## 1sweea2023010

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### Executive summary — financial system resilience and recent performance
- Sweden’s financial system "weathered the COVID-19 pandemic well."
- Strengths:
  - "Strong macro-fundamentals."
  - Regulatory capital buffers "exceeding minimum requirements by a wide margin."
  - "Ample liquidity reserves of banks."
  - "Prompt market liquidity support measures by the authorities."
- Outcome: financial system "exit[ed] the COVID-19 crisis without a significant impact on profitability, including loan portfolio losses."

### Structural features of the financial system
- Banking sector:
  - Banking sector assets were "around 300 percent of Gross Domestic product (GDP) at end-2021."
  - Five largest banks account for "over seventy-five percent of deposits and lending."
  - Domestic lending: "64 percent" of banks’ assets.
  - Mortgages: "about fifty percent of banks’ lending portfolios."
  - Mortgages repricing: "65 percent of which being repriced within one year."
  - Covered bonds financing mortgages: "SEK 2,500 billion as of June 2021."
  - Banks’ corporate loans: "over SEK 750 billion are loans to CREs (around 7 percent of total assets)."
  - Nordic-Baltic exposure: "20 percent of assets committed to the region."
- Non-Bank Financial Institutions (NBFIs):
  - "Insurance and Pension Funds (ICPF) together hold more than 150 percent of GDP in assets."
  - Investment funds: "about 110 percent of GDP" in equity portfolios; ICPF "40 percent of GDP."
  - Investment funds have "almost tripled in size since 2015."
  - Non-bank fintech mortgages: "around 2 percent of the total stock of mortgages."
- Securities markets and central bank holdings:
  - Sweden hosts one of the thirteen EU Central Clearing Counterparties (CCPs)—Nasdaq clearing.
  - Market described as "shallow, with just about 10 dealers and few market makers," limited transparency, many private placements and OTC trades.
  - Riksbank holdings: "405 billion SEK" of Swedish sovereign debt.
  - Riksbank holds "about 20 percent of the total outstanding volume of SEK covered bonds."

### Macro-financial risks and adverse scenario
- General risks:
  - "Tighter monetary conditions will test the financial system."
  - "High corporate leverage and household debt create structural and cyclical risks."
  - Most residential and commercial mortgage loans have "variable interest rates."
- Adverse scenario design elements:
  - De-anchoring of inflation expectations; "continued shortages due to supply chain constraints"; "persistently high energy and food prices"; "lower real estate prices"; "a period of negative growth."
- Adverse scenario calibration highlights:
  - "a three standard deviation shock from the baseline for the cumulative two-year growth rate of GDP."
  - Output contracts by "10 percentage points relative to the baseline by 2023."
  - Consumer price inflation grows by "3.7 percentage points."
  - Monetary policy shock: "up to 400 basis points for the Swedish repo rate" in scenario description.
  - Housing market "38 percent lower after two years."

### Commercial real estate (CRE) vulnerabilities
- Funding and exposures:
  - CRE increasingly market financed; non-bank debt share "surpassing 40 percent of total debt."
  - CRE bonds account for "about half of corporate bond market value."
  - Foreign holdings of CRE bonds "53 percent"; about "55 percent of all bonds are euro-denominated."
  - Average loan duration "about 3.5 years"; bond maturity "about 5 years."
  - "About 55 percent of bonds are in foreign currency against about 6 percent of lending."
- Stress test outcomes:
  - Increase in rates and fall in earnings reduces Interest Coverage Ratio (ICR) "to below one in 20–35 percent of medium and large CREs."
  - CRE ICR falls below threshold "1.5" and debt-at-risk "fluctuates between 20–35 percent of total" under adverse shock.
  - Banks’ internal models "do not fully capture feedback and amplification effects of CRE exposures."
- Flowback and collateral capacity (sensitivity analysis):
  - CRE property value 2020: "SEK 2.11 trillion."
  - Estimated 2021 property value applying 10 percent growth: "SEK 2.32 trillion."
  - Apply 20 percent decline → stressed property value: "SEK 1.85 trillion."
  - Apply haircut of 30 percent → total pledgeable collateral: "SEK 1.3 trillion."
  - CRE existing bank funding backed by collateral: "768 billion SEK."
  - Existing credit facilities with banks: "SEK 200 billion."
  - Remaining available collateral: "337 billion SEK."
  - Outstanding CRE bonds: "660 billion SEK."
  - Market financing maturing over next three years: "SEK 230 billion."
  - SEK 230 billion maturing over next three years is fully covered by remaining pledgeable collateral ("SEK 337 billion").
  - If banks absorb SEK 230 billion maturing market financing:
    - Baseline scenario: CET1 capital will deplete by "1 percentage point."
    - Adverse scenario: CET1 capital will be depleted between "2.8 and 3.9 percentage points."

