## _cr1469 ## Source details **Canonical URL:** [_cr1469](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2014/_cr1469.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2014/_cr1469.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2014/_cr1469.pdf.json) --- ### Overview of the stress-testing module - Purpose: Report on the stress testing module of the 2013 FSAP Update for Canada: coverage, tail risks, quantification, assumptions and models, and recommendations to enhance authorities’ stress testing frameworks. - Coverage: - Banking: six largest Canadian banks that together constitute over 90 percent of the banking system by assets. - Life insurance: three largest life insurers, with over 60 percent market share in premiums. - Mortgage insurer: Canada Mortgage and Housing Corporation (CMHC), with a 70 percent market share in premiums. - Test types: - Banks: Bottom-up (BU) and Top-down (ToD). Three ToD models by BoC, IMF, and OSFI. - Life insurers and CMHC: BU solvency stress tests. - Risk factors assessed: - BU banks: credit, market, operational risks affecting earnings and solvency. - ToD tests: credit and market risks; BoC ToD also assessed second-round effects from idiosyncratic funding runs, asset fire-sales, and interbank counterparty credit losses. - Sensitivities compared alternative parameterizations and different approaches to deriving RWAs under stress (regulatory versus economic). ### Common assumptions, data, and scenarios - Identical assumptions across approaches for stress and baseline scenarios and input risk parameters (including prescribed PDs and bank-projected LGDs). - Consolidation and timing: - Banking tests: consolidated data as of October 2012, restated to reflect early full adoption of Basel III for solvency tests. - Life insurers and CMHC: end-2012 valuations. - Horizon and scenarios: - Five-year horizon with two scenarios: baseline and stress. - Baseline: IMF WEO projections as of February 2013. - Stress: model-driven severe crisis originating outside Canada, triggering domestic household/house-price materialization; cumulative decline in real GDP over a three-year period representing the most severe recession in the last 35 years; GDP growth returns gradually after three years. ### Banking sector — solvency stress tests: main quantitative findings - All approaches: under severe economic distress, all banks would fall below the Canadian “all-in” CET1 supervisory threshold; recapitalization needs are manageable. - Definition: “All-in” = capital including all Basel III regulatory adjustments required by 2019 (no phase-ins) but retaining Basel III phase-out rules for non-qualifying capital instruments. - Regulatory thresholds: - Canadian “all-in” supervisory threshold: 7 percent for first three years, 8 percent for last two years. - IMF ToD outcomes: - Recapitalization needs peak in 2016 at 30 percent of 2012 gross income or 150 percent of 2012 net income (corresponding to 2½ percent of 2015 nominal GDP). - IMF ToD: system-wide “all-in” CET1 ratio would fall by 2½ percentage points in 2015 relative to the base year or 5½ percentage points relative to the baseline scenario in 2015. - BU outcomes (selected): - Five banks fall below supervisory threshold by 2015; capital shortfall small. - System-wide “all-in” CET1 declines by 180 basis points during the recession (2013-2015) vs 2012 “all-in” CET1. - System-wide decline of 430 basis points vs 2015 “all-in” CET1 in the baseline scenario. - Peak recapitalization to reach supervisory threshold: 9 percent of 2012 gross income in 2016 (40 percent of 2012 net income) = 0.7 percent of 2016 nominal GDP. - IMF ToD (alternate summary): - All banks fall below supervisory threshold by 2015. - System-wide “all-in” CET1 declines by 250 basis points during recession (2013-2015) vs 2012. - System-wide “all-in” CET1 declines by 540 basis points vs 2015 baseline CET1. - Recapitalization peak: 32 percent of 2012 gross income in 2016 = 150 percent of 2012 net income = 2.5 percent of 2015 nominal GDP. - OSFI ToD recapitalization peak: 20 percent of 2012 gross income in 2016 = 90 percent of 2012 net income = 1.5 percent of 2016 nominal GDP. - Drivers of losses and capital changes: - Credit losses (charges for impairment) are primary driver across approaches; RWAs for credit risk a major driver in IMF ToD. - Loss concentration: around 65 percent of peak losses (2015) from Canadian exposures (consumer loans, construction, manufacturing); around 25 percent from U.S. exposures (business loans). - Dividend payout assumptions materially affect capitalization outcomes. ### Banking sector — RWAs, PDs, loss computation and methodological notes - RWAs for credit risk under stress: - Calculated using Basel II IRB formula translating downturn LGDs, changes in PDs, asset correlation and maturity adjustment into stressed RWAs. - “Standardized exposures” added to IRB exposures to produce total exposures by asset class. - Percentage changes in calculated RWAs applied to base-year realized RWAs to project RWAs over the horizon. - PDs and mapping: - BoC estimated/forecasted PDs (sectoral default rates) used across approaches; corporate defaults modeled after Djoudad and Bordeleau (2013); household PDs from HRAM (Faruqui, Liu and Roberts, 2012). - Forecasted PDs provided as delta PDs applied to 2012 PIT PDs reported by banks. - Mapping between economic sectors and new Basel II asset classes used because PDs were available by sector while downturn LGDs and effective maturity available by Basel II asset classes. - Credit losses (IMF ToD): - Expected losses = PIT PDs × downturn LGDs × EAD by asset class; expected losses assumed fully provisioned and enter income statement immediately. - Loan loss reserves (net of realized losses on 2012 NPLs) serve as first line of defense. - Sensitivity analyses use alternative LGDs and regulatory parameter calibrations (e.g., TTC PDs). - Income statement satellite models: - Econometric panel regressions translated macro variables into interest income/expense, trading income, FX effects, non-interest items; taxes at 2012 effective tax rate if net income positive and zero otherwise. - Dividend policy and capital actions: - No capital actions allowed in stress scenario except commitments made before stress horizon; dividends set constant as base year unless CET1 falls below supervisory threshold, in which case capital conservation buffer rules apply. - OSFI restated results to apply the CCB rule on an annual basis for all banks due to inconsistent bank application. ### Banking sector — liquidity and MFRAF (BoC macro-financial risk assessment framework) - MFRAF purpose and modules: - Module 1: Core credit model (credit losses). - Module 2: Funding Liquidity Risk (runs when Balance Sheet Liquidity (BSL) < 100 percent; liquidity losses assumed 2.25 percent of RWAs; sensitivity 1 percent of RWAs). - Module 3: Network Effects (interbank counterparty defaults spillovers). - BSL metric: - Ratio of (liquid assets + illiquid assets × fire-sale discount) to liabilities maturing over next six months; run trigger if BSL < 100 percent. - Supervisory CET1 threshold used for run triggers: 7 percent. - BSL calibration broadly aligned with LCR haircuts/run-off rates but with Canadian-specific differences and a 6-month horizon. - MFRAF implementation: - Run in stress year 2 and year 3 under baseline and adverse liquidity scenarios; network module run in stress year 2 only. - Inputs: credit losses from BU exercise (PDs, LGDs, EAD); collective allowances for unidentified losses excluded from MFRAF charge for impairment. - MFRAF key quantitative outcomes: - Baseline liquidity: limited aggregate CET1 impact; one bank materially affected (200 basis points funding loss + 125 bps credit loss could breach 4 percent regulatory threshold). - Network marginal impact: 21–29 basis points on “all-in” CET1 for most exposed counterparties. - Adverse liquidity scenario: cumulative credit losses of 215 bps over two years augmented by cumulative funding liquidity and network losses of 235 bps. - Marginal system-wide impact of funding liquidity and network effects: ~35 basis points (benchmark) and 235 basis points (adverse). - Official liquidity support estimates: - Baseline: Can$5 billion–Can$9 billion. - Adverse (very extreme): Can$86 billion–Can$155 billion. - Sensitivities and caveats: - MFRAF outcomes sensitive to fire-sale discounts, roll-over rates, and BSL dynamics; year-3 BSL proxies from BU may overstate resilience because they omit MFRAF year-2 outcomes. ### Life insurers — macro solvency stress test results - Sample: Great West Life, Manulife, Sun Life (market share: 76% by assets; >60% by premiums). - Valuation and capital metric: Canadian valuation regime as of 1 January 2013; capital per MCCSR. - Aggregated MCCSR: - 2012 aggregated value: 215 percent. - Baseline projection end-2017: 288 percent. - Adverse scenario: aggregated MCCSR declines to 199 percent in 2015, stabilizes thereafter. - Supervisory target: 150 percent (MCCSR). All companies remain above 150 percent in adverse scenario. - Net income under adverse scenario: - Remains positive each year. - Declines by 20 percent in 2013, rises thereafter; end-2017 net income 15 percent higher than 2012. - Key adverse drivers: share price declines and higher policyholder lapse rates; from 2014 onward interest rate and credit spread movements are important. ### CMHC stress test results - Approach: BU stress tests using CMHC internal models; same macro assumptions as banks and insurers with house-price shocks larger in Toronto and Vancouver. - Capital adequacy (MCT): - Drops sharply in 2013 and 2014 but remains above minimum regulatory level of 100 percent; improves from 2015 onward. - Net income: - Turns significantly negative in 2013; recovers to positive in 2016 and 2017. - Management actions considered: re-pricing new business; concentrating underwriting in lower-risk loans. - Liquidity risk: considered small given monitoring and payout timing; claims payout occurs eight to nine months after arrears information. ### Scenarios and key macro shocks (five-year horizon, 2013–2017) - Baseline: - 2013 average growth: 1.7 percent. - Post-2013 annual growth accelerates to slightly below 2½ percent. - Unemployment natural rate: about 6¾ percent. - Adverse (U-shaped recession): - U.S. peak unemployment: 12.4 percent in 2016Q2-Q3. - Canadian economy: 9 quarters of negative growth, gradual recovery over last 2½ years. - National house prices decline by 34 percent over first 3 years. - Toronto and Vancouver prices decline by an additional 20 percent (54 percent total). - Canadian unemployment peaks at 13.2 percent at the beginning of year 4 before decreasing gradually. ### Sensitivity analyses — key findings - Positive asset correlation (most severe parameterization): - System-wide “all-in” CET1 falls by 450 basis points during downturn (2013-2015) vs base year. - Falls by 740 basis points vs 2015 baseline CET1; most banks would be significantly below supervisory threshold by end-2013. - Using BU LGD dynamics: aggregate CET1 falls by 390 basis points over first three years. - Using regulatory parameters (TTC PDs) and excluding loan loss reserves as first line of defense: system-wide CET1 declines by 100 basis points over five years. - Interest rate shock in banking book: aggregate loss ~5 percent of CET1 capital. - Market risk shock (trading book and AFS): aggregate loss rates 1.8 percent (trading) and 1.6 percent (AFS) of mark-to-market portfolio value; both less than 10 percent of CET1 capital. - Incremental risk charge shock: RWAs change for IRC ranges from -10 percent to 50 percent across banks; IRC RWAs very small so CET1 impact marginal. ### Differences across approaches (BU, IMF ToD, OSFI ToD) - RWAs: - IMF ToD: economic definition of RWAs (risk-sensitive to default rates non-linearly); no distinction between standardized and IRB exposures. - BU: regulatory formulas and credit migration models. - OSFI: uses risk weights from prior BU exercise; methodology closer to BU. - Charge for impairment: - IMF and BU use same PIT PDs for expected losses but IMF may understate impairment because it: (i) excludes collective provisions for unidentified losses, (ii) uses downturn LGDs from IRB formula instead of BU stressed LGDs, (iii) assumes loan loss reserves net of losses on existing NPLs serve as first defense. - OSFI follows accounting standards algorithmically and calibrationally closer to IMF than BU. - Income and dividend projection differences arise from modeling choices; IMF generally most conservative on net interest income. ### Recommendations and policy implications (selected) - Coverage and coordination: - Include major regulated entities at federal and provincial level in a regular, common stress testing exercise with federal–provincial collaboration. - Data enhancements: - OSFI Research Unit to collect longer time series and more granular data (e.g., trading income, realized/unrealized gains/losses) to improve market risk monitoring and model validation. - OSFI ToD framework improvements: 1. Add econometric/model-based forecasting for income statement items (including credit losses) to validate BU results. 2. Adopt both regulatory and economic concepts for key credit risk inputs; consider economic definition of RWAs to align with Pillar 2 and improve measurement/aggregation of material risks. 