banks, not subject to supplementary leverage rule and with lower Tier 1 leverage requirement, show less resilience; their leverage (total capital over total assets) falls below their required minimum of 4 percent. ### Dividend payout behavioral sensitivity and policy implications - Main simulation assumption: banks continue dividend payouts at long term historic average levels (approximately 40 percent of net earnings, excluding share buybacks); balance sheets grow up to 4 percent per year (vs. historic average growth of 7.5 percent). - Alternative simulation: shareholder payouts reduced to zero. - Keeping shareholder payouts at zero for the duration of the crisis would save an average of 60 b.p. of CET1 by Q2 2022. - Reduction in shareholder payouts helps preserve capital in Baseline and Adverse scenarios while allowing banks to continue extending credit. - Higher retention of earnings or temporary moratorium on shareholder payouts recommended to ensure preparedness for longer duration crisis and loan portfolio growth. ### Conclusions on solvency results - Banking system has solid capital buffers; system-wide capital shortfall is relatively small across scenarios. - Range of potential capital shortfall (as percent of GDP) across scenarios: - 0.52 percent of GDP in Adverse Sensitivity Scenario 1 - up to 0.81 percent of GDP in the most severe double-recession scenario (10 banks, all non-GSIBs, below CET1 minima) - Increase in dividend payouts, share buybacks, and CET1 reductions in recent years (especially among Non-GSIBs) increases procyclicality of capital planning. - Potential capital shortfall in terms of GDP would be up to 0.2 percentage points higher if banks maintain average dividend payout ratios of 40 percent. - Even under adverse scenarios, most banks earn enough interest income to offset credit risk related losses; major adjustment channel is reduction in non-interest administrative costs (e.g., staffing, salaries, bonuses). - Leverage requirements become more binding during stress; absent temporary supplementary leverage relief, affected banks would need to: retain higher share of profit, raise additional equity, or shrink balance sheets — actions that may induce system-wide effects (asset fire sales, funding liquidity shocks). ### Liquidity risk analysis and stress testing overview - Objective: assess banks’ ability to sustain severe funding shocks and continue providing liquidity to customers over a 30-day horizon using cash flow-based stress tests and structural ratios (Basel III LCR, funding concentration). - HQLA composition: nearly three-quarters of HQLA consists of highly liquid assets (such as Treasury securities); much HQLA comprised of Treasury securities, cash and central bank reserves. - Non-GSIBs hold the smallest relative amount of highly liquid assets; market trading banks have the highest share of HQLA. - Funding structure: - Reliance on domestic deposits is predominant for most domestic banks (G-SIBs and Non-GSIBs). - Market trading banks and majority of foreign-owned banks often depend on repo markets and secured funding. - COVID-19 crisis and Fed market support led to a US$1 trillion increase in bank deposits by households and corporates (as of Q1 2020). - Structural liquidity risks: - All banks in the sample have LCR ratios above 100 percent; some benefitted from a 70 percent outflow multiplier applied to smaller banks (mostly Non-GSIBs). - G-SIBs tend to have marginally lower LCR due to maturity mismatch add-on and higher assumed outflows (absence of 70 percent multiplier). - Maturity mismatch add-on applied to selected banks (17 out of 33) requires additional liquid assets equivalent to 1 percent of total liabilities on average. - Non-GSIBs have considerable structural liquidity risks: large contractual funding gaps and largest off-balance sheet committed facilities coupled with lowest expected utilization rates. - Excluding off-balance sheet commitments, 30 day contractual funding gap is on average 50 percent of total assets. - Overall, 30 day funding gap is close to 40 percent of total assets; non-GSIBs have highest unsecured funding gap, largely sourced from stable retail deposits which are typically insured and sticky. - Funding concentration and channels of potential risk: - Foreign banks rely substantially on secured lending inflows and are vulnerable to disruptions in secured funding markets. - Federal Home Loan Banks (FHLBs) are an important short-term wholesale funding provider; potential withdrawal of funding from FHLBs could reduce funding to commercial banks, though IMF analysis using supervisory data found potential liquidity squeeze due to withdrawal from FHLBs for the six largest G-SIBs to be insignificant. - Liquidity stress tests include scenarios such as closure of repo markets for non-HQLA collateral (results discussed in Liquidity Stress Testing section). *Source: IMF staff estimates and analysis as presented in the provided content.* ### 94. The gap between contractual inflows and outflows at the 30-day horizon excluding ### 1usaea2020007 - 94. The gap between contractual inflows and outflows at the 30-day horizon excluding ### Contractual funding gap and off-balance sheet exposures - The contractual funding gap at the 30-day horizon (excluding retail and operational deposits) is high and is mostly driven by off-balance sheet financing facilities. - On-balance sheet net cash and securities flows (including retail, sight and operational deposits) over a 30-day period constitute on average around half of banks’ balance sheet. - Off balance sheet credit and liquidity facilities constitute another 25 percent of Total Assets (TA). - Most off-balance sheet commitments are credit lines to corporations and households (credit cards), loans to other financial institutions, including mutual, investment, hedge funds. - Banks can estimate expected utilization of such lines for LCR purposes, creating discretion in assumed outflow rates. ### Evidence from past crises and utilization risk - Historical evidence (GFC and COVID-19 stress period) indicates that in stress more counterparties utilize revolving credit facilities and credit card accounts as loans of last resort. - Banks with a low historical utilization rate of credit and liquidity facilities may underestimate liquidity risks during stress (higher outflows than expected). - Many facilities are committed but may have covenants or be uncommitted (bank can cancel). Collective cancellation could raise corporate default rates. - LCR disclosure data indicate some Non-GSIBs have a high share of credit and liquidity facilities coupled with low expected utilization rates. ### Liquidity stress testing methodology - Cash flow tests were conducted over a wide range of scenarios and used public LCR cash flows disclosure templates to calculate system-wide average inflow/outflow parameters and haircuts on HQLA. - Haircuts on liquid assets were calibrated using non-linear estimation techniques (e.g., Markov regime switching models) and based on collective amounts sold; haircuts are much higher during market turmoil. - Key stress assumptions include wholesale funding shock, high utilization of credit lines, asset fire-sales, and partial closure of repo markets. - Stand-alone liquidity stress tests do not simulate redistribution of liquidity within the banking system (no second-round inflow increases from withdrawal at other banks). ### Key stress test scenarios and parameter assumptions - Scenario A: Gradual increase in utilization ratios of credit and liquidity facilities to reflect banks providing liquidity to cash-strapped companies. - Scenario B: Withdrawal of wholesale funding, including partial closure of repo markets. - FRB ran additional tests using IMF scenarios: (i) LCR-based shock with more severe outflow rates on contingent liquidity items; (ii) same as (i) plus partial closure of repo markets (no repos with MBS, agency, corporate securities). ### Liquidity stress test results — system and bank-level outcomes - A gradual increase in utilization of credit and liquidity facilities leads to significant liquidity shortage in several individual banks, but system-wide impact is contained. - Many banks (including G-SIBs) would need additional liquidity to provide funding if drawdowns exceed 30 percent. - Depending on share of committed/uncommitted lines and assumed conversion factors, additional depletion of CET1 ratios would be from 20 (minimum of 5 percent drawdown) to 250 basis points were banks to allow full drawdown of the lines. - The test assumed banks’ inflows/outflows follow LCR rates except for higher standardized outflow (utilization) rates applied for credit and liquidity facilities; full amount of HQLA without haircut is used to cover the gap; test does not distinguish between committed/uncommitted lines. ### G-SIB-specific findings from FRB-IMF analysis - G-SIBs have enough liquidity to withstand severe LCR-like liquidity shock coupled with additional contingent liquidity and committed line outflows over 1-, 5-, and 30-day horizons. - Compared to a 35-bank public LCR sample (where some Non-GSIBs were illiquid), G-SIBs show no gap after HQLA sales and do not need to repo assets; liquidity gap and impact on CET1 are zero across all scenarios and horizons. - Closure of the repo market for non-treasury securities would lead to a small and short-lived cash flow gap in several banks; worst observed cash flow gap before a bank needs to repo treasury securities would be from 0.09 percent of total assets (one day) to 0.27 percent (one week). - This repo-closure gap is fully covered by other inflows on the 30-day horizon (no shortage observed). - Liquidating affected (Treasury) assets in stressed markets would lead to an insignificant 9 basis points decline of CET1 on average (because the assets liquidated are treasury securities). - If repo markets including Treasury securities were completely closed, affected banks would face higher asset liquidation needs (probability assessed as very low). - Withdrawal of funding from FHLBs itself would not lead to a cashflow gap. ### Extreme combined scenarios and thresholds - Closure of the repo market combined with very high outflow rates from credit and liquidity facilities (40 percent and above) could render one G-SIB illiquid under the 30-day horizon. - Under the 30-day scenario, all G-SIBs would retain positive cash flow gap if outflow shock on credit facilities remains below 40 percent. - CET1 capital impairment solely due to liquidation losses is about 4.5 basis points; marked-to-market losses comparable — combined CET1 impact of 9 basis points in the illustrated scenario. ### Interconnectedness, contagion, and network effects - Direct funding and credit contagion among G-SIBs is small; failure of one G-SIB would not directly cause default of another. - Enhanced interconnectedness analysis (two-stage: funding liquidity risk via cash flows; then network contagion) considered unsecured, secured, and contingent exposures among six largest G-SIBs. - Asset fire-sales and network contagion could increase CET1 losses: proportional liquidation (slicing) yields CET1 impact of about 9 basis points for affected banks; waterfall liquidation (sell most liquid first) yields higher network contagion impact up to 25 basis points of CET1 loss — still with no bank becoming insolvent in the modeled cases. - Overall “liquidity pipeline” risk is high because banks provide liquidity to financial and nonfinancial customers that cannot access central bank facilities; failure to grant credit lines would create non-linear feedback effects and further corporate defaults. *Source: 1usaea2020007 (IMF staff estimates and FRB calculations).* ### 107. Network analysis in this section quantifies the potential spillovers ### 107. Network analysis in this section quantifies the potential spillovers ### Overview and scope - Network analysis quantifies the potential spillovers between the U.S. banking system and large counterparty banking systems through several types of exposures. - Spillovers here refer to the capital impairment incurred by an entity or a system due to distress in another banking system. - Analysis is based on BIS consolidated and locational statistics (both residency and nationality basis) and uses the 10 largest counterparty banking systems for which bilateral consolidated and locational interbank exposures are available. - Three exposure definitions are used: - Exposure Type 1: Spillovers between U.S.-owned and Foreign-owned Banking Systems — banking system defined at parent country level (without intra-group operations); against counterparty banking systems on ultimate counterparty/guarantor residency. - Exposure Type 2: Banking system defined at parent country level: exposures reorganized to include exposures of branches and subsidiaries domiciled outside the parent country along with the parent country exposures; against counterparty banking systems based on counterparty residency. - Exposure Type 3: Based on residency-based exposures, where foreign branches and subsidiaries are included under the host banking system; banking system defined at host country level; against counterparty banking systems based on counterparty residency. ### Scenario and simulation assumptions - The scenario considers the effect of a severe credit shock combined with a funding shock. - Assumptions: - 100 percent of the interbank lending provided to the banking system in distress is not recovered (loss-given-default parameter is 1.0). - Assets are liquidated to meet the funding gap at a 50 percent discount during an asset fire sale. - Banking systems will be able to roll-over 65 percent of the lost funding through other means such as raising equity (therefore, the share of lost funding that is not recovered is 0.35). - For a sensitivity analysis, a less severe scenario assumes: - loss-given-default rate of 50 percent, - banking systems roll-over 90 percent of lost funding (share not recovered = .1), - assets are liquidated at a 10 percent haircut. ### Key findings — Inward spillovers into the U.S. banking system - Inward spillovers into the U.S. banking system from other large counterparty banking systems are limited on average. - Concentration: sources of potential vulnerabilities are concentrated in a few large foreign banking systems such as the United Kingdom and Japan; at a much lower intensity, vulnerabilities may also arise from Canada, Germany, and France. - Potential inward spillovers into the U.S. banking system range between 2–8 percent of initial regulatory capital of the U.S. banking system depending on the type of exposure. - This capital impairment is transmitted mostly via the credit channel (direct and second-round credit exposures). - Incorporating funding shocks increases inward spillovers into the U.S. banking system by one-third. ### Key findings — U.S. banking system as a source of contagion (outward spillovers) - The U.S. banking system has the potential to be a source of significant contagion to large banking counterparts. - A credit shock originating in the U.S. banking system results in an impairment of about 10 percent of the initial regulatory capital of recipient foreign banking systems on average. - Additional amplification through the funding channel amounts to about one tenth of the capital impairment from the credit shock. - Contagion effects vary by exposure definition: - Average impairment stemming from contagion risk to foreign-owned banks could increase from 10 percent of capital to 40 percent of initial capital when contagion risks to host banking systems is considered. - Contagion risks are concentrated: - Canadian-owned, Japanese-owned, and French-owned banks (latter to a lesser intensity) have the highest potential contagion risks due to a hypothetical systemic shock to U.S.