## EXECUTIVE SUMMARY AND RECOMMENDATIONS ## Source details **Canonical URL:** [EXECUTIVE SUMMARY AND RECOMMENDATIONS](https://www.imf.org/-/media/files/publications/cr/2022/english/1gbrea2022003.pdf) ## Other formats - [Markdown version](/-/media/files/publications/cr/2022/english/1gbrea2022003.pdf.md) - [Structured JSON version](/-/media/files/publications/cr/2022/english/1gbrea2022003.pdf.json) --- ### Context and scope - FSAP conducted right after the second Covid wave and a third lockdown, focusing on balance sheet resilience of major institutional sectors. - Core analysis tool: stress tests comparing two adverse scenarios versus a baseline based on the October 2021 WEO assumptions. - Stress test coverage: - Corporates and households: financial sector exposures to indebted corporates and less creditworthy households and potential systemic implications. - Banks and insurers: resilience to solvency and liquidity pressures. - Climate-related risk assessment for banks and insurers: both physical and transition risks. ### Adverse scenarios (as described) - Adverse Scenario 1 (Adv. – Scarring): Protracted recession with lasting economic scars; pandemic controlled not earlier than late 2022 for advanced economies and by end of 2023 for rest of world; Real GDP growth of only 0.5 percent in 2021; recovery by only 1.6 percent in 2022; scarring lowers potential output growth by 0.3% relative to pre-COVID and raises natural unemployment rate. - Adverse Scenario 2 (Adv. – Tightening GFC): Surge in global inflation and sharp tightening of global financial conditions; equity prices decline as policy tightening becomes inevitable; sterling depreciation contributes to goods price inflation; by 2025 U.K. real GDP is still 2 percent lower than under baseline. - Baseline comparator: October 2021 WEO assumptions. ### Key findings — cross-sectoral - Pandemic policy response was effective and helped support financial stability. - Remaining and emerging vulnerabilities warrant further analysis and enhanced data availability as most pandemic support measures have expired. - Concentration of vulnerabilities: - SMEs, particularly in sectors hardest hit by the pandemic. - Low-income households. - Under adverse scenarios: - Estimated corporate liquidity and equity gaps could double compared with baseline. - Household mortgage risks could increase sizably. ### Non-financial corporates (NFCs) — balance sheets, vulnerabilities, and stress tests - Aggregate developments: - NFC private nonfinancial corporate debt: 78 percent of GDP in 2020 (up seven percentage points), peak 88 percent in 2008; declined to about 73 percent of GDP as of end-2021 Q3. - Corporates' liquidity position improved by 11 percentage points of GDP in 2020. - Composition in 2020: close to half of debt increase was short-term loans; debt securities increased by about three percentage points of GDP. - Creditor structure: - FSAP estimates NBFIs provide slightly more loans to NFCs than banks; NBFIs important lenders to SMEs. - Role of public support (examples and magnitudes): - CJRS: 2020 Total cost £46 billion; To SMEs £31 billion (65%); To SMEs in ORBIS sample £4.6 billion. 2021 Total cost £23 billion; To SMEs £18 billion (65%); To SMEs in ORBIS sample £2.6 billion. - Business rates relief: 100 percent relief for retail, leisure, hospitality, nurseries, and pubs from April 2020 to June 2021; 66 percent reduction for remaining nine months of 2021-22 fiscal year (July 2021 – March 2022). - Empirical findings (SMEs): - SMEs estimated to face liquidity shortfall about 2 percent of turnover and equity gap about 1½ percent of turnover in 2022–23. - Accommodation sector: liquidity gap 4 percent of turnover and equity gap 3 percent of turnover in 2022–23 under baseline dynamics. - Under adverse scenarios, liquidity and equity gaps could increase to 3-4 percent of turnover. - Stress-test scenario outcomes for SMEs: - Protracted recession with scarring: share of illiquid SMEs in accommodation increases from 20 percent (baseline 2021) to >30 percent; total liquidity shortfalls ~2.7 percent of turnover; equity gap reaches 3.2 percent of turnover (more than double baseline). - Inflationary and tightening scenario: total liquidity shortfall peaks at 3.7 percent of turnover in 2023; SME equity gap reaches 0.9 percent of turnover in 2025. - Transmission: - NFC financial stress could increase PDs in banks’ corporate loan portfolios; corporate defaults could lead to bank losses, but banking sector is well capitalized to absorb them. ### Corporate PD estimation (matched bank-corporate sample) - Sample and caveat: matching corporate financials to bank loan exposures dropped many observations; results may not be representative. - Corporate profit/regression results (selected coefficients): - Return on assets: -3.637***, -3.632*** - Relative size (asset to sectoral average): -0.151**, -0.152** - Fixed assets to asset ratio: 1.824***, 1.793*** - Sales growth: -0.977***, -0.957*** - Observations: 12,884; 12,908. Pseudo R-squared: 0.086, 0.086. - Sectoral PD impacts: - Recreation and accommodation sectors see largest PD increases by end-2025 vs pre-pandemic: - by 3¾ - 4¾ percentage points under baseline and protracted recession with scarring scenarios. - around 2½ percentage points under the inflationary and tightening scenario. ### Households — balance sheets, arrears, and stress results - Macro-level trends: - Households' financial assets: 285 percent of GDP end-2010 → 307 percent of GDP end-2019 → 349 percent of GDP end-2020. - Households' financial liabilities: 97 percent of GDP end-2010 → 86 percent of GDP end-2019 → 93 percent of GDP in 2020. - Households' net financial wealth: 256 percent of GDP at end-2020. - Mortgage concentration and structure: - At end-2019 U.K. household debt: 143 percent of disposable income. - Mortgages account for >70 percent of household debt; >80 percent of mortgages lent by banks. - Empirical determinants (selected regression results): - Employment equation: Lag Pr (Employment = 1): 0.64***. - Consumption regression: Lag gross income: 6.2e-6***; Net income: 0.07***; Saving (stock): -6.03e-6***; Age: 13.5***. - Pr (Mortgage arrear =1): Mortgage payment to gross income ratio (DSTI): 0.001*; Mortgage to house value (LTV): 0.007*; Mortgage interest rate: 7.99***. - Mortgage arrears mapping and LGR estimation: - Historical actual mortgage arrears (2012–2019): 1.6 percent (calibration target). - Baseline LGR: about 17 percent of repossessed mortgage portfolio; represents about 0.3 percent of total outstanding mortgages when average arrears ≈1.3 percent. - Adverse inflationary/tightening scenario: LGR 21 percent; average mortgage arrears peak 2.8 percent; repossessed share 0.9 percent of outstanding mortgages. - Repossession loss assumption: repossessed house value assumed 25 percent below market value. - Scenario impacts on mortgage arrears: - Protracted recession with scarring: average mortgage arrear probability similar to baseline (effects from lower income offset by lower interest rates). - Inflationary and tightening scenario: average probability of mortgage arrears peaks at 2.8 percent in 2022 (more than double baseline and above GFC peak). - Distributional findings: - Bottom income quintile has highest PD of arrears; increased savings in 2020 concentrated in top two quintiles; bottom quintile dissaved. - Transmission: - Banks hold >80 percent of mortgage balances; given current bank capitalization, projected mortgage losses would be absorbable. ### Banking system solvency and stress testing — methodology and key results - Banking system metrics (start point end-2020): - Aggregate CET1 ratio: 15.6 percent. - NPL ratio: 1.8 percent. - Return on assets: about 0.4 percent in recent years. - Solvency stress test scope and assumptions: - Top-down solvency stress-test of eight major U.K. banks and building societies; five-year horizon (2021–2025). - Risk channels: credit risk, market risk, interest rate risk. - Results reported on a fully loaded basis (IFRS9 transitional arrangements not accounted for). - Quasi-static balance sheet assumption; gross exposures grow in line with nominal GDP; banks can build capital only through retained earnings; dividend payout rule: 30 percent under positive profits and capital ratios above hurdle rates, otherwise zero. - PD and LGD modelling: - Satellite PD models link PDs to macro variables (GDP growth, unemployment, house price growth, exchange rate, interest rates, inflation). - Mortgage PDs under scenarios: - Domestic mortgage PDs increase by 4pp at start of 2022 under adverse scenario 1; under adverse scenario 2 peak at 7.7 percent by end-2023. - LGD for mortgages (PiT): LGD_bt = (1 − %highLTV_b) * LGD0 + %highLTV_b * [1 − (1 − LGD0) * min(HousePrice_t / HousePrice_0, 1)]. - Solvency stress test results (aggregate): - Aggregate CET1 ratio declines at low points (2022) of adverse scenarios: - by 2.0 percentage points under adverse scenario 1, - by 5.0 percentage points under adverse scenario 2, - declines driven mainly by loan and market losses. - Despite declines, aggregate CET1 ratio remains above estimated aggregate hurdle rates in all years. - At individual level, two banks fall slightly below hurdle rates before conversion of AT1 into CET1 under most severe scenario. - AT1 conversion effect: conversion increases low-point aggregate CET1 ratio by 30bp; one bank recovers above hurdle after AT1 conversion. - Bank-level CET1 shortfalls: CET1 shortfalls amount to 0.08 (0.035) percent of GDP in 2022 (2023) respectively in the severe scenario; factoring AT1 conversion shortfalls drop to zero in 2022 and remain at 0.035 percent of GDP in 2023. - Fintech overlay results: - Fintech shock: bank credit market share decreases by 3.5 percent in 2021, 2022 and 2023 (cumulated 10 percent). - System-wide capital depletion rises to 2.5 percent (from 2 percent at low point) when adding Fintech Overlay under adverse scenario 1. - By end of five-year horizon CET1 ratio would be 1.6 lower under Fintech Overlay. ### Liquidity resilience — banking and insurance - Banking liquidity: - Liquidity Coverage Ratios (LCRs) are currently well above regulatory standard of 100 percent for 110 domestic banks surveyed. - Under progressively severe haircut and outflow scenarios, almost all banks maintain high ‘total currencies’ liquidity ratios; seven banks experience stressed LCRs below 100 percent in some severe scenarios (only moderately). - Most extreme combined scenario: GBP 46 billion gap between net outflows and HQLAs over 30-day horizon. - FX single-currency analysis: with regulatory cap on inflows, aggregate FX liquidity gap could be overstated; removing cap shrinks base-case aggregate FX liquidity gap to GBP 5 billion; most extreme combined scenario FX gap about GBP 20 billion. - Insurance liquidity (variation margin focus): - Sample of five large life insurers: even sizable upward shifts in interest rates as single stress to swap positions would not cause systemic liquidity stress given existing buffers. - For a 100-bps shock, more insurers would have to liquidate on aggregate 7 percent of their highest-quality sovereign bonds. - Encumbrance: around 10 percent of sovereign bonds in highest credit steps encumbered; corporate bonds encumbered share below 4 percent. - PRA experience March 2020: insurers "stopped investing cash inflows" and "withheld dividend payments"; insurers avoided asset sales; PRA plans to require specific liquidity data from certain insurers. ### Insurance solvency stress test — methodology, shocks, and results - Sample and coverage: - Top-down solvency stress test covering 14 large insurers (8 life, 6 general); market coverage ~70 percent by gross written premiums. - Aggregated balance sheet assets: GBP 1,879bn total (GBP 1,738bn to groups predominantly life business). - Pre-stress solvency: all 14 participants record solvency ratios well above regulatory threshold of 100 percent (with LTG measures and transitionals). - Scenario shocks (selected values): - Equity shocks: U.K. Scenario 1: -19.5% ; Scenario 2: -15.8%; US/Euro area Scenario 1: -25.0% ; Scenario 2: -15.0%. - Sovereign bond spread shocks (percentage points): U.K. Scenario 1: 0.80% ; Scenario 2: 0.50%. - Property shocks (price changes): Residential domestic Scenario 1: -14.6% ; Scenario 2: -8.4%; Commercial domestic Scenario 1: -29.7% ; Scenario 2: -20.1%. - Results — Scenario 1 ("scarring"): - Life insurers more affected than general insurers. - Median life firm: excess of assets over liabilities declines by 17 percent; assets-to-liabilities ratio declines from 106.4 to 103.6 percent. - Median life SCR ratio drops from 158 to 116 percent; two life insurers post-stress SCR ratios below 100 percent with aggregated capital shortfall GBP 9bn. - Median general insurer assets-to-liabilities ratio after stress: 126.1 percent (down from 129.0 percent). - Results — Scenario 2 (tightening): - Aggregate impact milder; most life insurers see higher solvency ratios due to interest rate rise reducing liabilities. - Median life firm SCR ratio after stress: 187 percent (up from 158 percent). - Median general insurer SCR ratio marginally declines from 149 to 146 percent. - Sensitivity — default of largest banking counterparty: - Excess of assets over liabilities declines on average by 3 percent for life insurers and <2 percent for general insurers. - SCR ratio declines by 4 and 2 percentage points in life and general samples respectively. - Liquidity stress test for life insurers (variation margin scenarios): - No systemic liquidity stress for sample; for 25–100 bps shocks insurers may need to use sovereign holdings or group liquidity; individual firms could be pressured if derivative margin calls coincide with stressed outflows. ### Climate-related vulnerabilities — transition and physical risk analysis - Transition-risk methodology: - "Climate Minsky moment" assumption: switch in expectations to a high and steep carbon price path; climate Minsky moment assumed in year 2024; long-term simulation to 2050 but impacts estimated within five-year horizon. - Scenarios: NGFS Phase I ('1.5° with Carbon Dioxide Removal') and Phase II (Net Zero 2050); analyses use REMIND-MAgPIE, GTAP-E and Climate Credit Analytics (CCA) model suite. - Banks (sample of eight large banks) — transition results: - Average loan portfolio loss: 1.1 or 3.6 percent depending on carbon price path steepness (Phase I vs Phase II). - Banks’ market losses (equity and corporate bond holdings): on average 2.5 or 4 percent under same scenarios. - Phase I combined economic losses across corporate exposures: GBP 31bn; Phase II losses > GBP 90bn. - Sensitivity: raising all risk premia by ½ under NZ2050 raises banks’ aggregate credit losses to 5.8 percent (GBP 126 billion). - Insurers — transition results: - Phase I sample-wide valuation loss: close to GBP 40bn; life insurers account for ~GBP 38bn. - Phase II valuation losses: GBP 66bn. - Equity holdings decline on average by up to 11 percent in Phase II; across asset classes loss corresponds to around 2 to 4 percent for most insurers in sample. - Investment funds and pensions: - Investment funds (sample ~2,000): average portfolio loss ~0.32 percent under Phase I (dispersion from +0.5 to -2 percent excluding outliers). - Defined benefit pension funds (sample ~70): losses on average 2 or 3.5 percent depending on scenario (Phase I/Phase II). - Residential mortgage portfolio (transition sensitivity and EPC-based exercise): - Sensitivity combinations (NZ2050/DNZ × pass-through 100%/50% × cost median/max). - Worst-case combination (DNZ + MAX cost + 100% pass-through): weighted average valuation impact almost 5 percent (regional range ≈2–11 percent; some local areas almost 20 percent). - LGD impacts: best case LGDs increase ≤ 0.5 percentage point; worst case LGDs increase ≈ 1 percentage point. - Aggregate loan loss provisions increase between 5.6 percent (best case) and almost 17 percent (worst case). - Physical risk findings: - Sovereign bond holdings under RCP 8.5 to 2050: mean temperature only → average drop 0.6 percent in banks’ sovereign portfolios (range 0.3–1.2 percent); if variability rises too, average impact up to 3 percent with half banks >5 percent. - Insurers’ catastrophe exposures: expected mean annual losses — GBP 2.3bn (US hurricane), GBP 0.8bn (European windstorm), GBP 0.4bn (U.K. flood); 1-in-200-year pre-reinsurance losses: GBP 26bn (US hurricane), GBP 13bn (EU windstorm), GBP 5bn (UK flood). - Scenario of +30 percent severity and frequency increases mean annual losses up to 50 percent pre-reinsurance. - Caveats: - Results conditional on 'orderly' transition scenarios; outcomes likely less benign under 'disorderly' transition. - Analyses are top-down, first-order, and do not model second-round macroeconomic feedbacks or policy responses. - Data limitations (e.g., insurer asset sector granularity) constrain precision. ### Macro-financial feedbacks and iterative procedure - Iterative algorithm linking bank solvency stress test to SVAR (foreign block, domestic block, two banking variables). - Credit growth bridge model (1994Q2–2019Q4) uses changes in CAR and NPLR to project credit growth; SVAR estimation period 2000Q1–2019Q4. - Macro-financial feedback findings (first adverse scenario): - Real GDP growth: reduced by additional 0.65 percentage points in 2022 and 0.37 percentage points in 2023. - Unemployment: higher by 0.24 percentage points in 2022 and 2023. - PDs of domestic portfolios: increased by 33 basis points in 2022 and 43 basis points in 2023. - Capital ratios: reduced by 57 basis points on average across scenario horizon. ### RWAs, market risk, P&L modelling, and other technical elements - RWAs: - STA exposures: project balance sheet growth, structural FX growth, triggered credit lines. - IRB exposures: use Basel formula translating TTC PDs/LGDs into stressed RWAs; TTC PDs updated based on PiT PDs with smoothing parameter. - Market risk: - FVPL and FVOCI securities: mark-to-market modified duration approach; credit spreads stressed only for adverse scenario 1; spread shocks split equally over first three years. - Equity holdings revalued based on country-specific equity paths for 17 jurisdictions and world. - Accounting class determines capital impact (realized vs unrealized losses). - P&L components: - Interest income/expense models driven by bank rate; projected implied net interest margin ratio increases across scenarios (stronger in adverse scenario 2). - Satellite models for fee/commission income and non-interest expense use real GDP growth as driver. - Fintech overlay includes competition proxy (bank credit share) and assumed market share loss leading to reduced interest expense and fee income; main channels reducing capital ratios are interest expense ratio and fee & commission income ratio. - Stress-test behavioural assumptions: - Quasi-static balance sheet in main exercise; banks only build capital via retained earnings; no new equity issuance assumed. ### Data, validation, and supervisory recommendations (selected highlights) - Data gaps and needed improvements: - Reduce unidentified exposures in Who-to-whom NBFI statistics (ONS supported by BOE and FCA) — MT. - Augment banks' data reporting on non-financial corporate exposures; standardize corporate and industry identifications — BOE — MT. - Collect granular consumer credit data by lender and product, with loan performance and borrower credit conditions — BOE, FCA — MT. - Enhance usability of micro-data collected for bank stress testing via revised validation/plausibility rules and stricter resubmission criteria — BOE — NT. - Improve granular data on credit risk (loan-level non-mortgage retail), interest rate risk, and market risk — BOE/FCA — MT. - Consolidate internal toolkit for top-down stress testing to run independent exercises covering systemically relevant entities — BOE/PRA — MT. - Deepen analysis of risks outside last decades’ experience (e.g., stagflation with abrupt tightening) — BOE/PRA and FCA — MT. - Expand supervisory reporting for insurers to allow top-down liquidity analyses (e.g., derivative holdings, cash definitions) — PRA — NT. - Develop tools for top-down analysis of climate-related risks across authorized financial firms and gather exposures/management data — BOE, FCA, TPR — MT. - Analyse network effects in propagation of climate-related risks — BOE, TPR — MT. - Provide guidance on risks to be considered within insurers’ ORSA, including indirect climate risks and litigation risks — PRA — MT. - PRA-specific recommendations: - Enhance data quality checking and supervisory reporting completeness/consistency for insurers (S.06.02, S.02.01, S.08.01). - Augment focus on liquidity risks and require specific liquidity data from certain insurers, especially annuity writers and insurers with large derivative holdings. ### Risk Assessment Matrix — selected risks and staff likelihoods - Global resurgence of COVID-19 variants: Likelihood = Medium; expected impact: prolonged weakness for contact-intensive sectors, corporate bankruptcies, bank losses, weaker credit growth. - Disorderly transformations (post-COVID): Likelihood = Medium; expected impact: reshuffling of global value chains, higher production costs, lower potential output, corporate insolvencies, bank asset-quality deterioration. - De-anchoring of U.S. inflation expectations: Likelihood = Medium; expected impact: higher debt service/refinancing costs, defaults, real estate corrections, mark-to-market losses. - Higher frequency/severity of natural disasters (climate): Likelihood = Medium; expected impact: higher PDs for corporates and households, collateral value declines, higher bank credit losses; potential sharp reassessment of asset values in disorderly transition. *Source: EXECUTIVE SUMMARY AND RECOMMENDATIONS (1gbrea2022003).