3. Consider dividend distribution rule adjustments to allow recapitalization by cutting dividends and avoid supervisory CET1 breaches. - BU consistency improvements: - Ensure uniform implementation across banks for exposure coverage by economic sector, dividend rule application, stress testing RWAs for standardized exposures, and consistent calculation/guidance on collective allowances for unidentified losses. - BoC / MFRAF development: - Calibrate liquidity losses in MFRAF more meaningfully (e.g., use mark-to-market losses). - Embed MFRAF in a macroeconomic model (DSGE or econometric) or estimate panel VARs to capture feedback loops and internal consistency across years. *Source: INTRODUCTION AND OVERVIEW and excerpts from _cr1469 (2013 FSAP Update for Canada), IMF* ### INTRODUCTION AND OVERVIEW _______________________________________________________________ 5 ### INTRODUCTION AND OVERVIEW ### Overview of Stress Tests - Purpose: Report on the stress testing module of the 2013 FSAP Update for Canada, describing coverage, tail risks, quantification, assumptions and models, and recommendations to enhance authorities’ stress testing frameworks. - Coverage: - Banking: the six largest Canadian banks that together constitute over 90 percent of the banking system by assets. - Life insurance: the three largest life insurance providers, with over 60 percent market share in terms of premiums. - Mortgage insurer: Canada Mortgage and Housing Corporation (CMHC), with a 70 percent market share in terms of premiums. - Types of stress tests applied: - Banks: Bottom-up (BU) and Top-down (ToD). - Top-down models: three ToD models developed by the Bank of Canada (BoC), the IMF, and the Office of the Superintendent of Financial Institutions (OSFI). - Life insurers and CMHC: participated in BU solvency stress tests. - Risk factors and sensitivity analyses: - BU bank tests: credit, market, and operational risks impacting earnings and solvency. - ToD tests: credit and market risks; BoC ToD also assessed second-round effects from idiosyncratic funding runs, asset fire-sales, and interbank counterparty credit losses. - Sensitivity analyses compared alternative parameterizations and different approaches to deriving risk-weighted assets under stress (regulatory versus economic approaches). ### Common assumptions and data - Identical assumptions across approaches for stress and baseline scenarios and input risk parameters, including prescribed probabilities of default and bank-projected loss-given defaults. - Consolidation and timing: - Banking sector tests conducted on consolidated basis data as of October 2012, restated to reflect early full adoption of Basel III for solvency tests. - Life insurers and CMHC tests conducted based on end-2012 valuations. - Horizon and scenarios: - Two scenarios over a five year horizon: a baseline and a stress scenario. - Baseline: IMF World Economic Outlook projections as of February 2013. - Stress: model-driven simulation of a severe crisis originating outside Canada, triggering materialization of a key domestic risk (household finances and housing prices). - Result: cumulative decline in real GDP over a three-year period representing the most severe recession in the last 35 years; after three years of recession, GDP growth rate gradually returns to positive levels. ### Banking sector — Solvency stress tests: main findings - Overall result: all tests suggest that, under severe economic distress, all banks would fall below the Canadian “all-in” CET1 supervisory threshold, but resulting recapitalization needs are manageable. - Definition: “All-in” = capital calculated to include all of the Basel III regulatory adjustments that will be required by 2019 (i.e., no phase-ins) but retaining the Basel III phase-out rules for non-qualifying capital instruments. - Regulatory thresholds used: Canadian “all-in” supervisory threshold of 7 percent for the first three years, and 8 percent for the last two years. - Key quantitative outcomes: - Under the IMF approach, recapitalization needs peak in 2016 at 30 percent of 2012 gross income or 150 percent of 2012 net income (corresponding to 2½ percent of 2015 nominal GDP). - Recapitalization to bring four banks up to regulatory minimum (first breached in 2016 under IMF approach, mainly due to D-SIB surcharge) is five times smaller than system-wide recapitalization needs—i.e., ½ percent of 2015 nominal GDP. - IMF ToD model is most conservative: system-wide “all-in” CET1 ratio would fall by 2½ percentage points in 2015 relative to the base year or 5½ percentage points relative to the baseline scenario in 2015. - Drivers of losses and capital changes: - Effects on capital ratios during negative growth are largely driven by RWAs for credit risk in the IMF ToD, and by credit losses in all approaches (impact of recession on default rates and NPLs). - Losses concentrated geographically and by sector: around 65 percent of losses at their peak (2015) come from Canadian exposures (mostly consumer loans, construction, and manufacturing) and around 25 percent from U.S. exposures (mostly business loans). - Dividend payout assumptions materially affect capitalization outcomes in OSFI ToD and BU exercises. ### Banking sector — Liquidity and funding stress tests: main findings - Aggregate liquidity resilience: in aggregate, banks could withstand severe funding and market liquidity shocks characterized by withdrawal of funds and haircuts on liquid assets similar to emerging liquidity standards. - BoC MFRAF contribution: - MFRAF assesses solvency impact of funding liquidity and contagion pressures (liquidity module) and spillovers (network module) as second-round effects. - This is a novel framework modeling credit-liquidity interactions with interbank linkages. - Liquidity-module results: - Baseline liquidity scenario: limited additional impact on banks’ CET1 ratio in aggregate; one bank is significantly affected due to prior credit losses and a mismatch of maturing liabilities vs. sellable assets. - Adverse liquidity scenario: marginal liquidity impact brings four banks’ “all-in” CET1 below 4.5 percent. - Network (spillover) effects: - Marginal impact on “all-in” CET1 ranges between 21 and 29 basis points. - Low spillover effects explained by significant collateralization and hedging of interbank exposures, generally small interbank positions, and limited exposures to other Canadian financial institutions and non-Canadian bank counterparties. - Sensitivity: banks face substantially greater solvency pressures when asset fire-sale discounts and roll-over rates are increased beyond the baseline adverse parametrization. ### Recommendations and policy implications (summary from overview) - The exercise includes recommendations for Canadian authorities to enhance stress testing frameworks, drawn from the joint BU and ToD analyses and sensitivity checks. (Detailed recommendations appear in later sections of the source document.) *Source: INTRODUCTION AND OVERVIEW, _cr1469 (2013 FSAP Update for Canada), IMF* ### 8. The three large life insurance companies show quite robust results in the stress test. In ### _cr1469 - 8. The three large life insurance companies show quite robust results in the stress test. In ### Life insurance stress-test results - Baseline scenario: aggregated MCCSR capital ratio increases from an aggregated 2012 value of 215 percent to 288 percent at the end of the projection horizon in 2017. - Adverse scenario: solvency ratios of all companies remain well above the supervisory target of 150 percent, based on the MCCSR. - On aggregate, total MCCSR ratios decline from 215 percent in 2012 to 199 percent in 2015, stabilizing thereafter. - Net income under the adverse scenario: - Remains positive in each year of the projection horizon. - Declines by 20 percent in 2013 and rises in each year thereafter. - Expected to recover quickly from its lows in 2013. - Key drivers of adverse impact: share price declines and adverse policyholders’ lapse rates add most to the overall impact on insurers’ capital and net income under the adverse scenario. ### Canada Mortgage and Housing Corporation (CMHC) stress test - Bottom-up stress tests by CMHC: - Value of claims under the stress scenario assessed to be much larger than under the baseline scenario, lowering the solvency ratio significantly. - Capital adequacy based on the Minimum Capital Test (MCT) drops sharply but remains above the minimum regulatory level of 100 percent. - Net income turns significantly negative in 2013, but recovers quickly and is positive again in 2016 and 2017. - Potential management actions in severe stress include: re-pricing new business and concentrating underwriting in lower risk loans. ### Stress-testing framework — recommendations (summary of Table 1) - OSFI — Banking stress test: - Complement OSFI top-down stress testing framework for banks with economic (risk-sensitive) concepts of key credit risk input parameters and econometric, model-based approaches. (Para 38, 40, 41, 46, 48, 62) - Start collecting longer time series including more granular data (e.g., trading income). (Para 46, 62) - Ensure consistent implementation of key elements of the BU stress testing exercise across different banks. (Para 62) - Enhance the liquidity stress testing framework by running tests based on the LCR and NSFR on a regular basis. (Para 77) - OSFI — Insurance stress test: - Provide a comprehensive set of assumptions to be applied by participating companies in a harmonized manner; expand analytical approaches used to verify stress test results against supervisory data. (Para 85, 86) - BoC: - Find a meaningful way of calibrating liquidity losses. (Para 67, 77) - Model the Balance Sheet Liquidity ratio in an internally consistent manner. (Para 77) - Embed MFRAF in a macroeconomic model (DSGE or econometric). (Para 77) - General: - Include major regulated entities at federal and provincial level in a regular, common stress testing exercise involving collaboration between relevant federal and provincial authorities. (Para 62) ### Scenarios used (five-year horizons) - Two macroeconomic scenarios consistently applied in BU and IMF ToD approaches: - Baseline scenario: consistent with the IMF country desk projections for the stress tests horizon following the February 2013 World Economic Outlook update. - Adverse (tail risk) scenario: generated using the BoC’s (DSGE) models and staff judgment; characterized by a U-shaped recession caused by a significant deterioration of the euro area crisis; incorporates combined simultaneous movements in all risk factors. - Baseline scenario key figures and assumptions: - 2013 average rate of growth expected to be a modest 1.7 percent. - After 2013, annual growth expected to accelerate to slightly below 2½ percent. - Unemployment natural rate estimated at about 6¾ percent. - Stress scenario description and key outcomes: - U.S. peak unemployment at 12.4 percent in 2016Q2-Q3. - Canadian economy experiences 9 quarters of negative growth and recovers gradually over the last two and a half years of the 5-year stress horizon. - National house prices decline by 34 percent over the first 3 years of the stress scenario horizon. - Prices in Toronto and Vancouver decline by an additional 20 percent (54 percent in total). - Canadian unemployment rate rises to peak at 13.2 percent in the beginning of the fourth year before decreasing very gradually afterwards. ### Banking sector — solvency stress-test methodology (three-pronged approach) - Common features: - Capital position depends on net income after dividend payout and on RWAs. - Charge for impaired credit is usually the main loss driver in net income. - RWAs increase under stress mainly reflecting changes in credit quality; economic definition also accounts for interactions between PDs and LGDs and higher correlation in asset quality during stress. - The three approaches: - The IMF ToD solvency test: - Balance sheet-based approach similar to Schmieder et al. (2011). - Assesses solvency of individual banks through changes in net income and RWAs. - Attempts to base framework on “economic” measures of solvency, both capital and RWAs. - The OSFI ToD solvency test: - Follows a template similar to BU and IMF approaches. - Income statement items, including the charge for impairment, calculated based on projected balance sheet items using loan dynamics prescribed in the stress scenario. - Charge for impairment projected using a calibrated law of motion for loan loss reserves. - RWAs for credit risk calculated using risk weights from the previous year’s BU stress scenario applied to exposures consistent with the stress scenario. - Bottom-up test: - Six largest banks used internal models to stress-test income statement, balance sheet, RWAs, and some parameters of expected losses (e.g., LGDs). - PD and LGDs were not stressed for credit risk against the tail risk and baseline scenarios (credit quality migration only). - No capital actions to offset stress allowed, other than dividend distribution restrictions imposed by the capital conservation buffer rule. - Market risk RWAs stressed by setting VaR to Stressed VaR when GDP growth rate is negative; incremental risk charge calculated based on stressed correlations. - Several single-factor tests: (i) interest rate shock in the banking book, (ii) market risk shock in trading book, AFS securities and CVA, (iii) incremental risk charge RWAs. ### Coverage, data, and capital definitions - Coverage: - Stress tests used supervisory consolidated data of individual firms. - Covered six major commercial banks, accounting for about 90 percent of the banking sector’s assets. - Data cut-off: - Fourth quarter of fiscal year 2012 (i.e., the end of October 2012). - Capital definition: - OSFI’s “all-in” Basel III CET1 application. - Data on capital and RWAs were restated by the banks to reflect Basel III calculation implemented in January 2013. - Solvency stress tests conducted against hurdle rates consistent with both the Basel III transition schedule (the “regulatory threshold”) and Canadian “all-in” CET1 supervisory threshold, including accelerated implementation and the common equity surcharge associated with D-SIB status effective January 1, 2016. - Table 2 excerpts (regulatory and supervisory capital requirements): - I. Canadian "all-in" minimum Common equity tier 1 (CET1): 7.0% (2013), 7.0% (2014), 7.0% (2015), 7.0% (2016), 7.0% (2017) - II. DSIB surcharge (CET1): 1.0% (2016), 1.0% (2017) - III. Canadian “all-in” CET1 supervisory threshold: 7.0% (2013), 7.0% (2014), 7.0% (2015), 8.0% (2016), 8.0% (2017) - IV. Basel III phase-in minimum CET1: 3.5% (2013), 4.0% (2014), 4.5% (2015), 4.5% (2016), 4.5% (2017) - V. Capital conservation buffer: 0.6 (2015), 1.25% (2016) [as presented in table: "0.6125% 1.25%"] - VI. Regulatory threshold (IV.+V.