-owned banks. - When foreign banking systems including branch and subsidiary operations are considered, the U.K. banking system has the highest potential for contagion. - Host banking systems in Canada, the United Kingdom, and Japan (France and Germany to a lesser extent) have large potential for contagion, emphasizing cascading effects via branch and subsidiary operations. - Belgium also has large potential contagion risks owing to cross-border dollar funding activities conducted through Belgium banks’ foreign operations. ### Sensitivity analysis results - Under the less severe scenario (LGD = 50 percent; roll-over = 90 percent; haircut = 10 percent): - Foreign banking systems would lose about 5 percent to 15 percent of their initial regulatory capital due to contagion from the U.S. banking system. - This is about a one-half reduction in losses compared to the severe scenario. - Contagion remains larger in the U.K., Japan, and Canada. - Potential capital impairment of the U.S. banking system is about one-half lower in this scenario (ranges from 1–3 percent of initial capital of the U.S. banking system). ### Bank-level (BHC-level) contagion analysis — scope, caveats, and findings - Separate simulation uses publicly available data on cross-border claims against foreign banking systems (data from FFIEC 009a report). - Caveats: - Analysis captures only first-round direct exposures; absence of liabilities-side data and bilateral bank-to-bank exposures means cascading effects are not captured. - Simulations limited to a credit shock scenario; assumes 25 percent of the credit provided to the foreign banking systems is not recovered (LGD = 25 percent). - Findings: - Some concentrated inward spillovers due to hypothetical distress in foreign banking systems transmitted through a credit shock, though spillovers are modest on average. - Domestic G-SIBs have particularly larger direct inward spillovers emanating from the Japanese banking system. - On average, potential capital impairment incurred solely due to direct exposures by a G-SIB is about 1.2 percent of their initial regulatory capital. - Other domestically owned BHCs on average have potential capital impairment of less than 1 percent of their initial capital. - Intermediate holding companies (IHCs) on average are susceptible to potential inward spillovers due to direct exposures at about 1 percent of initial capital. - These potential spillovers could amplify in the presence of large counterparty exposures vis-à-vis other banks in the network; significant concentrations exist among IHCs against potential spillovers emanating from their parent banking systems. ### Liquidity stress testing for U.S. mutual funds — objectives and sample - Objective: assess whether mutual funds can withstand severe but plausible redemption shocks, identify which types of funds are potentially more vulnerable to liquidity risk, and estimate the extent to which funds can transmit shocks to the financial system. - Emphasis on fixed income mutual funds. - Sample: 2,743 funds for a net asset value of about US$6.4 trillion as of end-2019, covering the entire mutual fund universe tracked by the Investment Company Institute. - Sample subdivided into nine categories: IG corporates, HY corporates, loan funds, global bond funds, EM funds, government bond funds, municipal bond funds, mixed funds, and multi-strategy funds. ### Mutual funds stress test methodology — calibration and assumptions - Stress tests built on three pillars: calibration of the redemption shock, composition of asset sales, and price impact of sales. - Managers compare level of redemptions with level of highly liquid assets; managers liquidate assets according to a liquidation strategy. - Sales have negative price impact depending on amounts sold and market absorption capacity. - Two approaches to calibrate redemption shocks: - Historical approach (homogeneity assumption): within each of nine fund categories, funds face the same redemption shock calibrated as the average of the worst 3 percent net flows observed by funds in the category. - Resulting levels of redemptions range from 7 percent of NAV for municipal bond funds to more than 15 percent for HY and EM bond funds. - Historical approach (heterogeneity assumption): each single fund faces an idiosyncratic shock based on its own historical net flows. - Adverse scenario approach: uses the banking sector adverse scenario to estimate redemption shocks via projected returns and flow-return relationship. - Under the adverse scenario, most funds face levels of redemption below 3 percent. - Under the adverse scenario, all funds face redemption shocks at the same time, which could result in a large amount of forced sales. - Robustness: other thresholds and methods are used resulting in twelve different redemption shocks. ### Mutual funds — liquidity demands from derivatives exposures - Variation margins on swap and forward exposures estimated for a sample of 10 funds with large derivative exposures totaling US$41 billion in total assets. - Assumed shocks: 50 basis points interest rate increase and a 1 percent depreciation of the U.S. dollar against all currencies. - Variation margins calculated using duration of each instrument based on SEC form N-PORT as of end-2019. - Assumption: variation margins can only be paid in cash. ### Sample statistics (selected) - Net asset Value (US $ bn) and Number of funds by category (sample totals): - Corp. IG: 2,427; 608 - Mixed funds: 1,752; 792 - Municipal: 799; 567 - Multisector: 432; 182 - Government: 326; 161 - Corp. HY: 257; 192 - Global: 247; 87 - Loan funds: 91; 58 - EM funds: 66; 96 - Total: 6,398; 2,743 - Historical approach — redemption shocks by fund category (returns, flow sensitivity to returns, net flows; median net flows under heterogeneity approach): - Municipal: Returns 6.8; 5.8; Flow sensitivity to returns -1%; Net flows 0.5; 0.7 - Mixed funds: Returns 8.6; 6.7; Flow sensitivity to returns -10%; Net flows 0.3; 2.8 - Corp. IG: Returns 12.9; 10.3; Flow sensitivity to returns -2%; Net flows 0.7; 1.1 - Multisector: Returns 13.2; 9.9; Flow sensitivity to returns -3%; Net flows 0.6; 0.9 - Loan funds: Returns 13.3; 12.3; Flow sensitivity to returns -1%; Net flows 0.9; 0.8 - Global: Returns 14.3; 11.8; Flow sensitivity to returns -3%; Net flows 0.3; 0.6 - Government: Returns 14.4; 11.3; Flow sensitivity to returns 1%; Net flows 0.3; -0.5 - HY: Returns 15.0; 11.8; Flow sensitivity to returns -7%; Net flows 0.6; 4.8 - EM funds: Returns 17.5; 13.8; Flow sensitivity to returns -11%; Net flows 0.9; 10.3 - Adverse scenario: redemption shocks in % of NAV. Median net flows under the heterogeneity approach; positive values indicate outflows. *Source: IMF staff.* ### 121. Redemptions are compared to funds’ holdings of highly liquid assets to assess their ### Redemptions are compared to funds’ holdings of highly liquid assets to assess their ability to withstand shocks ### Redemption coverage ratio and liquidity measurement - Redemption Coverage Ratio (RCR) is defined as Highly liquid assets to redemption, both in percent of NAV. - Highly liquid assets are estimated at fund-level using the composition of the portfolio and applying liquidity weights derived from the Basel III framework for the calculation of High-Quality Liquid Assets (HQLAs). - When funds have an RCR below one, a liquidity shortfall is computed as the difference between the redemption shock and the available highly liquid assets. ### Fund liquidation approaches (assumptions) - Vertical slicing (pro rata): sell each asset class in proportion to its weight in the fund’s portfolio. - Waterfall: sell most liquid assets first. - Mixed approach: use cash first and then follow a slicing approach. ### Market liquidity and price impact methodology - Price impact is estimated by comparing sale volumes to market depth. - Market depth is positively related to the ratio of average daily trading volumes to asset volatility. - Market depth is measured under average trading conditions and during stress periods. ### Main results: ability to withstand severe redemption shocks - Under the historical approach nearly all funds would be able to withstand severe redemptions, with the exception of high yield (HY) and loan mutual funds. - Overall, more than 90 percent of the funds would have enough highly liquid assets to meet investors’ redemptions. - Most funds exposed to HY and leveraged loans would not have enough highly liquid assets and would need to sell liquid securities in their portfolio, assuming they do not use any liquidity risk management tools. - The result remains valid when shocks are calibrated at category level (homogeneity) and at fund-level (heterogeneity). - Under the adverse scenario (very mild shock): less than 3 percent of NAV for most funds. - Only 1 percent of funds would not have enough highly liquid assets to meet redemptions, all HY funds. ### Loan funds and credit lines - Loan funds invest mainly in leveraged loans, which are less liquid than corporate bonds and have longer settlement (average of 10 days according to LSTA). - Most loan funds have credit lines in place, usually committed lines from banks and mostly shared with other funds. - Shared credit lines could provide liquidity buffers but create the risk that multiple funds draw on the same line simultaneously. ### Derivatives, variation margins, and liquidity demands - A 1 percent depreciation of the USD and a 50 bp increase in interest rate would cause variation margins on the derivatives portfolio that would range between 3 percent and 10 percent of the NAV. - For several funds the variation margins would be more than 50 percent of available cash and cash equivalents, depleting funds liquidity buffers. - Results are illustrative: sample is small and focuses on simple derivatives; more complex derivatives (such as swaptions) were not considered. ### Leverage under the SEC proposed absolute VaR approach - Under the SEC proposed rule, synthetic leverage is indirectly limited by a VaR limit of either 150 percent of a reference benchmark or 15 percent of the one-month VaR of a fund (absolute VaR). - For funds using absolute VaR: - For a portfolio with a VaR of 5 percent, the fund could lever up to 3x times to reach the 15 percent VaR limit (5 times for a VaR of 3 percent etc.). - A simulation indicates the 15 percent VaR constraint would be binding only if asset volatility is very high (about 25 percent annualized volatility). - The current proposal includes an exemption allowing some funds to be leveraged up to three times provided that (i) funds disclose in their prospectuses that they are not subject to the proposed limit on fund leverage risk and (ii) funds would be subject to sale practice rules. - Box example: if asset volatility is 4 percent, a fund could lever up to 5.6 times the NAV since the VaR would be 2.7 percent. ### Impact of funds’ forced sales on markets - Under the slicing approach (pro rata sales), mutual funds exposed to less liquid asset classes (EM bonds, HY corporate bonds, leveraged loans) would sell large amounts, creating downward pressure on prices. - Under the waterfall approach (sell most liquid assets first), price impact is more limited but remaining investors end up with less liquid portfolios, amplifying first-mover advantage. - Price impact magnitudes: - Under slicing, the price impact on underlying markets ranges between 50 to 200 basis points in normal times, and between 150 to 700 basis points during stress periods. - Under waterfall, price impact is muted: less than 100 bps under normal conditions and less than 200 bps under stress for most asset classes. ### Adverse scenario and market impacts - Under the adverse scenario and the slicing approach: - Price of HY bonds would decline by more than 300 bps, mainly due to sales from HY funds (220 bps). - IG bond prices would decline by about 120 bps, due to combined selling of IG bond funds (70 bps) and mixed funds (40 bps). - Impact on sovereign bond market is muted due to limited sales and deeper liquidity. ### Second-round effects - Asset sales reduce fund returns, causing additional outflows. - Overall, second-round effects are limited under normal trading conditions. - Under stress conditions, additional outflows could be above 3 percent of NAV for IG, HY and loan funds. - The higher second-round effect for IG corporate bond funds is explained by the large size of IG corporate bond funds (US$2,427 billion). ### Vulnerability and interconnectedness findings - Vulnerable funds: funds likely to be in distress when other funds (or the market) are in distress. - EM bond funds are likely to experience large outflows when other fund categories are in distress. - IG corporate bond funds are vulnerable to distress affecting municipal and government bond funds. - Spreader funds (systemic): funds for which other funds are likely to be in distress when the spreader is in distress. - Systemic categories include IG corporate bond funds, multi-strategy bond funds and to a lesser extent municipal bond funds, mixed funds and global funds. - Funds most exposed to liquidity risk (HY, EM and loan funds) are not systemic because distress in these funds tends to induce investor flows into safer funds (government or IG corporate bond funds). - Interconnectedness analysis based on returns: - HY bond funds appear to be net receivers of spillovers and hence more vulnerable. - Government, municipal and corporate bond funds appear to be net senders of spillovers to the rest of the fund industry. ### Market risk stress testing for Money Market Funds (MMFs) - Post-2014 reform: non-government institutional MMFs required to maintain a floating NAV; however, CNAV still dominates the market. - VNAV prime funds account for 15 percent of the industry. - Tax-exempt municipal MMFs account for less than 1 percent. - MMF holdings by type: - Prime MMFs: mainly commercial paper and certificate of deposits. - Tax-exempt MMFs: municipal debt. - Government MMFs: U.S. Agency debt and repo, UST debt and repo. - Treasury MMFs: UST debt and repo. - MMFs exposures and concentrations: - Discount notes issued by FHLB account for 16 percent of U.S. MMFs holdings. - MMFs provide about 60 percent of FHLB funding through those instruments. - Sponsored repos (via FICC) have increased; top three banks account for 50 percent of repos. - SEC rule for CNAV MMFs: must be able to maintain a mark-to-market NAV within 0.5 cent of US$1, i.e., the shadow NAV must not fluctuate by more than 0.5 percent. - MMF stress-test methodology: - Applied to 208 funds. - Two shocks: (i) an increase in interest rates, and (ii) a widening of spreads on non-collateralized instruments held by MMFs. - Interest rate and credit spread shocks are calibrated based on the largest daily increase observed in 2008 and are both equal to 100 bps. - Credit spread is applied to all MMF holdings excluding UST and U.S. agency debt as well as UST and U.S. agencies repo. - Impact measures use duration of each instrument to estimate mark-to-market losses from the shocks. *Source: IMF staff calculations and analysis as presented in the IMF chapter.