* ### EXECUTIVE SUMMARY AND RECOMMENDATIONS ________________________________________________ 9 ### EXECUTIVE SUMMARY AND RECOMMENDATIONS ### Context and Scope - The FSAP was conducted right after the second Covid wave and a third lockdown, focusing on balance sheet resilience of major institutional sectors. - Core analysis tool: stress tests assessing potential vulnerabilities under two adverse scenarios versus a baseline scenario based on the October 2021 WEO assumptions. - Stress test coverage: - Corporates and households: financial sector exposures to indebted corporates and less creditworthy households and potential systemic implications. - Banks and insurers: resilience to solvency and liquidity pressures. - Climate-related risk assessment for banks and insurers: both physical and transition risks (complementing the BOE’s climate work). ### Adverse Scenarios (as described) - Protracted recession with a prolonged pandemic. - Sharp tightening of global financial conditions. - Baseline comparator: October 2021 WEO assumptions. ### Key Findings - The comprehensive policy response to the pandemic was effective and helped support financial stability. - Remaining and emerging vulnerabilities warrant further analysis and enhanced data availability, especially as most pandemic support measures have expired and the economy undergoes structural transformations. - Concentration of vulnerabilities: - SMEs, particularly in sectors hardest hit by the pandemic. - Low-income households. - Under adverse scenarios: - The estimated corporate liquidity and equity gaps could double, compared with the baseline scenario. - Household mortgage risks could increase sizably. - Banking system capitalization: - Current levels of bank capitalization are high and would help to absorb losses if risks materialize. - Under adverse scenarios the banking system capital ratios could decline by 2.0 to ### Analytical Focus and Methods - Balance sheet resilience and financial stability analysis driven by stress testing. - Climate-related risk analysis covered both transition and physical risks for banks and insurers. - The analysis emphasizes the interplay of macro-financial/structural conditions and financial vulnerabilities. *Source: EXECUTIVE SUMMARY AND RECOMMENDATIONS (1gbrea2022003)* ### 5.0 percentage points, driven mainly by loan and market losses. Despite this decline, system ### 1gbrea2022003 - 5.0 percentage points, driven mainly by loan and market losses. Despite this decline, system ### Bank capital and solvency findings - System capital ratios decline by 5.0 percentage points, driven mainly by loan and market losses. - Despite this decline, system capital ratios would remain above estimated aggregate hurdle rates. - At the individual level, two banks fall slightly below their hurdle rates—before conversion of AT1 instruments into CET1 capital—under the most severe scenario. - Initial macroeconomic shocks could be amplified through weaker credit growth if macro-financial effects are at play. - Top-down solvency stress test covered 14 larger U.K. insurers and applied two severe scenarios to insurers’ balance sheets as of end-2020, covering around 70 percent of the market. ### Liquidity resilience - The banking system is overall liquid and resilient to sizable withdrawals of funding and haircuts to liquid assets. - Liquidity Coverage Ratios (LCRs) are currently well above the regulatory standard of 100 percent for the 110 domestic banks surveyed. - Almost all banks would maintain high ‘total currencies’ liquidity ratios under progressively severe scenarios (rising haircuts and increasing outflows); only a few banks would experience LCRs moderately below 100 percent in some scenarios. - Analysis by single currency (no formal regulatory threshold) reveals potential FX liquidity shortfalls that would require more granular information to quantify accurately. ### Insurers: solvency and liquidity stress results - In the “scarring” scenario: - Life insurers are considerably more affected than general insurers. - While all life insurers would still sufficiently cover their liabilities with assets, the excess of assets over liabilities declines by more than 15 percent for the median firm. - Solvency ratios of two firms would drop below the 100 percent threshold. - Lower interest rates increase liabilities, partly offset by the Matching Adjustment rising with higher credit spreads. - General insurers: balance sheet impact is smaller; solvency ratios remain well above 100 percent. - In the “tightening financial conditions” scenario: - Aggregate impact on both sectors is milder; most life insurers would see higher solvency ratios. - Sharp increase in interest rates compensates for losses on investment assets as liabilities decline with higher discount rates. - For the median general insurer, the solvency ratio declines marginally. - Analysis does not account for the effect of higher claims inflation on general insurers’ earnings. - Life insurers and variation margin: - Analysis of five large life insurers shows even sizable upward shifts in interest rates, as a single stress to swap positions, would not cause systemic liquidity stress given existing sufficient buffers. - Individual firms might need to liquidate sovereign bond holdings or rely on group liquidity. - Liquidity risks increase when margin calls from other derivative types occur simultaneously or combine with stressed outflows (policy surrenders, catastrophe events, or lower premia). ### Climate-related vulnerabilities - Analysis focused mainly on transition risks linked to exposures to corporate counterparts, using two orderly transition scenarios from NGFS (‘1.5° warming with carbon dioxide removal’, and an orderly transition to net zero by 2050 with 2x higher carbon prices). - For banks’ corporate loan portfolios (sample of eight large banks): - Average loss would be 1.1 or 3.6 percent, depending on the steepness of the carbon price path. - For banks’ market losses (equity and corporate bond holdings): - Could represent, on average, 2.5 or 4 percent of their portfolios under the same scenarios. - For a sample of 70 defined benefit pension funds: - Losses would represent, on average, 2 or 3.5 percent of their portfolios. - For insurers: - Across all asset classes, loss in the investment portfolio would correspond to around 2 to 4 percent for most insurers in the sample. - Equity holdings would on average decline by up to 11 percent. - General insurers’ catastrophe exposure: - Largest exposures toward US hurricanes and European windstorms. - A combination of higher severity and frequency of natural disasters (each up by 30 percent) would increase future annual losses of general insurers by up to 50 percent. - Caveats: - Results conditional on adoption of ‘orderly’ transition scenarios; outcomes likely less benign under a ‘disorderly’ transition. - Analysis focused on sector and company level using NGFS GDP path but did not explicitly model other macroeconomic variables. - Suggestion to compare these top-down results with BOE’s Climate Biennial Exploratory Scenario (a bottom-up exercise). ### Adverse macro scenarios and macro-financial risks - Baseline scenario draws from the October 2021 WEO forecast. - Adverse Scenario 1 (Adv. – Scarring): - Protracted recession with lasting economic scars; pandemic assumed controlled not earlier than late 2022 for advanced economies and by end of 2023 for the rest of the world. - Real GDP growth of only 0.5 percent in 2021; recovery by only 1.6 percent in 2022. - Scarring manifests as lower potential output growth by 0.3% with respect to the pre-COVID period and higher natural unemployment rate. - Adverse Scenario 2 (Adv. – Tightening GFC): - Surge in global inflation and sharp tightening of global financial conditions driven by higher energy and commodity prices, reduced globalization, and rising term premia. - Equity prices flat near term but decline as policy tightening becomes inevitable; sterling depreciation contributes to goods price inflation. - By 2025 (the end of the risk horizon), U.K. real GDP is still 2 percent lower than under the baseline. - Main macro-financial risks identified: - Global resurgence of COVID-19 variants. - Tightening of global financial conditions if global inflation risks persist. - Supply-demand mismatches, commodity price rises, and potential protracted inflationary environment. ### Policy recommendations (selected highlights from Table 1) - Continue reducing the size of unidentified exposures in experimental statistics on NBFI balance sheets (Who-to-whom data by ONS). — ONS supported by BOE and FCA — MT - Consider augmenting banks' data reporting on non-financial corporate exposures, particularly standardizing reported corporate and industry identifications. — BOE — MT - Collect granular data on consumer credit by type of lender and product, with attention to loan performances and borrower credit conditions. — BOE, FCA — MT - Enhance usability of micro-data collected for bank stress testing via revision of validation and plausibility rules and stricter criteria for data resubmissions by banks. — BOE — NT - Improve availability and quality of granular data on credit risk (particularly at loan level for non-mortgage retail exposures), interest rate risk and market risk. — BOE/FCA — MT - Complete and consolidate internal toolkit for top-down stress testing to run independent full-fledged top-down exercises covering all systemically relevant entities. — BOE/PRA — MT - Deepen analysis of risks outside last decades’ experience (e.g., stagflation with abrupt tightening) to assess firms’ readiness. — BOE/PRA and FCA — MT - Expand supervisory reporting for insurers to allow comprehensive top-down analyses of liquidity risks (e.g., data on derivative holdings, definitions of cash). — PRA — NT - Develop tools for top-down analysis of climate-related risks across all relevant authorized financial firms, including gathering data on exposures and management of climate-related risk. — BOE, FCA, TPR — MT - Analyse network effects in propagation of climate-related risks across the financial system. — BOE, TPR — MT - Provide guidance on risks to be considered within an insurer’s ORSA, including indirect climate risks and litigation risks. — PRA — MT *Source: IMF staff technical note (1gbrea2022003).* ### 6. This chapter analyzes post-COVID-19 pandemic vulnerabilities of the non-financial ### 6. This chapter analyzes post-COVID-19 pandemic vulnerabilities of the non-financial ### Overview: scope and objectives - Focus: post-COVID-19 pandemic vulnerabilities of the non-financial corporate (NFC) sector and transmission channels for risks to financial stability. - Objectives: - Assess both liquidity and solvency risks of corporates at the firm level. - Test systemic resilience of indebted corporates. - Conduct firm-level panel regressions on a large sample of British firms to: - explore determinants of liquidity and equity gaps, including role of macro-financial conditions; - assess liquidity and solvency risks under various macro-financial stress test scenarios; - characterize risks to financial stability. ### Key aggregate developments in NFC balance sheets - Corporate debt and liquidity changes: - Corporate debt increased by seven percentage points of GDP in 2020. - NFC private nonfinancial corporate debt reached 78 percent of GDP in 2020 (up seven percentage points), down from a peak of 88 percent in 2008. - Corporate debt further declined during 2021, to about 73 percent of GDP as of end-2021 Q3. - Corporates' liquidity position improved by 11 percentage points of GDP in 2020. - Composition and dynamics: - Close to half of the increase in corporate debt in 2020 was in short-term loans, followed by market-raised debt securities (long-term loans fell while debt securities increased by about three percentage points of GDP). - At end-2019, about one-third of corporate debt was short-term loans (down from almost half in 2008). - Debt service to income ratio dynamics mirrored the debt level; decline in corporate profits in 2020 contributed to rise in debt burden. - Large corporate debt ratio in 2021 is now lower than end-2019 level. ### Credit provision and creditor structure - Creditor composition: - Both banks and nonbank financial institutions (NBFIs) are significant creditors to NFCs. - FSAP estimates NBFIs provide slightly more loans to NFCs than banks (combined multiple data sources). - NBFIs are important lenders to SMEs, represent a small but material share, and tend to focus on clients with shorter credit histories and weak collateral. - Lack of granular NBFI data prevents further analysis by firm size or sector. - Bank lending shifts in 2020: - SMEs' bank loans increased by £49 billion in 2020; large corporates' bank loans declined by £4 billion. - Corporate debt securities increased by £40 billion in 2020. - Equity liabilities increased by £74 billion in 2020. - Among sectors, real estate and professional services hold the largest share of outstanding bank loans; pandemic-hit sectors (recreation, accommodation, wholesale and retail trade, transportation) increased bank credit significantly in 2020. - Additional bank credit was largely backed by government loan guarantees. ### Role and design of public support measures - Public support mitigated aggregate corporate vulnerabilities: - Grant schemes, business rates relief, CJRS (furlough), government guarantee programs, Bank Rate cuts, and Term Funding Scheme supported SMEs and corporates. - Example business rates relief: 100 percent relief for retail, leisure, hospitality, nurseries, and pubs from April 2020 to June 2021; 66 percent reduction for remaining nine months of 2021-22 fiscal year (July 2021 – March 2022). - These measures helped avoid massive business failure; total number of company insolvencies remains below pre-COVID level. - For empirical analysis, four major support measures are applied to individual firms: - CJRS: individual firm receipts proportional to wage bills, adjusted by sectoral shares of furloughed workers and total CJRS receipts of SMEs in the sample. - Table 2 CJRS costs: - 2020 Total cost £46 billion; To SMEs £31 billion (65%); To SMEs in ORBIS sample £4.6 billion - 2021 Total cost £23 billion; To SMEs £18 billion (65%); To SMEs in ORBIS sample £2.6 billion - Business rates reliefs: based on sectoral business rate-to-turnover rate, adjusted for relief months and rates. - Grants: proportional to business rates reliefs and adjusted to match total grants disbursed. - Government-guaranteed loans: applied at sectoral level via sectoral growth rates of MFIs' lending; Table 3 shows sectoral CBILS & BBLS disbursements and MFIs’ net lending to SMEs and large corporates (Total CBILS & BBLS* 72.1; MFIs’ net lending to SMEs 48.7; MFIs’ net lending to large corporates -4.0). ### Empirical approach and vulnerabilities definitions - Firm-level structural models estimate counterfactual 2020 firm profits absent government support, then incorporate support measures. - Key profitability model (ROA): ROA_i,s,t = α·ROA_i,s,t−1 + β·FirmChar_i,s,t−1 + γ·MarcoFinan_s,t + dummy_s + ε_i,s,t - Firm characteristics: leverage (liability-to-asset ratio), relative size (total assets to sectoral average), fixed assets ratio, sales-to-assets, sales growth. - Macro-financial indicators: sectoral GVA growth rate, inflation, oil price growth rate, short-term interest rate, nominal effective exchange rate, financial condition index. - Definitions of financial stress: - Liquidity gap (illiquidity): current assets insufficient to cover net operational cash outflows and debt services. - Equity gap (insolvency): negative equity (liabilities exceed assets). - The analysis uses book values and focuses on changes induced by COVID-19; firms with pre-pandemic gaps were excluded from COVID-induced gap calculations. ### Main empirical findings: SME vulnerabilities and impact of support - FSAP concentration of vulnerabilities: - Vulnerabilities concentrated in sectors hardest hit by the pandemic; could intensify under adverse scenarios. - SMEs estimated to face: - Liquidity shortfall of about 2 percent of turnover and an equity gap of about 1½ percent of turnover in 2022–23. - In the accommodation sector, liquidity gap 