### Banking system solvency and capital results
- Solvency stress test scope and calibration:
  - Data cut-off: "December 31, 2021."
  - Institutional perimeter: the SIBs (Swedbank, SEB, Handelsbanken) and the 2 largest mortgage banks (SBAB Bank and Länsförsäkringar Bank), covering "about 75 percent of the banking sector’s assets."
  - Hurdle rates (CET1): "ranging from 7.0 to 10 percent" reflecting CRR and phased-in buffers.
  - Tax rate: "20.6 percent" effective in 2021 applied over the horizon for positive net income.
  - Dividend cap: "Maximum allowed dividend payout equal to 2021 dividend payout ratio, capped at 40 percent."
  - Risk weight floors applied: "25 percent on mortgage loans and residential CRE; 35 percent on commercial CRE."
- Key results:
  - Baseline: CET1 ratio trends from "18.7 percent to 19.5 percent."
  - Adverse scenario: aggregate CET1 ratio declines "by about 6.2 percentage points by the 3rd year."
  - No bank’s capital ratio falls below the hurdle rate in the adverse scenario.
  - Major contributors to CET1 decline (system level, cumulative three years):
    - Credit risk provisioning: "about 8.2 percentage points decline."
    - Risk-weighted assets: "2.5 percentage points."
    - Losses from trading portfolio: "0.2 percentage points."
  - Drivers include decline in Net Interest Income (NII) and decline in Net Fee and Commission Income under stress.
- Risk-weight density concerns:
  - Average risk weights (as of June 2021) "stood at just 23 percent", "ten percentage points lower than the EU average of 34 percent."
  - FI set risk weight floors: "25 percent for mortgage exposures and residential CRE and 35 percent for commercial CRE."
  - Median contribution of additional requirements to largest five banks’ capital requirements: "34 percent", with one bank reaching "63 percent."
  - "Overall capital would decrease by over 620 bps following the adverse scenario." (Executive summary phrasing.)

### Banking liquidity and cash-flow stress tests
- Structural liquidity metrics:
  - All banks meet "the 100 percent minimum LCR requirement."
  - Weighted average LCR: "SEK about 119 percent, EUR 163 percent, USD 145 percent."
  - All banks compliant with "100 percent NSFR requirement."
  - Aggregate stable funding needs around "SEK 5 trillion."
  - Retail deposits contribute "SEK 2.2 trillion" to stable funding; wholesale funding "SEK 1.85 trillion."
  - Deposit insurance in sample: "SEK 2.6 trillion" (implied contingent liability).
  - Share of wholesale funding in three major banks: "63 percent."
  - Retail funding in sample: "34 percent."
  - Average Asset Encumbrance (AE) for 5 banks: "23 percent", down from "25 in 2016"; AE reaches "58 percent" for one bank.
  - A decline in collateral value by 30 percent would lead to an additional encumbrance of "4 percent of assets."
- Cash-flow stress testing approach:
  - Horizons: "5 days, 4 weeks, and 3 months."
  - Five scenarios: Baseline, Contractual, Macroeconomic, Idiosyncratic, Idiosyncratic (no inflow).
  - Retail deposit run-off severe scenario comparable to Lehman: retail term deposits "10 percent over 30 days" and "20 percent for demand deposits."
  - Unsecured short-term wholesale funding run-off: "100 percent"; secured wholesale funding outflow: "20 percent."
- Findings:
  - Banks can withstand mild and medium liquidity outflows with existing counterbalancing capacities, but liquidity position becomes weaker beyond one month.
  - Some banks are prone to liquidity shortfalls even in the short-term (below 30 days) due to derivatives positions or large off-balance sheet exposures.
  - CBC declines by "10 p.p. of total assets" even in the baseline scenario.