+II.): 3.5% (2013), 4.0% (2014), 4.5% (2015), 6.1% (2016), 6.8% (2017) ### PDs, loss projections, and modeling links - PDs: - PDs estimated and forecasted by the BoC represent a key link between the real economy and the banking sector. - Sectoral PDs were consistently used across different approaches to calculate credit losses, including in the BoC MFRAF. - Sectoral PDs were defined as sectoral default rates. - Corporate default rates modeled using an approach derived from Djoudad and Bordeleau (2013). - Household PDs derived using the Household Risk Assessment Model (HRAM) linking household defaults to macro and financial variables (Faruqui, Liu and Roberts, 2012). - Forecasted PDs were first-differenced (delta PDs) and provided to banks which applied delta PDs to point-in-time (PIT) PDs in 2012. - Credit losses (bottom-up approach): - Calculated as an increase in allowances for identified and unidentified impaired credit by economic sectors. - Charge for impairment entering the income statement set equal to assumed increase in allowances for identified losses and banks’ assumptions about collective allowance for unidentified impairment. - Assumed increase in allowance for identified losses calculated as the greater of banks’ projected losses and the expected losses. - Expected losses calculated as a product of PIT PDs, stressed LGDs and exposures at default (standardized and IRB) by economic sectors. - Banks instructed to apply BoC forecast of delta PIT PDs by economic sectors to their own estimate of PIT PDs in the base year. - Stressed LGDs came from banks’ internal models and were consistent with the scenarios. - RWAs: - Affected by changes in Through-the-Cycle (TTC) PDs and changes in credit quality of exposures. - TTC PDs updated for new PIT PDs. - For IRB exposures, performing credits redistributed among borrower rating buckets and IRB buckets based on banks’ internal models or judgment. - For standardized exposures, migration of agency ratings for some exposures was done. - TTC PDs and downturn LGDs were not recalibrated. - Performing credits migrated to default status at rates consistent with the scenarios. - Coverage of exposures included drawn and undrawn commitments. - Exposures denominated in foreign currencies or measured using Mark-to-Market conventions (e.g., derivatives) recalculated to reflect scenario variables. *Source: _cr1469 - 8. The three large life insurance companies show quite robust results in the stress test. In* ### 28. Full implementation of the credit valuation adjustment (CVA) charge was assumed to ### _cr1469 - 28. Full implementation of the credit valuation adjustment (CVA) charge was assumed to ### CVA and counterparty credit risk assumptions - Full implementation of the credit valuation adjustment (CVA) charge was assumed to take effect from 2012 even though implementation has been delayed for regulatory capital reporting. - RWAs for counterparty credit risk and CVA were calculated based on Basel III rules. - Banks treated all central clearing counterparties (CCPs) as qualified CCPs unless they had reason to believe otherwise. - The Current Exposure Method (CEM) was used for calculating credit counterparty risk RWAs. - The Standardized CVA capital charge was used with CEM-based exposure at default being used to calculate CVA capital requirements. - The mark-to-market component of CEM-based exposures was changed according to the scenarios. ### Market risk and operational risk assumptions - Market Risk RWAs: - VaR and incremental risk charge (IRC) assumptions were scenario-dependent. - During periods of negative GDP growth rates, VaR was set to the base year stressed VaR. - During periods of positive GDP growth rates, VaR was set to the base year VaR. - Stressed VaR was assumed to stay constant over the stress period. - Stressed IRC was recalculated to reflect obligor correlations projected to be consistent with the stress scenarios. - Operational risk: - Charge for operational risk was calculated using the Standardized Approach. - The derivation of the charge reflected gross income consistent with the earnings projections provided in the income statement consistent with the scenarios. ### Income statement projection rules and components - Projections were consistent with corresponding scenarios. - Charge for impairment: - Equal to the increase in allowances for identified impairment and the collective allowance for unidentified impairment. - Net interest income: - Reflected assumed defaults in exposures, portfolio changes corresponding to the scenarios, and assumed shocks to funding costs, credit risk premia, and customer behavior. - Trading income: - Projected consistent with financial variables in scenarios and reflected fair value gains/losses and other changes in fair value of assets (full portfolio revaluation consistent with the stress scenario). - Unrealized gains/losses for available-for-sale securities: - Consistent with financial variables in scenarios and reflected in accumulated other comprehensive income (OCI). - Non-interest income related to capital markets: - Consistent with financial variables in scenarios (examples: mutual fund fees, underwriting fees, securities commissions and fees). - Other income statement items: - Projected using banks’ judgment. - Foreign currency translation: - Projected consistent with foreign exchange rates and reflected in OCI. ### Management actions, dividends, and capital actions under stress - Management actions: - Not allowed in the stress scenario except commitments made before the beginning of the stress horizon. - Banks could use projected management actions under the baseline scenario. - In the stress scenario, banks were not allowed to project the use of any capital actions designed to offset the impact of the stress scenario except for dividend distribution restrictions imposed by the capital conservation buffer rule. - Only capital actions representing commitments made before Stress Year 1 were allowed (e.g., acquisitions, dividend payments). - Dividends: - Dividends paid per share set constant throughout the stress horizon and consistent with dividends paid in the base year, except when a bank falls below the Canadian “all-in” supervisory threshold. - If below the threshold, the capital conservation buffer (CCB) rule for dividends distribution was applied on a quarterly basis (Table 4). - OSFI indicated inconsistent bank application of the CCB rule (some quarterly, some annual); OSFI restated results applying the CCB rule on an annual basis for all banks (quarterly data unavailable). ### Prescribed one-time shock single-factor tests (bottom-up) - Interest rate shock in the banking book: - Isolated impact of changes in interest rates (level, slope and shape of the yield curve in Table 5) on re-pricing of assets (including Canadian and non-Canadian sovereign bonds), liabilities and off-balance sheet positions. - Banks applied the interest rate shock to each bucket of the difference between interest rate sensitive assets and liabilities and used dollar duration and dollar convexity to calculate the impact. - Market risk shock in trading book, AFS securities and OTC derivatives: - Isolated impact by risk factor (equity, interest rate, credit spread, commodity and foreign exchange rate shocks). - No inter-risk diversification benefit and no management actions assumed. - Full revaluation based on shocks specified in Table 6. - Incremental risk charge shock: - Simulated impact of obligor correlations increasing by 25 percent on a relative basis. ### Key scenario shocks (as specified in Tables 5 and 6 excerpts) - Tenor scenario shocks (bps) for CAD/US/EUR/JPY (examples extracted): - Overnight: +100 +200 +125 +50 - 3 month: +100 +200 +125 +50 - 5 year: +250 +300 +300 +150 - 10 year: +350 +350 +350 +200 - Market risk factor price shocks (examples): - Equities: -30% (some indices), +80% (one entry), -30%, -40%, 90%, -40%, -50% - Commodities: -60%, 100%, -65%, -25%, -40% - Interest Rates: 270% (normalized; based on a 1-month at-the-money swaption on a 5yr USD interest rate swap; shock assumed to apply to all currencies) - Credit Spreads: various entries including +200, +800, +600, +400, +400, +150, N/A+200 - FX: +10% (USD appreciates), +20% (USD appreciates), -15% (EUR depreciates), -10% (GBP depreciates), -15% (JPY appreciates) - Implied volatility absolute shocks listed for indices and commodities (table entries such as Nikkei 225, S&P500, TSX, MSCI Asia Ex-Japan, STOXX50, Energy, Base Metals, Precious Metals, Grains and others) with country cross entries CAN:US:EU/UK:JPY showing values like Overnight:-750 -500, 3 month:-85 -10 -30 -5, etc. ### Results of the bottom-up (BU) solvency stress test - Overall outcomes: - Five banks would fall below the Canadian “all-in” supervisory threshold by 2015, with the capital shortfall being small. - System-wide “all-in” CET 1 declines by 180 basis points during the recession (2013-2015) in comparison to 2012 “all-in” CET1. - System-wide decline of 430 basis points in comparison to 2015 “all-in” CET1 in the baseline (non stress) scenario. - All banks would fall below the Canadian “all-in” supervisory threshold in 2016 due to introduction of the D-SIB surcharge with the aggregate capital shortfall of 80 bps. - Four banks would recover above the Canadian “all-in” CET1 threshold of 8 percent in 2017. - During the whole stress testing horizon, only one bank would fall below the regulatory threshold (by 20 bps) in 2017 due to D-SIB surcharge introduction and a convergence of Basel III regulatory ratios to supervisory threshold. - Recapitalization needs: - Total recapitalization needed to bring all banks to the Canadian “all-in” supervisory threshold peaks at 9 percent of their 2012 gross income in 2016, or 40 percent of their 2012 net income. - This corresponds to 0.7 percent of 2016 nominal GDP. - Drivers of losses: - Charges for impairment: - Subtract 150 bps in 2013 from CET1 (three times higher than in the base year). - From 2014, credit losses’ contribution amount to more than 200 bps. - Increase driven by individual and collective charges for identified losses (equal to expected losses) on exposures in construction and manufacturing sectors and consumer loans in Canada, and business loans in the U.S., driven by higher default rates and smaller recovery rates during recession. - RWAs: - Reduce CET1 ratio by 90 bps and 45 bps in the first two years respectively. - Aggregate RWAs increase in the first two years by 11 percent and 8 percent respectively; driven by increases in RWAs for credit risk in corporate sector and consumer loans sector (other retail). - RWAs in the last three years are stable (likely reflecting higher growth in exposures offset by an increase in credit quality). - RWAs for market risk increased by 22 percent in the first years; market risk comprises around 5 percent of total RWAs, so the CET1 impact was limited. - Dividend distribution: - Quantitative impact similar to that of the increase in RWAs during the first two years. - Dividend distributions have a negative impact throughout the stress horizon due to positive net incomes. - Net income drops significantly by 2015 compared to the base year, mostly driven by large increase in charges for impairment. - Net interest income declines mainly due to lower demand for credit; non-interest income and non-interest expense move little by 2015 but increase by the end of the period reflecting recovery. - Trading income had a positive impact as banks did not experience material trading book losses during stress. ### Sensitivity analysis results - Interest rate shock in the banking book: - Aggregate loss from materialization of interest rate risk corresponds to around 5 percent of CET1 capital. - Market risk shock in trading book, AFS securities and OTC derivatives: - Aggregate loss rate (loss over mark-to-market value of portfolio) equal to 1.8 percent for trading book and 1.6 percent for AFS securities. - Both losses mostly driven by equity and credit spreads and amount to less than 10 percent of CET1 capital. - CVA on OTC derivatives would double; impact on capital position small as total loss is less than 1 percent of total CET1 capital. - Incremental risk charge shock: - Change in RWAs for IRC ranges from -10 percent to 50 percent across banks. - IRC RWAs are very small so even largest increase results only in marginal CET1 decline. ### IMF top-down stress test approach - Framework: - Balance sheet based stress testing spreadsheet model similar to Schmieder et al (2011). - Assesses solvency of individual banks through changes in net income and RWAs. - Based on “economic” measures of solvency for capital and for RWAs. - Credit RWAs: - RWAs for credit risk were calculated by Basel II asset classes using the IRB formula. - Economic definition of credit RWAs used: in the benchmark case TTC PDs were replaced by the PDs used for calculation of expected losses. - Sensitivity analysis calculated RWAs using regulatory parameters and stressed LGDs and positive asset correlation. ### Credit losses computation under the IMF top-down framework - Definition: - Credit losses entering the income statement as charge for impairment are defined as expected losses. - Expected losses calculated using PIT PDs, downturn LGDs and exposure at default by economic sector over the scenario horizon. - Expected losses are assumed to be fully provisioned (full amount enters the income statement and losses cannot be distributed over time). - Loan loss reserves (net of realized losses on 2012 NPLs) serve as the first line of defense against credit losses. - Steps to compute expected losses: 1. Ideally, downturn LGDs by economic sectors used; since such data do not exist, downturn LGDs by Basel II asset classes (provided by OSFI) were mapped into LGDs by new Basel II asset classes (Table 7). 2. Central bank projections of delta PDs by economic sectors were applied to base year (2012) bank-specific PIT PDs by economic sectors supplied by banks in the BU exercise. 3. PIT PDs by economic sectors were mapped into PIT PDs by new Basel II asset classes. 4. Exposures by economic sectors were mapped into exposures by new asset classes (using the same mapping). 5. Expected losses by asset classes were calculated using PIT PDs, downturn LGDs and exposures by new Basel II asset classes. - Sensitivity analysis: - Uses PIT PDs by economic sectors, stressed LGDs by economic sectors reported by banks for BU test and exposure at default by economic sectors to calculate expected losses by economic sectors. - Exposure at default: - Taken from the BU exercise. - Takes into account credit risk mitigation (e.g., reclassifying exposures according to guarantor exposure class, reducing exposures by collateral for certain OTC derivatives and repo-style transactions). - Exposures account for an estimate of potential future changes to that credit exposure (undrawn commitments). - Notes and caveats: - Using weighted averages of PDs and LGDs rather than PDs and LGDs by economic sectors will introduce some overestimation of expected losses. - Original Basel II asset classes were not used; new Basel II asset classes combine corporate and SMEs into “corporates”, and combine other retail excluding QRR and QRR into “other retail”. *International Monetary Fund — _cr1469 (excerpt provided in source content)* ### 40. RWAs for credit risk under stress were calculated for individual banks using Basel II IRB ### _cr1469 - 40. RWAs for credit risk under stress were calculated for individual banks using Basel II IRB ### RWAs calculation methodology - RWAs for credit risk under stress were calculated for individual banks using Basel II IRB formula, translating downturn LGDs, changes in PDs, changes in assets correlation and the maturity adjustment parameter into stressed RWAs in economic terms. - “Standardized exposures” were added to “IRB exposures” to calculate total exposures by asset classes. - To project RWAs over the stress horizon, percentage changes in calculated RWAs were applied to base year, realized RWAs. - Projected levels of calculated RWAs were used in capital requirement calculations under two scenarios. - Forecasts of changes in PIT PDs were taken from the BoC and applied to PIT PDs in the base year reported by banks for the BU test. - Downturn LGDs and average effective maturity were provided by OSFI from regulatory returns. - Assumptions: - Asset compositions reflect dynamics of assets reported by banks in the BU exercise. - Banks will not replace maturing loans with different assets that have a different risk weight. - No RWA optimization was assumed. ### Mapping, data constraints, and sectoral aggregation - Data constraints on PDs for Basel II asset classes precluded calculation of RWAs on a Basel II asset class level. - PIT PDs were available only for economic sectors; other parameters (downturn LGDs, effective