4 percent of turnover and equity gap 3 percent of turnover in 2022–23. - Under adverse scenarios, liquidity and equity gaps could increase to 3-4 percent of turnover. - Policy impact (accommodation sector example): - Without policy support: - 43 percent of SMEs in accommodation would have faced liquidity shortfalls in 2020; 33 percent in 2021. - 9 percent and 12 percent of SMEs in accommodation would have encountered negative equities in 2020 and 2021, respectively. - With policy support: - Share of illiquid SMEs in accommodation reduces to 6 percent in 2020 and 20 percent in 2021. - Estimated negative-equity SMEs in accommodation reduce to 5 percent in 2020 and 8 percent in 2021. - Aggregate SME gap dynamics under baseline: - SME liquidity gap: 0.6 percent of turnover in 2021 → 1.9 percent in 2022 → 1.7 percent in 2025. - SME equity gap: 1 percent of turnover in 2021 → 1.4 percent in 2025. - Note: analysis assumes sectoral compositions gradually return to pre-pandemic levels by 2024; structural shifts not considered. ### Stress-test scenarios and implications - Two adverse scenarios (described in Chapter two) with no discretionary policy assumed; firms weakened mainly via lower revenues and rising financing costs. - Protracted recession with scarring scenario: - Financial stress of SMEs intensifies, especially in hard-hit sectors. - Share of illiquid SMEs in accommodation increases from 20 percent (baseline 2021) to more than 30 percent. - Total estimated liquidity shortfalls increase to about 2.7 percent of turnover and remain around this level over the medium term. - Estimated equity gap reaches 3.2 percent of turnover over the medium term (more than double baseline). - Inflationary and tightening financial conditions scenario: - Impact concentrated on leveraged firms where higher interest rates and risk premia outweigh stronger near-term growth. - Despite smaller share of stressed firms, total estimated liquidity shortfall peaks at 3.7 percent of turnover in 2023 when output growth turns negative. - Estimated SME equity gap reaches 0.9 percent of turnover in 2025. - Transmission to financial sector: - NFC financial stress could increase probability of default in banks’ corporate loan portfolios. - Corporate defaults could lead to some losses to the financial sector, but the sector is well capitalized to absorb them. *Source: https://www.imf.org/-/media/files/publications/cr/2022/english/1gbrea2022003.pdf* ### 19. The FSAP further explores the financial sector's exposures to corporate ### 19. The FSAP further explores the financial sector's exposures to corporate vulnerabilities. ### Corporate exposures and empirical PD estimation - With detailed information on the bank's corporate exposures (reported by major banks), the FSAP matches some corporations' financial conditions (from ORBIS) with their outstanding loans with major banks at end-2019. - Due to inconsistent reporting by banks on corporate identifications and incomplete financial information from ORBIS, a large part of the information is dropped during the matching process; results may not be representative of the whole corporate population or banks' corporate exposures. - The FSAP estimates the relationship between bank-defined default status of a corporate's loans and the corporate's financial indicators and macro-financial conditions (see Table 4). Based on the empirical analysis, the FSAP projects each corporate loan's probability of default (PD, based on bank’s definition of loan default) over the medium term under baseline and two adverse scenarios. - Table 4. Corporate Profit Regression—Default on Bank Loans (coefficients) - Return on assets: -3.637***, -3.632*** - Liability to assets ratio: 0.169, 0.171 - Liability to equity ratio: -0.0000167 - Relative size (asset to sectoral average): -0.151**, -0.152** - Fixed assets to asset ratio: 1.824***, 1.793*** - Sales to assets: 0.00737 - Sales growth: -0.977***, -0.957*** - Sectoral GVA growth: -4.354**, -4.154* - Constant: -4.950***, -4.922*** - Observations: 12,884; 12,908 - Pseudo R-squared: 0.086, 0.086 - Note: *** (**) (*) denotes 1 (5) (10) percent significance level. - Key findings on corporate PDs and sectoral impact: - Sharp increase in the average PD given firms' financial conditions in 2020; actual firm insolvencies were very low, aided by comprehensive policy actions. - Estimated average PD improves from 2020 to 2021 but gradually increases throughout the projection period. The decrease from 2020 to 2021 is due to improved economic growth; further increases from 2022 onwards reflect persistent rises in leverage ratios, which are positively correlated with PD. - Recreation and accommodation sectors see the most significant increases in estimated PD by end-2025 compared with pre-pandemic levels: - by 3¾ - 4¾ percentage points under the baseline and protracted recession with scarring scenarios - around 2½ percentage points under the inflationary and tightening of financial conditions scenario. - Current capitalization levels at banks are high; the sector is well capitalized to absorb these losses. - Due to data limitations, the FSAP cannot conduct a similar analysis on NBFIs to assess their exposures to corporate vulnerability. *Source: IMF staff calculations.* --- ### HOUSEHOLDS ### Macro-level household balance sheet trends - Households' financial assets: - Increased from 285 percent of GDP at end-2010 to 307 percent of GDP at end-2019, and further to 349 percent of GDP at end-2020. - Households' financial liabilities: - Declined from 97 percent of GDP to 86 percent of GDP from 2010 to 2019, but increased to 93 percent of GDP in 2020. - Households' net financial wealth: 256 percent of GDP at end-2020. - Note: Large contraction in GDP in 2020 contributes to increases in household financial assets and liabilities relative to GDP. Analysis focuses on financial assets and liabilities (real assets considered when estimating mortgage losses). ### Concentration of vulnerabilities and headline findings - Vulnerabilities concentrated in low-income households; an abrupt tightening of financial conditions would exacerbate household financial stress. - In the baseline scenario, mortgage arrears would increase moderately in 2021 and 2022, comparable with historical averages. - Bottom income quintile has the highest average probability of default. - Under the inflationary and tightening of financial conditions scenario, average probability of mortgage arrears would exceed the peak level during the GFC. - Given banks' exposure to mortgage loans and current capitalization, financial losses from mortgage default could be absorbed. ### Development of household balance sheets (pre- and during pandemic) - At end-2019, U.K. household debt: 143 percent of disposable income (second highest among G7 after Canada). - Debt burden (debt to disposable income ratio) declined by about 15 percentage points from 2009 to 2019, driven mainly by mortgages; consumer credit increased slightly. - Mortgages account for more than 70 percent of household debt; more than 80 percent of mortgages lent by banks. NBFIs take an important share in unsecured consumer credit. - Household financial assets increased significantly in 2020 by more than 40 percentage points of GDP; total outstanding mortgage debt increased by about five percentage points of GDP. - Debt burden remained stable on average but increased moderately among low-income households. 2020H2 NMG household survey: the lowest income quintile reported an average mortgage-to-income ratio that was 2.5 times income greater than the average ratio reported by the lowest income quintile in the 2019H2 survey; for the whole sample the ratio barely changed. ### Policy support and mortgage market structure - Government support measures during pandemic: - CJRS covered up to 80 percent of wages of workers not working due to the pandemic while keeping their jobs. - Universal Credit (UC) increased by £20 per week; conditions to access UC were relaxed. - Temporary liquidity support and guidance for mortgage payment deferral schemes. - Mortgage market recommendations (introduced in 2014): - Limiting mortgages with loan-to-income (LTI) ratios of 4.5 percent or higher to 15 percent of new mortgage lending. - An affordability test that ensures households can still afford mortgages if, at any point over the first five years of the loan, the mortgage rate was to be 3 percentage points higher than the reversion rate at origination. - Higher-risk mortgage segments have declined since the GFC; mortgages at LTV > 95 percent stayed low. ### Empirical determinants of household debt-at-risk - Regression framework estimated using NMG survey data: - Employment probability and consumption equations (Equations (2) and (3)) use lagged employment/consumption, household characteristics (gross income, net income, outstanding mortgages and consumer credit, age, number of adults, number of children), and macro-financial indicators (unemployment rate, inflation). - Employment probability is adjusted to match macro forecast unemployment rates under baseline or adverse scenarios. - Table 5. Household Regressions (selected coefficients and statistics) - Pr (Employment = 1): Lag Pr (Employment = 1): 0.64***; Lag Pr (Employment = 1) appears also as -0.35*** in another column. - Consumption: Lag gross income: 6.2e-6***; Net income: 0.07***; Saving (stock): -6.03e-6***; Age: 13.5***; Inflation: 0.10***; Constant: -847.46***; Observations: 1,726; R-squared: 0.72. - Pr (Mortgage arrear =1): Mortgage payment to gross income ratio (DSTI): 0.001*; Mortgage to house value (LTV): 0.007*; Mortgage interest rate: 7.99***; Consumer credit: 0.008; Saving (stock) effect shown in consumption regression; Age: -0.01***; Inflation: 0.10***; Constant: -0.55***; Observations: 11,765; R-squared: 0.05. - Overall observations: Employment equation observations 6,776; R-squared 0.53 for employment regression. - Note: *** (**) (*) denotes 1 (5) (10) percent significance level. - Modeling of household dynamics (summary of implementation) - Employment and income: Gross income evolves at average wage growth and is scaled by change in employment probability (Equation (4)). If prob(E_i,t=1)=0, gross income projected from unemployment insurance and inflation rules. - Consumption and saving: Increase in household saving stock equals household net income after consumption; the analysis assumes no new mortgages or changes in consumer credit. - Mortgage and house value: Outstanding mortgage stock reduced by principal in each payment; mortgage interest rate adjusted when current fixed rate expires; assumption of only one remortgage for each existing mortgage loan over forecast to 2025; house value grows with national average house prices projected under each scenario. ### Mortgage arrears definition and adjustment - Mortgage arrear defined as households reporting more than two months behind mortgage payments, excluding payment holidays, according to the NMG survey. - Reported share of households behind mortgage payments: average 14 percent between 2012 and 2019 (survey), versus about 1.6 percent share of loans in arrears in mortgage lending statistics from FCA. Estimated mortgage arrears are adjusted for this difference. ### Role of government support measures post-pandemic - In the NMG survey, reported share of mortgages in arrears is one percentage point (unadjusted) lower than in 2019 despite GDP contraction and business closures. - Average mortgage arrears projected to increase moderately in 2021 and 2022 while remaining comparable with historical averages; from 2023 onwards average mortgage arrears would decline and stay low over the medium term. - Analysis focuses on existing mortgage debt stock at end-2020 and does not assume new mortgage loans; average mortgage arrears expected to gradually decline as mortgage payments reduce outstanding stocks while household incomes grow. ### Distributional aspects - Bottom quintile income group: - Least reduction of mortgage arrears in 2020 and highest projected average probability of arrears over the projection period. - Increased savings in 2020 concentrated in the top two income quintiles; bottom income quintile dissaved. - Projected probability of mortgage arrears in 2021 and 2022 about one percentage point higher than the top quintile. ### Stress analysis and scenario outcomes - Channels: household employment status (linked to output growth) and debt burden (linked to interest rates). - Under the protracted recession with scarring scenario: - Lower-income quintiles face higher unemployment risk; income shortfalls impair debt servicing; however, interest rates decrease further, alleviating some debt burdens. FSAP finds average mortgage arrear probability would be like the baseline scenario. - Under the inflationary and tightening of financial conditions scenario: - Near-term output growth and lower unemployment improve income, but abrupt tightening and rapid increase in interest rates significantly deteriorate household financial conditions because about a quarter of residential mortgages would be repriced each year during the forecast horizon in the sample. - The average probability of mortgage arrears would peak to 2.8 percent in 2022, more than double the level in the baseline scenario and higher than the peak level during the GFC. ### Financial sector exposures to household vulnerabilities - Banks hold more than 80 percent of mortgage balances; banks are primary lenders to households. NBFIs have greater exposure to unsecured consumer credit (riskier part of household debt). - Given banks' mortgage exposure and current capitalization (noted in Chapter five), financial losses from mortgage defaults under projected scenarios could be absorbed. - Data limitations prevent detailed analysis of how consumer credit vulnerabilities could transmit to the financial sector and prevent analogous stress analysis for NBFIs. *Source: IMF staff calculations.* ### 33. Not all mortgage arrears will end up with repossession or default. In fact, the number of ### 33. Not all mortgage arrears will end up with repossession or default. In fact, the number of ### Mapping mortgage arrears into repossession and LGR estimation - Method steps used by the FSAP to map projected probability of mortgage arrear into repossession status: - First, a threshold for arrear probability is calculated to ensure that the share of projected arrear probability above this threshold from 2012 to 2019 matches the actual percentage of mortgage arrears, i.e., 1.6 percent. - Second, the dummy for mortgage arrears is one if the projected arrear probability is above the threshold from 2021 to 2025. - Third, the top 7 percent of mortgage arrears (ranked by arrear probability) are cast into default/repossession to estimate the maximum potential impact (as experienced in 2008). - Finally, once a mortgage is cast into repossession, the mortgage loss is estimated as the difference between the remaining outstanding mortgage and the repossessed house value (assumed 25 percent below the market value). LGR is calculated as the total mortgage losses on repossessed loans as a share of the total repossessed loans. ### Key empirical points on repossession and arrears - Historical average ratio: mortgages in repossession are, on average, 3 percent of the number of mortgages in arrears between 2012 and 2019. - Peak share of repossessions: 7 percent in 2008. - Calibration target for projected arrears: match historical actual mortgage arrears rate of 1.6 percent (2012–2019). ### Loss Given Repossession (LGR) estimates and scenario outcomes - Baseline scenario: - Estimated LGR would reach about 17 percent of repossessed mortgage portfolio. - This represents about 0.3 percent of total outstanding mortgages. - Occurs when the average probability of mortgage arrears reaches about 1.3 percent. - Inflationary and tightening of financial conditions scenario (adverse): - LGR would amount to 21 percent. - Average probability of mortgage arrears would peak to 2.8 percent. - Repossessed share would be 0.9 percent of outstanding mortgages. - Assumption on repossession loss: 25 percent loss during the mortgage repossession procedure relative to the market value (including lower selling prices and administrative costs). - Caveats on LGR estimation: - Repossession cost could be over-estimated under the baseline scenario with no national-wide house price corrections. - In adverse scenarios, losses could be mitigated if lenders delay selling repossessed properties until house prices have partially recovered. - Further breakdown of financial losses to different parts of the financial sector is impossible due to lack of mortgage provider information in the NMG survey. - Given large share of banks in mortgage lending and banks’ high capitalization levels, these losses would be mostly absorbable. ### Bank solvency and stress-test overview - Banking system metrics: - Aggregate CET1 ratio: 15.6 percent at the end of 2020. - NPL ratio: 1.8 percent. - Return on assets (recent years): about 0.4 percent. - Non-systemic sector aggregate CET1 ratio: around 17 percent. - Non-systemic sector aggregate liquidity coverage ratio: over 200 percent. - Non-systemic sector represents less than 10 percent in terms of assets. - Solvency stress test scope and assumptions: - Top-down solvency stress-test of the eight major U.K. banks and building societies (STeM). - Time horizon: five-year. - Scenarios: three five-year horizon scenarios (baseline and two adverse scenarios). - Risk channels assessed: credit risk (loan exposures and securities held at amortized cost), market risk (revaluation of debt securities held at fair value and valuation changes in open foreign positions), and interest rate risk. - Results reported on a fully loaded basis (IFRS9 transitional arrangements are not accounted for). - Quasi-static balance sheet assumption: allocation of assets and composition of funding sources remain constant as of the cut-off date. - Gross exposures assumed to grow in line with nominal GDP growth. - Banks can build capital buffers only through retained earnings. - Dividend policy under test: dividend payout set at 30 percent under positive profits and capital ratios above hurdle rates; otherwise no dividend payout. - No new equity issuance or share repurchases assumed. - IMF exercise does not take account of corrective management actions banks might take under stress. ### Capital requirements and hurdle rates - Individual banks’ hurdle rates comprise: - Pillar 1 CET1: 4.5 percent. - Bank-specific uplifts to the CET1 minimum as set by the Prudential Regulation Authority (Pillar 2A). - Any applicable global or domestic systemically important institution buffers (G-SIB, O-SII, and SRB). - FSAP approach: no adjustment to hurdle rates for IFRS9 in the solvency stress test exercise to ensure conservatism and cross-country comparability. ### Credit risk modelling approach and exposures - Loan portfolio composition: - Loan portfolio accounts for about 56 percent of total