### Investment funds and market liquidity risks
- Sector size and trends:
  - Sample: 171 funds, total NAV "SEK 1.223 bn (EUR 120 bn)" as of end-2021 supervisory data.
  - Between December 2017 and December 2021, total assets of Swedish investment funds "increased by 54 percent."
  - Equity funds increased by "70 percent"; short-term bond funds about "50 percent"; long-term bond and mixed funds about "30 percent."
  - In March 2020, about "thirty open-end funds were temporarily closed, corresponding to over SEK 120 billion in managed assets."
- Liquidity stress test calibration and metrics:
  - Redemption shocks based on ES/VaR historical net flows with homogeneity and heterogeneity assumptions.
  - Main focus: homogeneity assumption calibrated at the "3% ES" with redemption shocks ranging from "7.8% percent for Mixed funds to ~14% percent for HY bond funds."
  - RCR (Redemption Coverage Ratio) = Highly Liquid Assets / Redemption shock.
  - Highly liquid assets estimated with liquidity weights derived from the Basel III framework.
- Main results and vulnerabilities:
  - "Around 75 percent of funds would have enough highly liquid assets to meet investors’ redemptions."
  - "Up to 25 percent of funds considered in the analysis could experience a liquidity shortfall following an adverse shock."
  - Some funds’ liquidity shortfall would be "above 10 percent" of NAV, the maximum temporary borrowing limit foreseen by the European UCITS Directive.
  - Vulnerable funds: portfolios "not sufficiently diversified or heavily exposed to unrated or poorly rated debt securities."
  - Category-specific shares of funds with RCR < 1 (selected exact outcomes):
    - Homogeneous 3% ES — HY: "85%" of funds with RCR < 1.
    - Homogeneous 3% ES — Alternative: "5%".
    - Homogeneous 3% ES — Corporate bond: "7%".
    - Government funds: "0%" across scenarios.
  - Historical table: total funds with RCR < 1 — Homogeneity (ES 3%): "45 funds (26% of funds) representing 8% of NAV"; Heterogeneity (ES 3%): "33 funds (19% of funds) representing 8% of NAV."
- Market depth and price impact:
  - Price impact estimated using market depth MD(τ) formula and SELMA volumes.
  - Assumption: half of corporate bonds traded are from CRE companies due to SELMA reporting limitations.
  - Results sensitive to trading volume assumptions and to liquidation strategy (pro-rata vs waterfall).
- Supervisory observations:
  - FI found some fund managers may not perform thorough liquidity risk assessments and may assume large volumes of unrated corporate bonds can be liquidated quickly.
  - FI published a supervisory report in June 2021 with recommendations and is following up on implementation.

### Policy recommendations and implementation priorities
- Data, modelling and supervisory enhancements:
  - "Enhance the comprehensiveness and periodicity of CRE data (e.g., on rents, vacancies, and transaction prices) and to integrate multisource data into a single database." — FI — Immediate
  - "Use CRE stress tests to inform banks solvency assessment." — FI — Immediate
  - "Consider alternative modelling approaches to capture the spillover to consumption and corporates profitability while more granular data on households become available." — FI — Short-term
  - FI to review and approve comprehensive changes in PD and LGD models following IRB guidance changes; implement more comprehensive onsite inspections.
- Liquidity, funds and market infrastructure:
  - "Develop tools to analyze banks’ contingent liquidity risks from derivatives and corporate exposures." — FI — Short-term
  - "Require asset managers to perform regular liquidity stress tests for different market scenarios, and review and challenge such liquidity stress tests." — FI — Medium-Term
  - "Complete their analytical framework to assess market impact of collective funds’ reaction to episodes of stress." — FI — Medium-Term
  - "Improve quality and granularity of the information collected especially for funds (e.g., monthly flows, and returns), monitor the duration-times-spread of funds invested in non-liquid asset classes, improve liquidity tracking of specific asset types (e.g., debt instruments issued by CRE companies), and maintain adequate information on assets in institutions’ portfolios." — FI — Short-term
  - "Require investment funds to offer redemption terms that are more aligned with the liquidity profile of their portfolio (e.g., notice periods); consider price- and quantity-based measures as a second line of defense (e.g., swing pricing and gates); and provide guidance on liquidity stress tests." — MoF, FI — Medium-Term
- Implementation priorities for authorities:
  - Provide guidance and require adoption of LMTs aligned to funds’ liquidity profiles.
  - Require regular, comparable liquidity stress testing for asset managers and supervisory review and challenge.
  - Enhance FI’s analytical capacity to assess market impact of collective fund actions and analyse interlinkages between funds and other financial institutions.