* ### 142. All U.S. MMFs would be able to withstand large shocks to yields. Under a 100 bps ### All U.S. MMFs would be able to withstand large shocks to yields ### MMF stress test findings - Under a 100 bps interest rate shock, U.S. MMFs NAV would not fluctuate more than 0.16 percent, due to the low duration of MMF portfolio (about 0.1 year on average). - Under a combined interest rate and spread shock of 100 bps each, prime retail funds would see their NAV fall up to 0.27 percent, within the allowable range. - Reverse stress tests estimate the interest rate shock required to produce a 0.5 percent deviation from US$1: - Interest rates would need to rise by more than 600 bps on average to produce such a deviation. - For MMFs with the highest duration, the required shock would be about 350 bps. ### Liquidity risk for Prime MMFs (observations) - Liquidity risk was not assessed for Prime MMFs (Institutional prime MMFs which do not use constant net asset value). - During early March 2020, institutional prime MMFs experienced very large outflows while facing challenges selling assets due to strains in short term money markets. - As a result of combined asset- and liability-side stresses, some prime MMFs received sponsor support in March 2020; two affiliate banks purchased assets from the MMFs to improve their liquidity position. - After multiple government agency steps to support the economy, liquidity stress receded and prime MMFs had inflows starting early April 2020. ### United States: Results of the MMF Stress Test (key statistics from Table 2) - Total size: 3,812 (USD bn); No. of funds: 208 - Aggregate averages and maxima for impact on NAV: - Average impact under 100 bps shock: 0.09% - Max impact under 100 bps shock: 0.16% - Average impact under 100 bps shock + 100 bps spread: 0.11% - Max impact under 100 bps shock + 100 bps spread: 0.27% - Reverse stress test (bps) average: 319; min: 60; max: 660 - By fund type (Size in USD bn; No. of funds; Average/Max impacts under shocks; Reverse stress test bps): - Treasury Retail: Size 91; No. of funds 140; 100 bps shock Average 0.10%; Max 0.14%; 100 bps shock+100 bps spread Average 0.10%; Max 0.14%; Reverse stress test bps 353 / 551 / 1 (Min/Average/Max ordering in table) - Prime retail: Size 460; No. of funds 270; 100 bps shock Average 0.09%; Max 0.14%; 100 bps shock+100 bps spread Average 0.17%; Max 0.27%; Reverse stress test bps 364 / 622 / 2 - Prime instit.: Size 605; No. of funds 380; 100 bps shock Average 0.08%; Max 0.11%; 100 bps shock+100 bps spread Average 0.15%; Max 0.22%; Reverse stress test bps 444 / 675 / 0 - Gov. retail: Size 627; No. of funds 370; 100 bps shock Average 0.08%; Max 0.15%; 100 bps shock+100 bps spread Average 0.08%; Max 0.15%; Reverse stress test bps 326 / 710 / 0 - Treasury Instit.: Size 781; No. of funds 400; 100 bps shock Average 0.10%; Max 0.16%; 100 bps shock+100 bps spread Average 0.10%; Max 0.16%; Reverse stress test bps 319 / 560 / 0 - Gov. Instit.: Size 1,248; No. of funds 520; 100 bps shock Average 0.09%; Max 0.15%; 100 bps shock+100 bps spread Average 0.09%; Max 0.15%; Reverse stress test bps 329 / 742 / 0 - Sources: CRANE, IMF staff - Note: Impact of a 100 bps interest rate shock on the NAV of the MMF and impact of a combined 100 bps interest rate shock and 100 bps increase in yields on uncollateralized instruments. *Sources: CRANE, IMF staff; content excerpted from the IMF FSAP document.* --- ### The insurance solvency stress tests (overview and main findings) ### Objective and tools - Objective: To quantify risks to the insurance sector using scenario analyses, sensitivity tests, and exposure analyses. - Additional insurance-specific sensitivity analyses included: - (i) a prolonged period of low interest rates; - (ii) a lapse and surrender event with liquidity outflows from the life insurance sector; - (iii) the default of the largest banking counterparty; and - (iv) a stock-take on exposures to carbon-intense sectors. - The results of these analyses are not added to the outcome of the main stress scenario, although materialization could coincide with (i) or (ii), or both. ### Valuation and accounting context - Statutory accounting and U.S. GAAP do not require a fully market-consistent valuation of assets and liabilities. - Under statutory accounting: - Liabilities of P&C insurers are generally not discounted. - Life insurance liabilities are discounted with a rate set when the policy is sold or based on expected return of associated assets, resulting in average discount rates above current market rates. - Amortized cost is the predominant regime for fixed income assets; unrealized gains or losses are not recognized. - Impairment rules differ from market-consistent regimes: investment assets are impaired only when fair value loss is deemed other than temporary; impaired bonds cannot be written back up after recovery. ### Sample and scope - Stress test sample: 50 insurance groups (21 groups predominantly active in life insurance; 22 in Property & Casualty; 7 health insurers with market share of about 45 percent). - Sub-samples: life insurers with a high share in VA business (6 companies) and foreign insurers (6). - Cut-off date for analyses: December 31, 2018. ### Adverse scenario and market risk parameters - Scenario builds on the narrative and severity of the banking sector stress test; focus is on investment assets. - Market risk stresses include shocks to bond holdings (sovereigns, municipals, corporates), securitizations, equity, property, and other investments (hedge funds, private equity). All stresses are assumed to occur instantaneously. - Some shocks were slightly adjusted for operational applicability at end-2018 (e.g., equity shock re-calibrated downward; yield increases on fixed-income instruments slightly lower than bank trading book shocks). - Table 3 indicates impairment/default and yield increase parameters by instrument and NAIC bucket (values as presented in source). ### Modeling assumptions - Data sources: publicly available consolidated regulatory returns and NAIC asset-by-asset data (Schedule D). - Modeling steps included: - Mark-to-market impairment for holdings in equity, fixed-income instruments below investment grade, and other investment assets (Schedule BA assets). - Default losses in the corporate bond and securitizations portfolio as well as on mortgage loans. - The U.S. regulatory framework does not include a consolidated group capital requirement; stress test produces balance sheet impact as reduction in statutory capital. - No mitigating effects from hedging, profit-sharing, or management actions were modeled (stress test likely gives a maximum impact). ### COVID-19 timing note - The stress test was performed before the COVID-19 outbreak started; a Box summarizes COVID-19 impacts on the insurance industry (claims, operational challenges, lower investment returns, reinvestment risk, liquidity pressures, and legal/regulatory risks). ### Stress test results (aggregate) - In the adverse scenario, the life sector experiences a substantial hit on its statutory capital but is largely shielded by the current valuation and capital framework. - Aggregated reduction in statutory capital: US$226 billion, which equals: - 30.9 percent of the sample’s statutory capital; - 3.9 percent of consolidated balance sheet assets; - 1.1 percent of the U.S. GDP. - Many balance sheet items are not sensitive to market price fluctuations under statutory accounting; results would differ under a fully market-consistent valuation. - Absence of a group capital framework in the U.S. complicates mapping to a risk-based solvency regime. Recent solvency ratios of U.S. solo companies tended to be well above regulatory thresholds; based on current calibration of the risk-based capital, it can be expected that even in this adverse scenario the vast majority would still continue to be adequately capitalized. *IMF staff analysis and figures as presented in the source document.* ### 159. Stress test results are very heterogenous in the life and P&C sector. In the life sector, ### 1usaea2020007 - 159. ### Stress test results: heterogeneity across insurance sectors - Life sector: capital declines by US$74.3 billion (-35.7 percent). - P&C sector: capital declines by US$149.8 billion (-31.7 percent). - Health sector: capital declines by US$1.4 billion (-2.8 percent). - Distribution within sectors: - Life (21 groups): decline ranges from 14 to more than 60 percent; median decline 32 percent; 50 percent of companies range between -21 and -36 percent. - P&C: median company loses 19 percent of capital; sample range from -5 to -53 percent. - Health: median company capital declines by 1 percent; most affected company declines by 8 percent. - Note: Some groups active in both life and non-life businesses explain outliers across sectors. ### Sources of capital reduction by shock - Life sector: shock to other investment assets (Schedule BA) contributes most to overall reduction in statutory capital. - P&C sector: stock price decline more relevant on aggregate, but sector-wide impact somewhat overstated because equity exposures are small for most P&C firms and driven by a very small number of larger companies with sizable investments. - Mortgage defaults: contribute 10 percent of the overall impact in the life sector; companies in P&C and health barely engage in such lending activities. ### Sensitivity analysis — Low-for-Long (life sector) - Historical net investment spread (2016–18): averaged 1.1 percent. - Projection assumptions: - About 7 percent of bond investments roll over every year. - Net portfolio yield likely to decline at a slightly higher pace than the guaranteed interest rate. - Projection outcome: - By 2021, the net investment spread could drop below 1 percent, only slightly above the recent minimum of 0.93 percent observed in 2017. ### Sensitivity analysis — Lapse/Surrender event (life sector) - Termination rates historically: hovering about 6 percent recently, with no significant difference between individual lines and group business. - Sample: same 21 life insurance groups as the macrofinancial scenario stress test. - Scenario 1 assumptions: - Lapse rates for life policies redeemable at book value without adjustment would double compared to 2018 rates. - Policies with surrender charge of at least 5 percent: lapse rates increase by 50 percent. - Alternative scenarios modeled include: - Scenario 2: lapse shocks correspond to maximum termination rate observed 2009–2018 and the 75th percentile. - Scenario 3: increase of lapse rate by two standard deviations and one standard deviation, respectively. - Scenario 4: lapse rate equal to 200 and 150 percent of each company’s mean lapse rate between 2009 to 2018. - Assumed insurer reaction to net cash outflows: - Liquidate investment assets via a waterfall: sell up to 50 percent of U.S. Treasury bonds, then U.S. GSE issues and municipals, followed by corporate bonds starting with NAIC 1 rating category. - Cash positions and deposits kept unchanged. - Estimated aggregate liquidations across scenarios: - U.S. Treasury bond sales range from US$23–33 billion. - U.S. GSEs sales range from US$13–15 billion. - Corporate bonds sales range from US$4–11 billion. - Firm-level outcomes: - Rough clustering into three groups: - ~one third can meet outflows by selling portions of U.S. Treasury bonds. - Another group sells U.S. Treasuries up to 50 percent cap and some U.S. GSEs. - Another four to six companies (depending on scenario) would need to liquidate corporate bond portfolios, potentially at a discount due to restricted liquidity. ### Vulnerabilities to weather-related catastrophes (P&C sector) - Relevant vulnerable lines by gross premiums: homeowners’ multiple peril (most relevant), commercial multiple peril, allied lines. - Loss ratio volatility: allied lines exhibit highest volatility with peaks above 150 percent in 2005 and 2017. - Samples used: - (a) 538 solo entities part of 22 large and diversified P&C groups (stress-test sample). - (b) 44 small, regionally concentrated P&C insurers focused in nine Southeastern jurisdictions. - Modeling approach: - One-off effect of a very severe event simultaneously hitting all “vulnerable” states equally (instead of modeling increased frequency). - Analysis does not model cumulative series of events, physical damage to insurers’ own properties, or second-round economic effects. - Use of insurers’ own hurricane impact estimates: insurers report impacts at 1-in-50, 1-in-100, 1-in-250, and 1-in-500-year occurrence probabilities; underlying assumptions vary across firms. - Aggregate NAIC-provided statistics include RBC coverage ratios before and after stress, and available capital with and without reinsurance recoverables. ### Findings on hurricane impacts - Large and diversified P&C insurers: - A simultaneous 1-in-50-year event: 8.1 percent reduction of available capital (gross); 3.9 percent reduction (net, after recoverables). - For 1-in-250 and 1-in-500-year events: net capital impacts increase to 5.8 percent and 8.6 percent, respectively. - In the 1-in-250-year event, four out of 538 insurers would drop below the regulatory minimum (triggering supervisory action). - Small and regionally concentrated P&C insurers: - 1-in-50-year event: available capital declines by 81.4 percent (gross); net impact after reinsurance recoverables is 5.7 percent. - 1-in-250-year event: net impact on available capital is 58.6 percent. - 1-in-500-year event: net impact on available capital is 164.3 percent. - In the 1-in-250-year event, ten out of 44 companies would record a shortfall in capital adequacy. - Policyholder protection: - Insurance guarantee funds and, where present, dedicated catastrophe funds (funded by the P&C insurance sector) would ensure policyholder claims are paid, though delays and disruptions in policy renewals could occur and require close supervisory attention. *Source: IMF staff calculations based on NAIC data.