assets. - Mortgage loans account for about 54.9 percent of the total exposures at default (EAD) of loan portfolios. - Corporate loans: 32.5 percent of credit exposures. - Retail: 12.6 percent. - By geography: about 67.5 percent of total exposures are domestic; remainder split across several countries with large fractions in the Euro Area, United States and Hong Kong. - Total RWAs attributed to credit risk: almost 76 percent. - Satellite models for PDs: - Based on cross-country panel regressions of probabilities of default (PDs) taken from banks’ IFRS 9 submissions (one-year PDs, exposure weighted averages). - Dependent variable: logistic transformation of PDs. - Explanatory macro variables: GDP growth, unemployment rate, house price growth, exchange rate change, interest rates and inflation; different lag structures and country/region fixed effects included. - Estimation period: 2014:Q1–2020:Q1 (pandemic period intentionally excluded). - Countries included vary by loan type consistent with cross-border exposures. - Final specifications chosen on goodness of fit and statistical/economic significance. ### PD projections and key dynamics under scenarios - Mortgage PDs: - Domestic mortgage PDs increase by 4pp at the start of 2022 under adverse scenario 1. - Under adverse scenario 2, domestic mortgage PDs initially fall and then peak at 7.7 percent by the end of 2023. - Foreign mortgage PDs peak at 8.1 percent in 2022 under adverse scenario 1 and reach 9.9 percent in 2023 under scenario 2. - Retail PDs: - Domestic retail PDs reach 24 percent and 22 percent at the peak under adverse scenario 1 and 2 respectively. - Foreign retail PDs increase to 8.2 percent and 6.7 percent respectively. - Corporate PDs: - Domestic corporate PDs increase to 4.3 percent and 3.5 percent under adverse scenario 1 and 2 respectively. - Foreign corporate PDs increase to 4.2 percent and 3.9 percent respectively. - Mapping PDs to bank-level: - Aggregate PD paths are mapped to bank PDs based on starting points at the bank-asset class level (25 segments considered). - Mapping uses standard score (z-score) of aggregate PDs and individual banks’ starting PDs to ensure projected PDs remain within the [0, 1] range. ### Robustness checks and alternative linkages - Robustness checks performed include: - Using full sample period (2014:Q1–2020:Q4) with a dummy for pandemic quarters. - Estimating cross-country regressions using subsamples of countries with similar characteristics. - Using longer time series with various PD proxies (e.g., Moody’s EDFs, aggregate U.K. NPLs, write off rates). - Different approaches produced varying dynamics but broadly consistent ranges of PD changes. - Corporate stress test linkage: - Aggregate corporate PD paths of domestic exposures replaced by sectoral level PDs for each scenario as a robustness exercise. - Mapping of sectoral PD paths to bank-level PDs uses standard z-score and is applied at sector-bank level based on starting PDs. - Corporate stress test affects only the domestic corporate portfolio, which corresponds to less than 14 percent of the total loan portfolio. - Corporate stress test PDs initially drop under the three adverse scenarios but increase after 2022, reaching highest points at the end of the five-year horizon — contrasting with satellite model PD timing. ### LGD and provisioning approach - PiT LGD for mortgages: - Model links starting LGD (LGD0) to country-level house price path (House Price_t) and fraction of mortgage loans with high LTV per bank (%highLTV_b) at cutoff date: - LGD_bt = (1 − %highLTV_b) * LGD0 + %highLTV_b * [1 − (1 − LGD0) * min(HousePrice_t / HousePrice_0, 1)] - Fraction with high LTV accounts for overcollateralization; LGD of overcollateralized mortgages does not increase with house price drops. - Non-mortgage exposures: - PiT LGDs take the maximum LGD observed during sample period 2014–2020. - IFRS9 provisioning in the FSAP framework: - Expected credit loss calculated on a 12-month horizon for stage 1 assets and on a lifetime horizon for stage 2 and stage 3 assets. - Loan loss provisions projected using banks’ stressed stage transition probability matrices for each asset segment. - Evolution of transition matrices over the scenario horizon linked to projected PiT PD for each scenario based on the beta-linking approach. - Perfect scenario foresight is assumed to simplify provisioning projections. *Source: Extract from FSAP chapter on mortgage arrears, repossession mapping, LGR estimation, and bank solvency stress testing.* ### 48. RWAs for credit exposures are treated differently for exposures under the ### 48. RWAs for credit exposures are treated differently for exposures under the standardized approach (STA) and the internal ratings-based approach (IRB) ### RWAs and credit risk modelling - RWAs for STA exposures project: - balance sheet growth, - structural FX growth, - triggered credit lines. - RWAs for IRB exposures: - use the Basel formula to translate credit parameters (e.g., TTC PDs, TTC LGDs, correlation, maturity, and scaling factors) into stressed RWAs. - TTC PDs for non-defaulted exposures are updated based on PiT PDs and a smoothing parameter that reflects their sensitivity to PiT PDs. - RWAs for market risk, operational risk and other RWAs are assumed to grow in line with nominal GDP growth. - 30 %ℎ푖푔ℎ퐿푇푉푏 is calculated as the fraction of loans with LTV higher than 70%, based on U.K.-mortgages and applied to non-U.K. mortgages under the assumption of similar mortgage granting policy across country portfolios. - When transition matrices are not available at the bank-asset class level for the starting point, asset-class level transition matrices (same across banks) are used; starting-point transition matrices are re-escalated to match starting PiT PDs. ### LGD and loss projections - Figure references indicate projected LGDs under scenarios (source: BoE, IMF staff calculations). --- ### B. Market Risk Modelling Approach - Market risk module captures valuation changes of debt securities from changes in: - risk-free interest rates, - credit spreads. - Accounting classes and methods: - FVPL and FVOCI debt securities: mark-to-market approach using a modified duration approach based on residual duration, the relevant bond yield, and stressed spreads. - Spreads are only stressed for adverse scenario 1. - Stressed spreads are consistent with the macroeconomic scenario and equal to those applied for the insurance stress test. - Shocks to credit spreads are split equally over the first three years of the stress test horizon. - Bank-specific interest rate hedge ratios for FVOCI portfolios are accounted for using 2021 BoE-provided information. - Amortized cost (AC) securities: credit risk approach with provisions to cover expected loss as asset quality deteriorates. - FVOCI of corporate securities: also subject to the credit risk approach. - Equity and other market exposures: - Domestic and foreign equity holdings (FVPL and FVOCI) revaluated based on country-specific equity paths for 17 jurisdictions and for the world to determine equity gains/losses. - Module captures valuation changes in open positions in foreign currencies and commodities. - Capital impact depends on accounting class: - Losses from FVPL portfolios are realized losses, affect net profits, and are subject to taxation and dividend payout. - Unrealized losses from FVOCI portfolios affect capital through other comprehensive income. - Relevant yield for a portfolio is proxied via linear interpolation between the short- and long-term bond yields of a given scenario. --- ### C. Modelling of P&L Components - Interest income and expense module: - Captures interest rate risk in the banking book (IRRBB). - Econometric models estimated for aggregate historical interest income and interest expense ratios. - Key driver for both ratios is the bank rate. - Projected paths conditional on scenarios suggest increases in implied net interest margin ratio under the three scenarios, with a more pronounced increase for the second adverse scenario. - IRRBB impact on net interest income estimated by measuring repricing gaps up to the five-year scenario horizon; banks’ maturity profile assumed unchanged over the stress testing period. - Satellite models: - Fee and commissions income ratio, other non-interest income ratio, and non-interest expense ratio models use real GDP growth as key driver. - Projected paths: - Decrease in non-interest income in the first year of the scenario, followed by an increase in subsequent years under the baseline and adverse scenario 1. - Under adverse scenario 2, non-interest income initially higher than in adverse scenario 1 but drops towards end of horizon, consistent with GDP path. - Similar projected paths for non-interest expense ratio. - Aggregated P&L ratio paths are mapped at bank level using the standard z-score from starting points. - Fintech Overlay implementation: - Models for interest expense ratio and fee and commissions ratio include a proxy for competition: the change in a bank’s credit share of total credit to the private non-financial sector. - This competition proxy is assumed constant over the scenario horizon in the main exercise; modified only in fintech overlay analysis. - Selected model statistics (examples from P&L/PD models table): - Observations for certain logit(PD_gq) models: 252, 352, 189. - R-squared values reported: 0.950, 0.849, 0.921. - Several coefficients and significance levels are shown (e.g., logit(PD_gq)(t-1) coefficients 0.280*** and 0.665***). --- ### D. Solvency Stress Tests Results - System resilience: - Aggregate CET1 ratio starting point (2020): 15.6 percent. - Aggregate CET1 ratio declines at low points (2022) of adverse scenarios: - by 2.0 pp under adverse scenario 1, - by 5.0 pp under adverse scenario 2, - these declines are before conversion of AT1 instruments into CET1 capital. - In both adverse scenarios, aggregate CET1 ratio remains above estimated aggregate hurdle rates in all years of the scenario horizon. - Some AT1 instruments convert into CET1 under adverse scenario 2, increasing the low-point aggregate CET1 ratio by 30bp. - Bank-level impacts: - Under adverse scenario 1: higher credit losses and lower interest income on accrual loans compared to baseline; all banks remain above hurdle rates. - Under adverse scenario 2: decline in capital ratios driven mainly by higher credit losses combined with market valuation losses from abrupt interest rate increases and equity price decreases. - Following 2022 decline, capital ratios trend upward driven by increased net interest income and market valuation gains from equity price recovery. - Two banks fall below hurdle rates in 2022 or 2023, with CET1 shortfalls amounting to 0.08 (0.035) percent of GDP in 2022 (2023), respectively. - Factoring in AT1 conversion, the trigger would activate for one bank, bringing its CET1 ratio back above the hurdle rate; CET1 shortfalls would drop to zero in 2022 and remain at 0.035 percent of GDP in 2023. - Link to corporate stress test: - Results very similar when linking PD paths from corporate stress testing to bank solvency stress test. - Under baseline: aggregate CET1 ratio is lower by 41 bps at the end of the five-year horizon. - Under adverse scenario 1: CET1 ratio is higher by 26 bps at the low point. - Under adverse scenario 2: difference is almost negligible. - Banks most affected are those with larger shares of domestic corporate loans. - Scenario 2 characterization: - Scenario 2 corresponds to a relatively short-lived burst of stagflation with abrupt tightening of financial conditions and market volatility. - This scenario falls outside common recent advanced-economy experience and was chosen to test preparedness of regulators and supervised entities. - Exercise nature: - Stress test is structured to give conservative estimates and is not meant to provide forecasts. - The exercise is static and does not consider management actions by banks that could prevent CET1 ratios from falling below hurdle rates. --- ### Recommendations to strengthen BoE top-down capacity - The BoE could invest in strengthening top-down stress testing capacity despite consolidated bottom-up framework (annual cyclical scenarios (ACS) and biennial exploratory scenarios (BES) since 2016). - Key areas for further development and improved data availability/quality: - Credit risk: more granular data for certain segments, particularly retail non-mortgage for domestic and foreign exposures. - Interest rate risk: more granularity in maturity buckets by type of asset and liability to better assess sudden interest rate shocks. - Market risk: improved quality of key parameters (e.g., duration of securities) and detailed information on hedges. - Objective: complete and consolidate in-house analytics to cover all relevant portfolios and P&L components, enabling independent, higher-frequency top-down stress tests and progressive coverage of systemically relevant entities and interactions. --- ### E. Fintech Overlay - Motivations and risks: - Fintech developments, particularly Open Banking (OB), can drive efficiency gains and potential financial stability risks (competition, deposit switching, pressure on interest expense, lower fees and commissions). - Sudden entry of large platform-based technology companies into financial services could pose risks. - Methodology: - Anchored in econometric models for interest expense ratio and fees and commissions income ratio, including the competition proxy (bank credit share of total credit to the private non-financial sector). - Empirical relationship: greater banking-sector concentration → lower interest expenses and higher fee and commissions income (higher market power). - Fintech overlay shock assumptions: - Market share of bank credit decreases by 3.5 percent in 2021, 2022 and 2023, resulting in a cumulated drop of 10 percent. - Note: A two standard deviation shock is equal to 3.5 percent. - The market share loss is assumed driven by larger growth in non-bank credit (not a decrease in bank credit). - No business model changes and no impact on interest income ratio from increased competition are assumed. - Results: - Banks could experience erosion in net income margins and fee and commissions income, reducing capital ratios over a short-term horizon. - System-wide capital depletion would rise to 2.5 percent, from 2 percent at the low point, when adding the Fintech Overlay under adverse scenario 1. - By the end of the five-year horizon the CET1 ratio would be 1.6 lower under the Fintech Overlay. - The interest expense ratio and fee and commissions income ratio are the main channels through which stronger competition would affect capital ratios. - This is a partial equilibrium analysis; technological adaptation could allow banks to offset pressures through new opportunities from OB. *Source: UNITED KINGDOM — IMF staff calculations, BoE, FINREP, COREP, and related modelling described in the provided content.* ### 63. The solvency bank stress test was complemented by an assessment of macro-financial ### 1gbrea2022003 - 63. The solvency bank stress test was complemented by an assessment of macro-financial ### Macro-financial feedback methodology - The FSAP team implemented an iterative algorithm linking the bank solvency stress-testing framework to a macro structure represented by a SVAR (structural VAR), following Catalan and Hoffmaister (2021). - SVAR specification: - Blocks: foreign macroeconomic variables, domestic macroeconomic variables, and two banking sector variables. - Foreign block: US real GDP, oil price, US policy rate. - Domestic block: U.K. real GDP, unemployment rate, CPI, real exchange rate, bank rate. - Banking sector variables (entered as exogenous regressors): interest income to asset ratio and lending (credit) growth. - Block exogeneity assumed for foreign variables (no impact or lagged effects from domestic variables). - Estimation period: 2000Q1 to 2019Q4 in quarterly growth rates (except interest rates and interest income ratio). - Lag specification: two lags. - Iterative procedure: - Bank-level stress-test outputs (aggregated CAR and NPLR outcomes) feed into a credit growth model to produce a credit path. - The credit path is input to the SVAR to produce amended macro paths (e.g., real GDP, unemployment). - Amended macro paths are used to run another round of bank-level stress tests; iterate until convergence. - The initial projections for interest income ratio and credit growth used the GFM; differences between initial and final paths measure inferred feedback effects. - Caveat noted: use of a model with built-in macrofinancial linkages (GFM) to construct projections in the "assumed" absence of macrofinancial feedbacks limits comparability and interpretation. ### Credit growth bridge model - Estimated period: 1994Q2 – 2019Q4. - Model variables: - Change in capital ratios (CAR). - Change in non-performing loans ratio (NPLR). - An autoregressive term. - Purpose: project credit growth conditional on aggregated CAR and NPLR outcomes from the stress test; feed resulting credit path into the SVAR for second-round effects. ### Macro-financial feedback findings (first adverse scenario) - Real GDP growth: - Reduced by additional 0.65 percentage points in 2022. - Reduced by additional 0.37 percentage points in 2023. - Unemployment rate: - Higher by 0.24 percentage points in 2022. - Higher by 0.24 percentage points in 2023. - Probability of Default (PD) of domestic portfolios: - Increased by 33 basis points in 2022. - Increased by 43 basis points in 2023. - Capital ratios: - Reduced by 57 basis points on average across the scenario horizon. ### Bank liquidity stress test (LCR-based) results - Sample: 110 domestic banks surveyed. - Aggregate LCRs: - Currently well above the regulatory standard of 100 percent. - Under combinations of three “haircut” scenarios and four “outflows” scenarios, U.K. banks generally maintained high ‘total currencies’ liquidity ratios. - Tail outcomes: - For seven banks the stressed LCR would fall below 100 percent in some severe scenarios (only moderately). - Most extreme combined scenario: a gap of GBP 46 billion between net outflows and HQLAs over the 30-day horizon. - FX single-currency analysis (no formal regulatory threshold): - With regulatory cap on inflows, FX liquidity shortfalls could be overstated for matched FX transactions. - Removing the cap (for sizing the issue): - Base case (with regulatory weights): aggregate FX liquidity gap shrinks to about GBP 5 billion. - Most extreme combined scenario: aggregate FX liquidity gap about GBP 20 billion. - Recommendation/observation: - Addition of further details to the PRA reporting scheme on liquidity over the 30-day horizon (PRA 110) would help identify items where caps on inflows are excessive and better quantify FX liquidity gaps. ### Insurance solvency stress test: scope and sample - Exercise: top-down (TD) solvency stress test for the U.K. insurance sector on a consolidated basis. - Participants: 14 large insurers (8 life insurers, 6 