*Source: EXECUTIVE SUMMARY, 1sweea2023010 — IMF staff summary of Sweden FSAP content.*

### EXECUTIVE SUMMARY __________________________________________________________________________ 5

### EXECUTIVE SUMMARY

### Financial system resilience and recent performance
- Sweden’s financial system "weathered the COVID-19 pandemic well."  
- Key strengths cited:
  - "Strong macro-fundamentals."
  - Regulatory capital buffers "exceeding minimum requirements by a wide margin."
  - "Ample liquidity reserves of banks."
  - "Prompt market liquidity support measures by the authorities."
- Outcome: financial system "exit[ed] the COVID-19 crisis without a significant impact on profitability, including loan portfolio losses."

### Structural features of the financial system
- Banking sector:
  - Banking sector assets were "around 300 percent of Gross Domestic product (GDP) at end-2021."
  - The five largest banks (Svenska Handelsbanken, SEB, Swedbank, Nordea and Danske Bank’s Swedish branches and mortgage companies) account for "over seventy-five percent of deposits and lending."
  - Domestic lending constitutes "64 percent" of banks’ assets.
  - Mortgages represent "about fifty percent of banks’ lending portfolios."
  - Mortgages: "65 percent of which being repriced within one year."
  - Large banks finance mortgages mainly via covered bonds, reaching "SEK 2,500 billion as of June 2021."
  - Banks’ corporate loans: "over SEK 750 billion are loans to CREs (around 7 percent of total assets)."
  - Banks have significant exposure to the Nordic-Baltic region: "20 percent of assets committed to the region."
- Non-Bank Financial Institutions (NBFIs):
  - Insurance and Pension Funds (ICPF) and investment funds:
    - "ICPF together hold more than 150 percent of GDP in assets" (note: phrasing in source: "Insurance and Pension Funds (ICPF) together hold more than 150 percent of GDP in assets").
    - "All NBFIs have large equity portfolios (about 110 percent of GDP for investment funds, and 40 percent of GDP for ICPF)."
  - Investment funds have "almost tripled in size since 2015."
  - Mortgages provided by non-bank fintech amount to "around 2 percent of the total stock of mortgages."
- Securities markets:
  - Sweden hosts a major securities market and "one of the thirteen European Union Central Clearing Counterparties (CCPs)—Nasdaq clearing."
  - Market imperfections noted: market is "shallow, with just about 10 dealers and few market makers," lacks transparency, has many private placements and OTC trades.
  - Riksbank holdings: "The Riksbank also owns a large share of Swedish sovereign debt (405 billion SEK)."
  - Riksbank holds "about 20 percent of the total outstanding volume of SEK covered bonds."

### Macro-financial risks and adverse scenario
- General risks:
  - "Tighter monetary conditions will test the financial system."
  - "High corporate leverage and household debt create structural and cyclical risks."
  - Most residential and commercial mortgage loans have "variable interest rates."
- Adverse scenario design elements:
  - Rise in term and risk premiums due to "a de-anchoring of inflation expectations."
  - "Continued shortages due to supply chain constraints."
  - "Persistently high energy and food prices."
  - "Lower real estate prices."
  - "A period of negative growth."