* ### 178. A further asset-side shock centered around a default of the largest banking ### A further asset-side shock centered around a default of the largest banking counterparty ### Asset-side shock: scope and limitations - The analysis considered a further asset-side shock centered on a default of the largest banking counterparty for each insurance company in the sample. - The scenario is not homogenous because the largest banking counterparty differs for each insurer. - The analysis shows only the direct impact of one isolated default; second-round effects or a more widespread contagion after a major bank’s default are not factored in. ### Sample and data - Sensitivity analysis performed for the 50 insurance groups which were also subject to the macrofinancial stress test. - NAIC provided detailed asset exposures based on Schedule D, enabling identification of equity and bond exposures towards individual banks. ### Shock assumptions (haircuts by exposure type) - Equity exposures: assumed to lose their entire value (100 percent haircut). - Unsecured bond exposures: LGD assumed to be 50 percent. - Subordinated bonds: assumed to suffer a 100 percent haircut. - Secured bonds: market value assumed to decline by 15 percent. ### Direct capital impact on insurers - For the vast majority of insurance companies, the default of the largest banking counterparty has only a minor direct impact on the capital position. - Life sector: - Median life company would lose 1.2 percent of its capital. - Half of the life sector’s firms range between 0.8 and 1.8 percent (interquartile range). - P&C and health sectors: - Median insurer would experience a 0.5 percent loss in capital (both sectors). - Outliers: - Some insurers exhibit much higher losses, particularly where an insurer holds a participation in a large bank. - Figure 49 (text summary): reduction in statutory capital ranges shown with Min-Max, Interquartile range, Median, Mean (visual data implied but numeric specifics preserved above). ### Exposure analysis: carbon-intense investments (stock-take) - The FSAP undertook a stock-take of carbon-intense investments of the U.S. insurance sector without assuming a change in pricing of carbon-intense assets. - Identification method: - Used the “FFI The Carbon Underground Top 200 List” to identify companies by carbon footprint (largely fossil fuel producers and mining companies). - Matched bonds and shares issued by those companies against insurers’ holdings reported in Schedule D. - Included additional securities when the issuer name included terms like “oil”, “coal”, or “petroleum”. ### Size and share of identified carbon-intense assets - Carbon-intense bond exposures: 3.5 percent of all corporate bond exposures. - Carbon-intense equity exposures: 1.6 percent of the equity portfolio. - Identified carbon investments account for 1.1 percent of the sample’s total balance sheet assets. - Caveat: other transition risks may exist in sectors not included due to missing data on direct or indirect carbon emissions. ### Transition and repricing risks beyond direct carbon exposures - Repricing risks could affect mortgage loans or mortgage-backed securities with collateral in areas more frequently hit by windstorms, floods, or wildfires. - Municipal bonds in affected regions or debt issued by local utility providers could also be repriced. ### Systemic risk, interconnectedness, and contagion analysis: scope - The systemic risk and interconnectedness analysis complements balance sheet stress tests by assessing transmission of risks across the financial system. - Two complementary analyses: - Contagion analysis between banks, non-banks, and nonfinancial companies to account for direct and indirect cross-sectoral interconnectedness. - Market-based analysis to capture correlation and co-movement patterns in financial asset prices across institutions and sectors. ### Contagion between banks, non-banks, and nonfinancial corporates: overview - Analysis centers on assessing resilience of banks and non-banks when facing potential stress in the corporate sector. - Rising vulnerabilities identified in the U.S. corporate sector, notably among highly leveraged firms. - Focus on the impact of corporate sector stress on banks and non-bank financial institutions, including their reaction to stress. ### Shock transmission modes and scenario - Two transmission modes: - Instant shock (e.g., simultaneous credit rating downgrades) leading to fire-sales. - Gradual adjustment via portfolio rebalancing leading to less severe effects. - The analysis focuses on an instant market shock lasting for one month leading to immediate asset liquidation. ### Exposures and transmission channels - Direct exposures to the U.S. corporate sector are concentrated among non-banks. - Financial institution exposures primarily relate to: - Investment in nonfinancial firms’ equities. - Holdings of corporate debt instruments (corporate bonds, commercial paper). - Mutual funds, life insurers, and pension funds: relatively large exposures to corporate securities. - Banks: exposures mainly through bank term loans and revolvers (committed and uncommitted credit lines). - Non-bank financial institutions would suffer larger direct losses in case of corporate-sector stress than banks. - Transmission channels include: - Direct cross-sectoral exposures. - Indirect linkages via similar asset holdings, market reaction, asset liquidation, and mark-to-market losses. - Mutual fund redemptions inducing asset liquidations, causing falling asset prices and mark-to-market losses for other institutions. - Banks potentially facing liquidity pressures from committed credit lines to mutual funds. ### Price impact and potential losses from mutual fund liquidations - Estimated mutual fund asset liquidations following corporate sector stress: about US$97 billion. - Liquidated amounts vary by asset type and fund type, with EM, HY, and EM funds suffering the largest redemption pressures. - Estimated price impacts across asset classes range between 0.002 percent and 4.8 percent (asset-class specific values referenced in Appendix XVII). - Estimated mark-to-market and sector losses under the one-month market sentiment shock: - Mutual funds: liquidation losses amount to about US$0.9 billion (about 1 percent of the original value of assets sold). - Banks: resulting mark-to-market losses close to US$10.8 billion (roughly equivalent to 0.06 percent of total assets). - This would reduce banks’ CET1 ratio by about 0.1 percentage points. - Insurance companies: shock may roughly translate into losses at about 1 percent of insurance sector total assets. - Under a systemic credit risk-related stress (increase of annual default rates of leveraged corporates of up to 6 percent): - Banks would face US$230 billion losses on commercial and industrial loans and CLO holdings. - This scenario assumes banks would provide at least part of their US$760 billion committed lines to corporates. - Five Category IV banks (Non-GSIBS) would require recapitalization. - Insurance sector would be hit by around US$300 billion losses, noting insurers do not have to mark these to market given existing regulatory asset valuation treatments. ### Caveats and nonlinearities - Contagion dynamics are highly nonlinear and difficult to quantify. - Price impact estimation relies on assumptions about market absorption capacity based on historical patterns. - Liquidation strategies and willingness to cut credit lines during stress are difficult to predict. - Insurance companies do not need to actively mark-to-market all assets; losses would mainly materialize upon disposal. ### Stress losses and mispricing risks (structured products) - Systematic mispricing of risk can compound stress losses (lesson from global financial crisis where default correlation underestimation amplified losses). - The analysis estimates default correlations based on realized defaults and compares them to correlations implicitly used in CLO tranche ratings (Box 3). - Estimated default correlations are used to adjust CLO tranche ratings and reprice holdings across financial institutions. ### Box 3 summary: CLO tranches, pricing of risk, and implications - CLOs have increased in popularity; more than half of leveraged loan issuance ends up in CLOs. - CLOs are held by a wide range of investors; banks tend to hold mainly triple-A-rated CLO tranches, while non-bank investors hold lower-rated tranches. - Default correlations are key for pricing CLO tranche risk; lower-quality ratings exhibit higher default correlations. - Default correlations were estimated using realized defaults following methodology in Lucas (1995) and related literature. - Applying estimated default correlations to adjust CLO tranche ratings allows computation of potential equity losses across financial institutions based on reported CLO holdings. *Source: IMF staff calculations based on NAIC data and FSAP analysis (content unit: 1usaea2020007).* ### Box 3. CLO Tranches, the Pricing of Risk, and Implications for Financial Institutions ### Box 3. CLO Tranches, the Pricing of Risk, and Implications for Financial Institutions (concluded) ### Estimation results: Default correlations by credit rating - Estimated 1-year ahead default correlations, by credit rating: - A-AAABBBBBBC-CCC - A-AAA0.250.110.240.600.99 - BBB0.110.310.400.591.00 - BB0.240.400.631.292.19 - B0.600.591.292.694.54 - C-CCC0.991.002.194.548.69 - Estimated 2-year ahead default correlations, by credit rating: - A-AAABBBBBBC-CCC - A-AAA0.290.240.661.161.38 - BBB0.240.890.921.371.97 - BB0.660.921.793.003.96 - B1.161.373.004.976.50 - C-CCC1.381.973.966.509.70 - Estimated 5-year ahead default correlations, by credit rating: - A-AAABBBBBBC-CCC - A-AAA0.360.631.351.931.83 - BBB0.631.021.882.862.74 - BB1.351.883.835.625.43 - B1.932.865.628.398.48 - C-CCC1.832.745.438.489.90 - Sources: Caceres et al. (2020b); and IMF staff calculations. ### Complementary market-based contagion analysis (methodology and sample) - Objective: - Assess indirect spillover risks between domestic banking entities and large foreign entities due to co-movement in asset prices using publicly available daily equity price returns. - Methodology: - Uses Diebold and Yilmaz’s (2014) approach to evaluate directional co-movement through equity price returns. - A financial spillover from firm A to firm B is defined as the share of variation in firm B’s equity returns shocks attributable to contemporaneous or preceding shocks to firm A’s equity returns. - Analysis stresses idiosyncratic shocks and excludes co-movement driven by common factors. - VAR estimated using a lasso-estimator (Zou and Hastie, 2005). Estimations control for global conditions using the VIX index. - Model specification (as in text): - 퐴퐴(퐿퐿)푌푌푡푡 + 퐵퐵(퐿퐿)푋푋푡푡 = 휀휀 - 퐷퐷퐻퐻 = [�푙푙푖푖,푗푗퐻퐻�] - 푋푋푡푡 = [푉푉푉푉푋푋, ...] - Y is a vector of equity returns for all firms, X is the VIX, A(L) and B(L) are lag polynomials, ε is an error term, and DH is the H-step ahead generalized forecast error variance decomposition matrix. - Builds generalized forecast-error variance decomposition (GVD) using Pesaran and Shin (1998) to identify uncorrelated structural shocks to FCIs; GVD is order invariant. - Spillover metric: the percent of firm j’s total inward spillovers attributable to firm i (fraction of H-month ahead forecast error variance of firm j’s returns accounted for by innovations in firm i’s returns). - Sample and estimation details: - Sample includes 160 large financial sector entities with asset size above US$100 billion in 20 countries. - Daily equity returns from 2015 through end-2019, estimated as log differences in equity prices. - To control for differences in trading hours across countries, two-day averages are used (Forbes and Rigobon, 2002). - Estimations control for global conditions by using the VIX index. - Several sub-samples assessed separately to explore domestic and cross-border equity return co-movement. ### Market-based results (directional co-movement and spillovers) - Key empirical findings (2015–19, controlling for VIX): - Equity return spillovers are relatively stronger from the U.S. G-SIBs into domestic as well as foreign financial entities. - Inward spillovers from the U.S. G-SIBs into U.S. Non-GSIBs are relatively high. - A few Non-GSIBs with larger credit card operations reveal higher spillovers. - Large U.S. non-bank financial entities are well connected with domestic G-SIBs through equity return co-movement. - On average, spillovers emanating from Non-GSIBs and other U.S. non-bank financial entities are relatively subdued compared to spillovers from U.S. G-SIBs. - Conclusion: strong market-based interconnectedness between G-SIBs and other domestic financial entities suggests value in monitoring market-based as well as exposure-based contagion. - Aggregate relative spillover matrix (Figure 54: Relative Spillovers, 2015–19; From: rows; To: columns): - Header groups: US GSIBs | US non-GSIBs | US Nonbank Financial Sector | Foreign GSIBs | Foreign non-GSIB banks | Foreign Nonbank Financial Sector - US GSIBs0.250.030.030.080.010.02 - US non-GSIBs0.370.050.050.110.020.02 - US Nonbank Financial Sector0.400.050.070.140.020.03 - Foreign GSIBs0.270.030.040.280.040.05 - Foreign non-GSIB banks0.820.100.120.850.150.14 - Foreign Nonbank Financial Sector0.620.080.100.590.080.12 - Cross-border interconnectedness results: - Market-based interconnectedness reveals higher spillovers on average from U.S. G-SIBs to foreign non-GSIB banks compared to spillovers potentially transmitted through other groups. - Spillovers from U.S. G-SIBs into foreign G-SIBs are large, but spillovers into other foreign financial and domestic financial entities on average are relatively higher. - Spillovers from foreign G-SIBs into domestic financial entities are present but at relatively lower intensity than those from domestic G-SIBs. - Network clustering: many U.S. banks are closely connected with major domestic and foreign banks and are centrally clustered (proximity to network center suggests larger co-movement levels). - Overall emphasis: U.S. G-SIBs act as potential risk transmitters; vulnerabilities could emanate into domestic banks from foreign G-SIBs as well. ### Conclusions and policy-relevant findings (paragraphs 203–215) - COVID-19 stress and structural vulnerabilities: - COVID outbreak