maturity, etc.) were available for Basel II asset classes only. - A mapping from economic sectors into Basel II classes (Table 7) was devised to construct new Basel II asset classes: - Each new asset class has an effective maturity and a downturn LGD calculated as a weighted average of corresponding Basel II asset class items from regulatory returns. - PDs of new Basel II asset classes were calculated as weighted averages of PDs by economic sector. - This mapping allows calculation of changes in RWAs by asset classes as a function of sector-specific PD, downturn LGD, asset correlation and maturity adjustment (for non-retail sectors), ensuring RWAs and expected losses are consistently calculated using the same parameters. ### Sensitivity analysis and alternative parameterizations - Sensitivity tests varied IRB formula parameters: PIT PDs, downturn LGDs, asset correlation, maturity adjustment, and effective maturity. - Scenarios tested included: - Different LGDs that follow dynamics reported by banks in the BU. - Positive asset correlation (see Asset correlation below). - Use of regulatory calibration where TTC PDs replace PIT PDs. - A scenario where loan loss reserves were assumed not to serve as the first line of defense against credit losses. - Data constraints on sectoral effective maturity and downturn LGDs (available for Basel II asset classes only) precluded calculation of RWAs changes on an economic sectoral level. ### Asset correlation treatment - Basel II regulatory formulas assume average asset correlation decreases with PDs. - Sensitivity analysis replaced the negative impact of increasing PDs on asset correlation with a positive impact of the same magnitude (sign change only) to simulate the alternative empirical/economic argument that asset correlation may increase with PDs. - The positive relationship increased RWAs for credit risk. ### Treatment of other RWAs and non-IRB components - RWAs for other exposures (trading book, equity, securitization, market risk, operational risk) were taken from the BU test because they are not straightforward to model with the IRB formula. ### Income statement projection approach (satellite models) - Simple econometric "satellite" models translated macroeconomic scenarios into projections for most income statement items; where relationships could not be established, standardized behavioral assumptions were applied. - Key projection rules and models: - Charge for impairment = credit losses/expected losses, calculated using downturn LGD (OSFI), PDs provided by banks per BoC instructions, and EAD reported by banks for the BU exercise. - Interest income: panel regression of y-o-y growth rate of interest income on y-o-y growth rate of (total loans × loan interest rates) and fixed effects; projections of loans and interest rates taken from scenarios. - Interest expenses: panel regression of y-o-y growth rate of interest expenses on y-o-y growth rate of (total deposits × deposit interest rates) and fixed effects; deposits assumed to grow at y-o-y growth rate of loans. - Non-interest income (excluding trading income): projected as average share of nominal GDP in the last 5 years; nominal GDP projection taken from scenarios. - Non-interest expense: projected as average share of the balance sheet, with balance sheet growth rate equal to growth rate of loans; loan projection taken from scenarios. - Trading income: FSAP definition included OSFI trading income, realized gains/losses on instruments held for “other-than-trading purposes,” and two comprehensive-income items: (i) changes in unrealized gains/losses on AFS (net of reclassification) and (ii) derivatives designed as cash flow hedges (unrealized gains/losses net of reclassification). Y-o-y growth rate of trading income modeled on y-o-y growth rates of TSX Index and nominal GDP, with dummy variables for a 2007 structural break and CIBC losses. - Foreign currency translation effects projected using panel regression of y-o-y growth rate of FX valuations on y-o-y growth rate of CAD/USD exchange rate. - Taxes set at the effective tax rate (share of net income) in 2012 in case of positive net income and zero otherwise. ### Balance sheet, asset allocation, and dividend policy - Balance sheet asset allocation assumed constant across scenarios; maturing exposures replaced with similar ones. - Banks’ balance sheets assumed to grow in line with total loans; loan growth assumed equal across banks and taken from scenarios. - Dividend payouts: - Payable out of current year’s profit using adjusted IMF capital conservation rule. - Maximum allowed dividend payout equal to dividend payout ratio in 2012. - If a bank fell below the Canadian “all-in” supervisory CET1 threshold of 7 percent (8 percent from 2016 onward) before dividend distribution, it was considered capital constrained and followed a schedule of dividend payouts (Table 8). If dividend distribution caused breach, payout limited to avoid breaching threshold (applies only if net income positive). If net income negative, no dividend payout. If above threshold, bank paid maximum-allowed proportion of dividend. - Effective dividend payout rate in 2012 (EDPR) and assumed dividend payout schedules are summarized in the source (table). ### Results and key statistics - All banks would fall below the Canadian “all-in” CET1 supervisory threshold during severe economic distress; recapitalization needs would be manageable. - Timing and magnitudes: - All banks would fall below the Canadian “all-in” CET1 supervisory threshold by 2015. - System-wide “all-in” CET 1 declines by 250 basis points during the recession (2013-2015) compared to 2012 “all-in” CET1. - System-wide “all-in” CET 1 declines by 540 basis points in comparison to 2015 “all-in” CET1 in the baseline scenario. - Banks’ capital position recovers from 2016, increasing by 60 bps by 2017. - Four banks would fall below the regulatory threshold in the last two years of the horizon (mainly due to the D-SIB surcharge imposed in 2016). - Recapitalization needs: - Peak recapitalization needed to bring all banks to the Canadian “all-in” CET1 supervisory threshold peaks at 32 percent of their 2012 gross income in 2016. - This corresponds to around 150 percent of their 2012 net income. - This corresponds to 2.5 percent of 2015 nominal GDP. - Recapitalization needs to bring banks to the regulatory threshold also peak in 2016 but are five times smaller than needs to reach the supervisory threshold. - Drivers of capital deterioration: - In recession years, deterioration is mainly driven by increases in RWAs and in charges for impairment. - Aggregate RWAs for credit risk increase by 44 percent in the first year, by 18 percent in the second year, and by 7 percent in the third year. - Large first-year RWAs increase driven by an increase in default rates at recession start, particularly for exposures to Canadian financial institutions and the corporate sector in Canada and abroad. - Credit losses: - Net income becomes negative in 2015 and 2016. - Loan loss reserves cushion the first year but are exhausted in the first period of the stress horizon. - Credit losses peak at the end of recession (2015), when they are more than four times larger than expected losses in 2012 and more than seven times larger than the TTC measure of expected loss (product of TTC PDs, downturn LGDs and exposures). - Losses are concentrated in Canada and the U.S.: around 65 percent of peak losses (2015) come from Canadian exposures and around 25 percent from U.S. exposures. - Canada accounts for around 75 percent of exposures while the U.S. accounts for around 15 percent of exposures. - Income cushioning: - Net interest income and non-interest income cushioning falls sharply in first three years due to lower credit demand. - The effect on capital is cushioned by falling non-interest expenses (relative to RWAs). - Trading income (including realized and unrealized gains/losses) had limited impact historically; trading income negative in 2013 and positive from 2014 onward but too small to affect capital positions significantly. - Modest negative impact of dividend distribution felt in the first year only due to capital conservation rule kicking in from 2014 onwards. *Source: _cr1469 - 40. RWAs for credit risk under stress were calculated for individual banks using Basel II IRB* ### 51. Sensitivity analysis suggests that, under the most severe parameterization, i.e., of ### _cr1469 - 51. Sensitivity analysis suggests that, under the most severe parameterization, i.e., of ### Sensitivity analysis — overview and scenarios - Sensitivity analyses (Figure 3) were performed with respect to different LGDs (that follow dynamics of LGDs reported by banks in the BU), positive asset correlation and a regulatory risk parameter input where, instead of PIT PDs, TTC PDs were used. - The most severe parameterization is the intuitive assumption that asset correlation moves in the same direction as PDs (positive asset correlation). ### Key sensitivity findings - Positive asset correlation scenario: - The system-wide “all-in” CET1 ratio would fall by 450 basis points during the economic downturn (2013-2015) relative to the base year CET1 ratio. - The system-wide “all-in” CET1 ratio would fall by 740 basis points in comparison to 2015 CET1 in the baseline scenario. - Most banks would fall significantly below the Canadian “all-in” CET1 supervisory threshold by end-2013. - By comparison, in the benchmark case reported above, the same banks would go below the regulatory threshold only in the last two years of the stress horizon. - Using LGDs dynamics from the BU test: - The aggregate “all-in” CET1 ratio would fall by 390 basis points over the first three years. - Using regulatory parameters (TTC PDs and regulatory RWAs) and assuming loan loss reserves were not considered as the first line of defense against credit losses: - Resulted in a decline of the system-wide “all-in” CET1 ratio by 100 basis points over the five year stress horizon. - Note: Assuming that loan loss reserves cannot cushion credit losses has the positive impact on the system-wide CET1 of 20 bps only. ### OSFI Top-down Stress Test — methodology highlights - Income statement items (excluding the charge for impairment) were calculated based on corresponding balance sheet items projected using variables in the stress scenario. - Credit losses: - Calculated as charge for impairment consistent with regulatory standards. - Charge for impairment was projected using an algorithm (Box 1) that simultaneously projects the balance sheet loan book, performing, non-performing and impaired loans and loan loss reserves using regulatory formulas. - RWAs: - RWAs for credit risk were projected by using risk weights and exposures consistent with the stress scenario. - Exposures by Basel II asset classes were projected to grow in line with performing loans and risk weights were calculated by dividing RWAs and exposure for each asset class reported by banks in the 2012 OSFI stress test. - In sensitivity analysis, RWAs were calculated by asset classes using IRB formulas and regulatory parameters of IRB formulas—the exposures and downturn LGDs were taken from the BU exercise, TTC PDs were updated by using PIT PDs over the stress horizon. It was assumed that there are no standardized exposures. - RWAs for market and operational risk were projected using OSFI’s internal models. RWAs for market risk during bad times (negative GDP growth) were calculated by doubling the SVaR. Operational risk followed the standardized approach depending on gross income. - Income statement projection rules (selected): - Charge for impairment: projected as described in Box 1. - Interest income: projected using projected interest rates (on business, personal, mortgage and other loans) and corresponding assets consistent with the stress scenario; only mortgage interest rate was adjusted for an assumed fall in mortgage interest rates. - Interest expense: projected using projected interest rates (on subordinated debt, deposits) and corresponding liabilities; only subordinated debt interest rate was adjusted for an assumed fall. - Non-interest income and non-interest expenses: projected by applying base-year ratios to projected other assets and projected total assets respectively. - Trading income: first stress year set to the average loss observed in the last 10 years; subsequent years set to the smallest trading income (excluding trading losses) over the same period. - Taxes: set at the effective tax rate (share of net income) in 2012 in case of positive net income and zero otherwise. - Dividends: followed the capital conservation rule—if CET1 below the Canadian “all-in” supervisory threshold, dividends were reduced per the rule; otherwise, dividends paid consistent with dollar per share amount in the base year. ### OSFI Top-down Stress Test — balance sheet and loan-book projection - Balance sheet projection two-step approach: - Step 1: Project loan book and deposits. Loan book consists of net business loans, net personal (consumer) loans and net mortgage loans; projections for each component were made using the algorithm described in Box 1. Deposits were assumed to grow in line with nominal GDP. - Step 2: Calculate base-year ratios of other balance sheet items to loans or deposits, hold these ratios constant and apply them to projected loans or deposits to project the rest of the balance sheet items. - Box 1 algorithm (summary of steps used to project loan book and impairments): - Project gross loans using base-year gross loans and a nominal growth rate supplied for the stress scenario. - Project loans that will default using performing loans and PDs, with delta PDs calculated as weighted averages from BoC-provided delta PDs and base-year PDs derived from historical default data (2003-2012). - Project net write-offs based on gross impaired loans and a calibrated write-offs ratio; recalibrate if algorithm results in negative impaired loans. - Calculate gross impaired loans as previous impaired loans plus defaulted loans minus net write-offs, with write-offs adjusted for an LGD proxy (ratio of loan loss reserves to impaired loans) calibrated from 2012 OSFI data. - Project loan loss reserves using the calibrated LGD proxy and impaired loans. - Derive charge for impairment from the law of motion for loan loss reserves (loan loss reserves t+1 minus loan loss reserves t plus net write-offs t+1). - Obtain performing loans as gross loans minus loan loss reserves. ### OSFI Top-down Stress Test — results and drivers - Aggregate outcomes: - OSFI’s solvency stress testing results suggest that five banks would fall under the Canadian “all-in” CET1 supervisory threshold by the end of the third year. - The results in the downturn period (2013-2015) are similar to BU results. - The system-wide “all-in” CET1 ratio in the OSFI ToD model declines by 200 basis points by 2016 in comparison to the 2012 level, with no banks staying above the Canadian “all-in” CET1 supervisory threshold throughout this period. - By the end of the recovery period, all banks fall below the Canadian “all-in” CET1 supervisory threshold of 8 percent and two banks fall below the regulatory threshold (mostly due to introduction of DSIB surcharge and convergence of the regulatory threshold to “all in” metric). - Recapitalization needed to bring all banks to the Canadian “all-in” supervisory threshold peaks at 20 percent of their 2012 gross income in 2016 or 90 percent of their 2012 net income which corresponds to 1.5 percent of 2016 nominal GDP. - Main drivers of results (years of downturn): - RWAs: - Have the largest negative impact on the CET1 ratio in the first year due to an increase of RWAs for credit risk equal to almost 20 percent. - In following years RWAs are mostly stable and do not affect the capital positions of the banks materially. - Charges for impairment: - Have a similar impact on the CET1 ratio in the first year and are the most important driver of the solvency ratio in the following years. - This is mostly due to an increase in losses on business loans driven by a large increase in default rates. - Dividends: - Realized dividend distribution has a modest negative impact on capitalization throughout the stress test horizon. - Dividends under the capital conservation rule were materially lower than dividends paid out in the base year. - The modest net negative impact reflects offsetting positive net income, reductions in non-interest expenses, a significant fall in net interest income over the whole horizon, a one-time fall in non-interest income and trading income in the first year, and a large increase in charges for impairment. *Source: IMF staff compilation from the provided document excerpt.