### Commercial real estate (CRE) vulnerabilities
- CRE funding and exposures:
  - Corporate sector and banking system solvency stress tests highlight "pockets of vulnerabilities due to exposure to CREs and low risk weight density of banks."
  - CREs increasingly rely on market funding, especially "short-term debt."
- Stress test outcomes:
  - An increase in rates and fall in earnings reduces Interest Coverage Ratio (ICR) "to below one in 20–35 percent of medium and large CREs, impairing their ability to service debt."
  - Bank internal models "do not fully capture feedback and amplification effects of CRE exposures."
- Possible contagion channel:
  - If market funding dried up, CREs likely to "draw down bank credit lines and to request further loans," potentially increasing banks’ exposures and eroding capital.

### Banking system solvency and liquidity
- Solvency stress test:
  - "Overall capital would decrease by over 620 bps following the adverse scenario."
  - Although banks have high capital buffers as measured by "CET1/REA," "low risk weights imply that these buffers are not as high measured in SEK," which "would limit banks absorption capacity during systemic crises."
- Liquidity position:
  - "Banks have ample liquidity and could withstand severe liquidity shocks."
  - Liquidity stress tests show banks’ "central bank reserves shield them from mild to severe liquidity shocks."
  - Vulnerabilities remain from "banks with committed credit lines to corporates, and sizeable derivative exposures for horizons above 1 month."

### Investment funds and market liquidity risks
- Size and role:
  - Investment funds play "a significant role in providing access to the corporate bond market."
  - Between December 2017 and December 2021, total assets of Swedish investment funds "increased by 54 percent."
  - Equity funds increased by "70 percent"; short-term bond funds about "50 percent"; long-term bond and mixed funds about "30 percent."
- Liquidity stress test findings:
  - "Up to 25 percent of funds considered in the analysis could experience a liquidity shortfall following an adverse shock."
  - Vulnerabilities located in portfolios "not sufficiently diversified or heavily exposed to unrated or poorly rated debt securities."
  - If liquid assets cannot cover redemptions, funds "could trigger fire sales, especially if their portfolios are invested in securities with limited market depth."
  - Data limitations: "Limited data does not allow a proper study of the effect of such redemptions on asset prices."
- Historical episode:
  - In March 2020, about "thirty open-end funds were temporarily closed, corresponding to over SEK 120 billion in managed assets."

### Key policy recommendations (high-level)
- Enhance data and modelling:
  - "Enhance the comprehensiveness and periodicity of CRE data (e.g., on rents, vacancies, and transaction prices) and to integrate multisource data into a single database." — FI — Immediate
  - "Use CRE stress tests to inform banks solvency assessment." — FI — Immediate
  - "Consider alternative modelling approaches to capture the spillover to consumption and corporates profitability while more granular data on households become available." — FI — Short-term
- Strengthen infrastructure and supervisory tools:
  - "Develop tools to analyze banks’ contingent liquidity risks from derivatives and corporate exposures." — FI — Short-term
  - "Require asset managers to perform regular liquidity stress tests for different market scenarios, and review and challenge such liquidity stress tests." — FI — Medium-Term
  - "Complete their analytical framework to assess market impact of collective funds’ reaction to episodes of stress." — FI — Medium-Term
  - "Improve quality and granularity of the information collected especially for funds (e.g., monthly flows, and returns), monitor the duration-times-spread of funds invested in non-liquid asset classes, improve liquidity tracking of specific asset types (e.g., debt instruments issued by CRE companies), and maintain adequate information on assets in institutions’ portfolios." — FI — Short-term
  - "Require investment funds to offer redemption terms that are more aligned with the liquidity profile of their portfolio (e.g., notice periods); consider price- and quantity-based measures as a second line of defense (e.g., swing pricing and gates); and provide guidance on liquidity stress tests." — MoF, FI — Medium-Term

*Source: EXECUTIVE SUMMARY, 1sweea2023010 — IMF staff summary of Sweden FSAP content.*

### 13. Sweden’s financial sector enters the current economic juncture with generally solid

### 13. Sweden’s financial sector enters the current economic juncture with generally solid

### Financial sector fundamentals and regulatory measures
- Banks have structurally higher profitability than their European peers and high regulatory capital and liquidity positions that exceed regulatory minima (Box 1, Figure 2).
- These positions worsened only slightly during the Covid-19 crisis, sustained by timely policy measures by the regulatory authorities:
  - full release of the counter-cyclical capital buffer;
  - allowing banks to temporary fall below the minimum liquidity coverage ratios (LCR);
  - recommendation that banks postpone dividend payments;
  - temporary exemption from amortization requirements.
- The exemption from amortization requirements expired in August 2021.