amplified existing structural vulnerabilities via adverse macroeconomic and financial shocks, exposing highly leveraged borrowers and lenders to default risks. - U.S. regulatory and supervisory agencies took swift actions to mitigate effects on financial markets. - FSAP analysis highlighted changes compared to previous crises, differences in simulated shocks, and remaining structural vulnerabilities. - Household and consumer credit: - Household mortgage debt decreased substantially since the GFC, but other consumer credit forms are rising. - Auto loans now exceed US$1 trillion, and student loan debt is rising. - Consumer credit segments could pressure household debt servicing capacity under tightened financial conditions and high unemployment. - Banks’ stress tests identify credit losses related to consumer credit and mortgages as highest contributors to overall credit losses. - Corporate sector vulnerabilities: - Corporate sector leverage is at its historic peak; leveraged finance was growing rapidly. - Increased risk-taking, weakening underwriting standards, and weaker investor protections have allowed less creditworthy firms to increase leverage via leveraged loans, private loans and CLOs. - Covenant protections weakened and credit quality of new loans deteriorated in light of COVID-19. - Important data gaps exist on direct and indirect exposures to leveraged and private loans across financial subsectors. - Corporate stress tests indicate up to 12 percent of corporates may face financial troubles in a prolonged recession (due to a second wave of COVID-19 infections). - Banking system preparedness and stress test findings: - U.S. banking system entered COVID-19 crisis with strong capital and liquidity buffers. - Banks showed ability to extend credit to the real sector at the crisis outset. - FSAP stress test results are comparable to FRB (DFAST/CCAR); market-data proxies (EDFs) yield only marginally more conservative loan portfolio loss estimates vs accounting data. - CET1 and leverage ratios depend significantly on net income and expense before credit/market risk losses. - Largest loan losses stem from credit card-related net charge-offs, followed by residential real estate and commercial and industrial net charge-offs. - Loan loss provisions contribute only a smaller fraction of CET1 decline. - Smaller domestic banks (Non-GSIBs) face more volatile CET1 and leverage ratios during stress than G-SIBs. - Non-GSIBs rely more on interest income from loans and have higher exposure to credit card and small business loans (highest credit loss rates in adverse scenarios). - G-SIBs and Trading Banks derive a larger revenue share from trading books and are subject to marking-to-market losses and counterparty defaults. - Policy implication: ensure Non-GSIBs have enough capital to support lending and absorb losses—achievable via conservative capital planning and reducing/stopping shareholder payouts during crisis. - Banks’ capital shortfalls are moderate in Baseline and Adverse sensitivity scenarios, ranging from 0.3 to 1.3 percent of GDP. - Liquidity and market functioning: - Banks maintain healthy liquidity buffers and withstood severe funding outflows, though market liquidity for mortgage, corporate, and sovereign bonds dried up. - G-SIBs and Non-GSIBs have large amounts of stable deposit funding; some trading and foreign-owned banks rely on repo markets. - Partial closure of repo market (only Treasury securities accepted as collateral) would not significantly affect G-SIB liquidity except when banks cannot sell large amounts of Treasury securities outright. - The Federal Reserve’s swift intervention mitigated initial market dysfunction. - Importance: ensure all large banks, including Non-GSIBs, have enough liquidity buffers to continue providing funding in stress. - Network contagion and cross-border vulnerabilities: - U.S. banking system’s vulnerability to shocks from other banking systems is relatively contained, but the U.S. plays a larger role as source of contagion. - Network analysis indicates that capturing exposures by consolidation and ownership levels reveals pockets of vulnerability and potential amplification channels. - Market-based equity-return analysis shows U.S. banks are closely connected with large foreign banks and centrally clustered. - Risks migrating to non-bank financial institutions: - Risk migration observed since the GFC: highly leveraged institutions (investment/hedge funds), mortgage lenders, and other non-bank funding providers may face higher liquidity and solvency risks during the COVID-19 downturn. - Mutual funds and insurance sector vulnerabilities: - Mutual funds are highly interconnected with other financial institutions; some fund categories face liquidity mismatches due to investment in less liquid assets while offering daily redemptions—aggregate distress could affect financial stability. - Parts of the insurance sector are vulnerable to severe market shocks and prolonged low interest rates despite some mitigating valuation regime effects. - Analysis complexity arises from absence of a group capital requirement for insurance groups. - FSAP recommendations for insurance sector: - NAIC should develop and perform insurance solvency stress tests on a consolidated basis, in line with forthcoming group capital standards. - Further work on liquidity stress tests for insurers should be pursued. - Public disclosures should be enhanced by requiring insurers to disclose market risk and interest rate sensitivities in a more harmonized manner. - Systemic risk heterogeneity: - Not all institutions are equally resilient: banks generally have adequate capital and liquidity buffers, while some risk takers (certain mutual funds, insurers) are more vulnerable. - A marked rise in corporate stress would impact non-bank financial institutions more severely, with a more moderate impact on banks—though some large domestic banks could be substantially affected. - G-SIBs have substantial liquidity buffers, but high utilization of credit and liquidity facilities could lead to liquidity shortfalls in some banks. - Life insurers would face significant statutory capital reductions in the adverse scenario; a few less-diversified P&C insurers could face capital shortfalls after severe natural disasters. - Most investment funds could withstand severe redemptions, though high yield and loan funds would face significant shortfalls. *Source: IMF staff calculations and analysis as presented in the Box titled "CLO Tranches, the Pricing of Risk, and Implications for Financial Institutions" in the provided FSAP material.* ### 2. Channels of ### 2. Channels of risk propagation ### Methodology: PPNR, credit, market, and fee income projections - FRB uses its own satellite models for: credit risk projections, bank interest rates and net interest margins, market risk, and banks’ fee & commission income. - FRB projects PPNR components using supervisory models that take the FRB’s scenarios and firm-provided data as inputs. - FRB projects the paths of these variables as a function of aggregate macroeconomic variables included in the CCAR scenarios. - FRB calculates projected pre-tax net income by combining projections of revenue, expenses, loan-loss provisions, and other losses. - FSAP team projected pre-tax net income at bank level by combining projections of PPNR components and loan loss provisions derived based on forecasted charge-offs and recoveries (57 separate specifications). - The model’s core set of regressions are used to forecast financial ratios related to pre-provision net revenue (PPNR), returns on AFS securities, and provisions conditional on macroeconomic conditions, lagged value of the ratio, and firm-level controls. - FSAP team forecasted 26 PPNR-related ratios separately: - 8 interest income ratios - 7 interest expense ratios - 8 non-interest income ratios - 3 non-interest expense ratios - FSAP team forecasted charge-offs and recoveries ratios separately for 15 ratios (30 ratios in total) at industry-level. - The autoregressive nature of the specifications implies that the projected ratios will converge to their long-run steady state value. - Solvency–funding cost interaction: bank-specific funding costs (interest expenses) conditional on stressed capital position. ### Tail shocks and scenario design - FRB scenarios are based on FRB policy statement and include inter alia severe tail shocks such as increase in the level of unemployment by at least 4 p.p. up to a level not less than 10 percent. This corresponds to 1-in-100 years scenario. - The adverse scenario features a severe recession that occurs concurrently with significant financial market stress and a sharp housing and equity market correction and is characterized by a slow recovery. Main triggers: deterioration of U.S. corporate debt markets and simultaneous downturns in Europe and China. - The scenario is to a large extent similar to FRB’s severely adverse scenario in terms of severity (less than 1 percent probability of occurrence). - Sensitivity analysis includes: - Default of the largest counterparties - Fintech impact on income and IT expenses - Market shock on corporate loans, including CLOs/LLs - Decreased reliance on FHLB funding ### Risks and buffers: credit, traded risks, market stress, profit/loss recognition - Credit risk: - Estimated according to the FRB’s stress testing framework. - Framework is based upon accounting classification of assets. - CLOs and securitization exposures are included. - Off-balance sheet exposures using baseline and stressed Credit Conversion Factors (CCFs) are included. - Credit risks estimated based on 15 charge-off and recoveries specifications for: first lien and junior lien residential mortgages, home equity lines of credit (HELOC), construction loans, multifamily and non-residential commercial mortgages, credit cards, other consumer loans, commercial and industrial (C&I) loans, loans to foreign governments, loans to depository institutions, agriculture loans, other residential real estate loans, and all other loans. - Traded risks: - Mark-to-market valuation of securities (from shocks to interest rates and credit spreads). - For banks with large trading books, trading book exposures are shocked through Global Market Shocks (GMS) with losses recognized in the first quarter of the planning period. - DFAST applies a largest counterparty default shock (LCPD) to the trading firms and two other firms with substantial process and/or custodial operations. - Realized gains on AFS securities estimated separately following the original CLASS model; realized AFS gains and losses reflect asset price shocks, credit events, behavioral decisions about asset sales, and accounting judgment. - Market stress: - Shocks to risk-free interest rates, exchange rates, credit spreads, commodities, and equity prices are incorporated. - Profit/loss recognition: - Losses/gains are recognized in the same quarter that a shock hit. - Evolution of RWAs: - RWAs for credit risk evolve according to STA approach as well as balance sheet growth requirements embedded into scenario. ### Balance sheet, growth assumptions, and taxes - Balance sheet and RWA projections: - Growth path of assets over the stress testing horizon is used to forecast balance sheet variables and RWAs. - FSAP team uses: (1) long-run historical asset growth; (2) zero growth in balance sheet in the stress testing horizon; (3) forecasting balance sheet growth to reflect dynamics of the nominal GDP path in the scenario. - Interest income from non-defaulting loans is 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. - Net fee and commission income and other income evolve with macroeconomic conditions and banks’ balance sheets. - No change in business models (no rebalancing of portfolio). - Starting in DFAST 2020, projections are based on the assumption that firms’ balance sheets remain unchanged throughout the projection period. - In March 2020, FRB amended stress testing requirements to assume that a firm maintains a constant level of assets over the projection horizon and that a firm will not pay any common dividends or make any issuance of common or preferred stock. - Tax rate: Assumed at 21 percent in the forecasting horizon. - The effective corporate income tax rate is used. ### Regulatory impact and behavioral adjustments - Regulatory impact: - Stress test results are compared against regulatory minima of CET1 and leverage ratios. - G-SIB buffer is included into 4.5 percent CET1 minima and can be depleted. - No conversion of additional Tier 1 capital is assumed during the stress horizon. - If banks’ capital ratio falls below regulatory minimum during the stress test horizon, banks are not able to return funds to shareholders (dividend payments as well as share buybacks). - Stress test results are compared against a hurdle rate of 4.5. - In addition to the hurdle rate, these BHCs are also subjected to a capital conservation buffer (CCB also includes the G-SIBs surcharge in the case of G-SIBs). - Behavioral adjustments: - Dynamic Balance Sheet: In line with 2019 FRB methodology. - FSAP Dynamic Balance Sheet: Balance sheet size is assumed to grow at historical industry growth rate. - Dividend Policy: - FRB: Payout ratio set by 2019 FRB methodology. - FSAP: Payout ratio projected based on a partial adjustment model with a payout rate of 45 percent for a portion of the net income. Specification: (0.9*(previous period’s dividends) + ((1-0.9)*(0.45*net income))). ### Calibration, reporting, and outputs - Calibration of risk parameters: - Parameter definition: Net charge off ratios for credit risk (accounting definition). - Accounting portfolios. - Capital definition according to national implementation of Basel principles, including CET1, Tier 1, leverage ratio and total CAR. - Capital components that are no longer eligible for additional Tier 1 and Tier 2 capital components follow Basel III transitional path. - Reporting format for results (solvency test outputs): - Bank-by-bank: - Minimum CET1, Tier 1, CAR, and leverage ratio - Composition of P&L - Capital ratios - Profitability metrics: ROE; ROA; NII - Contribution of key drivers to aggregate CET1 capital ratios - Number of banks and share of total assets below hurdle rates - Capital shortfall in terms of nominal GDP - System-wide and by groups of banks: - CET1, Tier 1, CAR, and leverage ratio - Distribution of capital ratios (box plots) - Profitability metrics: ROE; ROA; NII *Source: 1usaea2020007 - 2. Channels of risk propagation* ### 2. Channels of risk ### 2. Channels of risk ### Methodology for risk propagation and time horizon - Calibration of redemption shock and comparison to level of highly liquid assets. - Price impact due to asset sales. - Second-round effects based on flow-performance relationship. - Time horizon: Instantaneous shock. ### Tail shocks and scenario analysis - Adverse scenario: same as the banking sector scenario but converted to monthly frequency. - Pure redemption shock: severe outflows based on historical distribution: 3 percent expected shortfall (average of 3 percent worst net flows). ### Risks assessed and buffers - Risks/factors assessed: - Market risk: interest rates, share prices, credit spreads. - Liquidity risk: severe redemption shock. - Buffers: - Level of highly liquid assets. - Behavioral adjustments: - Choice of liquidation strategy used: slicing (prorata), waterfall (most liquid assets first) and mixed approach (cash then slicing). - Liquidity Management Tools are not taken into account. ### Reporting format for results - Output presentation: - Number of funds with a redemption coverage ratio below one (ratio of highly liquid assets to redemptions). - Price impact of asset sales. - Redemptions due to second-round effects. --- ### Mutual fund Sector: Vulnerability analysis and contagion ### Institutional perimeter and data - Institutions included: All fixed income and mixed mutual funds covered by Morningstar (2,733 funds with total net assets of about US$6.3 billion) and fixed-income ETFs. - Market share: About 100 percent of the fixed income and mixed fund sector. - Data: Commercial data (Morningstar). - Reference date: December 31, 2019. ### Channels of risk propagation (fund-level network methods) - Methodology: - CoVaR applied to fund flows and returns by fund category to identify most vulnerable funds and most contagious. - Diebold-Yilmaz methodology applied to funds to identify most vulnerable funds and most contagious. - Tail-dependence using copula. - Time horizon: Monthly data. ### Tail shocks (copula approach) - Scenario analysis: - For Copula approach: expected net flows conditional on a fund category facing net flows worse than the 3 percent expected shortfall. ### Risks, buffers, behavioral adjustments (fund contagion analysis) - Risks/factors assessed: (not specified in the section). - Buffers: (not specified in the section). - Behavioral adjustments: (not specified in the section). ### Reporting format for results (fund contagion analysis) - Output presentation: - Representation of interconnectedness among funds by fund category. - Identification of fund categories most vulnerable to distress from other categories. --- *Italic: Source: 1usaea2020007 - 2. Channels of risk (PDF chapter content).* ### Appendix Table VIII.3. Sensitivity Scenario 2 ### Appendix Table VIII.3. Sensitivity Scenario 2 ### Quarterly projections (variable units as in source) - 2019Q4 — Real GDP Growth: 2.1 percent (Q-o-Q annualized); Unemployment Rate: 3.5 percent; 3-Month Treasury Rate: 1.6 percent; 10-Year Treasury Bond Yield: 1.8 percent; BBB Corporate Bond Yield: 3.3 percent; Stock Market Index (2019Q4=100): 100.0; House Price Index (2019Q4=100): 100.0 - 2020Q1 — Real GDP Growth: -4.8 percent (Q-o-Q annualized); Unemployment Rate: 3.8 percent; 3-Month Treasury Rate: 1.1 percent; 10-Year Treasury Bond Yield: 1.4 percent; BBB Corporate Bond Yield: 3.3 percent; Stock Market Index (2019Q4=100): 96.8; House Price Index (2019Q4=100): 101.2 - 2020Q2 — Real GDP Growth: -66.8 percent (Q-o-Q annualized); Unemployment Rate: 22.8 percent; 3-Month Treasury Rate: 0.1 percent; 10-Year Treasury Bond Yield: 0.5 percent; BBB Corporate Bond Yield: 5.8 percent; Stock Market Index (2019Q4=100): 72.9; House Price Index (2019Q4=100): 88.3 - 2020Q3 — Real GDP Growth: 0.0 percent (Q-o-Q annualized); Unemployment Rate: 26.5 percent; 3-Month Treasury Rate: 0.1 percent; 10-Year Treasury Bond Yield: 0.7 percent; BBB Corporate Bond Yield: 7.2 percent; Stock Market Index (2019Q4=100): 62.5; House Price Index (2019Q4=100): 83.9 - 2020Q4 — Real GDP Growth: 29.5 percent (Q-o-Q annualized); Unemployment Rate: 24.5 percent; 3-Month Treasury Rate: 0.1 percent; 10-Year Treasury Bond Yield: 0.7 percent; BBB Corporate Bond Yield: 7.5 percent; Stock Market Index (2019Q4=100): 63.0; House Price Index (2019Q4=100): 80.8 - 2021Q1 — Real GDP Growth: 21.6 percent (Q-o-Q annualized); Unemployment Rate: 22.2 percent; 3-Month Treasury Rate: 0.1 percent; 10-Year Treasury Bond Yield: 0.8 percent; BBB Corporate Bond Yield: 7.4 percent; Stock Market Index (2019Q4=100): 65.8; House Price Index (2019Q4=100): 77.0 - 2021Q2 — Real GDP Growth: 7.3 percent (Q-o-Q annualized); Unemployment Rate: 20.7 percent; 3-Month Treasury Rate: 0.1 percent; 10-Year Treasury Bond Yield: 0.8 percent; BBB Corporate Bond Yield: 6.9 percent; Stock Market Index (2019Q4=100): 67.1; House Price Index (2019Q4=100): 73.2 - 2021Q3 — Real GDP Growth: 4.2 percent (Q-o-Q annualized); Unemployment Rate: 19.6 percent; 3-Month Treasury Rate: 0.1 percent; 10-Year Treasury Bond Yield: 0.8 percent; BBB Corporate Bond Yield: 6.6 percent; Stock Market Index (2019Q4=100): 68.4; House Price Index (2019Q4=100): 69.8 - 2021Q4 — Real GDP Growth: 3.2 percent (Q-o-Q annualized); Unemployment Rate: 18.8 percent; 3-Month Treasury Rate: 0.1 percent; 10-Year Treasury Bond Yield: 0.9 percent; BBB Corporate Bond Yield: 6.3 percent; Stock Market Index (2019Q4=100): 69.7; House Price Index (2019Q4=100): 66.6 - 2022Q1 — Real GDP Growth: 2.8 percent (Q-o-Q annualized); Unemployment Rate: 18.1 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.0 percent; BBB Corporate Bond Yield: 5.8 percent; Stock Market Index (2019Q4=100): 70.9; House Price Index (2019Q4=100): 65.0 - 2022Q2 — Real GDP Growth: 2.5 percent (Q-o-Q annualized); Unemployment Rate: 17.5 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.1 percent; BBB Corporate Bond Yield: 5.5 percent; Stock Market Index (2019Q4=100): 72.2; House Price Index (2019Q4=100): 64.7 - 2022Q3 — Real GDP Growth: 2.3 percent (Q-o-Q annualized); Unemployment Rate: 17.0 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.2 percent; BBB Corporate Bond Yield: 5.2 percent; Stock Market Index (2019Q4=100): 73.5; House Price Index (2019Q4=100): 65.6 - 2022Q4 — Real GDP Growth: 2.2 percent (Q-o-Q annualized); Unemployment Rate: 16.4 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.4 percent; BBB Corporate Bond Yield: 4.9 percent; Stock Market Index (2019Q4=100): 74.7; House Price Index (2019Q4=100): 66.6 - 2023Q1 — Real GDP Growth: 2.1 percent (Q-o-Q annualized); Unemployment Rate: 15.9 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.5 percent; BBB Corporate Bond Yield: 4.6 percent; Stock Market Index (2019Q4=100): 76.0; House Price Index (2019Q4=100): 68.2 - 2023Q2 — Real GDP Growth: 2.1 percent (Q-o-Q annualized); Unemployment Rate: 15.4 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.5 percent; BBB Corporate Bond Yield: 4.5 percent; Stock Market Index (2019Q4=100): 77.3; House Price Index (2019Q4=100): 70.7 - 2023Q3 — Real GDP Growth: 2.1 percent (Q-o-Q annualized); Unemployment Rate: 14.9 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.5 percent; BBB Corporate Bond Yield: 4.4 percent; Stock Market Index (2019Q4=100): 78.5; House Price Index (2019Q4=100): 73.3 - 2023Q4 — Real GDP Growth: 2.1 percent (Q-o-Q annualized); Unemployment Rate: 14.4 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.5 percent; BBB Corporate Bond Yield: 4.3 percent; Stock Market Index (2019Q4=100): 79.8; House Price Index (2019Q4=100): 75.8 - 2024Q1 — Real GDP Growth: 2.1 percent (Q-o-Q annualized); Unemployment Rate: 13.9 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.5 percent; BBB Corporate Bond Yield: 4.2 percent; Stock Market Index (2019Q4=100): 81.1; House Price Index (2019Q4=100): 78.4 - 2024Q2 — Real GDP Growth: 2.0 percent (Q-o-Q annualized); Unemployment Rate: 13.4 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.6 percent; BBB Corporate Bond Yield: 4.2 percent; Stock Market Index (2019Q4=100): 82.3; House Price Index (2019Q4=100): 80.9 - 2024Q3 — Real GDP Growth: 2.0 percent (Q-o-Q annualized); Unemployment Rate: 12.9 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.6 percent; BBB Corporate Bond Yield: 4.1 percent; Stock Market Index (2019Q4=100): 83.6; House Price Index (2019Q4=100): 83.4 - 2024Q4 — Real GDP Growth: 2.0 percent (Q-o-Q annualized); Unemployment Rate: 12.4 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.6 percent; BBB Corporate Bond Yield: 4.0 percent; Stock Market Index (2019Q4=100): 84.9; House Price Index (2019Q4=100): 86.0 - 2025Q1 — Real GDP Growth: 2.0 percent (Q-o-Q annualized); Unemployment Rate: 12.0 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.6 percent; BBB Corporate Bond Yield: 3.9 percent; Stock Market Index (2019Q4=100): 86.1; House Price Index (2019Q4=100): 88.5 - 2025Q2 — Real GDP Growth: 2.0 percent (Q-o-Q annualized); Unemployment Rate: 11.5 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.6 percent; BBB Corporate Bond Yield: 3.8 percent; Stock Market Index (2019Q4=100): 87.4; House Price Index (2019Q4=100): 91.0 - 2025Q3 — Real GDP Growth: 1.9 percent (Q-o-Q annualized); Unemployment Rate: 11.1 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.6 percent; BBB Corporate Bond Yield: 3.7 percent; Stock Market Index (2019Q4=100): 88.7; House Price Index (2019Q4=100): 93.4 - 2025Q4 — Real GDP Growth: 1.9 percent (Q-o-Q annualized); Unemployment Rate: 10.8 percent; 3-Month Treasury Rate: 0.0 percent; 10-Year Treasury Bond Yield: 1.6 percent; BBB Corporate Bond Yield: 3.6 percent; Stock Market Index (2019Q4=100): 89.9; House Price Index (2019Q4=100): 95.8 *Source: Appendix Table VIII.3. Sensitivity Scenario 2 (provided content).* ### 1. Loan growth ### 1. Loan growth ### Estimation results: Selected PPNR and Return on AFS items - Lagged dependent variable coefficients (selected series): - 0.445*** (real estate loans) - 0.332** (other loans) - 0.836*** (US Treasuries) - 0.751*** (MBS) - 0.771*** (Other securities) - 0.656*** (Trading-related) - 0.882*** (Service charges for deposit accounts in domestic offices) - 0.548*** (trading revenue) - 0.809*** (Fees and commissions from securities brokerage) - 0.970*** (Investment banking and etc.) - 0.109 (Fees and commissions from insurance and reinsurance activities) - 0.061 (Asset sales income) - 0.793*** (Securitization income) - Interest rate and market factor effects (selected): - 3M T-bill rate: 0.263***, 0.421***, 0.019***, 0.050***, 0.023***, 0.018***, 0.003* - Terms spread: 0.261***, 0.137**, 0.016*** - Change in BBB spreads: -0.074***, -0.006*, -0.058***, -0.013 - Change in VIX: -0.001***, -0.000 - Constant terms (selected): - 0.310***, 1.507***, 0.003, 0.173***, -0.002, 0.072***, 0.023**, 0.059***, 0.018**, 0.010***, 0.047***, 0.108***, 0.002 - Model statistics (selected): - Observations: 1,317; 1,372; 1,904; 1,915; 1,926; 1,862; 2,765; 2,765; 2,765; 2,765; 301; 1,575; 2,765 - R-squared: 0.447; 0.402; 0.772; 0.690; 0.705; 0.526; 0.772; 0.334; 0.640; 0.883; 0.041; 0.011; 0.631 - Number of entities: 34, 35, 33, 34, 33, 35, 35, 35, 35, 35, 30, 11, 57, 35 (as reported in table rows) ### Estimation results: Interest expense, non-interest income and balance sheet items - Lagged dependent variable coefficients (selected deposit and expense series): - Retail deposits: 0.846*** - Wholesale dep: 0.772*** - Other dep: 0.692*** - Repo: 0.812*** - Trading-related wages: 0.681*** - Fixed assets: 0.160 - Other: 0.213** - Return on AFS assets (selected): 0.017, 0.060 - Interest rate effects: - 3M T-bill rate: 0.026***, 0.027***, 0.061***, 0.056***, 0.086*** - Other coefficients: - Real GDP growth: -0.044* (selected) - Change in 10Y Yield: -0.430** (selected) - Change in equity prices: 0.010*** (selected) - Model statistics (selected): - Observations: 1,925; 1,953; 2,058; 1,961; 2,068; 950; 947; 950; 2,764 - R-squared: 0.889; 0.823; 0.893; 0.864; 0.791; 0.086; 0.080; 0.012; 0.026 - Number of entities: 34, 33, 35, 35, 35, 35, 35, 35, 35 ### Charge-off specifications (selected loan types) - Lagged dependent variable coefficients (selected): - Residential real estate: first lien: 0.905*** - Residential real estate: junior lien: 0.799*** - HELOC: 0.885*** - CRE real estate: construction: 0.820*** - CRE real estate: multi family: 0.775*** - CRE real estate: NFNR: 0.856*** - C&I: 0.881*** - Credit cards: 0.803*** - Other consumer: 0.707*** - Lease: 0.747*** - Other real estate: 0.476*** - Depositary corps: 0.371** - Agriculture: 0.615*** - Selected macro/market effects on charge-offs: - Change in unemployment: 0.078**, 0.090, 0.088***, 0.408***, 0.170***, 0.075***, -0.000 (across various loan types) - Commercial property price growth: -0.040**, -0.011**, -0.009***, -0.008 (selected) - House price growth: -0.004, -0.060**, -0.013* (selected) - Model statistics (selected): - Observations: 717, 178, 797, 979, 797, 579, 517, 959, 79, 79, 79 - R-squared: 0.897; 0.804; 0.943; 0.919; 0.811; 0.886; 0.793; 0.871; 0.724; 0.807; 0.240; 0.185; 0.375 ### Recoveries specifications (selected loan types) - Lagged dependent variable coefficients (selected): - Residential real estate: first lien: 0.746*** - Residential real estate: junior lien: 1.027*** - HELOC: 0.960*** - CRE real estate: construction: 0.905*** - CRE real estate: multi family: 0.752*** - CRE real estate: NFNR: 0.782*** - C&I: 0.838*** - Credit cards: 0.629*** - Other consumer: 0.811*** - Lease: 0.815*** - Other real estate: 0.660*** - Depositary corps: 0.145 - Agriculture: 0.452*** - Selected macro effects on recoveries: - Change in unemployment: -0.005, -0.001, -0.004, -0.018*** (selected) - House price growth: 0.002, 0.001, 0.003, 0.004 (selected) - Real GDP growth: 0.003, 0.000, 0.002, 0.003, 0.008 (selected) - Model statistics (selected): - Observations: 717, 078, 797, 979, 797, 579, 517, 957, 79 - R-squared: 0.657; 0.687; 0.841; 0.823; 0.562; 0.618; 0.727; 0.580; 0.717; 0.696; 0.436; 0.077; 0.284 ### Contribution to losses in terms of RWAs (scenario decomposition) - Components shown in figures for various bank groups (percent points of RWA contribution to changes in Tier 1 ratio): - Profit before losses - Credit losses - Market losses - Counterparty losses - Dividends - Changes in RWA - Bank groups illustrated include: - GSIBs - Trading Banks - Foreign Banks - Non-GSIBs - Visual ranges shown (examples from charts): - For some groups, contributions range from -10% to 25% (in pct pnt of RWA) - For others, ranges are narrower, e.g., -6% to 12% or -10 to 10 (as plotted) ### Mutual funds sample used in stress tests - Sample construction: - Based on Morningstar data for U.S. mutual funds in Morningstar global broad category group: Allocation, Taxable Bond and Municipal Bond. - Target date funds excluded. - Remaining 2,743 funds split into 43 Morningstar Global categories and mapped into ICI categories. - For stress tests, nine fund categories used: mixed, municipal, EM, HY, IG, loan, government, multi-strategy, global. - Sample summary (Appendix Table XI.2): - Net asset Value (US $ bn) and Number of funds by fund