general insurers). - Market coverage: around 70 percent in both sub-sectors (based on gross written premiums). - Aggregated balance sheet assets: GBP 1,879bn total, of which GBP 1,738bn attributed to groups predominantly life business. - Asset composition highlights: - Life insurers: - Unit-linked and index-linked insurance assets: 39 percent of total assets of life insurers. - Equity and participations: 16 percent. - Corporate bonds: 14 percent. - Sovereign bonds: 10 percent. - Sovereign bond exposures dominated by domestic exposures: 57 percent. - General insurers: - Sovereign and corporate bonds: 50 percent of assets. - US and Canadian government bond exposures: 46 percent and 20 percent, respectively. - Corporate bond credit quality: about 60 percent rated A or better; less than 3 percent speculative grade. - Solvency positions pre-stress: - All 14 participants record solvency ratios before stress well above the regulatory threshold of 100 percent (taking account of long-term guaranteed measures and transitionals in life sector). - SCR model usage: seven groups use a full internal model; seven apply a partial internal model. - Composition of eligible own funds: 69 percent unrestricted Tier 1 capital, 4 percent Tier 1 restricted, 25 percent Tier 2, 2 percent Tier 3. - Impact of LTG measures and transitionals (life insurers): - Eligible own funds increase by 109 percent when LTG measures and transitionals are included. - Matching Adjustment (MA) alone increases eligible own funds by around 75 percent and reduces the SCR by 39 percent. - SCR is lowered by 43 percent (aggregate effect noted) with LTG and transitionals. - Without LTG measures and transitionals, median SCR ratio of life insurers in the sample would be 62 instead of 152 percent (a 90 percentage point reduction). ### Insurance stress test scenarios and shocks - Scenarios: Scenario 1 (further deterioration of COVID-19 pandemic), Scenario 2 (tighter financial conditions with inflationary shock). For insurers shocks assumed instantaneous at reference date 31 December 2020. - Market risk shocks (selected values from specification): - Equity shocks: - United Kingdom: Scenario 1: -19.5% ; Scenario 2: -15.8% - United States, Euro area: Scenario 1: -25.0% ; Scenario 2: -15.0% - Other advanced economies: Scenario 1: -15.0% ; Scenario 2: -15.0% - Emerging economies: Scenario 1: -25.0% ; Scenario 2: -30.0% - Sovereign bond spreads (in percentage points): - United Kingdom: Scenario 1: 0.80% ; Scenario 2: 0.50% - Other low-yield advanced economies: Scenario 1: 0.70% ; Scenario 2: 0.30% - High-yield advanced economies: Scenario 1: 1.40% ; Scenario 2: 1.20% - Emerging and developing economies: Scenario 1: 1.60% ; Scenario 2: 1.80% - Property shocks (price changes): - Residential, domestic: Scenario 1: -14.6% ; Scenario 2: -8.4% - Commercial, domestic: Scenario 1: -29.7% ; Scenario 2: -20.1% - Residential, other countries: Scenario 1: -10.0% ; Scenario 2: -6.0% - Commercial, other countries: Scenario 1: -18.0% ; Scenario 2: -8.2% - Investment funds and alternatives (examples): - Alternative funds: Scenario 1: -8.0% ; Scenario 2: -5.0% - Private equity funds: Scenario 1: -10.0% ; Scenario 2: -12.0% - Infrastructure funds: Scenario 1: -5.0% ; Scenario 2: -3.0% - Corporate bond spread shocks by credit quality step (in percentage points, selected): - Non-financials, credit quality step 0: Scenario 1: 0.70% ; Scenario 2: 0.40% - Non-financials, credit quality step 1: Scenario 1: 0.85% ; Scenario 2: 0.50% - Non-financials, credit quality step 6: Scenario 1: 2.90% ; Scenario 2: 3.20% - Financials by CQS (in percentage points, selected): - Financials, CQS 0: Scenario 1: 0.85% ; Scenario 2: 0.70% - Financials, CQS 3: Scenario 1: 1.50% ; Scenario 2: 1.70% - Financials, CQS 6: Scenario 1: 3.20% ; Scenario 2: 3.60% - Additional single-factor shock: - Default of the largest banking counterparty (determined by S.06.02 issuer-group asset data). - Assumed haircuts and LGD: - Equity exposures: 100 percent haircut (full write-off). - Unsecured bonds LGD: 50 percent. - Secured bonds LGD: 15 percent. - Other on-balance sheet exposures LGD: 30 percent. - Sensitivity result not added to scenario results. ### Capital standard, modeling assumptions, and data used - Solvency framework: Solvency II (on-shored into U.K. law post-Brexit); assets and liabilities generally mark-to-market with allowance for LTG and transitional measures affecting discounting. - Main stress-test outputs: effect on own funds eligible for SCR coverage; SCR partially recalculated after stress because stresses affect capital requirement. - Data sources and templates used (QRTs and national templates): - Balance sheet (S.02.01) - Asset-by-asset investment holdings (S.06.02) - Derivative positions (S.08.01) - Cash-flow projections (S.13.01, S.18.01) - Impact of long-term guaranteed measures and transitionals (S.22.01, SR.22.03) - Own funds (S.23.01) - Calculation of the solvency capital requirement (S.25.02, S.25.03) - National specific reporting template SF.01.01 (standard formula calculation of the SCR by internal model users) *Source: IMF staff calculations, PRA data, BOE, FINREP, COREP, and IMF staff (excerpts from the referenced IMF chapter).* ### 80. For the TD stress test, the shocks specified in the scenario were applied to the ### 1gbrea2022003 - 80. For the TD stress test, the shocks specified in the scenario were applied to the ### Methodology for the TD stress test - Shocks from the scenario were applied to the investment assets and insurance liabilities. - Haircuts in line with the adverse scenario were applied to the market value of directly held assets. - A look-through was not applied; investment fund holdings were stressed with the corresponding shocks for the underlying asset classes. - Fixed-income assets were re-valued with the stressed term structure (per currency). - Technical provisions (except for unit-linked business) after stress were approximated with the stressed term structure including the matching or the volatility adjustment. - For unit-linked business, the decline in liabilities mirrored the market value loss of underlying assets. - Re-calculation of the SCR after stress was limited to selected risk modules: - In the market risk module, capital charges for equity risk, spread risk and property risk were proportionately adjusted in line with the change in exposures due to the stress. - The equity risk capital charge was corrected for the symmetric equity adjustment which changes from -0.5 to -10.0 percentage points after the fall in equity prices in scenario 1 and to -9.2 percentage points in scenario 2. - The capital charge for life underwriting risk was assumed to change proportionately with the technical provisions after application of the stressed discount curve. - All other components of the basic SCR, including the capital charge for counterparty default risk, non-life underwriting risk and operational risk were assumed unchanged. - For internal model users, SCR calculations including aggregation and resulting diversification effects were made in a simplified approach building on the standard formula. - Loss-absorbing capacity of deferred taxes was re-calculated based on modeled valuation losses in the excess of assets over liabilities. - Data proxies and limitations: - Solo templates of the largest domestic subsidiaries were used as a proxy where group-level templates are not reported. - Due to data limitations, not all product features could be fully incorporated in the approximation. ### Risk mitigation, derivatives, and TMTP - Insurers hedge large parts of their interest rate and market risks with derivatives. - For life insurers: - Market value of asset-side derivatives amounts to 5.8 percent of assets. - Liability-side derivatives constitute 5.6 percent of total liabilities. - Interest rate swaps account for 40 percent of the number of positions and 62 percent of the notional value (Figure 17 text also notes interest rate swaps account for 41 percent of positions and 65 percent of notional value in a caption). - For most life insurers in the sample, interest rate swaps could be re-valued after stress; for remaining ones, derivative reporting in S.08.01 was insufficiently detailed. - For general insurers: - Market values of derivatives are 0.6 percent and 0.5 percent of assets and liabilities, respectively. - These positions are predominantly used for hedging of currency risks; however, no currency shock was applied. - Transitional measure on technical provisions (TMTP): - TMTP is not re-calculated after stress. - Until 2031, insurers may apply the TMTP based on the difference between technical provisions under Solvency I and Solvency II; over 16 years the transitional deduction is reduced to zero. - The impact of TMTP is sizable for some U.K. life insurers. - After an adverse scenario comparable to scenario 1, re-calculation of the TMTP by life insurers could result in either higher or lower SCR ratios. - The actual impact of TMTP re-calculation could not be replicated in this TD stress test. ### Management actions and modeling choices - Dynamic management actions were not modeled; the stress test assumed a static balance sheet. - Examples of unmodeled management actions include: - Changes in underwriting standards. - Changes in reinsurance programmes. - Withholding profits. - De-risking of the balance sheet (e.g., selling equity or high-yield corporate bonds and buying sovereign bonds), which can substantially reduce required capital. ### Results of the Solvency Stress Test — Scenario 1 ("scarring") - Overall impacts: - Life insurers are considerably more affected than general insurers. - All life insurers would still sufficiently cover their liabilities with assets, but excess of assets over liabilities declines by 17 percent for the median firm, with considerable dispersion across the sample. - Assets-to-liabilities ratio for the median life company declines from 106.4 to 103.6 percent. - For unit-linked business, the decline in assets (and roughly equal decline in liabilities) amounts to around 13 percent for most life insurers. - For the median general insurer, assets-to-liabilities ratio after stress amounts to 126.1 percent (down from 129.0 percent). - Drivers: - Increase in corporate bond spreads contributes most to reduction in available capital for life insurers, but this is largely offset through the MA and resulting interest rate shock. - Without the offsetting effect, the most relevant shock for life insurers would be the decline in equity prices; for general insurers, the most relevant shocks are to corporate bond spreads due to lower stock market exposures. - Capital adequacy: - SCR ratio of the median life insurers drops from 158 to 116 percent. - Two life insurers have post-stress SCR ratios below 100 percent; aggregated capital shortfall of these two firms amounts to GBP 9bn. - For median general insurer, SCR ratio falls from 149 to 140 percent. ### Results of the Solvency Stress Test — Scenario 2 (tightening financial conditions) - Overall impacts: - Aggregate impact on both sectors is milder; most life insurers would see higher solvency ratios. - Median life insurer assets-to-liabilities ratio declines marginally from 106.4 to 105.9 percent; post-stress dispersion is wide (for half the sample the ratio either increases or decreases). - Median general insurer assets-to-liabilities ratio moves from 129.0 to 129.4 percent. - Drivers: - Sharp increase in interest rates generally compensates for losses on investment assets because liabilities decline with higher discount rates. - Market risk impact is mostly contributed by the interest rate shock lowering life insurers’ liabilities, outweighing market value losses in fixed-income portfolios. - For general insurers, interest rate effect and higher corporate bond spreads are relevant, though effects are smaller due to shorter durations of non-life liabilities. - Analysis focuses only on market risks and does not consider effect of higher claims inflation on general insurers’ earnings. - Capital adequacy: - Median life firm SCR ratio after stress would be 187 percent (up from 158 percent). - Only two life insurers face a falling SCR ratio. - Median general insurer SCR ratio declines marginally from 149 to 146 percent. - A lower capital requirement, mainly for spread risks, contributes substantially to the increase in life-sector SCR ratios. ### Sensitivity analysis — Default of largest banking counterparty - Method: - Used investment asset data from QRT S.06.02. - Assumed full write-off of equity exposures towards the banking group, a 50 percent loss-given-default on unsecured bonds, a 15 percent loss-given-default on secured bonds, and a 30 percent haircut on all other types of investment exposures. - SCR was not re-calculated after stress for this sensitivity analysis. - Results: - Excess of assets over liabilities declines, on average, by 3 percent for life insurers and by less than 2 percent for general insurers. - SCR ratio declines marginally by only 4 and 2 percentage points in the life and the general insurance sample, respectively. - The largest counterparty typically differs across insurers, implying individual stress per company rather than a systemic adverse scenario. ### Liquidity risks and liquidity stress test - Focus: - Analysis focused on need to meet variation margin calls for a single stress on interest rate swaps after a sudden rise of interest rates. - Derivatives, especially interest rate swaps, are an important risk-mitigating tool; life insurers are typically fixed-rate receivers and vulnerable to increases in interest rates which trigger variation margin calls. - Sample and reference date: - Smaller sample than solvency ST: five life insurance groups included, with market coverage of around 50 percent. - Reference date: end-2020. - Scenarios and assumptions: - Modeled three interest rate shocks over two time horizons: - 25 basis point increase overnight — only the most liquid assets (cash deposits) could be drawn upon; FSAP uses narrowest definition including only cash deposits. - 50 and 100 basis point increases unfolding over five days — insurers could liquidate some most liquid high-quality assets; liquid pool includes cash deposits plus unencumbered sovereign bonds of credit quality steps 0 and 1, revalued after the interest rate increase with an additional haircut. - Central counterparties may allow high-quality securities to meet margin calls (not explicitly modeled in narrow cash-only case). - Encumbrance levels: - Around 10 percent of sovereign bonds in the two highest credit quality steps are encumbered. - For corporate bonds the share encumbered is below 4 percent. - Encumbered assets comprise mostly U.K. government bonds. - Results: - No systemic liquidity stress for the overall sample of life insurers, but notable differences across individual firms. - Most insurers can meet margin calls on interest rate swaps with cash equivalents only, even with a 50-bps interest rate increase. - Assuming a 100-bps shock, more insurers would have to liquidate on aggregate 7 percent of their highest-quality sovereign bonds (unless counterparties accept securities as admissible assets). - Analysis used consolidated data; it is unclear how liquidity is allocated within insurance groups and whether liquidity is available at entities facing short-term needs. *Source: IMF staff calculations based on PRA data.* ### 96. The PRA’s experience from March 2020 indicated that insurers used the full range of ### 96. The PRA’s experience from March 2020 indicated that insurers used the full range of ### PRA experience in March 2020: liquidity actions and implications - Insurers "stopped investing cash inflows from premiums and withheld dividend payments" when faced with simultaneous margin call stresses. - Insurers "widely tried to avoid asset sales" — interpreted as evidence that regulatory incentives for buy-and-hold investments, particularly related to the MA, "have worked in practice." - Identified need: "To further analyze combined liquidity strains, exacerbated by reduced market liquidity and fungibility of certain assets, more granular liquidity data and a monitoring framework is needed, particularly for annuity writers and insurers with large derivative holdings." - PRA plans: "The PRA plans to require specific liquidity data from certain insurers, which would provide an opportunity to close these data gaps." ### Encumbrance levels and interest-rate swap variation margin metrics (visual summary from source) - Encumbrance Levels (In percent): visual axis labeled 0, 2, 4, 6, 8, 10, 12, 14 and categories including "Sovereigns, AAA", "Sovereigns, AA", "Corporates, AAA", "Corporates, AA". - Interest Rate Swaps: Variation Margins (In percent of Liquid Assets): visual axis labeled 0, 50, 100, 150 and variation scenarios "+25bps", "+50bps", "+100bps". - Variation margin comparisons shown as: - "Variation margin to cash (equivalents)" - "Variation margin to cash (equivalents) and sovereign bonds (AAA, AA)" ### Recommendations (From section F; numbered paragraphs preserved) - Paragraph 97: - "The PRA should continue its efforts to enhance the data quality checking process and ensure high-quality supervisory reporting by insurers." - Supervisory reporting should be "thoroughly scrutinized, and companies should be guided to report data more completely and consistently, particular regarding investment assets (S.06.02, in combination with S.02.01) and derivatives (S.08.01)." - Box 1 referenced for "further examples of how the supervisory reporting for insurers could be expanded to further facilitate a comprehensive analysis of risks and vulnerabilities in the insurance sector." - Paragraph 98: - "The PRA should augment its already strong focus on liquidity risks and further analyze combined liquidity strains, exacerbated by reduced market liquidity and fungibility of certain assets, and expand supervisory reporting, particularly for annuity writers and insurers with large derivative holdings." - It is necessary "to understand cash management arrangements and intra-group flows of liquidity." - Reiteration: "The PRA has announced plans to require specific liquidity data from certain insurers, which would provide an opportunity to close these data gaps." ### Box 1 — The Importance of Data in Support of Financial Stability Monitoring and Analysis (key points) - Role of data: "Data are fundamental for monitoring financial stability and analyzing risks and vulnerabilities across the financial system, both from a domestic and a global financial stability perspective." - Challenges in the U.K.: heterogeneity and complexity of the domestic financial system, deep international links, and London’s central role in the global financial network. - Banks: - PRA powers: require information or documents; make general rules applying to authorized firms. - Reporting framework: includes reports inherited from the Financial Services Authority (FSA) and EU frameworks (COREP and FINREP). - Recent additional data requirements: e.g., liquidity, Annual Cyclical Scenario (ACS) exercise. - Validation issues: "for data collections of more recent origin or falling outside of the regulatory reporting perimeter, the validation and plausibility checks in place do not necessarily ensure the quality and – ultimately - full usability of the data." - Example: Stress Test Data Framework (STDF) collections may need a "revision of the validation and plausibility rules (e.g., regarding the admissible values for identifiers), together with stricter enforcement." - Insurers: - Solvency II implementation in 2016 improved supervisory reporting, including "detailed asset-by-asset reporting on a quarterly basis." - PRA introduced national-specific reporting templates for monitoring internal model drift. - Remaining gaps: "most notably in the areas of liquidity risk and cross-border business." - Specific data issues: - "Amount of cash and cash equivalents is reported differently by firms depending upon their interpretation of cash equivalent." - "Derivative data, which is also available from trade repository data under EMIR, needs to be further enhanced and quality-checked to allow for a more robust monitoring of margin call risks." - "Data on cross-border business and intermediation channels is limited, complicating e.g., an assessment of the systemic relevance of Lloyds in foreign markets." - "For insurance intermediaries, including some of the larger brokers in the Lloyds and London market, liquidity reserves have been collected only ad-hoc during the pandemic." *Source: IMF staff.