### Non-Performing Loans (NPLs) and household credit risk
- NPLs remained flat through the Covid-19 period (Figure 2).
- Credit losses and non-performing exposures are structurally low in Sweden, driven by the full recourse provisions on mortgages.
- Fiscal support measures and relatively milder activity containment measures during the pandemic kept bankruptcies at low levels.
- Historical low household and corporate defaults and rising asset valuations materially affect banks’ estimates of risk parameters for capital requirements calculations.
- Historical data on credit losses do not capture the increase in indebtedness of households and leverage of corporates, making them more sensitive to interest rate rises than in the past.
- Households and housing market specifics:
  - Both house prices and total household debt in relation to income peaked in Q4 2021.
  - Interest rate payments in relation to disposable income remained at historically low levels due to low interest rates and limited amortization requirements.
  - "50 percent of mortgages have interest rate fixing time below one year."
  - Loan-to-value ratio is "still quite low, albeit rising (at around 70 percent, on average)."
  - Previous Finansinspektionen (FI) stress tests suggested most households have sufficient buffers to service their debt in case of income loss or mortgage rate increases.
- Amortization requirements (introduced in 2016) apply to new mortgages:
  - For loans with loan-to-value ratio over 70 per cent amortization requirement is 2 per cent;
  - When loan-to-value ratio is below 70 per cent the requirement drops to 1 per cent, until the loan-to-value ratio has reached 50 per cent.
  - In 2018, a stricter requirement was introduced.
  - Households with a LTI above 450 per cent have to amortize an extra per cent.
  - For a limited period, the requirement can be waived for individual households if special grounds exist.

### Corporate borrowing and commercial real estate (CRE)
- Corporate borrowing persisted through the pandemic albeit at a slower pace.
  - Average interest rate on loans stood at 1.4 percent.
  - Tighter conditions expected after the initial policy rate hike in April 2022 and further increases.
- The credit-to-GDP gap suggested some overheating in 2021, though numbers have been volatile and it was below trend on the later part of the year.
- CRE borrowing and market structure:
  - CRE sector borrowing accounts for a large part of corporate borrowing and has become increasingly market financed.
  - The share of non-bank debt has increased recently, surpassing 40 percent of total debt.
  - CRE bonds account for about half of corporate bond market value.
  - Foreign holdings of CRE bonds stand at 53 percent.
  - About 55 percent of all bonds are euro-denominated.
  - Among domestic investors, investment funds hold the largest share of CRE bonds, at around 21 percent.
  - Average loan duration is about 3.5 years, and bond maturity is about 5 years.
  - About 55 percent of bonds are in foreign currency against about 6 percent of lending.
- Vulnerabilities:
  - Exposure to foreign-denominated bonds and high foreign holdings exposes the sector to rapid selloffs during heightened global risk aversion.
  - Refinancing risks are emerging from the bond markets as spreads widen.
  - Ownership concentration and cross-ownership have increased, elevating associated risks.

### CRE earnings and market trends
- Sustainability of CRE revenues increasingly subject to risks:
  - Hybrid working models have stressed office rental markets.
  - Office vacancy rates rose from about 3 percent in 2019 to close to 8 percent in 2021 in Stockholm, with similar trends in Gothenburg and Malmö.
  - Office yields have been declining (Figure 6), affecting CRE firms’ credit ratings and ability to roll over debt securities in the local bond market.
- Some properties could be repurposed; strong population growth in Stockholm reinforces demand and may support future space markets. Supply constraints in central areas are noted.