* ### 61. Differences in the three sets of results are attributable to the different approaches of ### _cr1469 - 61. Differences in the three sets of results are attributable to the different approaches of ### Drivers of differences across the three approaches (BU, IMF ToD, OSFI ToD) - RWAs for credit risk: - IMF approach: uses an economic definition of RWAs for credit risk that assumes that RWAs are risk-sensitive with respect to default rates (in a non-linear way) and makes no difference between standardized and IRB exposures. - BU exercise: uses regulatory formulas to update TTC PDs and uses credit migration models to calculate RWAs for credit risk. - OSFI: uses the risk weights from last year’s BU stress test; RWAs calculation is closer to BU exercise. - All three approaches use the same RWAs for operational risk. - IMF ToD and BU use the same RWAs for market risk; OSFI has its own methodology for market risk RWAs. The difference in RWAs for market risk does not affect results in a material way. - Charge for impairment: - IMF and BU tests are consistent in approach to calculate charges for impairment using the same PIT PDs and exposures to calculate expected losses. - Three main differences cause IMF approach to underestimate impairment charge: 1. IMF approach does not include collective provisions for unidentified losses. 2. IMF approach does not use stressed LGDs from the BU exercise; instead uses downturn LGDs from the IRB formula (which may be higher or lower). 3. IMF assumes loan loss reserves net of losses on existing NPLs serve as the first line of defense against credit losses. - OSFI’s charges for impairment follow accounting standards more closely but values are more similar to IMF than BU due to lower projected recoveries in OSFI ToD’s model and idiosyncratic algorithm/calibration choices. - Other net income items: - Differences arise from modeling techniques and assumptions. - IMF approach is generally the most conservative for net interest income, non-interest income, and non-interest expense projections — largest gap on net interest income. - OSFI is most conservative in projection of trading income. - Unavailability of BU internal models prevents full comparison. - IMF ToD uses econometric justification (Figures 18–22 referenced) for explanatory variables; OSFI’s non-econometric approach may have unstable ratios making robustness assessment difficult. - Dividend distribution rules: - Different CCB rules materially affect results. - Two reasons for differing from OSFI/bank rules: 1. Banks unlikely to distribute dividends if it would make them fall below the supervisory threshold. 2. Annual CCB calculation makes it reasonable to assume dividends are paid out of the current year’s net income. ### Condensed mapping of key modeling differences (selected items from Table 9) - Interest income / expense / non-interest income / non-interest expense / trading income / taxes / OCI items / charge for impairment: each approach uses distinct modeling choices (panel regression models, projections consistent with BoC, held constant ratios, internal models, base-year effective rates, etc.) as specified in Table 9. - PDs: - BU: delta PDs projected by the BoC applied to PIT PDs in the base year; for RWAs: TTC PDs updated for forecasted PDs. - IMF ToD: delta PDs projected by the BoC applied to PIT PDs in the base year; for RWAs: same PIT PDs. - OSFI ToD: delta PDs projected by the BoC applied to PIT PDs in the base year; PDs were not used in calculation of RWAs for credit risk. - LGDs: - BU: Stressed LGDs for expected losses projected using internal models; downturn LGDs used for RWAs. - IMF ToD: Downturn LGDs used for both expected losses and RWAs. - OSFI ToD: Stressed LGD for expected losses calibrated; LGDs not used in RWAs for credit risk. - Exposures and their distribution across asset classes differ by approach (BoC forecasts, BU exercise inputs, distributions by economic sectors or Basel II asset classes). - Sensitivity analyses differ (e.g., interest rate shock in banking book, market risk shock in trading book; alternative RWAs assumptions including stressed LGDs and positive asset correlation). ### Recommendations and policy implications (Section E) - Coverage: - Authorities should collaborate with major provincial regulators and include major regulated entities at both federal and provincial level in a regular, common stress testing exercise. - Data gaps: - OSFI’s Research Unit should devote more resources to collection of longer time series of balance sheet and income statement data. - OSFI should collect more granular data on realized and unrealized gains and losses on instruments held for trading and available-for-sale to better understand and monitor market risks. - OSFI ToD framework improvements: 1. Add income statement items, including credit losses, to forecasting by incorporating econometric, model-based approaches such as IMF’s ToD analysis to enhance ability to validate BU results. 2. Credit risk framework should encompass regulatory and economic concepts in key credit risk inputs; apply economic definitions of RWAs for credit risk to align with Pillar 2 approaches and enable identification, measurement and aggregation of material risk types. 3. Consider a different dividend distribution rule so banks can recapitalize by cutting dividends and avoid falling below supervisory CET1 threshold. - BU stress test consistency: - OSFI should ensure consistent implementation across banks for: (i) coverage of exposures by economic sectors, (ii) a uniformly applied dividend distribution rule, (iii) stress testing RWAs for standardized exposures (finding a way to change risk weights), (iv) consistent calculation of charges for impairment and guidance on collective allowances for unidentified losses (not to be used as cushion against allowances for identified losses). ### Banking sector — liquidity and funding stress tests; MFRAF overview and application - MFRAF purpose: - BoC’s macro-financial risk assessment framework (MFRAF) assesses funding liquidity vulnerability, contagion risk (liquidity module), and network effects (network module) translating into losses in addition to credit losses (credit module). - Funding liquidity risk is modeled as an endogenous outcome of interactions among solvency risk, market liquidity risk and funding profiles. - MFRAF modules and sequencing: - Module 1: Core credit model — credit losses due to borrower defaults. - Module 2: Funding Liquidity Risk — losses due to interactions between funding strategies and solvency concerns (including information contagion). - Module 3: Network Effects — losses due to interbank counterparty defaults. - Implementation timing and logic: - MFRAF was run in stress year 2 and year 3 under two liquidity scenarios. - Funding liquidity module was run in both years (middle of each year). - Network module was run in stress year 2 only (end of year 2), because deriving interbank exposures at end of year 3 is not straightforward once the interbank market clears at end of year 2. - The funding liquidity module at end of year 2 accounts for credit losses in year 1, first-half losses in year 2, and creditors’ anticipation of remaining year losses. Funding liquidity module at end of year 3 accounts for accumulated credit risk, funding liquidity risk and network-contagion losses in year 2. - MFRAF inputs and interactions with BU exercise: - Credit losses in Module 1 used income data and expected loss parameters from the BU test (PDs, LGDs, exposures at default from BU). - Collective allowances for unidentified losses are not included in the charge for impairment in MFRAF, so credit losses in MFRAF are generally smaller; net income and dividends may be larger than in BU. - For banks affected by funding liquidity and network effects, net income, dividends and taxes lead to lower CET1 ratios in MFRAF compared to BU. - Starting capital position: net income (excluding charge for impairment) reported by banks in the BU exercise over 2013–2015 was front-loaded to the beginning of the year. - Important modeling notes and caveats: - Credit losses in MFRAF use expected loss formula rather than charge for impairment. - Since PD forecasts are random variables, distribution of PDs and losses was derived using errors from the PDs regressions. - Interpretation in year 3 may be problematic: MFRAF assumes income generated during year 3 is not affected by bank failures in year 2, which can understate additional erosion of solvency buffers. - Using expected losses instead of charge for impairment can overestimate the capital position before taxes and capital distribution, although it can also underestimate capital position if expected losses are larger due to negative collective allowances for unidentified losses. *Source: _cr1469 - 61. Differences in the three sets of results are attributable to the different approaches of* ### 67. Funding liquidity was translated into losses in the second module. As initial credit losses of ### _cr1469 - 67. Funding liquidity was translated into losses in the second module. As initial credit losses of ### Framework and modules - MFRAF translates funding liquidity into solvency outcomes through a sequential, three-module approach: - Credit module (initial credit losses in stress year 1 reduce capital). - Funding liquidity module (runs triggered if Balance Sheet Liquidity (BSL) < 100 percent; creditors may not roll over maturing claims). - Network module (counterparty defaults propagate via interbank exposures). - Supervisory threshold used for run triggers: supervisory CET1 ratio of 7 percent (used instead of the regulatory ratio). - BSL measure: ratio of (liquid assets + illiquid assets × fire-sale discount) to liabilities maturing over the next six months. - If BSL > 100 percent, no run is assumed; if BSL < 100 percent, there is a positive probability of non-rollover decisions by creditors. - Liquidity losses due to a run were assumed to be 2.25 percent of RWAs (sensitivity test used 1 percent of RWAs). - Information contagion: creditors update beliefs when observing another bank “defaulting” (CET1 < 7 percent), revising secondary market price perceptions to “low” or “high,” which can lower BSL and increase run probability. - Network exposures considered: deposits, traditional lending, reverse repos, bankers’ acceptances, cross-shareholdings, holdings of debt instruments, and OTC derivatives. ### BSL calibration and differences versus LCR - BSL calibration aimed to be broadly consistent with LCR factors using Net Cumulative Cash Flow (NCCF) data. - Mapped BSL asset categories to HQLA-like definitions (levels 1, 2A, 2B). - Applied haircuts and run-off rates corresponding to LCR calibration. - Included all off-balance sheet items of the LCR (category D). - Subtracted derivatives-related amounts from liquid assets. - Key differences from LCR reflecting Canadian specifics: - Augmented HQLA by reverse repos and securities borrowed with zero haircuts in baseline. - Illiquid assets can be used to cover maturing liabilities (not 100% haircut as in LCR). - Denominator covers liabilities maturing within next 6 months plus a proportion of each liability category deemed unstable by LCR. - Tied securities and encumbrance of liquid assets treated less strictly than under LCR. - Rationale for 6-month horizon: a 30-day horizon was considered not representative of maturing liabilities dynamics; a 6-month period better captures true dynamics for Canadian banks that rely on short-term funding. ### Application and scenario design - Applied to the six largest banks using consolidated supervisory data as of April 2013 (both domestic and foreign currency data). - Two scenarios: baseline liquidity scenario and adverse liquidity scenario. - Year 2 BSL calculated from NCCF data (liquid assets, illiquid assets, maturing liabilities). - Year 3 BSL elements proxied from BU exercise: (i) liquid assets grow with securities holdings, (ii) illiquid assets grow with exposure at default, (iii) maturing liabilities grow with interest-sensitive liabilities (BU proxies do not incorporate funding liquidity and network effects and may overestimate BSL in year 3). - RWAs for CET1 calculations taken from the BU exercise. ### Results — Baseline scenario (selected quantitative outcomes) - Aggregate finding: banks could in aggregate withstand severe funding and market liquidity shocks, but liquidity runs can produce large additional losses beyond pure solvency losses; network effects appear limited. - Year 2, liquidity module: - Only one bank significantly affected by funding liquidity risk, generating losses equal to 200 basis point of “all-in” CET1. - That bank’s 200 basis point funding loss plus credit losses of 125 bps would make it fall below the 4 percent regulatory threshold. - Information contagion did not materialize (only one bank experienced a run; other banks had high liquidity ratios). - Year 2, network module: - The failed bank in liquidity module causes deterioration in the capital of three banks with largest interbank exposure to it. - Marginal network impact on CET1 ranged between 21 and 29 basis points. - Interbank exposures are small due to collateralization and hedging; spillover risk limited. - Year 3, liquidity module: - Using BU-based proxies for year 3, additional liquidity stress