* ### Climate-related vulnerabilities — Transition risk: methodological overview and scope - Objective: assess the U.K. financial system’s exposure to transition risk via a "climate Minsky moment" approach over a five-year horizon. - Shock assumption: "a switch in the economic agents’ expectation from a low and relatively flat to a high and steep path for carbon prices, in the United Kingdom and at global level." - Time horizons and key dates: - Long-term simulation up to "2050." - Impacts estimated over a shorter term "within a five-year horizon." - "For all purposes, the climate Minsky moment was assumed to occur in the year 2024." - Impact mechanism: difference in valuations under two scenarios at the climate Minsky point represents "the potential asset price correction affecting all marketable assets and—via an approach à-la-Merton—the probabilities of default and credit ratings of companies." ### Covered institutions and datasets for the transition-risk exercise - Institutions covered: - "the same eight banks of the solvency stress test" - "eight large life insurers and seven large general insurers" - "a representative sample of investment and pensions funds (Table 9)." - Scenarios used: - NGFS 'Phase I' scenarios in the REMIND-MAgPIE version: "National Determined Contributions" (labelled 'BASE') and "1.5°C with Carbon Dioxide Removal" (labelled 'ADV'). - NGFS 'Phase II' scenarios also used for expanded analysis. - Modelling approach: - NGFS scenarios expanded via simulations of a global computable general equilibrium model "GTAP-E" to capture sectoral effects. - Heterogeneity within sectors captured at company level via the "Climate Credit Analytics (CCA) model suite." - Company financials and samples: - For NGFS Phase I run: "end-2019 financials (of about 1.2 million companies worldwide)." - For NGFS Phase II run: "end-2020 financials (of a stratified sample of more than 160,000 companies)." - Sampling assumptions: end-2019 and end-2020 financials assumed representative for the climate Minsky point (2024) with leverage adjustment for end-2019 to incorporate COVID-19 indebtedness during 2020. - Table 9 methodological mapping highlights: - Banks: corporate loan portfolio and securities portfolio analyses for transition risk; U.K. mortgage loan portfolio included under NGFS 'Phase II'. - Life insurers and general insurers: securities portfolio (stocks, corporate bonds, funds) analyzed for transition risk. - U.K.-domiciled funds (~2000 funds): securities' holdings (stocks, corporate bonds) analyzed. - Pensions funds (~70 DB schemes): securities' holdings analyzed. - T = Analysis of transition risk; P = Analysis of physical (chronic/acute) risk. *Source: IMF staff.* ### Box 4. The Climate Credit Analytics Model Suite ### Box 4. The Climate Credit Analytics Model Suite ### Model description and sector coverage - Climate Credit Analytics (CCA) is a climate scenario analysis and credit analytics model suite developed jointly by S&P Global Market Intelligence and Oliver Wyman. - CCA conditions financials of corporates on climate scenarios to evaluate corporate performance; conditioned financial statements are analyzed to assess risk to banking-sector lenders. - CCA applies a common framework across six sectors: Oil & Gas, Power generation, Metals & Mining, Auto manufacturing, Commercial Airlines, and a generic approach for the remaining non-financial corporate sectors. - Sector methodologies vary significantly to reflect different business activities, adaptation pathways, dynamics, and financial reporting. - Notable sectoral dynamics: in Oil & Gas there may be divestment and movement into new business lines; Automotive Manufacturing and Power Generation see resilient demand but require significant changes in products and production methods. - Acronyms used in CCA: DCF = Discounted Cash Flow; DtD = Distance to Default. ### Transition-risk results for banks (Phase I and Phase II scenarios) - Phase I (orderly NGFS scenario) results: - Losses on banks’ corporate loan portfolios: slightly higher, on average, than 1 percent, corresponding to GBP bln 24. - Market losses on equity holdings: 3.5 percent, on average. - Market losses on corporate bond portfolios: 1.6 percent, on average. - Combined economic losses on all corporate exposures (accrual-accounting and fair-valued portfolios): GBP 31 bln. - Phase II (NZ2050) results and comparison: - Banks’ corporate loan losses: increase from 1.1 to 3.6 percent (Phase I 1.5°C+CDR vs NZ2050), with credit losses rising from GBP 24 billion to almost 79 billion. - Banks’ market losses on equity and corporate bond holdings: increase from 2.5 (GBP 7 billion) to more than 4 percent (GBP 11.5 billion). - Total banks’ losses across all corporate exposures: increase from GBP 31 billion to more than 90 billion. - Sensitivity to risk premia (NZ2050 with higher risk premia): - Increasing all risk premia by ½ raises banks’ aggregate credit losses on corporate loan portfolios to 5.8 percent (from 3.6 percent under current risk premia), corresponding to GBP 126 billion (as opposed to 79 billion under current risk premia). ### Investment funds and defined-benefit pensions (Phase I) - For a large sample of U.K.-domiciled investment funds: - Average portfolio loss: about 0.32 percent of the overall portfolio. - Dispersion: after excluding outliers (below the 1st and above the 99th percentiles) across more than 2,000 funds and sub-funds, results span from a gain of 0.5 percent of the portfolio to a loss of around 2 percent. - Smaller sample of defined-benefit pension schemes: losses under Phase I are described as modest (see Box 5 in source for pension-specific detail). ### Insurance sector: methodology, exposures, and results - Methodology: - Analysis followed the approach used for banks and investment funds, covering all insurers of the solvency ST sample as of end-2020. - For most stocks and bonds held by insurers, either an issuer-specific shock or a sector-specific shock was derived from the climate change model. - Shocks specified as equity price changes or, for bonds, as spread increases and a default rate. - Universal shock applied for investment holdings with no economic sector reported (around 5 percent of all positions). - Only impact on asset valuation was estimated, not the impact on solvency ratios. - Reporting limitation: - Solvency II QRT S.06.02 requests economic sector at one-letter NACE level (e.g., “C – Manufacturing”), which dilutes sectoral sensitivity to climate change and carbon price paths. - Insurance sectoral investment exposures (selected values from NACE classification; exposure in GBP bn, percent of corporate exposures, percent of total assets): - C - Manufacturing: 158,517; 20.4%; 8.4% - K64.1.9 - Other monetary intermediation: 126,483; 16.3%; 6.7% - L - Real estate activities: 63,433; 8.2%; 3.4% - J - Information and communication: 62,738; 8.1%; 3.3% - D - Electricity, gas, steam and air conditioning supply: 48,546; 6.2%; 2.6% - H - Transporting and storage: 40,988; 5.3%; 2.2% - G - Wholesale and retail trade; repair of motor vehicles and motorcycles: 28,888; 3.7%; 1.5% - K65.1.2 - Non-life insurance: 26,202; 3.4%; 1.4% - K65.1.1 - Life insurance: 24,813; 3.2%; 1.3% - K64.9.9 - Other financial service activities, except insurance and pension funding n.e.c.: 22,892; 2.9%; 1.2% - K64.2.0 - Activities of holding companies: 21,445; 2.8%; 1.1% - E - Water supply; sewerage; waste management and remediation activities: 17,336; 2.2%; 0.9% - K66.1.9 - Other activities auxiliary to financial services, except insurance and pension funding: 16,530; 2.1%; 0.9% - L68.2.0 - Renting and operating of own or leased real estate: 16,303; 2.1%; 0.9% - B - Mining and quarrying: 14,291; 1.8%; 0.8% - Q - Human health and social work activities: 13,478; 1.7%; 0.7% - K66.1.2 - Security and commodity contracts brokerage: 9,839; 1.3%; 0.5% - F - Construction: 8,244; 1.1%; 0.4% - M - Professional, scientific and technical activities: 8,140; 1.0%; 0.4% - K64.9.2 - Other credit granting: 6,524; 0.8%; 0.3% - Other sectors (except public sector and mutual funds): 41,602; 5.4%; 2.2% - Phase I insurance valuation impacts: - Sample-wide valuation loss: close to GBP 40bn. - Valuation impact by asset class: - Equity holdings: decline by 4 percent on average. - Corporate bonds: decline by 2.5 percent on average. - Investment funds: decline by 1 percent on average. - Life insurance: around GBP 38bn of the total impact is seen among life insurers of the sample. - Relative losses: - Losses for most insurers range between 1 and 3 percent of total investments. - Median life firm: 2 percent combined investment loss. - Note: Some valuation losses may be borne by policyholders (especially in unit-linked life contracts) and some losses may be offset by changes in liability calculations (e.g., matching adjustment portfolios). - Phase II (NZ2050) insurance impacts: - Valuation losses total GBP 66bn. - Valuation impact by asset class: - Equity holdings: decline on average by 11 percent. - Corporate bonds: decline by 4 percent. - Investment funds: decline by 1.5 percent. - Across all asset classes, the loss corresponds to around 2 to 4 percent for most insurers in the sample; life sector impact larger than general sector. ### Interpretation, sensitivity, and methodological caveats - Results represent a single simulation covering a subset of relevant portfolios under an ‘orderly’ scenario and are not the most severe transition risks possible. - Impacts are highly sensitive to assumptions and methodological choices, including: - LGDs at the climate Minsky point are held constant in the exercise but could be modelled within the Merton framework (which could change results). - Reaction of volatility of market value of assets to the climate Minsky shock: volatility could increase, with ambiguous effects (potentially positive on market value of equity seen as a put option; negative on firms’ credit standing via reduced distance to default). - Increase in credit spreads in the exercise is determined by company migrations across rating classes, with spreads by rating class unchanged; spreads could themselves increase at the climate Minsky point. - Exercise captures first-order losses only; second-round effects and feedback loops are not considered. - Reaction of risk premia to a climate Minsky moment: - Sensitivity test increasing all risk premia by ½ under NZ2050 shows significant worsening (e.g., banks’ credit losses rising to 5.8 percent, GBP 126 billion). - Historical context: increases in implied equity risk premia of that size, or larger, have been recorded in the US equity market (Damodaran 2019 example cited in source). - Macroeconomic environment: - Exercise remains agnostic to broader macroeconomic effects from scenario switches; policy responses (monetary and fiscal) could have large, potentially offsetting or amplifying effects on GDP, inflation, and resource reallocation. ### Residential mortgage portfolio exercise (complementary analysis) - Purpose and logic: - Complementary exercise to capture transition risk in U.K. residential mortgage portfolios under NGFS scenarios, following the same climate Minsky logic used for corporates. - Property values are affected by increases in carbon prices via higher heating and electricity costs and/or costs to implement efficiency-improving measures, which affect mortgage Loss Given Default (LGD). - Data and approach: - Uses Energy Performance Certificate (EPC) database for England and Wales (similar database available for Scotland). - BAU scenario: carbon price paths in Europe under Phase II NGFS ‘National Determined Contributions’ (NDCs). - Climate-ambitious scenarios: ‘Net Zero 2050’ (NZ2050) and ‘Divergent Net Zero’ (DNZ). - Valuation impact (%VI) at single-property level: cumulated, discounted extra costs from higher heating and electricity bills (applied to building’s current CO2 emissions) if no improvements; if improvements implemented, value impact equals improvement costs (net of government subsidies) plus cumulated, discounted impact on bills (reduced by measures) and extra costs from residual CO2 emissions. - Current property values estimated by multiplying building total floor area from EPC by current price per square meter at local authority level. - %VI crossed with banks’ owner-occupied and buy-to-let mortgage loans, broken down by U.K. region and Loan-to-Value (LtV) band. - Key modeling assumptions and parameters: - Carbon price pass-through to property dwellers considered under full (100%) pass-through and reduced pass-through (e.g., 50%). - Discount rate set at 2.5 percent (approximately the current average interest rate on mortgage loans). - Effect on LGD depends on degree of collateralization: - For overcollateralized loans (LtV ≤ 100%) that remain overcollateralized after valuation impact: migration to higher LtV band (higher LGD). - For loans already undercollateralized (LtV > 100%) or becoming undercollateralized (100% - %VI < LtV ≤ 100%): expected recovery rate (100% - LGD) is affected in proportion to the drop in value. *Source: Oliver Wyman; IMF staff calculations based on PRA data.* ### 122. The sensitivity analysis explored the combination of different alternatives along three ### Sensitivity analysis of transition and physical climate risks (United Kingdom) ### Sensitivity analysis design - Explored combinations along three dimensions: adverse scenario (NZ2050 or DNZ); carbon price pass-through rate (100% or 50%); cost of the energy efficiency improvement measures (the median of the range indicated by the assessors in the energy performance certificate or the maximum of that range). - Produced eight combinations ranging from a ‘best case’ (NZ2050 + 50% pass-through rate + median cost of energy efficiency measures) to a ‘worst case’ (DNZ + 100% pass-through rate + max cost of energy efficiency measures). - Rationale note: The choice of the maximum indicative cost could be justified by potential systematic underestimation of appliance/material costs and labor costs by assessors. ### Impact on residential property valuations and mortgages - Pass-through rates are a crucial assumption: under the most extreme combination (DNZ + MAX cost + 100% pass-through rate) the weighted average impact on valuations is almost 5 percent and ranges by region from around 2 to around 11 percent; in some local areas it can reach almost 20 percent. - With carbon pass-through reduced to 50 percent, both average impacts and dispersion shrink considerably; valuation impacts drop approximately in the same proportion as the pass-through reduction. - Mapping valuation impacts into banks’ IFRS 9 LGD estimates (assuming unchanged PDs and using mortgage LtV distributions): - Under the ‘best case’ combination: LGDs increase no more than half of a percentage point. - Under the ‘worst case’ combination: LGDs increase by almost 1 percentage point, on average. - Aggregate loan loss provisions would increase between 5.6 percent (under the ‘best case’ combination) and almost 17 percent (under the ‘worst case’). - Caveats highlighted: - Mortgage PDs might plausibly increase at a climate Minsky point; this analysis assumes unchanged PDs and only considers asset valuation impacts. - Banks in the sample are national players with geographically well-diversified mortgage portfolios; smaller or less diversified banks could face concentration risks. - Results depend on the degree of overcollateralization; portfolios outside the sample could have different outcomes. ### Pension fund sector (transition risk) - Sample: ~70 corporate occupational defined benefit pension schemes (out of more than 5000 schemes), representing 1/3 of the segment in terms of total assets. - Data: aggregated by asset class and NACE classification; sector-level changes applied to sector-level holdings. - Results under NGFS scenarios: - ‘Phase I’ (1.5°C+CDR vs NDCs): weighted average portfolio loss at the climate Minsky point ≈ 2 percent, with individual scheme results ranging from –7 to 0 percent. - ‘Phase II’ (NZ2050 vs NDCs): weighted average portfolio loss ≈ 3.5 percent, with a range between –12.5 and 0 percent. ### Buildings’ energy efficiency and EPC data - EPC database for England and Wales contains 21.4 million certificates and 88.8 million recommendations (≈4 recommendations per certificate). - Certificates include CO2 emissions (current and potential), heating/hot water cost (current and potential), and total floor area (square meters). - Recommendations list indicative min-max costs in GBP for energy efficiency measures; information also relevant to estimate potential government subsidies such as the domestic Renewable Heat Incentive (RHI). - Government support: Green Grant covers up to 2/3 of the cost of chosen improvements, with a maximum government contribution of GBP 5,000. ### Sovereign bond holdings (physical risk — chronic) - Adverse scenario: RCP 8.5 with GMST increasing up to 2.2°C above pre-industrial levels in 2050 (analysis horizon to 2050). - Approach: simulated sovereign rating migrations driven by chronic physical risk (via GMST and its variability), translated into changes in credit spreads using current rating-based country risk premia; sovereign bond holdings repriced by applying discount rates incorporating changed credit spreads. - Findings: - If only mean temperature increases: overall modest drop of 0.6 percent in aggregate value of banks’ sovereign bond portfolios at the climate Minsky