### Risk-Weighted Assets (Box 1)
- Swedish banks’ Risk Weighted Assets (RWAs) density is the lowest in the EU and among the lowest worldwide:
  - As of June 2021, average risk weights (excluding the recently introduced risk weight floor for CRE lending) stood at just 23 percent, ten percentage points lower than the EU average of 34 percent.
- Drivers of low risk weights:
  - Large mortgage portfolios kept on banks’ balance sheets and loans to CRE companies constitute the bulk of Swedish banks assets (57 percent).
  - Long period without crises (last thirty years) and collateral price growth (over 45 percent during the last 5 years) have impacted banks’ estimates of PDs and LGDs for internal models.
- Supervisory response:
  - FI set risk weight floors at 25 percent for mortgage exposures and residential CRE and 35 percent for commercial CRE.
  - The risk weight floors significantly impact the largest five banks’ capital requirements: median contribution of the additional requirements is 34 percent, with one bank reaching 63 percent.
- Concerns:
  - Low risk-weight density limits loss absorption capacity during systemic crises and may underestimate risks and favor excessive risk taking.
  - Swedish banks identified the need for comprehensive changes in all existing PD and LGD models due to IRB guidance changes ("IRB repair").
  - FI will have to review and approve models before implementation and enhance processes for ongoing assessment of IRB model performance, including more comprehensive onsite inspections.

### Systemic Risk Analysis (SRA) and stress-testing scope
- SRA comprised stress testing exercises covering solvency and liquidity for several sectors (Figure 5).
  - Stress tests based on a macrofinancial scenario including domestic and global risks.
  - Banks: supervisory data incorporated into solvency analysis considering market, credit, and interest rate/funding risks; sensitivity analysis on CRE exposures.
  - Liquidity analysis used cash flow (maturity ladder) data.
  - Funds: supervisory information on portfolios complemented with commercial sources to derive asset characteristics and flows.
  - Macrofinancial linkages analyzed linking results across banks, households, CRE corporates, investment funds and bond markets.
  - Interconnectedness analysis focused on banks’ cross-border and domestic exposures.

### Macro-financial scenarios for stress tests
- Two macroeconomic scenarios for solvency stress tests (2022-2024): baseline and adverse.
  - Baseline aligned with April 2022 World Economic Outlook (WEO) projections; market-implied forward rates used for short- and long-term interest rates not in WEO; house prices path based on historical growth before the pandemic.
  - Adverse scenario reflects main risks in the Risk Assessment Matrix and envisages stagflation:
    - De-anchoring of inflation expectations in the U.S. and advanced European economies amid geopolitical tensions and supply-chain issues.
    - Sustained demand and widespread cost-push shocks in energy and food with second round effects lead to late but strong increase in advanced country policy rates (up to 400 basis points for the Swedish repo rate) triggering a sharp recession.
    - Financial conditions tighten, confidence retracts, risk premia spike, Swedish asset prices contract — housing market 38 percent lower after two years.
- Adverse scenario calibration:
  - Implies a three standard deviation shock from the baseline for the cumulative two-year growth rate of GDP.
  - Output contracts by 10 percentage points relative to the baseline by 2023.
  - Consumer price inflation grows by 3.7 percentage points.
  - Monetary policy shock calibrated to reflect the 90th percentile in the last available projection from the Riksbank.
  - Scenario based on Global Macro-Financial Model (GFM) disaggregated into forty national economies.

### CRE solvency stress test methodology and results
- Scope and data:
  - Assessment based on Orbis company financial data for over 20 thousand Swedish CRE firms; sample for stress tests comprised largest 100 companies holding aggregate assets of around 70 percent of GDP.
  - Ratios analyzed: return on assets (ROA), return on equity (ROE), interest coverage ratio (ICR), debt to EBIT, debt to equity (DE).
- Interest Coverage Ratio (ICR) metrics:
  - ICR computed as EBIT/Interest Expense.
  - ICR threshold of 1.5 times applied to account for funding and earnings vulnerabilities; ICR < 1 implies insufficient revenues to service debt without adjustments.
  - Debt categorized into risk buckets based on ICR; lower ICR implies higher probability of becoming non-performing.
- Stress test findings:
  - Under the adverse scenario, CRE sector displays less resilience in servicing its debt.
  - Under the adverse shock, ICR falls below the threshold 1.5 and debt-at-risk fluctuates between 20–35 percent of total, depending on the calibration (Figure 9).
  - Medium and large-size firms are affected in the same proportion by the shocks.
  - Results are indicative given data limitations; shocks applied include GDP/income shocks and interest shocks, with banks’ stress scenario shocks applied to firms’ balance sheets.
- Data limitations and assumptions:
  - No direct data for share of CRE foreign debt; proxied after share of FX debt in total corporate debt with adjustment — estimate that about a quarter of issued debt is in FX.
  - Banks’ off balance-sheet exposures not included.