leads to further capital declines. - All banks fall below the supervisory threshold due to credit losses; the same bank experiences second-round funding liquidity effects with almost the same quantitative effect as in year 2. - Note: BU-derived BSL for year 3 does not fully reflect MFRAF year 2 liquidity and network outcomes. ### Results — Adverse scenario (selected quantitative outcomes) - System-wide impact: - Cumulative credit losses of 215 bps over the two years are augmented by cumulative losses equal to 235 bps due to funding liquidity risk and network effects in severe market and funding distress. - Year 2: - Only one bank — the same idiosyncratic case — significantly affected by funding liquidity; contagion negligible in year 2. - Year 3: - Additional liquidity distress and decline in CET1 due to credit losses generate sizeable liquidity losses. - Four banks fall significantly below the regulatory threshold by end of year 3. - Distributional effects: - Marginal impact of funding liquidity risk and network effects on system-wide CET1 over two years: about 35 basis points in the benchmark scenario but 235 basis points in the adverse scenario. - Example probability shift: probability of sector losses equal to 1 percent of RWAs is roughly zero percent under solvency-only stress but increases to 15 percent when funding liquidity risk and network effects are included. ### Official liquidity support estimates - MFRAF used to estimate required official liquidity support to mitigate CET1 liquidity losses: - Baseline scenario: Can$5 billion to Can$9 billion, depending on market perception of illiquid asset prices. - Adverse scenario (very extreme): Can$86 billion to Can$155 billion. - These ranges are similar to liquidity support provided during the 2008/2009 crisis. - Note: higher starting capital ratios reduce ex-post liquidity support required. ### Key quantitative parameters and sensitivities to preserve - Supervisory CET1 threshold used for runs: 7 percent. - BSL run threshold: 100 percent. - Liquidity losses assumed when a run occurs: 2.25 percent of RWAs (sensitivity test: 1 percent of RWAs). - Example credit losses referenced: 125 bps for one bank in year 2; cumulative credit losses 215 bps in adverse scenario. - Network marginal impacts: between 21 and 29 basis points on CET1 for most exposed counterparties. - Aggregate marginal impact of funding liquidity and network effects: ~35 basis points (benchmark) and 235 basis points (adverse) on system-wide CET1. - Official liquidity support ranges: Can$5 billion–Can$9 billion (baseline); Can$86 billion–Can$155 billion (adverse). - Time horizon for BSL: liabilities maturing over the next six months. ### Recommendations and policy implications (selected) - Enhance liquidity stress testing: - OSFI should consider enhancing its liquidity stress testing framework; the LCR and NSFR metrics should be part of the stress testing framework. - Calibrate liquidity losses in MFRAF: - Current calibration of liquidity losses is arbitrary despite endogenous run mechanism; find a meaningful calibration approach (e.g., use mark-to-market losses during downturns). - Internal consistency for BSL dynamics: - A run in Stress Year 3 depending on BSL at start of Stress Year 3 should be modeled consistently; BU-based year 3 BSL data are inconsistent with MFRAF year 2 liquidity and network outcomes and BoC should incorporate these effects into BSL. - MFRAF model development: - BoC might embed MFRAF in a macroeconomic model to simulate stress scenarios and feedback loops, or estimate a system of ad-hoc equations (panel VAR for PDs and macro variables) to model feedback loops and sectoral behaviors; modeling individual bank equations could be easier than in a full general equilibrium DSGE framework. *Source: BoC, IMF Staff calculations* ### 78. The macro solvency stress test for life insurers follows assumptions comparable to the ### _cr1469 - 78. The macro solvency stress test for life insurers follows assumptions comparable to the ### Macro solvency stress test design for life insurers - The macro solvency stress test for life insurers follows assumptions comparable to the bank stress test, including the disorderly default of a European sovereign in the stressed scenario. - The adverse scenario focused mainly on variations of market variables: lower equity prices, higher credit spreads and low interest rates. - In addition, adverse rates of policyholders surrendering their life insurance policies were assumed. - Valuation framework: the Canadian valuation regime in place on 1 January 2013. - Capital projections: made in accordance with OSFI guidelines on the Minimum Continuing Capital and Surplus Requirement (MCCSR). - Companies were instructed not to make use of extraordinary management actions like capital increases or discontinuation of business lines. ### Sample and scope - Sample included the three large life insurance companies: Great West Life, Manulife and Sunlife. - Market coverage: in terms of assets, market share amounts to 76 percent; in terms of premiums, more than 60 percent of the market is covered. - Stresses applied at the group level including international business (substantial operations in the United States, as well as in Asia and Europe). ### Baseline and adverse-scenario results (life insurers) - Baseline scenario characteristics: gradually increasing interest rates, rising equity prices and stable or decreasing credit spreads. - Aggregated MCCSR capital ratio: - 2012 aggregated value: 215 percent. - Baseline projection at end-2017: 288 percent. - Adverse scenario aggregated MCCSR capital ratio: - Declines from 215 percent in 2012 to 199 percent in 2015, stabilizing thereafter. - Supervisory target: 150 percent (MCCSR). - Even in the adverse scenario, solvency ratios of all companies remain well above the supervisory target of 150 percent. - Variation of results among participating companies: rather low. ### Net income projections (life insurers) - Net income under the adverse scenario remains positive in each year of the projection horizon and is expected to recover quickly from its lows in 2013. - Aggregate net income changes: - Net income declines by 20 percent in 2013 (adverse scenario). - Net income rises in each year thereafter. - At end-2017, net income is 15 percent higher than in 2012. - Baseline scenario net income: expected to double within five years (2012–2017). ### Main drivers of solvency and income changes - In the first year of the projection horizon (2013), share price declines and adverse policyholders’ lapse rates add most to the overall impact on insurers’ capital and net income under the adverse scenario. - Less pronounced in first year: effect of lower risk-free interest rates (companies have built up substantial reserves). - From 2014 onward: interest rate and credit spread developments contribute most to deterioration of solvency rates vis-à-vis baseline. - Exchange rate movements (example provided: a 10 percent depreciation of the Canadian dollar versus the U.S. dollar) have a negligible effect on solvency ratios and net income given close currency matching of assets and liabilities. ### Management actions and aggregation challenges - Reported management actions include: re-pricing of new business, change in policy design (if permissible under current contract), and adjustment of dividend payouts. - Some management actions described as widely automatic (without substantial management discretion). - Extraordinary management actions (e.g., sale of business units) could be more relevant in a more severe scenario when net income turns negative or capital ratios drop below supervisory targets. - Such management actions make comparison and aggregation of results from a macroprudential perspective somewhat difficult. ### Recommendations regarding life-insurer macro stress tests - Authorities should expand the solvency stress testing framework for life insurers. - Improve consistency of results provided by participating companies in macro stress tests. - Management actions and their effect on overall results need to be reported by companies and analyzed by authorities, both individually and in combination. - Authorities should develop more approaches for validating bottom-up results, for example by exploring the potential use of a top-down stress test based on supervisory data. ### CMHC solvency stress test: design - The CMHC stress test uses broadly the same assumptions as used in the stress tests for banks and life insurers. - Focus: claims being paid out to mortgage lenders after realization of mortgage loan defaults. - Financial market shocks consistent with those used in the life insurance sector applied to CMHC’s investment portfolio. - GDP forecasting difference: CMHC used the average of quarterly projected growth rates instead of annual growth rates (timing pattern of the stress scenario is a bit shifted; overall impact over the 5-year horizon largely similar). - CMHC used historic experience to model effects of the adverse scenario on claims pattern and timing of defaults; unemployment rates were the most influential determinant. ### CMHC scenario specifics and assumptions - National house price shock complemented by larger house price declines for the Greater Toronto Area and the Greater Vancouver Area. - Market share assumed to be stable (note: previous crisis experience 2008/09 showed CMHC market share actually increased). - Stress applied at level of internal models and assumptions; CMHC deriving results based on internal modeling. - CMHC is enhancing its stress test management framework in cooperation with OSFI: plan to apply stress at level of individual policies instead of portfolio level. - Current stress-testing practice includes reverse stress testing (determining length of a downturn sustainable with current capital level) and both deterministic and stochastic scenarios. ### CMHC results and dynamics - Capital adequacy based on the Minimum Capital Test (MCT): - Drops sharply in 2013 and continues to fall in 2014, but remains above the minimum regulatory level of 100 percent. - Starts to improve steadily from 2015 onward. - Drivers of capital adequacy decline: - Available capital affected via build-up of claims provisions with direct impact on net income; most effect occurs in the first year of projection horizon. - Required capital increases in 2013 and 2014, mainly driven by higher requirements for policy liabilities. - Requirements for interest rate risk and balance sheet assets remain fairly stable. - Investment portfolio characteristics: - Less affected by market stresses due to conservative investments. - Asset allocation rebalanced every six months (strategic portfolio: 57 percent federal and provincial government bonds, and 21 percent corporate bonds). - Net income profile: - Turns significantly negative in 2013. - Recovers quickly and is positive again in 2016 and 2017. - Most decline related to claims provisioning; modest assumptions on new business also put pressure on profitability. - Liquidity risks: considered small. CMHC is informed on arrears of 90 days or more within one month; actual payouts would occur eight to nine months later. ### Key figures and labeled scenario categories (as presented) - MCCSR and Tier 1 charts label scenarios as: - A. Baseline - B1. Stress (interest and spread only) - B2. Stress (interest, spread and equity) - B3. Stress (interest, spread, equity and FX) - B4. Stress (interest, spread, equity, FX and lapses) *Source: IMF staff calculations and OSFI as presented in the source content.* ### Appendix I. Statistical Annex ### Appendix I. Statistical Annex ### Developments in Banking Sector - Credit intermediation in Canada is dominated by chartered banks; the six largest banks have diversified business models across personal & commercial banking, wealth management, capital markets, and insurance. - Household and Business Credit (Billions of CAD$) — chart components listed (no totals in source). - Net Income by Business Segment, FY 2012, Millions of Canadian dollars — geographic and segment breakdowns shown for RBCTD/BNS/BMO/CIBC/NBC/Desjardins: - Personal & Commercial: Canada; Personal & Commercial: U.S. and other; Wealth Management; Capital Markets; Insurance - Assets composition (Percent of total assets) for 2000M12, 2007M12, 2012M12 includes: - Cash & deposits: 6% - Private debt: 10% - Shares: 6% - Governement securities: 7% - Consumer loans: 10% - Business loans: 8% - Repos: 6% - Other loans: 9% - Insured mortgages: 6% - Uninsured mortgages: 11% - Derivatives: 9% - Other: 6% (presentation shows multiple years with these labels) - Liabilities (Percent of total liabilities) for 2000M12, 2007M12, 2012M12 includes: - Individual deposits: 27% - Other deposits: 32% - Deposit-taking institutions: 3% - Governement deposits: 1% - Derivatives: 7% - Obligations related to borrowed securities: 4% - Obligations related to assets sold under repo: 7% - Acceptances: 1% - Subordinated debt: 1% - Mortgages and loans payable: 6% - Other: 5% - Shareholders equity: 6% - Bank funding is dominated by individual and other deposits, with total deposits amounting to about two-thirds of banks’ balance sheets. - Foreign operations: - Canadian banks’ foreign operations have increased over time, notably in the U.S. - Regional exposure concentrated towards the United States, Latin America and the Caribbean. - Over the last five years, income volatility has been highest for U.S. operations. - The relative share of impaired loans and impairment losses is higher in the U.S. and other foreign markets than in Canada. - Net Income by Geography, FY 2012, Millions of Canadian dollars — shows Canada, U.S., Other breakdowns for major banks (RBCTD/BNS/BMO/CIBC/NBC). ### Developments in Insurance Sector - Gross written premiums (excluding reinsurance, in million Can$) 2008–2012 shown; life vs non-life series: - About two thirds of insurance premiums stems from the life sector; fluctuation only slightly since 2009. - More than one third of life insurance premiums comes from foreign operations, mainly the US and Asia. - Geographic breakdown of gross written premiums in life insurance (excluding reinsurance): - Canada: 63% - USA: 16% - Europe: 4% - Asia and other: 17% - Breakdown of gross written premiums in non-life insurance (2012 data): - Property: 33% - Automobile: 51% - Liability: 9% - Surety: 1% - Mortgage: 2% - Accident and Sickness: 2% - Other: 2% - Insurance sector assets (in million Can$), 2008–2012: life sector about ten times larger than non-life and has grown faster since 2008. - Solvency ratios (life insurance, in percent) 2008–2012: - MCCSR ratio - Total and MCCSR ratio - Tier 1 consistently above 200 percent. - Asset allocation of life insurers (2012 data): - Cash and Cash Equivalents: 2% - Bonds and Debentures: 31% - Mortgage Loans: 7% - Common Shares: 3% - Investment Properties: 2% - Segregated Funds Net Assets: 40% - Reinsurance Assets: 4% - Other investment assets: 8% - Other assets: 3% - Market concentration: - Market shares life insurance (excluding reinsurance and foreign companies, 2012 data): - Great-West: 24% - ManuLife: 40% - Sun Life: 23% - Other Canadian companies: 13% - Market shares mortgage insurance (2012 data): - CMHC: 73% - Genworth: 22% - Canada Guaranty: 5% ### Stress Testing Results — Solvency and Liquidity (Banks) - Table 11. Summary of Banks’ Stress Testing Results — Solvency stress test and Liquidity stress test (MFRAF, individual and network effects) summary items: - Number of banks falling below supervisory threshold (year): - IMF top down: 6 (2015) - OSFI top down: 5 (2015) - Bottom up: 5 (2015) - Number of banks falling below regulatory threshold (year): - IMF top down: 4 (2016) - OSFI top down: 2 (2017) - Bottom up: 1 (2017) - Recapitalization needs (supervisory threshold): - % 2012 gross income: 30% (IMF top down); 20% (OSFI top down); 9% (Bottom up) - % 2012 net income: 150% (IMF top down); 90% (OSFI top down); 40% (Bottom up) - % nominal GDP: 2.5% (IMF top down); 1.5% (OSFI top down); 0.7% (Bottom up) - Recapitalization needs (regulatory threshold): - % 2012 gross income: 7% (IMF top down); 3% (OSFI top down); 0.4% (Bottom up) - % 2012 net income: 30% (IMF top down); 15% (OSFI top down); 2% (Bottom up) - % nominal GDP: 0.5% (IMF top down); 0.2% (OSFI top down); 0% (Bottom up) - Baseline vs Adverse scenarios: - Number of banks falling below regulatory threshold: - Baseline: year 1: 1; year 2: 2 - Adverse: year 1: 11; year 2: 14 - Cummulative impact (year 2 & 3) on system-wide CET1: - Solvency risk: 215 bps (both scenarios listed as 215 bps) - Funding liquidity and network effects: 35 bps (Baseline); 235 bps (Adverse) - Liquidity needs (estimated by the BoC): - Baseline: $5-$9 bn in each year - Adverse: $90-$160 bn in each year - Figures show scenario paths for main macro variables for Canada, U.S., Euro Area, Other; scenarios include Baseline scenario and Stress scenario for: - GDP growth, Unemployment rate, House prices (CREA) growth, Equity price (TSX) growth, CAD/USD exchange rate, BoC target interest rate, U.S. GDP growth, U.S. unemployment rate, Euro area GDP growth, Euro area unemployment rate, Oil price (West Texas International). ### IMF Top-Down Income Statement Models and Drivers - IMF Top Down model charts for income statement components across individual banks (RBC, TD, BNS, BMO, CIBC, NBC) show: - Y-o-Y growth rate of interest income vs Loans x Lending Rates. - Y-o-Y growth rate of interest expense vs Deposits x deposit rates. - Y-o-Y change in trading income vs TSX and nominal income. - Non-interest income in proportion of nominal GDP and non-interest expense in proportion of balance sheet. - Loan and deposit growth assumptions; note: Growth rate of loans in 2012 is much higher than the growth rate of deposits due to IFRS implementation in 2011 Q4. - Loans vs balance sheet growth assumptions; note: Growth rate of loans in 2012 is much higher than the growth rate of balance sheets due to IFRS implementation in 2011 q4. - Drivers of stress test results — Contributions to CET1 Change (2012–2017) displayed for: - NET INTEREST INCOME, NONINTEREST EXPENSE, NONINTEREST INCOME, TRADING INCOME, PROVISIONS FOR CREDIT LOSSES, OTHER (fx, dividends, taxes), RWAS CHANGE IN CET1, CET1 (%)-right. - Drivers of stress test results — Contributions to Net Income (2012–2017) displayed for: - NET INTEREST INCOME, NONINTEREST EXPENSE, NONINTEREST INCOME, TRADING INCOME, PROVISIONS FOR CREDIT LOSSES, FX VALUATIONS, TAXES, DIVIDENDS, NET INCOME. ### Net Income and RWAs — Comparisons Across Approaches - Figures compare IMF top down, OSFI top down, and Bottom up projections for 2012–2017 for: - Interest income; Interest expense; Non-interest income; Non-interest expense; Trading income; Charge for impairment; Taxes; Net income after taxes; Dividends; RWAs for credit risk; RWAs for market risk; RWAs for operational risk. - Scales shown in the source for monetary aggregates (units in figures) include large magnitudes (e.g., up to 1,800,000,000 for some series). ### Parameters of RWAs and Expected Losses - Weighted average PDs, in % — IMF top down, OSFI top down, Bottom up comparisons shown for 2012–2017. - Weighted average LGDs, in % — comparisons shown. - Sectoral PDs (time series 1997–2015) include sectors: Agriculture; Construction / Real estate; Manufacturing; Retail / Small business loans; Wholesale trade; Accommodations (service); Consumer loans; Residential mortgages. - Exposure growth projections 2013–2017 show comparisons: - IMF top down; OSFI top down; Bottom up; Bottom up (Basel II); BoC credit growth forecast. - Note: 2013–2017 series includes negative and positive percentages; example labels include -6.0%, -4.0%, -2.0%, 0.0%, 2.0%, 4.0%, 6.0%, 8.0% on charts. ### Recapitalization Needs — Results - Recapitalization needs to reach supervisory threshold (as percent in gross income), 2012–2017: - IMF top down; OSFI top down; Bottom up comparisons plotted. - Recapitalization needs to reach regulatory threshold (as percent in gross income), 2012–2017: - IMF top down; OSFI top down; Bottom up comparisons plotted. ### Liquid and Illiquid Assets Haircuts — BSL Metric (Table 12) - Calibration of haircuts for Bad state and Good state across asset classes: - Cash: Deposits with Bank of Canada, bank notes and items in transit — Bad state/Good state: (no numeric haircut listed in table header). - B1. Marketable Securities/Liquid assets - Canada: - T-bills: 98% / 100% - Bonds: 98% / 100% - Stripped coupons and residuals: 98% / 100% - Canada Mortgage Bonds: 97% / 100% - NHA MBS: 97% / 100% - Government of Canada guaranteed stripped coupons and residuals: 96% / 100% - Securities issued by a provincial government: 96% / 100% - Provincial government - stripped coupons and residuals: 94% / 100% - Securities guaranteed by a province: 96% / 100% - Provincial government guaranteed - stripped coupons and residuals: 94% / 100% - Securities issued by a Municipal government (High Rating): 95% / 100% - Securities issued by a Municipal government (Low Rating): 75% / 85% - Corporate and foreign-issuer bonds (High Rating): 75% / 85% - Corporate and foreign-issuer bonds (Low Rating): 40% / 50% - Securities issued by the U.S. Treasury: 96% / 100% - Equities: 40% / 50% - B2. Marketable Securities/Liquid assets - USA: includes many categories with Bad state/Good state haircuts, e.g. Total Cash and Cash Equivalents: 100% / 100%; U.S. Treasuries & Fully Guaranteed Agencies: multiple entries at 100% / 100%; Government Sponsored Enterprises Bills/Notes/Bonds: 75% / 85%; Corporate Bonds AAA rated - U.S. Dollar Denominated: 75% / 85%; BBB-AA rated - U.S. Dollar Denominated: 40% / 50%; etc. - B3. Marketable Securities/Liquid assets - Other: Total Cash and Cash Equivalents: 100% / 100%; Qualifying marketable securities from sovereigns, central banks, public sector entities: 100% / 100%; Domestic sovereign or central bank debt in domestic currency: 75% / 85%; Qualifying corporate bonds rated AA- or higher: 75% / 85%; Equity securities: 40% / 50%. - C1. Illiquid assets - various currencies (Bad state / Good state examples): - Deposits with banks: 100% / 100% - Other government securities: 50% / 85% - MBS: 50% / 75% - ABS: 0% / 40% - Corporate CP: 50% / 85% - Corporate bonds: 0% / 40% - Bankers' acceptances, promissory notes, commercial paper (High Rating): 50% / 85% - Bankers' acceptances, promissory notes, commercial paper (Low Rating): 0% / 50% - Precious metals: 0% / 0% - Loans: 0% / 0% - Reverse repos: 90% / 100% - Securities borrowed: 90% / 100% - Derivatives: 0% / 0% - C2. Illiquid assets - US (Bad state / Good state examples): - ABS AAA rated: 25% / 60% - ABS BBB-AA rated: 0% / 40% - CDO- AAA rated: 0% / 40% - CMBS- AAA rated: 25% / 60% - Private label CMOs- AAA rated: 50% / 75% - Trust preferred securities: 0% / 40% - CD, BA, CP, ABCP: 0% / 85% - Adverse calibration and Baseline calibration cells present with percentage entries in the source table (e.g., Baseline calibration includes many 100% entries). - Table 12 labeled "Liquid and Illiquid Assets of the BSL Metric—Haircuts Calibration." ### Outflows of BSL Metric — Run-off Rates Calibration (Table 13) - Deposits — Baseline / Adverse run-off rates: - Demand / Notice Deposits — Retail — Primary Deposits (relationship): - Insured chequing: 3% / 5% - Insured investment savings accounts: 3% / 5% - Uninsured deposits: 5% / 8% - Other Deposits (non-relationship): - Insured investment savings accounts: 5% / 8% - Uninsured investment savings accounts: 10% / 15% - Others: 10% / 15% - Small Business Enterprises — Primary Deposits: - Insured demand / chequing: 5% / 8% - Uninsured deposits: 10% / 15% - Term Deposits — Retail — Primary Deposits (relationship): - Fixed Term: 3% / 5% - Cashable: 3% / 5% - Uninsured deposits: 5% / 8% - Term Deposits — Other Deposits (non-relationship): - Fixed Term: 5% / 8% - Cashable: 5% / 8% - Others (i.e. uninsured deposits): 10% / 15% - Small Business Enterprises — Term Deposits: - Primary Deposits (relationship) Fixed Term: 5% / 8%; Cashable: 5% / 8%; Uninsured deposits: 10% / 15% - Other Deposits (non-relationship) Fixed Term: 10% / 15%; Cashable: 10% / 15%; Others: 10% / 15% - Wholesale Funding — Baseline / Adverse: - Wholesale Demand/Notice Deposits Corporate/Commercial deposits: 25% / 50% - Other: 100% / 100% - Financial Secured: 25% / 50% - Financial Unsecured: 100% / 100% - Other wholesale Secured: 25% / 50% - Other wholesale Unsecured: 40% / 80% - Maturing funding from FI sponsored ABCP & securitization: 100% / 100% - Swapped Intra-bank Deposits: 100% / 100% - Other Liabilities: - Banker's Acceptances: 100% / 100% - Repurchase Agreements: 100% / 100% - Securities lent: 100% / 100% - Securities sold short: 100% / 100% - Derivatives related amounts: 0% / 0% - Others: 0% / 0% - LCR Category D. (off-balance sheet items) — Baseline / Adverse: - Liquidity needs related to downgrade triggers embedded in financing transactions, derivatives and other contracts (up to 3 notch downgrade): 100% / 100% - Market valuation changes on derivatives transactions (largest absolute net 30-day collateral flows realised during the preceding 24 months): 100% / 100% - Valuation changes on non-Level 1 posted collateral securing derivatives: 20% / 40% - Excess collateral held by a bank related to derivative transactions that could contractually be called at any time by its counterparty: 100% / 100% - Liquidity needs related to collateral contractually due from the reporting bank on derivatives transactions: 100% / 100% - Increased liquidity needs related to derivative transactions that allow collateral substitution to non-HQLA assets: 100% / 100% - ABCP, SIVs, conduits, SPVs, etc: Liabilities from maturing ABCP, SIVs, SPVs, etc (applied to maturing amounts and returnable assets): (100% / 100%) applied to maturing amounts; Asset Backed Securities (including covered bonds) applied to maturing amounts. - Currently undrawn committed credit and liquidity facilities provided to: - retail and small business clients: 5% / 10% - non-financial corporates, sovereigns and central banks, multilateral development banks, and PSEs: 10% / 20% (source shows 10% / 20%) - banks subject to prudential supervision: 40% / 80% - other financial institutions (include securities firms, insurance companies): 40% / 80% - other legal entity customers, credit and liquidity facilities: 100% / 100% - Other contingent funding liabilities (such as guarantees, letters of credit, revocable credit and liquidity facilities, etc): - Trade finance: 5% / 10% - Customer short positions covered by other customers’ collateral: Any additional contractual outflows — Net derivative cash outflows: 100% / 100% - Any additional contractual outflows Already included in Assets. ### Stress Test Matrix (STeM) — Solvency and Liquidity Risks (Table 14, partial) - Banking sector solvency risk — Institutional Perimeter: - Institutions included: 6 commercial banks (RBC, TD, BNS, CIBC, BMO, NBC) - Market share: 93 percent of total banking sector’s assets - Data and baseline date: - Bottom-Up by Financial Institutions: Banks’ own data; Consolidated banking group; Baseline date: 2012 Q4, restated to reflect Basel III calculations - Top-Down by Authorities: Supervisory data; Consolidated banking group; Baseline date: 2012 Q4, restated to reflect Basel III calculations - Top-down by FSAP Team: Supervisory data; Consolidated banking group; Baseline date: 2012 Q4, restated to reflect Basel III calculations *Source: OSFI, BoC, Haver, banks’ annual reports, IMF Staff calculations* ### 2. Channels of Risk Propagation ### 2. Channels of Risk Propagation ### Methodology - Banks’ internal models with OSFI guidance - OSFI top-down approach similar to balance sheet-based approach - Balance sheet-based approach similar to Schmieder et al (2011) ### Satellite Models for Macro-Financial linkages - Macro-financial linkages: forecasted delta PDs (as a function of macroeconomic variables) were taken for the BoC - P&L variables were estimated using banks’ internal models or judgment and mostly use macroeconomic variables to forecast income and expenses by business line; charge for impairment included specific and collective increase in allowances (calculated as maximum of projected losses and expected losses, where expected losses are a - Macro-financial linkages were incorporated in the forecast of variables (loans and delta PDs in particular) that are used in the algorithm OSFI uses to calculate charge for impairment (for business, personal and mortgage loans) through loan loss reserves law of motion where: - (i) loan loss reserves in period (t+1) are a function of (calibrated) constant share of impaired loans in (t+1); - impaired loans in (t+1) are a function of impaired loans in period t and loans that default during period t (depend on delta PDs provided by the BoC) net of write-offs in (t+1); - write-offs in period (t+1) are a function of (calibrated) constant share of impaired loans in period t - Macro-financial linkages: forecasted delta PDs (as a function of macroeconomic variables) were taken for the BoC and applied to point-in-time PDs by economic sectors in the base year (2012) provided by banks in the BU test; Delta PDs were provided by economic sectors in Canada, the U.S., Euro Zone, Latin America and the rest of the World - P&L variables were forecasted using panel regression of y-o-y growth rate on y-o-y growth rate of a product of total loans and product of point-in-time PDs (forecasted PDs provided by the BoC applied to point-in-time PD in 2012), stressed LGDs (forecasted by banks but consistent with stress scenario) and exposures (drawn and undrawn) and increase in collective for unidentified losses determined by judgment; Exposures were reported by economic sectors in Canada, the U.S., Euro Zone, Latin America and the rest of the World; Mark-to-market gains or losses for available-for-sale (AFS) securities were projected to be consistent with the financial variables defined in the Baseline and Stress scenarios and reflected in the accumulated