point, with individual bank results ranging from 0.3 percent to 1.2 percent. - If mean temperature increase is accompanied by an analogous increase in variability: average impact could rise to 3 percent, with half of the banks experiencing an impact larger than 5 percent. ### Insurers’ technical reserves (physical risk — acute and chronic) - U.K. general insurers’ largest exposures among sample of eight general insurers/reinsurers: expected mean annual losses of GBP 2.3bn for US hurricanes and GBP 0.8bn for European windstorms; expected mean annual loss for domestic floods GBP 0.4bn. - Extreme events: 1-in-200-year loss amounts before reinsurance — GBP 26bn for a US hurricane and GBP 13bn for a European windstorm; GBP 5bn for a 1-in-200-year domestic flood. - Reinsurance mitigates a large share of extreme losses: effectively around 30 percent of the losses of a 1-in-200-year event would be borne by the U.K. insurer who underwrote that risk (i.e., more than 70 percent recovered from reinsurers, mostly international). - Sensitivity analysis parameters: parametric increase in severity and/or frequency of 10/30 percent for US hurricanes, European windstorms, and U.K. floods; loss distributions estimated via gamma and beta distributions for severity and Poisson for frequency; simulated 1,000 loss years to derive new tail distributions after stress. - Results: - Assuming a 30 percent increase in both severity and frequency, mean annual loss before reinsurance would increase by up to 50 percent, with similar effects across the three perils. - Net (after reinsurance) impact cannot be precisely estimated due to bespoke, non-proportional reinsurance contract features. Insurers often reinsure large shares of U.K. flood exposure far out in the tail; some insurers use reinsurance caps for European windstorms, resulting in higher retention for certain low-probability, high-impact events. ### Key numeric highlights - Eight sensitivity combinations from three binary choices (scenario × pass-through × cost). - Weighted average valuation impact under most extreme combination: almost 5 percent; regional range around 2 to around 11 percent; some local areas almost 20 percent. - Pass-through reduction to 50% reduces impacts roughly proportionally. - LGD increases: up to half of a percentage point (best case) and almost 1 percentage point (worst case). - Aggregate loan loss provisions increase: 5.6 percent (best case) to almost 17 percent (worst case). - Pension schemes: Phase I weighted average loss ≈ 2 percent (range –7 to 0 percent); Phase II weighted average loss ≈ 3.5 percent (range –12.5 to 0 percent). - Sovereign portfolio impacts: 0.6 percent average drop (range 0.3–1.2 percent) if only mean temperature rise; up to 3 percent average if variability also rises, with half of banks >5 percent impact. - Insurers’ exposure: expected mean annual losses — GBP 2.3bn (US hurricane), GBP 0.8bn (EU windstorm), GBP 0.4bn (UK flood); 1-in-200-year losses before reinsurance — GBP 26bn (US hurricane), GBP 13bn (EU windstorm), GBP 5bn (UK flood). - Increase in mean annual loss if severity and frequency each increase by 30 percent: up to 50 percent before reinsurance. *Source: IMF staff calculations and analyses as presented in the United Kingdom FSAP chapter.* ### 134. While the U.K. plays a leading role in the analysis of climate risks for the insurance ### 1gbrea2022003 - 134. While the U.K. plays a leading role in the analysis of climate risks for the insurance ### Climate risk modeling and disclosure challenges - Supervisory and industry data gaps remain, e.g., insurance exposures split by peril and geography, as well as correlations and interdependencies. - Insurers often consider climate change predominantly as a catastrophe risk, which is described as a "too narrow perspective." - Interdependencies and indirect risks require closer attention, e.g., the impact of extreme weather variability on global supply chains. - Many climate impact models assume that economies will fully adapt; this assumption is characterized as "rather optimistic" given the current state of adaptation across the world. - Models indicate that "adapting later will be significantly more expensive and, in some cases, hardly possible." - Transition risk strategies have been adopted by a growing number of insurers, but "only few have a strategy for litigation risk or adaptation." - Climate risk disclosures are expanding but approaches remain "rather heterogenous," making it difficult for investors to systematically incorporate those disclosures into investment and risk management. - "Many smaller companies still must catch up in their approach to disclosures." ### Box 7. Flood Re (summary of scheme and metrics) - Flood Reinsurance Scheme (Flood Re) provides domestic flood reinsurance coverage; joint initiative of the U.K. Government and the insurance industry; established by the Water Act 2014; operational through Flood Re Limited since April 2016. - Purpose: promote availability and affordability of flood insurance for eligible homes and manage transition to risk-sensitive pricing for household flood insurance by 2039. - Reinsurance offered at a subsidized fixed rate to U.K. household insurers, resulting in an expected underwriting loss every year for Flood Re Limited; nevertheless, the company earned profits before tax of GBP 142m and GBP 61m in the financial years 2020/21 and 2019/20, respectively. - The expected loss and Flood Re’s cost for retrocession are financed through a GBP 180m levy on U.K. household insurers. - Since 2016, GBP 67m in claims were paid out, benefiting more than 350,000 households. - Availability of household insurance has improved in flood risk areas; "80 percent of households with previous flood claims pay now at least 50 percent less premiums." - Company SCR ratio: coverage of the SCR stood at 1,251 percent at end of financial year 2020/21, "up by more than 700 percentage points from the previous year" (partly driven by switch from the standard formula to an internal model which led the PRA to waive the previously prescribed capital add-on). - Asset-side risks are minor: cash and short-term deposits account for 93 percent of total assets. - Regionally concentrated underwriting risks are largely mitigated through retrocession. - Going forward: critical to introduce increasingly risk-sensitive elements to avoid cliff effects at the end of the scheme’s projected lifetime in 2039. - Flood Re evaluates strategy and effectiveness every five years; last review in 2019 proposed mechanisms expected to incentivize property flood resilience, including: - "Build Back Better": claims payments would include an additional amount for resilient repair, which would typically exceed the cost of the original damage. - Discounted premiums to reward households that have adapted their homes to be more resilient to flooding. - The levy on ceding insurers could be reviewed on a three-year cycle in line with procurement of Flood Re’s reinsurance program, potentially reducing the levy in the future. - DEFRA has initiated and consulted an amendment of the Flood Re scheme in early 2021. - Sources: IMF staff based on Flood Re’s disclosures. ### Recommendations (C. Recommendations) - Bank of England (BOE) should "accelerate the development of its own analytics" to remain at the frontier of climate-related analyses in the financial sector. - BOE has led global effort since Governor Carney’s Lloyd’s speech in September 2015, supported NGFS, and launched the Climate Biennial Exploratory Scenario. - BOE could complement current framework with more investments in internal analytical tools: equip itself with a suite of in-house models (macro, sectoral, micro) to run independent (top-down) scenario-based analyses of climate-related risks on financial institutions and their propagation across the financial system. - Important to deepen understanding of how financial firms’ climate-related risks will be influenced by public policies, particularly transition risks (e.g., use of revenues from carbon taxes, energy efficiency measures, other decarbonization policies) and physical risks (e.g., future role of Flood Re and general disaster prevention policies). - PRA should develop further guidance for insurance companies on risks to be considered in their Own Risk and Solvency Review (ORSA), including indirect climate risks and litigation risks. - Indirect effects of climate change remain to be fully understood by insurers and supervisors. - Important for U.K. as a reinsurance hub: vulnerability of global supply chains to more frequent/severe weather events; second-order effects could lead to substantial business interruption claims and significant macroeconomic implications. - U.K. insurers active in liability insurance: crucial to fully understand climate-related risks covered by corporate liability insurance and Directors and Officers (D&O) policies. - Litigation risks were excluded from this FSAP’s stress test because they are difficult to quantify in a top-down climate stress test. - Industry practice needs to emerge on how to measure litigation risks (stand-alone or component of transition risk), how to reserve for it given recent jurisprudence developments, and how to disclose it. - DEFRA should further promote transition of Flood Re to reward investments in flood resilience with premium reductions and introduce a build-back-better policy. - Such policies normally require definition of best-practice standards and certifications which should be widely promoted. ### Risk Assessment Matrix (selected entries) - Conjunctural shocks and scenarios (likelihood and expected impact summaries): - Global resurgence of the COVID-19 pandemic: Likelihood = Medium. - Expected impact: Demand for contact-intensive sectors remains low for longer; firms face prolonged increase in production costs; corporate bankruptcies and longer-term unemployment increase; bank losses on domestic and cross-border exposures materialize; banks’ capital declines, depressing recovery with weaker credit growth (second-round effects). - Disorderly transformations (post-COVID): Likelihood = Medium. - Expected impact: Reshuffling of global value chains increases production costs and contributes to inflation; permanent reshoring and less trade reduce potential output; prolonged unemployment and corporate insolvencies weigh on banks’ asset quality. - De-anchoring of inflation expectations in the U.S.: Likelihood = Medium. - Expected impact: Higher debt service and refinancing costs lead to increasing defaults; severe real estate price correction leads to loan losses; mark-to-market losses on debt securities; higher interest rates could lead to losses. - Rising commodity prices amid bouts of volatility: Likelihood = Medium. - Expected impact: Persistent rise in price of imports passes through to U.K. domestic inflation; volatility and rising risk premia increase debt service burden for banks' counterparts and losses in banks' bond portfolios. - Structural risks (selected): - Cyber-attacks on critical infrastructure/institutions: Likelihood = Medium. - Expected impact: Disruptions undermine confidence, negatively affect asset quality; funding market freeze and spike in risk premia amid counterparty risk concerns. - Higher frequency and severity of natural disasters related to climate change: Likelihood = Medium. - Expected impact: Damages from increasingly frequent/severe hazards (esp. floods in the U.K.) and from increasing surface temperatures and extreme weather events affect probabilities of default of corporates and households and the value of their collateral, leading to increase in banks’ credit losses. - "The global policy response to mounting evidence of climate change impact on the economy leads to a sharp acceleration of the transition to a low-carbon economy, determining a drastic reassessment of asset values and causing significant losses in equity and bond portfolios with large concentrations in high-carbon sectors." - Stronger impact from Brexit: Likelihood = Medium. - Expected impact: Market fragmentation increases cost of financial services; continuing uncertainty about adjustment path decreases business investment and weighs on potential growth. - RAM explanatory note: "The relative likelihood is the staff’s subjective assessment... ('low' ... below 10 percent, 'medium' ... between 10 and 30 percent, and 'high' ... between 30 and 50 percent)." ### Banking Sector: Solvency Test (selected matrix entries) - Institutional perimeter: - Institutions included: "Eight major banks and building societies." - Market share: "Approximately 75 percent of PRA-regulated banks’ lending to the United Kingdom real economy." - Data and baseline date: Effective date: end-December 2020. Data: Banks’ submissions as part of the Annual Cyclical Scenario (ACS), performed by the BOE, FINREP, COREP, HBRD. - Scope of consolidation: Global consolidated group basis, except for Santander U.K. plc, whose parent is supervised by a foreign authority. - Channels of risk propagation and methodology: - IMF Top-down: "IMF Solvency Stress Test Workbox (Balance-sheet based approach)." - BOE Bottom-up: "Standard BOE approach that uses a dynamic balance sheet approach." Solvency stress test also includes a traded risk stress calibrated to be consistent with macro scenario shocks. - Satellite models for macro-financial linkages (Top-down description): - Credit Risk: Satellite models link credit risk variables with macroeconomic variables per asset class for domestic exposures and per geographical location for foreign exposures. Models consider different sample periods, including and excluding 2020; when 2020 is included a dummy for quarters affected by pandemic is added. Selected models project loan losses under various scenarios and are augmented with corporate stress testing outputs. - Market risk: Valuation losses from full revaluation of sovereign securities, corporate fixed income debt securities and equity holdings are calculated using a Mark to Market (MTM) approach for fair-valued securities. Valuation losses of securities held at amortized cost are calculated using a credit risk approach. Valuation changes in open positions in foreign currency, commodities and equities are estimated based on fluctuations in exchange rate, commodity prices, and equity prices under the scenarios. - Interest rate risk: A gap analysis is conducted based on asset/liability structure broken into funding sources and time to re-pricing buckets. Interest margin shocks vary per scenario. - Other P&L components: Interest income is calculated via estimation and projection of lending/borrowing rates (via satellite models), applied to new and variable rate loans. Residual income components (e.g., net fee and commission income) are either estimated via satellite models. Non-performing loans do not generate any income. - Bottom-up note: "Banks use their own models to comprehensively project their P&L and capital results. The Bank also uses its own set of econometric models to form a judgment around" (text continues in original). *Source: IMF staff based on text provided in the content unit.* ### 1. Macrofinancial feedback effects. The team developed a ### 1. Macrofinancial feedback effects ### Framework and modules - The exercise accounts for macrofinancial feedback effects following a framework similar to Catalan and Hoffmaister (2020). - The exercise is comprised of two modules that are integrated with the Workbox: - Credit Growth model: Elasticities of credit growth to macroeconomic variables and bank sector variables (CAR and NPL). - SVAR (Structural Vector Auto Regression): Elasticities of macroeconomic variables to aggregate bank loan. ### Credit risk and internal stress-test models - A range of internal stress test models are used to project credit losses; mechanics vary but all take economic scenario variables as inputs to project credit impairment charges over the horizon. - Model outputs are used to form judgements on results and the reasonableness of submissions from participating firms. - Three internal models cover U.K. mortgages (each designed differently) providing alternative views. - Two alternate models cover corporate exposures, primarily focusing on U.K. exposures. ### Interest rate risk, net interest income, and other P&L components - Interest rate risk: A gap analysis is conducted based on granular data on asset/liability structure and time to re-pricing buckets. - Net interest income: Calculated via estimation and projection of lending/borrowing rates. - Other P&L components: Residual income components (e.g., net fee and commission income) will be either estimated/projected or assumed to stay at the level observed for 2020. - Non-performing loans will not generate any income. ### Stress test horizon - Stress test horizon: 5 years (2021-2025) 5 years (2021-2025) ### Scenarios (tail shocks / scenario analysis) - Three macroeconomic scenarios (baseline and two adverse) agreed with the authorities. - Baseline: Based on the October 2021 WEO projections. - Scenario 1: Adverse with scarring. - Pandemic recedes in the first half of 2021 as vaccination campaigns pick up, but new variants emerge; vaccines adaptation takes longer than anticipated. - Pandemic assumed to be under control not earlier than late 2022 for advanced economies, including The United Kingdom., and by the end of 2023 for the rest of the world. - Global trade depressed; acceleration of de-globalization; weaker global activity prompts sharp increases of risk premia exposing financial and fiscal vulnerabilities. - Domestic post-Brexit adjustment difficulties further lower GDP growth over the short-term; market fragmentation increases the cost of financial services. - Compound effects result in real GDP growth of only 0.5 percent in 2021. - Real GDP recovers by only 1.6 percent in 2022. - Scarring: lower potential output growth by 0.3 percent with respect to the pre-COVID period and higher natural unemployment rate. - Scenario 2: Adverse with sudden tightening of global financial conditions. - Pandemic in global rearview mirror; consumer spending picks up; drawdown of savings and gradual government support withdrawal. - Low investment, business failures, and skill mismatches reduce global spare capacity. - Energy and commodity prices rise on a sustained basis; localization of value chains reduces globalization’s role in productivity gains and disinflation. - Major central banks accommodate rising inflationary pressures in near term; policy rates remain near zero while term premia rise sharply. - Abrupt increase in borrowing cost of corporates and sovereigns; tightening of global financial conditions weighs on investment; unemployment remains elevated. - Central banks finally raise short term rates rapidly [by 2022/2023]; uncertainty about quantitative tightening creates upwards pressure on term premia and long-term rates. - Financial strains on households with variable rate mortgages; equity prices decline as policy tightening becomes inevitable. - Sterling