category: - Corp. IG: 2,427 (608 funds) - Mixed funds: 1,752 (792 funds) - Municipal: 799 (567 funds) - Multisector: 432 (182 funds) - Government: 326 (161 funds) - Corp. HY: 257 (192 funds) - Global: 247 (87 funds) - Loan funds: 91 (58 funds) - EM funds: 66 (96 funds) - Total: 6,398 (2,743 funds) - Data used (2017–2019 monthly): - Flows, net asset value, portfolio composition, returns. - Sample includes funds alive as of end-2019. - Net flows computation: f_t = FLLFALF_t / NAV_{t-1} - Net flows with absolute value above 50% excluded. ### Portfolio composition and credit quality treatment - Portfolio split (highest level): cash, equities, bonds, other. - Fixed income split: government, municipal, corporate, securitized, cash and equivalents, derivatives. - Credit rating allocation heuristic: - Highest credit rating allocated first to government portfolio, then to corporate bonds, then to securitized products. - Treatment of derivatives and leverage: - If cash part negative (due to leverage), set cash = 0% and cap other parts at 100%. - If cash allocation > 100%, bound cash at 100%. ### Mutual fund liquidity stress test methodology — Calibration of redemption shocks - Historical (homogeneity assumption): - Shock calibrated on distribution of net flows across funds in same category. - Calibration based on the 3 percent Expected Shortfall (ES) = average worst flows below the 3rd percentile. - Robustness: ES at 1 percent and 5 percent, and worst 1, 3, 5 percent net flows (“VaR approach”). - Historical (heterogeneity assumption): - Shock calibrated on each fund’s own historical data using 3 percent ES; robustness at 1 percent and 5 percent and percentiles. - Each fund subject to 12 different redemption shocks (Appendix Table XII.1). - Main focus: homogeneity assumption at 3 percent ES, with redemption shocks ranging from 7 percent for Municipal funds to 17 percent for EM bond funds (reported range for main focus). ### Calibration table highlights (Appendix Table XII.1) - Homogeneity assumption (net outflows in % of NAV, 1st, 3rd, 5th percentiles and ES/VaR measures shown in table): - Example figures (selected from table): - Municipal: 11 (ES), 7 (3rd percentile), 5 (1st percentile) — as reported in table cells - Mixed funds: 15, 9, 7 - Corp. IG: 21, 13, 10 - Multisector: 22, 13, 10 - Loan funds: 19, 13, 11 - Global: 23, 14, 11 - Government: 24, 14, 11 - HY: 23, 15, 12 - EM funds: 26, 17, 14 - Heterogeneity assumption and percentile-based values also reported in table (see table for exact cell-level numbers). ### Adverse scenario calibration and projection of net flows - Adverse scenario approach: - Use adverse scenario from banking sector stress test to project macrofinancial variables over five years (quarterly), convert to monthly changes for the liquidity stress test horizon. - Monthly projected changes obtained using a scaling factor (example shown: √20 / √60 = 0.57 in the text). - For EM, HY and securitized yields: - Projection based on shock to BBB yields multiplied by conversion factor. - Conversion factor example: ∆BBB projected / ∆BBB Oct.08 = 93 / 163 = 0.57 - ICE Bank of America Merrill Lynch indices used to retrieve yield information. - Projected macrofinancial monthly changes (Appendix Table XII.3, selected): - Quarterly => Monthly changes shown in table: - 3-Month Treasury Rate: -78bps quarterly change => -45.0bps monthly change - 10-Year Treasury Bond yield: -103bps quarterly change => -59.5bps monthly change - BBB Corporate Bond Yield: 161bps quarterly change => 93.0bps monthly change - Equities: -30% quarterly change => -17.3% monthly change ### Computation of funds’ returns under adverse scenario (Appendix Table XII.4) - Average monthly returns by fund category under the adverse scenario (sample results): - EM: -11% (Average), -10% (Median), Min -20%, Max -1% - Global: -3% (Average), -1% (Median), Min -27%, Max 4% - Gov: 1% (Average), 0% (Median), Min -4%, Max 14% - HY: -7% (Average), -7% (Median), Min -12%, Max 0% - IG: -2% (Average), -1% (Median), Min -12%, Max 2% - Loan: -1% (Average), -1% (Median), Min -2%, Max 0% - Mixed: -10% (Average), -11% (Median), Min -22%, Max 9% - Multi: -3% (Average), -2% (Median), Min -24%, Max 2% - Muni: -1% (Average), 0% (Median), Min -16%, Max 3% - Total: -4% (Average), -2% (Median), Min -27%, Max 14% - Drivers noted: - Large negative returns for EM and HY bond funds (large increase in yields). - Large negative returns for mixed funds (large decline in equity prices). - Government and municipal bond funds: little change or positive returns. - Loan funds: relatively low shocks to returns due to very short duration (less than 0.5). ### Computation of funds’ net flows under adverse scenario - Flow-performance relationship estimated using Fama-MacBeth two-step methodology: - Monthly cross-sectional regressions with twelve lags for returns and flows and lagged fund size as control. - Regression specification: f_{i,t} = α + Σ_{k=1}^{12} β_k f_{i,t-k} + Σ_{h=1}^{12} γ_h r_{i,t-h} + log(aUM_{i,t-1}) + ε_{i,t} - Parameter estimation: - Time series average of coefficients yields parameters for flow-performance relationship. - For all fund categories, the parameter for lagged returns is significant at the 5 percent level. - Projected net flow outcomes under the adverse scenario (summary): - EM and HY funds would experience sizable outflows. - Corporate and mixed funds would face more limited outflows. - Other fund categories would face small outflows or inflows (government bond funds could see inflows). *Source: IMF staff (extracted from 1usaea2020007 - 1. Loan growth).* ### Appendix Table XII.5. Flow-Performance Relationship and Net Flows ### Appendix Table XII.5. Flow-Performance Relationship and Net Flows in the Adverse Scenario ### II. Ability of funds to withstand shocks: the liquidity bucket approach - High liquid assets measured at fund level using liquidity weights defined in the context of the Liquidity Coverage Ratio for banks; liquidity weights taken from the Basel Committee. - Note: "For example, in the U.S corporate debt securities are not included in Level 2A assets (liquidity weight of 85 percent) but only to Level 2B (liquidity weight of 50 percent)." - Fund-level net flows in the adverse scenario (Net flows in %): - EM 0.85: -10.3% - Global 0.25: -0.6% - Gov 0.27: 0.5% - HY 0.64: -4.8% - IG 0.71: -1.1% - Loan 0.91: -0.8% - Mixed 0.26: -2.8% - Multi 0.55: -0.9% - Muni 0.53: -0.7% - Redemption Coverage Ratio (RCR) concept: when RCR < 1, the fund does not have enough highly liquid assets to cover redemptions without selling less liquid assets; liquidity shortfall defined as difference between redemption shock and stock of highly liquid assets. - For funds using derivatives, adverse scenario used to estimate potential variation margins; assumption that funds can only post cash as margins. - Variation margin first-order approximation for interest rate swaps and FX forwards provided (formulas as in source). - Basel Committee liquidity weights by asset and rating (as presented): - Sovereign bonds: AAA-AA 100%, A 85%, BBB 50%, Below BBB 0% - Corporate bonds: AAA-AA 85%, A 50%, BBB 50%, Below BBB 0% - Securitized: AAA-AA 85%, A 0%, BBB 0%, Below BBB 0% - Cash: 100% (implied in ordering) ### III. Liquidation strategies and price impact of funds sales - Liquidation strategies described: - Slicing (prorata): sell each asset class in proportion to portfolio weight. - Waterfall: sell most liquid assets first (ordering based on HQLA liquidity weights). - Mixed approach: cash first, then slicing. - Trade-offs: - Slicing preserves portfolio profile but may force sales of less liquid assets, increasing price impact. - Waterfall mitigates immediate price impact but leaves remaining investors with a less liquid portfolio. - Waterfall ordering (based on HQLA liquidity weights and source ordering): - Cash → AAA-AA sovereign bonds → A sovereign bonds → AAA-AA corporate bonds → BBB sovereign bonds → A corporate bonds → AAA-AA securitized assets → BBB corporate bonds → unrated sovereign bonds → corporate bonds → securitized assets. - Liquidity management tools (LMTs) such as in-kind redemptions, swing pricing, suspension of redemptions, or credit lines are not taken into account in the stress test. - Price impact estimation methodology: - Market depth MD(τ) = ξ * (ADV / σ) * sqrt(τ) following Cont and Schaaning (2017). - Market depth increases with ADV and with the square root of the time horizon; decreases with volatility. - Correction: floor on price impact during fire sales set at 50 percent (when prices fall that much, opportunistic investors step in). - Price impact measures by asset class (excerpted exact figures): - UST: - ADV (US$ bn): 545 - Average volatility: 0.28% - 2008 Volatility: 0.55% - Market Depth (US$ bn): 77,857 and 39,636 (two horizon entries) - Impact of $ 1bn of sale (in bps): 0.1 and 0.3 - Corp. IG: - ADV (US$ bn): 210 - Average volatility: 0.30% - 2008 Volatility: 0.65% - Market Depth (US$ bn): 2,800 and 1,292 - Impact of $ 1bn of sale (in bps): 3.6 and 7.7 - Corp. HY: - ADV (US$ bn): 120 - Average volatility: 0.31% - 2008 Volatility: 1.07% - Market Depth (US$ bn): 1,548 and 449 - Impact of $ 1bn of sale (in bps): 6.5 and 22.3 - Leveraged loans: - ADV (US$ bn): 30 - Average volatility: 0.18% - 2008 Volatility: 0.64% - Market Depth (US$ bn): 556 and 156 - Impact of $ 1bn of sale (in bps): 18.0 and 64.0 - EM debt: - ADV (US$ bn): 80 - Average volatility: 0.40% - 2008 Volatility: 1.36% - Market Depth (US$ bn): 750 and 221 - Impact of $ 1bn of sale (in bps): 13.3 and 45.3 - Municipal bonds: - ADV (US$ bn): 110 - Average volatility: 0.19% - 2008 Volatility: 0.64% - Market Depth (US$ bn): 2,316 and 688 - Impact of $ 1bn of sale (in bps): 4.3 and 14.5 - Securitized Agencies: - ADV (US$ bn): 2200 - Average volatility: 0.19% - 2008 Volatility: 0.44% - Market Depth (US$ bn): 46,316 and 20,000 - Impact of $ 1bn of sale (in bps): 0.2 and 0.5 - Securitized: - ADV (US$ bn): 2 - Average volatility: 0.11% - 2008 Volatility: 0.27% - Market Depth (US$ bn): 727 and 296 - Impact of $ 1bn of sale (in bps): 13.8 and 33.8 - Equities: - ADV (US$ bn): 320 - Average volatility: 1.12% - 2008 Volatility: 3.60% - Market Depth (US$ bn): 11,429 and 3,556 - Impact of $ 1bn of sale (in bps): 0.9 and 2.8 - Note: 2008 volatility estimated over September-December 2008. ### Second-round effects - Procedure: - Recalculate NAV of each fund after price impact of forced sales to reflect costs on remaining portfolio. - Negative returns induce a second wave of redemptions; magnitude determined by the flow-performance relationship. ### Appendix XIII. Mutual Fund Stress Tests Results (summary) - Results outline share of funds with RCR < 0, in percent, for 12 different redemption shocks under two assumptions (homogeneity and heterogeneity) and six shock definitions (ES and VaR at 1 percent, 3 percent, 5 percent). - Selected entries from Appendix Table XIII.1 (share of funds with RCR<0, in percent): - Under Homogeneity assumption (1st percentile ES/VaR and 3rd/5th percentiles shown in table format in source): - Multisector: 10% 5% 9% 4% - Loan funds: 96% 82% 82% 62% - HY: 90% 75% 66% 55% - EM funds: 20% 6% 11% 3% - Under Heterogeneity assumption (selected rows): - Multisector: 5% 2% 4% 2% - Loan funds: 82% 42% 64% 42% - HY: 75% 48% 56% 37% - EM funds: 6% 0% 3% 2% - Additional entries for other percentiles and categories are provided in the source table. - Data sources: Morningstar, IMF staff. - Note: table entries represent "Share of funds with RCR<0, in percent." ### Appendix XIV. Analysis of Vulnerabilities and Interconnectedness (Mutual Funds) - Two risk concepts: - Vulnerable funds: likely to be in distress when other funds or the market are in distress. - Spreader funds: when in distress, likely to cause distress in other funds. - Two methodologies: - Tail-dependence using copula (Student t-copula with ν degrees of freedom; correlation matrix Σ estimated by maximum likelihood). - Marginal distributions for fund flows: logistic distribution selected as best fit; pdf f(x; μ, a) given and parameters μ and a estimated by maximum likelihood. - Conditional expectation used to estimate expected net flows of category A given category B in distress: E(f_A | f_B < α) computed via numerical integration or Monte-Carlo simulations. - Example output interpretation: - EM funds identified as vulnerable (experience high outflows when IG and MISC categories are in distress). - Municipal bond funds not vulnerable (do not experience large outflows when others are in distress). - IG funds identified as spreaders (when IG funds are in distress, HY and EM funds experience high outflows). - Interconnectedness approach (Diebold-Yilmaz spillover analysis applied to fund returns and fund flows). - Weekly returns from Morningstar used. - VAR estimated using a lasso-estimator (Zou and Hastie 2005). - Generalized forecast-error variance decomposition (GVD) using Pesaran and Shin (1998); focus on 3-week ahead forecast error to measure spillovers. - Spillover measures fraction of H‑week ahead forecast error variance of firm j explained by innovations in firm i. ### Appendix XV. Sample Selection for Insurance Stress Tests - Sample construction: - Starting from NAIC Market Share Reports 2018 for life&health and P&C insurers, Top 25 companies from each sector identified; largest health insurers also identified with smaller sample due to concentration. - A company/group included in only one of three samples (life, P&C, health) based on highest premiums in that sector. - Variable annuity (VA) providers: included if premiums written in VA > 30 percent of total life&health premiums. - Subsidiaries of foreign insurance groups identified based on ultimate ownership. - Sample characteristics (Appendix Table XV.2; summary figures provided): - Number of groups: - Life: 21 - P&C (diversified): 22 - Health: 7 - Balance sheet assets (USD bn.) – selected percentiles and extremes: - Min: Life 3.7, P&C 8.9, Health 3.5 - 25th percentile: Life 135.6, P&C 20.3, Health 9.2 - Median: Life 195.0, P&C 36.0, Health 15.2 - 75th percentile: Life 254.9, P&C 67.0, Health 21.9 - Max: Life 577.9, P&C 321.4, Health 34.4 - Statutory capital / Assets – selected percentiles and extremes: - Min: Life 2.0%, P&C 3.3%, Health 32.5% - 25th percentile: Life 3.3%, P&C 23.8%, Health 41.9% - Median: Life 4.6%, P&C 26.8%, Health 46.2% - 75th percentile: Life 6.4%, P&C 33.3%, Health 47.2% - Max: Life 12.1%, P&C 56.7%, Health 48.5% - Representative lists of major groups in each sample provided in the source (omitted here as navigation/listing per pipeline rules). *Source: IMF staff calculations (content unit: Appendix Table XII.5 and associated appendices as provided).