other comprehensive income in the Shareholders’ Equity. - P&L variables: interest income and interest expense were projected using projected balance sheet items and corresponding interest rates; Balance sheet items were projected as a product of loans (taken from the stress scenario) or deposits (function of nominal GDP) and a (constant) share of each balance sheet item and loans (assets item) or deposits (liabilities item) in the base year. Trading income was projected as a product of projected securities on the asset side and the share of trading income and securities which was set, in 2013, to the average loss observed over the last ten years and, in 2014-2017 to the minimum value observed in the last ten years when banks’ had positive trading income. Similar logic was applied to project non-interest income and non-interest expense. - P&L variables: loan interest rates (for interest income), product of total deposits and deposit interest rates (for interest expense), equity prices and nominal GDP (for trading income), CAD/USD exchange rate (for FX valuations) as explanatory variables and fixed effects. Non-interest income was projected as an average share of nominal GDP in the last 10 years. Non-interest expenses were projected as an average share of the balance sheet, assuming that balance sheet growth rate is equal to the growth rate of loans; Projections of explanatory variables were taken from the scenarios; Charge for impairment was calculated as a product of forecasted default rates, downturn LGDs provided by OSFI and exposures provided by banks in the bottom-up exercise. ### Stress test horizon - 2013-2017 ### 3. Tail shocks — Scenario analysis - Baseline: January 2013 WEO baseline, real GDP growth for 2013 is 1.7 percent and 2.3 percent for 2014 - Adverse: Model-driven scenario was generated using the BoC model to simulate a U-shaped recession over 2013-2015, which represents the most severe recession in at least the last 35 years, driven by: - (i) a large foreign demand shock and decreasing commodities prices caused by a disorderly default in a peripheral euro area country and a subsequent recession in the euro area and the United States, - (iii) rising funding costs due to rising uncertainty and adverse confidence, - (iv) negative wealth effects affecting households and business. - The paths under the stress scenario of other relevant macroeconomic and financial variables are generated by the central bank using its own macroeconomic models. ### 3. Tail shocks — Sensitivity analysis - Credit risk shocks: increase obligor correlation by 25 percent on a relative basis - Interest rate risk in the banking book: steepening of the yield curve depending on currency (e.g., 100 bps widening in the short end of the curve; 350 bps widening in the long end of the curve for CAD) - Market risk shocks on Trading, AFS securities and CVA: - (i) 10 percent depreciation of the CAD against the U.S. dollar, 11 percent depreciation of the euro against the U.S. dollar, 13 percent depreciation of the GBP against the U.S. dollar, 20 percent appreciation of the U.S. dollar against the MXN, 9 percent depreciation of the U.S. dollar against the JPY; - (ii) stock market decline (Nikkei and S&P500 by 30 percent; TSX, MSCI by 40 percent and STOXX50 by 50 percent); - (iii) commodity price decline (energy, base metals, precious metals and grains by 60, 65, 25 and 40 percent respectively); - (iv) interest rates (depending on the currency and maturity) and credit spreads (depending on exposure) increase - Downturn LGDs were subject to additional shock to follow the dynamics of projected LGDs by banks in the BU test—new downturn LGDs were used for calculation of expected losses and RWAs for credit risk - Negative asset correlation was replaced by positive asset correlation in the IRB formula - Point-in-time PDs were replaced by through-the-cycle PDs updated to reflect point-in-time PDs in 2013-2017 ### 4. Risks and Buffers — Risks/factors assessed - Credit risk (households, corporates, sovereign, domestic and foreign exposures). - Market risk including equity, exchange rate and interest rate risk in the trading and banking book - Taxes: regulatory requirement - Taxes: set at the effective rate in the base-year ### 4. Risks and Buffers — Behavioral adjustments - Projected balance sheet growth were supposed to be consistent with the assumptions on credit growth provided by OSFI/BoC - Balance sheet items were projected using forecasted loans (assets items) or deposits (liabilities items) and a share of each balance sheet item to loans or deposits; loans were taken from the stress scenario whereas deposits were projected using nominal GDP - Balance sheet growth and deposits growth were assumed to be equal to credit growth provided by the BoC (generated using BoC models as part of model-driven scenario) - Any capital actions that are designed to offset the impact of the stress scenario on the bank were not allowed - The dividends paid per share were set to be constant throughout the Stress Years and consistent with the dividends paid in the Base Year. If constrained banks (CET1 < 7 or 8 percent) should have followed capital conservation rule - Dividend payout schedule follows capital conservation rule; banks could distribute maximum dividend amount equal to dividend payout ratio (dividends over net income) in the base-year; dividends were paid out only if bank records profits. - Asset disposals and acquisitions over time were not considered; the portfolio composition remained unchanged over time, with maturing exposures replaced with similar ones. ### 5. Regulatory and Market-Based Standards and Parameters — Calibration of risk parameters - The End of Base Year PDs used as a starting point are based on banks’ historical experience using annual exposure-weighted default rates (of non-defaulted exposures). Delta PDs, representing the year-over-year change in the annual point in time PDs were taken from the BoC - Through-the-cycle PDs were updated to reflect dynamics of point-in-time PDs - Delta PDs taken from the BoC (estimated and forecasted as a function of macroeconomic variables) - LGDs calibrated - Write-off calibrated - Market risk in period with negative GDP growth is added to the base years market risk RWAs - Operational risk calculation follows TSA which depends on gross income - Point-in-time delta PDs provided by the BoC applied to point-in-time PDs in 2012 provided by banks in the BU test - Downturn LGDs provided by OSFI - PIT PDs and downturn used for both credit losses and stressed RWA calculations - Positive asset correlation used in sensitivity analysis of RWAs for credit risk ### 5. Regulatory and Market-Based Standards and Parameters — Additional points - For calculation of RWAs for credit risk, banks projected changes in the credit quality of their exposures consistent with the various macroeconomic and financial factors provided in the Baseline and Stress scenarios (for example, IRB banks were allowed to move exposures into different IRB LGD/PD buckets in a fashion consistent with the stress scenario, but were not allowed to recalibrate IRB LGDs or through-the-cycle PDs) - The calculation of Market Risk RWA considered the following points: - VaR: During a period of negative GDP growth rates, VaR was set to the Base Year Stressed VaR, and during a period of positive GDP growth rates, VaR was set to the level of the Base Year VaR. - Stressed VaR: Stress VaR remained constant at the base year Stressed VaR level. - Banks recalculated the charge for operational risk to be consistent with the Baseline and Stress scenarios. For banks using the Standardized Approach (“TSA”), the derivation of the charge reflected Gross Income consistent with the earnings projections provided in the Income Statement supporting schedules for the relevant years of the scenario - Full implementation of the CVA charge—RWAs for CCR and CVA were calculated based on Basel III rules; Banks treated all CCPs as QCCPs unless they have reason to believe otherwise; The Current Exposure Method (CEM) was used for calculating CCR RWA, and the Standardized CVA capital charge with CEM-based EAD was used to calculate CVA capital requirements. - Risk-weighted assets were modeled using two approaches: - (i) by applying risk weights from 2012 BU stress test in the stress scenario, and - (ii) using IRB formulas by asset classes assuming there are no standardized exposures using downturn LGDs, through-the-cycle PDs and exposures reported by banks in the BU test - Risk-weighted assets: For computation of credit risk, RWAs under stress for each bank by asset classes were calculated using Basel II, IRB formula that translates downturn LGDs, changes in point-in-time PDs, changes in assets correlation and the maturity adjustment parameter into stressed RWAs in economic terms. “Standardized exposures” were added to “IRB exposures” to calculate total exposures by asset classes. Percentage changes in calculated RWAs was applied to the base year, real RWAs ### Banking sector: Liquidity risk — Institutional perimeter and data - Institutions included (Top-Down by Authorities): 6 commercial banks (RBC, TD, BNS, CIBC, BMO, NBC) - Market share (Top-Down by Authorities): 93 percent of total banking sector’s assets - Data and baseline date (Top-Down by Authorities): Data provided by banks for liquidity tests; Consolidated banking group as of April 2013. ### Banking sector: Liquidity risk — Channels of Risk Propagation and Methodology - MFRAF: funding liquidity risk (and network effects) is modeled as an endogenous outcome of the interaction between market liquidity risk, solvency risk and the structure of banks’ funding under the baseline and the adverse scenario; liquidity (and network effects) are translated into losses that affect capital position of each bank ### Banking sector: Liquidity risk — Tail shocks and calibration - The shock is endogenous and is represented by a run by short-term creditors conditional on the size of credit losses and liquidity measure which depend on calibrated fire-sales discounts, amount of liquid and illiquid assets and the value of liabilities maturing over the next 6 months; calibrated parameters of the liquidity measure are in large part consistent with parameters of revised Basel III LCR haircuts (on the asset side) and run-off rates (on the funding side). - Parameters are calibrated based on banks’ monthly balance sheet reports; Parameters of the liquidity measure are, in large part, consistent with the revised Basel III LCR; liquidity losses are assumed to be equal to 2.25% of RWAs - The “run point” is an increasing function of bank’s capital, liquid asset holdings, and the return on short-term debt and a decreasing function of the amount of short-term funding and the opportunity cost of short-term creditors ### Banking sector: Liquidity risk — Risks and Buffers - Risks (Top-Down by Authorities): Funding and market liquidity risk (including information contagion risk) due to solvency issues - Buffers (Top-Down by Authorities): Liquid assets ### Banking sector: Spillover risks — Institutional perimeter and data - Institutions included (Top-Down by Authorities): 6 commercial banks (RBC, TD, BNS, CIBC, BMO, NBC) - Market share (Top-Down by Authorities): 93 percent of total banking sector’s assets - Data and baseline date (Top-Down by Authorities): Data provided by banks in regulatory return on interbank exposures (exposures that arise from traditional lending estimated by entropy maximization algorithm, cross-shareholdings and off-balance sheet instruments such as exchange traded and OTC derivatives from OSFI’s Survey); Consolidated banking group as of April 2013. ### Banking sector: Spillover risks — Channels of Risk Propagation and Tail shocks - MFRAF: includes network externalities caused by counterparties’ default which depend on credit losses and losses caused by market and funding liquidity disruptions - Size of the shock (Top-Down by Authorities): Defaulting banks are unable to fully honor their interbank liabilities ### Banking sector: Spillover risks — Risks - Spillover risks/default ### Life insurance sector: Solvency risks — Institutional perimeter and data - Institutions included: 3 largest life insurers, Sunlife Financial, Great West Lifeco, and Manulife Financial - Market share: Over 60 percent of premiums written in 2012 - Data and baseline date: Insurers’ own data; Baseline date: 2012Q4 *Source: _cr1469 - 2. Channels of Risk Propagation* ### 2. Channels of Risk Propagation ### 2. Channels of Risk Propagation ### Methodology - Insurers’ internal models with OSFI guidance - N.A. - N.A. ### Stress test horizon - 2013-2017 - N.A. - N.A. ### Tail shocks #### Scenario analysis - Same stress scenario was applied as in the banking sector stress test - N.A. - N.A. #### Sensitivity analysis - Credit spread shocks: increase in Canadian, U.S., and European issuers’ spreads identical to the calibrations for the bottom-up analysis by banks: up to 3-4 the level of end-2012 credit spreads for 2013, gradually declining in the following years - Interest rate risk: decline in CAD, USD, Bund, and JPY interest rates in 2013, gradual increase from 2014 onwards - Equity index shocks: stock market decline (Nikkei and S&P500 by 30 percent; TSX, MSCI by 40 percent and STOXX50 by 50 percent) - Currency valuation shocks: 10 percent depreciation of the CAD against the U.S. dollar, 11 percent depreciation of the euro against the U.S. dollar, 13 percent depreciation of the GBP against the U.S. dollar, 20 percent appreciation of the U.S. dollar against the MXN, 9 percent depreciation of the U.S. dollar against the JPY - Policy holder behavior: stressed lapse rates, 20 percent higher than in baseline scenario - Combined effect of all shocks above. - N.A. - N.A. ### Risks and Buffers #### Risks/factors assessed - Credit risk (federal and provincial governments /agencies, corporates, domestic and foreign exposures). - Market risk including equity, exchange rate and interest rate risk - N.A. - N.A. #### Buffers - Capital - N.A. - N.A. --- ### MORTGAGE INSURANCE: SOLVENCY RISKS ### 1. Institutional Perimeter #### Institutions included - Large mortgage insurance company. - N.A. - N.A. #### Data and baseline date - Financial institution provided own data - Baseline date: 2012Q4 - N.A. - N.A. ### 2. Channels of Risk Propagation #### Methodology - Financial institution’s internal models with OSFI guidance - N.A. - N.A. #### Stress test horizon - 2013-2017 ### 3. Tail shocks #### Scenario analysis - Same stress scenario was applied as in the banking sector stress test - N.A. - N.A. #### Sensitivity analysis - Same as life insurers (except for higher lapses) - N.A. - N.A. ### 4. Risks and Buffers #### Risks - Credit risk (mortgage loans) - Market risk including equity, exchange rate and interest rate risk - N.A. - N.A. #### Buffers - Capital - N.A. - N.A. *Source: _cr1469 - 2. Channels of Risk Propagation* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2014/_cr1469.pdf_