depreciation in the United Kingdom reduces risk appetite of foreign investors and contributes to goods price inflation. - Potential output recovers as pandemic-related supply restrictions ease but does not fully recover to pre-COVID path. - The adverse scenarios are simulated using the IMF's Global Macro financial Model (GFM). - Traded risk stress is consistent with the macroeconomic scenario — no separate traded risk scenario. - Participating banks will be asked to submit stressed misconduct costs for known issues. ### Risks and buffers — positions and risk factors assessed - Credit risk (provision costs): Estimated according to Basel III framework. - Credit risk includes: (i) lending risk from exposures to sovereign, public entities, financial institutions, corporates, and other; (ii) mortgage-related lending. Positions include cross-border loan exposures; (iii) retail lending. - Sovereign risk: Mark-to-market valuation of securities (from shocks to interest rates and credit spreads) in trading book and Available for Sale/Fair Value Option (AFS/FVO) linked to macro scenario. - Market risk other than sovereign risk: Market stress from shocks to changes in interest rates, credit spreads, exchange rates, commodities, and equity prices. - Profits: Interest income declines for lost income from defaulted loans. - Interest rate risk in the banking book: Interest expenses increase due to rising funding costs linked to the macroeconomic scenario with empirically estimated pass-through. - Net fee and commission income, other income and non-interest expense evolve with macroeconomic conditions. - Market risk (additional detail): Direct losses due to market moves for fair valued banking book positions and on trading book positions; equity and debt including leveraged loans underwriting positions; investment banking revenues; losses from large single name defaults and defaults in specific groups of smaller counterparties. - Changes in valuation adjustments, principally CVA, FVA and PVA. Changes to market risk, counterparty credit risk and CVA RWAs. - No change in business models (no rebalancing of portfolio) in one stated approach; elsewhere banks’ submissions should reflect their corporate plans including cost or business changes and be adjusted appropriately to reflect changes in expected performance and execution in the stress scenario. - 2021 solvency stress test will include a comprehensive of banks’ profit projections that will include: - Net interest income. - Investment banking income. - Net fee and commission income and other income expenses. - Misconduct costs and other non-underlying costs. ### Behavioral adjustments and balance sheet assumptions - Balance sheet growth assumptions (one approach): Loan portfolios are assumed to grow uniformly across the in-scope banks at the nominal GDP growth rate of the scenarios, with no change in composition (except for new NPLs). - Balance sheet composition remaining constant over the stress test horizon (in one specification). - Banks can only accumulate capital through retained earnings. - Maturing assets are replaced by exposures of the same type and risk. - Statutory tax rates apply. - Dividends are linked to banks’ net profits: - Under positive profits and capital ratios above hurdle rates, the dividend payout is set at 30 percent. Otherwise, no dividend payout is assumed. - If a bank’s capital ratio falls below regulatory minimum during the stress test horizon, no prompt corrective action is assumed (in one stated approach). - Management actions are not incorporated in one approach; in another, banks’ submissions should reflect business-as-usual management actions and strategic management actions. - Banks’ stock of secured lending to U.K. individuals, consumer credit to U.K. individuals and lending to U.K. PNFCs should increase in each year of the stress projection by at least the growth rates provided by the Bank for these asset classes. The published growth rates assume there are no provisions or write-offs during the stress period. - In the 2021 stress test, banks should include ordinary dividend payments that they project their boards would approve in the stress scenario. - There is no mechanical link between the stress test results and the setting of capital buffers or other regulatory response; the Bank will consider each bank’s capital low point against their hurdle rates. ### Regulatory and market-based standards and parameters - Parameter definition: - Point-in-Time (PiT) PDs and LGDs for expected losses (numerator of the capital ratio) and Through-the-Cycle TtC PDs and LGDs for RWA (denominator). - Transition rates between stages 1-2-3 (under IFRS 9) are inferred from available information. - Domestic Corporate PDs are also derived from the output of the corporate stress test exercise, as a robustness check. - Parameter calibration: PDs and LGDs evolve with the macroeconomic and financial variables of the scenario. - Internal credit models will project PiT PDs and LGDs. Risk weighted assets are not modelled internally; participating firms do model these aspects and submit them as part of their projections. - IFRS 9 stage transitions are not modelled internally and are not requested as part of the results submission. - Regulatory standards: - Capital definition according to Basel III/PRA rulebook, including CET1, Tier 1, and total CAR. - Hurdle rates: Pillar 1 and 2A CET1 Requirements plus systemic buffers (G-SIB, O-SII, and SRB); leverage ratio requirements. - Results are reported on a fully loaded basis, i.e., IFRS9 transitional arrangements are not accounted for. - Banks are required to apply IFRS 9 in their starting position and throughout the projection period. - Hurdle rates/Reference points: Pillar 1 and 2A CET1 Requirements plus systemic buffers (G-SIB and SRB). ### Reporting format and output presentation - Output presentation includes: - Evolution of CET1, Tier 1, CAR for the aggregate banking system. - Decomposition of key drivers to aggregate net profits and aggregate CET1 capital ratios. - Cumulative impairment charges by bank for The United Kingdom. and other specific countries impacted by the scenario. - Number of banks and share of total assets below hurdle rates. - Individual firm-by-firm results from the stress test will be published in Q4 2021. - Aggregate information will also be published in Summer 2021. - The Q4 publication will include details of the impact of the stress on the U.K. banking system in aggregate, including a decomposition of key drivers to aggregate changes in the CET1 and Tier 1 leverage ratio and further details of aggregate impairments by asset class and geography. ### Annex IV — Liquidity Stress Test: Stressed LCR haircut scenarios (selected entries) - Annex IV. Table 1. Stressed LCR: Haircut Scenarios (percent) — Scenario 1 / Scenario 2 / Scenario 3 - Coins and banknotes: 100 / 100 / 100 - Withdrawable central bank reserves: 100 / 100 / 100 - Central bank assets: 100 / 100 / 100 - Central government assets: 100 / 100 / 95 - Regional government / local authorities’ assets: 100 / 95 / 90 - Public Sector Entity assets: 100 / 95 / 90 - Recognizable domestic and foreign currency central government and central bank assets: 100 / 100 / 100 - Multilateral development bank and international organizations assets: 100 / 100 / 90 - Qualifying CIU shares/units: underlying is coins/banknotes and/or central bank exposure: 100 / 100 / 100 - Qualifying CIU shares/units: underlying is Level 1 assets excluding extremely high-quality covered bonds: 95 / 95 / 85 - Alternative Liquidity Approaches: Inclusion of Level 2A assets recognized as Level 1: 80 / 80 / 80 - Extremely high-quality covered bonds: 93 / 90 / 85 - Qualifying CIU shares/units: underlying is extremely high-quality covered bonds: 88 / 80 / 80 - Regional government / local authorities or Public Sector Entity assets (Member State, RW20): 85 / 75 / 70 - Central bank or central / regional government or local authorities or Public Sector Entity assets (Third Country, RW20): 85 / 80 / 70 - High quality covered bonds (CQS2): 85 / 70 / 50 - High quality covered bonds (Third Country, CQS1): 85 / 80 / 60 - Corporate debt securities (CQS1): 85 / 80 / 70 - Qualifying CIU shares/units: underlying is Level 2A assets: 80 / 70 / 60 - Asset-backed securities (residential, CQS1): 75 / 70 / 60 - Asset-backed securities (auto, CQS1): 75 / 70 / 60 - High quality covered bonds (RW35): 70 / 60 / 50 - Asset-backed securities (commercial or individuals, Member State, CQS1): 65 / 60 / 55 - Corporate debt securities (CQS2/3): 50 / 40 / 30 - Corporate debt securities - non-interest-bearing assets (held by credit institutions for religious reasons) (CQS1/2/3): 50 / 30 / 30 - Shares (major stock index): 50 / 25 / 0 - Non-interest-bearing assets (held by credit institutions for religious reasons) (CQS3-5): 50 / 50 / 50 - Restricted-use central bank committed liquidity facilities: 100 / 100 / 100 - Qualifying CIU shares/units: underlying is asset-backed securities (residential or auto, CQS1): 70 / 60 / 50 - Qualifying CIU shares/units: underlying is high quality covered bonds (RW35): 65 / 60 / 55 - Qualifying CIU shares/units: underlying is asset-backed securities (commercial or individuals, Member State, CQS1): 60 / 60 / 60 - Qualifying CIU shares/units: underlying is corporate debt securities (CQS2/3), shares (major stock index) or non-interest-bearing assets (held by credit institutions for religious reasons) (CQS3-5): 45 / 40 / 35 - Deposits by network member with central institution (no obligated investment): 75 / 75 / 75 - Liquidity funding available to network member from central institution (non-specified collateralization): 75 / 75 / 75 *Source: 1gbrea2022003 - 1. Macrofinancial feedback effects. The team developed a / 1gbrea2022003 PDF content.* ### Annex IV. Table 2. Stressed LCR: Outflows Scenarios 1-4 (percent) ### Annex IV. Table 2. Stressed LCR: Outflows Scenarios 1-4 (percent) ### Retail deposit outflows (Unsecured transactions/deposits) - deposits where the payout has been agreed within the following 30 days: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - deposits subject to higher outflows — category 1: - Scenario 1: 10, Scenario 2: 20, Scenario 3: , Scenario 4: 20 - deposits subject to higher outflows — category 2: - Scenario 1: 15, Scenario 2: 30, Scenario 3: , Scenario 4: 30 - stable deposits: - Scenario 1: 5, Scenario 2: 10, Scenario 3: 5, Scenario 4: 10 - derogated stable deposits: - Scenario 1: 3, Scenario 2: 5, Scenario 3: 3, Scenario 4: 5 - other retail deposits: - Scenario 1: 10, Scenario 2: 20, Scenario 3: 20, Scenario 4: 20 ### Operational deposits (maintained for clearing, custody, cash management or comparable services) - maintained for clearing, custody, cash management or other comparable services in the context of an established operational relationship covered by DGS: - Scenario 1: 5, Scenario 2: 10, Scenario 3: 15, Scenario 4: 15 - maintained for clearing, custody, cash management or other comparable services in the context of an established operational relationship not covered by DGS: - Scenario 1: 25, Scenario 2: 25, Scenario 3: 35, Scenario 4: 35 - maintained in the context of IPS or a cooperative network — not treated as liquid assets for the depositing institution: - Scenario 1: 25, Scenario 2: 35, Scenario 3: 25, Scenario 4: 35 - maintained in the context of IPS or a cooperative network — treated as liquid assets for the depositing credit institution: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - maintained in the context of an established operational relationship (other) with non-financial customers: - Scenario 1: 25, Scenario 2: 25, Scenario 3: 35, Scenario 4: 35 - maintained to obtain cash clearing and central credit institution services within a network: - Scenario 1: 25, Scenario 2: 25, Scenario 3: 25, Scenario 4: 25 ### Non-operational deposits - correspondent banking and provisions of prime brokerage deposits: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - deposits by financial customers: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - deposits by other customers — covered by DGS: - Scenario 1: 20, Scenario 2: 20, Scenario 3: 40, Scenario 4: 40 - deposits by other customers — not covered by DGS: - Scenario 1: 40, Scenario 2: 40, Scenario 3: 60, Scenario 4: 60 ### Additional outflows - collateral other than Level 1 assets collateral posted for derivatives: - Scenario 1: 20, Scenario 2: 30, Scenario 3: 30, Scenario 4: 30 - Level 1 EHQ Covered Bonds assets collateral posted for derivatives: - Scenario 1: 10, Scenario 2: 25, Scenario 3: 25, Scenario 4: 25 - material outflows due to deterioration of own credit quality: - Scenario 1: 100, Scenario 2: 20, Scenario 3: 20, Scenario 4: 20 - impact of an adverse market scenario on derivatives, financing transactions and other contracts — hlba approach: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - impact of an adverse market scenario on derivatives, financing transactions and other contracts — amao approach: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - outflows from derivatives: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 ### Short positions and collateral-related outflows - short positions covered by collateralized SFT: - Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - short positions — other: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - callable excess collateral: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - due collateral: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - liquid asset collateral exchangeable for non-liquid asset collateral: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 ### Loss of funding on structured financing activities and related items - structured financing instruments: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - financing facilities: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - assets borrowed on an unsecured basis: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - internal netting of client’s positions: - Scenario 1: 50, Scenario 2: 50, Scenario 3: 50, Scenario 4: 50 ### Committed facilities - credit facilities to retail customers: - Scenario 1: 5, Scenario 2: 10, Scenario 3: 5, Scenario 4: 10 - credit facilities to non-financial customers other than retail customers: - Scenario 1: 10, Scenario 2: 10, Scenario 3: 20, Scenario 4: 20 - credit facilities to credit institutions — for funding promotional loans of retail customers: - Scenario 1: 5, Scenario 2: 10, Scenario 3: 10, Scenario 4: 10 - credit facilities to credit institutions — for funding promotional loans of non-financial customers: - Scenario 1: 10, Scenario 2: 20, Scenario 3: 20, Scenario 4: 20 - credit facilities to credit institutions — other: - Scenario 1: 40, Scenario 2: 60, Scenario 3: 60, Scenario 4: 60 - credit facilities to regulated institutions other than credit institutions: - Scenario 1: 40, Scenario 2: 75, Scenario 3: 75, Scenario 4: 75 - within a group or an IPS if subject to preferential treatment — within IPS or cooperative network if treated as liquid asset by the depositing institution: - Scenario 1: 75, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - credit facilities to other financial customers: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 ### Liquidity facilities - to retail customers: - Scenario 1: 5, Scenario 2: 15, Scenario 3: 10, Scenario 4: 15 - to non-financial customers other than retail customers: - Scenario 1: 30, Scenario 2: 40, Scenario 3: 50, Scenario 4: 50 - to personal investment companies: - Scenario 1: 40, Scenario 2: 50, Scenario 3: 50, Scenario 4: 50 - to SSPEs — to purchase assets other than securities from non-financial customers: - Scenario 1: 10, Scenario 2: 10, Scenario 3: 10, Scenario 4: 10 - to SSPEs — other: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - to credit institutions — for funding promotional loans of retail customers: - Scenario 1: 5, Scenario 2: 15, Scenario 3: 10, Scenario 4: 15 - to credit institutions — for funding promotional loans of non-financial customers: - Scenario 1: 30, Scenario 2: 40, Scenario 3: 50, Scenario 4: 50 - to credit institutions — other: - Scenario 1: 40, Scenario 2: 50, Scenario 3: 50, Scenario 4: 50 - within a group or an IPS if subject to preferential treatment — within IPS or cooperative network if treated as liquid asset by the depositing institution: - Scenario 1: 75, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - to other financial customers: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 ### Other liabilities - liabilities resulting from operating expenses: - Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - in the form of debt securities if not treated as retail deposits: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 - others: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 ### Outflows from secured lending and capital market-driven transactions (counterparty distinctions) - Counterparty is central bank — all collateral categories shown: - level 1 excl. EHQ Covered Bonds collateral: Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - level 1 EHQ Covered Bonds collateral: Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - level 2A collateral: Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - level 2B asset-backed securities (residential or automobile, CQS1) collateral: Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - level 2B covered bonds: Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - level 2B asset-backed securities (commercial or individuals, Member State, CQS1) collateral: Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - other Level 2B assets collateral: Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - non-liquid assets collateral: Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - Counterparty is non-central bank: - level 1 excl. EHQ Covered Bonds collateral: Scenario 1: 0, Scenario 2: 0, Scenario 3: 0, Scenario 4: 0 - level 1 EHQ Covered Bonds collateral: Scenario 1: 7, Scenario 2: 7, Scenario 3: 7, Scenario 4: 7 - level 2A collateral: Scenario 1: 15, Scenario 2: 15, Scenario 3: 15, Scenario 4: 15 - level 2B asset-backed securities (residential or automobile, CQS1) collateral: Scenario 1: 25, Scenario 2: 25, Scenario 3: 25, Scenario 4: 25 - level 2B covered bonds: Scenario 1: 30, Scenario 2: 30, Scenario 3: 30, Scenario 4: 30 - level 2B asset-backed securities (commercial or individuals, Member State, CQS1) collateral: Scenario 1: 35, Scenario 2: 35, Scenario 3: 35, Scenario 4: 35 - other Level 2B assets collateral: Scenario 1: 50, Scenario 2: 50, Scenario 3: 50, Scenario 4: 50 - non-liquid assets collateral — counterparty is central govt, PSE<=RW20, MDB: - Scenario 1: 25, Scenario 2: 25, Scenario 3: 25, Scenario 4: 25 - non-liquid assets collateral — another counterparty: - Scenario 1: 100, Scenario 2: 100, Scenario 3: 100, Scenario 4: 100 *Annex IV. Table 2. Stressed LCR: Outflows Scenarios 1-4 (percent).* --- _Source: https://www.imf.org/-/media/files/publications/cr/2022/english/1gbrea2022003.pdf_