* ### Appendix XVI. Solvency-Liquidity Network Model ### Appendix XVI. Solvency-Liquidity Network Model ### I. Data - FR2052a data on different types of secured financing transactions are aggregated into 4 buckets (HQLA1, HQLA2a, HQLA2b and Non-HQLA). HQLA is further disaggregated into more granular buckets using data from FR Y-9C. - Parameters such as Price Impact, Corwin-Schultz measures are estimated using TRACES data. - Interbank network exposures are sourced from bilateral exposures (“i-to-i”) for domestic G-SIBs via the BIS G-SIB hub database and enter the interbank network under separate exposure categories: lending-unsecured, ST money placement, issuer risk (equities and fixed income), and CDS. - A separate category for risk transfers is sourced from DTCC data. - Loss-given-default rates and the risk transfer parameter are proxied by the IMF team based on prior research, Moody’s defaults and recoveries and CreditEdge databases. - Data sources explicitly referenced: FR Y-9C, FR 2052a, TRACES, BIS G-SIB hub, DTCC. ### II. Risks and scenarios — Funding shock - Scenario structure: - Each scenario consists of a set of inflow/outflow/CBC (Counterbalancing capacity) parameters. - Two parameter types: (i) fixed and (ii) varying (changing by a 5 percent or 10 percent increment for sensitivity). - Embedded scenario tabs: Scenarios_Flows_LCR (same shocks to flows for all banks), Scenarios_Flows_All_Repo_Cls (same shocks to flows for all banks), Scenarios_Flows_BankName (customized per bank; not used for US FSAP). - Two main scenarios illustrated: - i) LCR scenario: - Uses average, system-wide LCR inflow/outflow rates. - Assumes each bank faces: increase in demand for committed facilities; withdrawal of wholesale funding; own credit rating downgrade and loss of rehypothecation rights; additional margin calls for derivative positions. - Assumes interbank network is not functioning (no redistribution of liquidity between banks). - Asset fire-sale prices applied to balances of other banks holding same security types (marked-to-market). - Stylized balance sheet used to determine CAR and liquidity position and simulate second-round effects. - ii) Closure of Repo market: - Bank unable to repo assets to obtain liquidity, except using HQLA1 treasury securities. - All other parameters same as in LCR scenario. - Variable sub-scenarios (same variable parameters applied): - a) Outstanding Draws on Revolving Credit Facilities (shocks from 100% to 0%); - b) Credit Facilities (shocks from 0% to 100%); - c) Retail Mortgage Commitments (shocks from 0% to 100%); - d) Liquidity Facilities (shocks from 0% to 100%); - e) Federal Home Loan Bank Advances (all types of HQLA) (shocks from 0% to 100%); - f) Draws on committed lines (shocks from 0% to 100%); - g) MTM Impact on Derivative Positions (shocks from 100% to 0%); - h) Loss of Rehypothecation Rights, Total Collateral Required Due to a Notch Downgrade Total Collateral Required Due to a Change in Financial Condition (all types of downgrades). (shocks from 0% to 100%); - i) Combined scenarios: all shocks as above. - Time-horizon and severity are embedded in scenario tabs and depend on data availability. ### III. Network (structure, balance sheet decomposition, credit shock) - Network overview: - System of N entities (domestic banks in US interbank network) with NxN interbank exposures matrix. - Analysis quantifies consequences of a hypothetical stress event (e.g., default of a bank) and assesses credit and funding related losses across the network. - Balance sheet decomposition: - Assets decomposed into: cash and CB reserves, unencumbered securities (available for liquidity purposes), reverse repos, collateral swaps, secured loans (‘rehypothecatable’), derivatives receivable, loans and all other assets (residual). - Liabilities decomposed into: unsecured and secured wholesale funding, wholesale borrowing on and off-shore, repos, operational and non-operational wholesale deposits, liquidity and credit facilities, derivatives payable and all other liabilities (residual). - Assets further decomposed into collateral types: Level 1, Level 2a, Level 2b, non-HQLA. Non-HQLA further decomposed by issuer/CUSIP. - Counterparty aggregation by sector: bank; mutual fund; insurance company; hedge fund; pension fund; CCP; other non-bank financial company; nonfinancial entity; other. - Balance sheet timing from FR2052a: three horizons — open + Day1; open+Day1....Day 5; Open + Day 1....Day 30. Other inflows/outflows outside those horizons treated as other assets/liabilities. - Simple credit risk shock formulation (stylized identity): - Fundamental identity (notation preserved from source): - ∑_{k, j=1}^{N} a_{i,j}(k) + ∑_{k} a_{i}(k) = ē_{i} + ∑_{k, j=1}^{N} l_{i,j}(k) + ∑_{k} l_{i}(k) - Direct credit loss when bank h defaults: - loss = λ_{i,h}(k) a_{i,h}(k) - After-shock balance sheet identity (notation preserved): - ∑_{k,j=1, j≠h}^{N} a_{i,j}(k) + ∑_{k} a_{i}(k) + (1−λ_{i,h}(k)) a_{i,h}(k) = ē_{i} − ∑_{k} λ_{i,h}(k) a_{i,h}(k) + ∑_{k,j=1}^{N} l_{i,j}(k) + ∑_{k} l_{i}(k) - Capital after direct credit losses denoted ē_{i}′ and compared to minimum threshold c*. If ē_{i}′ < c* then bank i is considered in default and cascade continues. ### IV. Funding risk shocks - Triggering condition: a bank’s capital falls below its minimum required solvency ratio and the bank experiences funding withdrawals over 1-, 5-, and 30-day periods (separate scenarios). - Applied parameter values and rules: - Wholesale funding outflows – commercial funding (no ability to borrow; maturing securities must be redeemed). - Wholesale borrowing (all counterparties) – 50,75,100 percent outflows. - Repos (non-HQLA) – maturing repos are not renewed. - Transactional, operational accounts – 2,5,10 percent outflows. - Non-operational accounts: 25,50,75 percent outflows. - Liquidity facilities provided to a bank – not available. - Credit facilities provided by bank – granted (to avoid reputational risk). - Derivatives payable – 25,50, 100 percent outflows. - Due to outflows, banks use cash/CB reserves and sell or repo securities to meet liquidity needs. - Stress assessment aims to evaluate effects on banks and non-bank financial intermediaries via funding, liquidity and solvency channels. ### V. Determination of Haircuts - Assumptions: - Bank can use up to the full amount of cash and CB reserves; haircuts determined by amount of assets liquidated. - Haircut types: - (i) Scenario based: term and risk premiums aligned with macroeconomic stress test scenario (realized immediately). - (ii) Security based: CB eligible assets — obtain liquidity from the Fed with minimal costs via repos (Fed collateral framework). - (iii) Security based: market repos — bank able to repo securities using market haircuts. - (iv) Security based: fire-sales — securities for which repos are not available (non-HQLA) may need market liquidation; haircuts determined by total amount of securities sold by given bank and other banks; calculations may assume 50 percent implementation shortfall. - Market liquidity measures used: - Cost and quantity dimensions. - Two widely-used price-impact measures referenced: - Amihud (2002) measure: ratio between absolute value of daily returns and daily trading volume. - Price impact measure (PI): slope coefficient of regression of price change on signed order flow; assigns signs to trades and suited for modeling seller-initiated fire sales. - PI estimated using daily TRACE trading data for asset classes: corporate bonds, agency, ABS, CMO, MBS, TBA. Treasuries sourced from Bloomberg. - Regime-switching model for aggregate price impact (per asset class j): - Steps: - Step 1: identify marketable asset classes and calculate PI at security level. - Step 2: average security-level PIs to obtain asset-class PI. - Step 3: estimate baseline Markov regime-switching model: - PIV_{t}^{j} = β_{0}^{j,s} + ε_{t}^{j,s}, with two regimes (non-stress / stress), regime-dependent constant β_{0}^{j,s} and variance σ_{s}^{2}. - Two-regime assumption (non-stress/high-liquidity and stress/low-liquidity); variance may vary across regimes or be same across regimes. - Aggregation and fire-sale impact: - Total amount of securities in asset class j liquidated by all banks: VRl_{aggregate}^{j} ≡ ∑_{i=1}^{N} VRl_{i}^{j} - Price impact for asset class j in regime s approximated as β_{0}^{j,s} ∙ VRl_{aggregate}^{j}, with a scenario-based floor to avoid unrealistic haircuts. - For idiosyncratic fire-sales (assumed non-stress regime): use β_{0}^{j,non-stress} ∙ VRl_{aggregate, non-stress}^{j}. - When market liquidity switches to stress regime, use β_{0}^{j,stress} ∗ VRl_{aggregate, stress}^{j}. - Notes and caveats: - Linear relationship assumption between PI and total fire sales is a simplifying assumption; may be unrealistic for very large trades. - Time horizon of sales linked to stress testing scenario (1, 5, 30 days) and pecking order of sales must be assumed, though current methodology does not model pecking order. ### VI. Estimated haircuts (selected estimates and measures) - Estimated Price Impact of Securities (volume-based per US$ 100 mil; Corwin-Schulz and Price Impact estimates shown as stress vs non-stress regime): - US Treasuries: Stress regime 0.00097; Non-stress regime 0.00001 - RMBS: pass-through: Stress regime 0.06058; Non-stress regime 0.01691; (additional columns in source: 0.00159, 0.00069) - RMBS: other: Stress regime 0.02440; Non-stress regime 0.00552; (additional: 0.00117, 0.00064) - CMBS: Stress regime 0.10874; Non-stress regime 0.01598; (additional: 0.00753, 0.00045) - ABS and other structured products: (additional values) 0.00053; 0.00012 - Corporate securities: Stress regime 0.12906; Non-stress regime 0.03047; (additional: 0.00336, 0.00004) - Data and calculations for price-impact/haircut estimates use FINRA TRACE, Bloomberg LP, and IMF staff calculations. - Market size of various securities summarized in source (SIFMA and IMF calculations referenced). ### VII. Network algorithm and cascade effects - Network algorithm representation: - N nodes with inter-node loans represented by N x N matrix X, generic element x_{ij}. - F_{t} = set of failed institutions at simulation round t; NF_{t} = set of not-failed institutions. - Credit shock simulation (simulation 1): - Initialize with institution h failing at t = 0; fraction λ of its debts are not repaid. - For each j ∈ NF_{t}, check if losses > capital; if yes, j defaults and is added to F_{t+1}. - Default condition expressed in source notation: - if ∑ λ e_{h t} > ē_{j} → j defaults too: j ∈ F_{t+1}, h ∈ F_{t} - Algorithm converges when F_{t} = F_{t+1}. - Credit-plus-funding shock simulation (simulation 2): - Previous shock compounded by funding-shortfall induced loss δρ x_{ih}. - Default condition at each stage (notation preserved from source): - H_f f ( ∑ λ e_{h t} + ∑ δρ e_{j h} ) > ē_{j} → j defaults too: j ∈ F_{t+1} - Post-shock recalculations: - Recalculate banks’ solvency positions after each shock: shareholders’ equity, CET1 ratio, total CAR. - Thresholds used: - Illiquidity: inability to maintain positive cash flows after use of available counterbalancing capacity within 1,5,30 days; - Inability to maintain CET1 ratio above minimum requirements (4.5 percent); - Inability to maintain positive shareholders equity. - Transmission channels: - Funding channel: bank facing liquidity shortage cancels credit lines to other banks/non-bank financial institutions. - Liquidity channel: asset fire-sales affect other financial intermediaries via marked-to-market pricing. - Solvency channel: failing institution would not redeem its own securities or deposits held by other banks and non-bank financial institutions. - Market liquidity computation details: - Two cost-based measures estimated: regression-based price impact and Corwin-Schultz (2012) high-low spread. - Price-impact regression (transaction level): - ∆Price_{s,t} = α + β Trading Volume_{s,t} + ε, where s = security; t = time between two transactions within a day. - Volume constraints to account for quantity dimension: - (1) Liquidation amount divided into transactions based on maximum per-transaction volume in sample for each asset class. - (2) Total amount liquidatable constrained by daily total trading volume observed in sample; G-SIBs assumed to access only a share of daily trading volume proxied by banking sector share in financial sector. - Uncertainty addressed by allowing a parameter range from half-to 5 times the threshold. - Corwin-Schultz high-low spread computed using two-day high and low prices with nonlinear transformations (formulas preserved in source). Negative estimated high-low spreads set to zero per Corwin-Schultz (2012) convention. - Calculations performed at securities transaction level for seven asset classes: Corporate, ABS, RMBS pass-through, RMBS other, CMBS, Agency, Treasuries (TRACE used for six asset classes; Treasuries from Bloomberg). *Source: Appendix XVI. Solvency-Liquidity Network Model (excerpts).* ### Appendix XVII. Network Algorithm for Contagion (Cross-Border ### Appendix XVII. Network Algorithm for Contagion (Cross-Border Interconnectedness) ### Overview of the network framework - The network consists of N nodes (each node representing a banking system/bank). - Inter-node (or interbank) loans are represented by the N x N matrix, X, with generic element x_ij (direct exposures across nodes). - Let F_t be the set of failed institutions and let NF_t be the set of not-failed institutions in round t of the simulations. ### Simulation 1 — Credit shock (initialization and failure propagation) - Initialization: - Assume institution h fails at t=0. - A fraction λ of its debts to the rest of institutions will not be repaid. - Failure propagation rule (as stated in the source): - For each not-failed institution j ∈ NF_t, check whether the amount of losses suffered by that institution is larger than the amount of capital of that institution. If so, the institution is driven to bankruptcy: - if � 휆휆푒푒 ℎ푡푡 >표표 푗푗 →푗푗 defaults too∶푗푗 ∈퐹퐹 푡푡+1 ℎ ∈퐹퐹 푡푡 - Convergence criterion: - The algorithm is said to converge once there are no further failures, i.e., F_t = F_t+1. ### Simulation 2 — Credit-plus-funding shock (compounded shocks including funding shortfalls and fire sales) - Extension of Simulation 1: - The credit shock is compounded by the funding-shortfall induced loss, δρx_ih. - At each stage of the simulation, an institution’s capital may be negatively affected by the asset fire sale. - Default condition (as stated in the source): - 퐻퐻푓푓 � 휆휆푒푒 ℎ푡푡 + �훿훿휌휌 푒푒 푗푗ℎ ℎ∈퐹퐹 푡푡 >표표 푗푗 →푗푗 defaults too∶푗푗 ∈퐹퐹 푡푡+1 ℎ ∈퐹퐹 푡푡 ### Methodological note - The appendix indicates the algorithm is based on Espinosa-Vega and Sole (2010).1 *Italic: Source — Appendix XVII. Network Algorithm for Contagion (Cross-Border Interconnectedness), content unit 1usaea2020007* --- _Source: https://www.imf.org/-/media/files/publications/cr/2020/english/1usaea2020007.pdf_