### Policy recommendations and monitoring priorities for CRE and banks
- Strengthen monitoring of corporate liabilities and ownership structure in the CRE sector:
  - Request better disclosure of firms’ liabilities, especially those in foreign currency.
  - Improve collection and analysis of financial data on CRE firms.
  - Enhance comprehensiveness and periodicity of CRE data (e.g., rents, vacancies, transaction prices) and integrate multisource data into a single database.
  - Better knowledge of ownership structure to identify interlinkages across firms and associated vulnerabilities.
- For IRB model governance and capital adequacy:
  - FI to review and approve comprehensive changes in PD and LGD models following IRB guidance changes.
  - Implement a robust process for ongoing assessment of IRB models’ performance, including more comprehensive onsite inspections to assess quality of deployment.

*Source: IMF staff analysis in "13. Sweden’s financial sector enters the current economic juncture with generally solid" (PDF chapter).*

### 28. This section explains the top-down solvency stress tests to assess the resilience of the

### Top-down solvency stress tests to assess the resilience of the largest five Swedish banks

### Scope of the tests
- Data cut-off date: December 31, 2021.
- Coverage: the SIBs (Swedbank, SEB, Handelsbanken) and the 2 largest mortgage banks (SBAB Bank and Länsförsäkringar Bank), which account for about 75 percent of the banking sector’s assets.
- Data sources: granular supervisory data at highest consolidation complemented by FI and Riksbank survey data on mortgages and securities holdings.
- Approach: balance sheet-based solvency stress testing assessing solvency of individual banks under scenarios through changes in net income and risk-weighted assets (RWAs).
- Caveat: matching and reconciliation of risk data from multiple sources is complex and subject to caveats.

### Stress test methodology
- Projection basis: modeled output of each bank’s balance sheet over the scenario horizon; pre-provision net revenue components projected using regression and structural models based on historical data.

- Loan loss provisions:
  - Provisions calculated as expected losses for all asset classes/economic sectors with Exposure at Default (EaD), including triggered credit lines, revolving facilities and guarantees.
  - Key risk parameters: Probability of Default (PD), Loss Given Default (LGD), Exposures at Default (EaD), RWA broken down by nine portfolios.
  - COREP reporting (09.02 and 08.02) used to assign risk parameters by portfolio, geography, modeling approach (IRB-Foundation, -Advanced), asset class, and obligor grade; obligor grades with an implied PD =1 are excluded.
  - Stressed conditions applied to non-defaulted exposures; no additional capital charge computed for defaulted assets to cover systematic uncertainty in realized recovery rates.

- Default rate projection approaches:
  - PD projections anchored at banking sector level and bank-specific starting points in distance-to-default space; one bank’s insured exposures have losses capped at 20 percent.
  - Mortgages: structural model (using DSR and LTV distributions for mortgages initiated/renegotiated in last six years) projects forward loss rates; model accounts for household affordability, house price shocks; approach based on Reserve Bank of New Zealand model.
  - Consumption loans: relative change in PD from mortgage structural model applied to consumption loans; LGD kept constant.
  - CRE exposures: PD paths derived from granular FI CRE stress test; PD obtained as change in fraction of firms with ICR < 1; stressed LGD in adverse scenario based on average LTV on CRE exposures and house price decline; LGD for real-estate-collateralized loans assumed not to decline under baseline.
  - Corporates: PDs sourced from Moody’s one-year Expected Default Frequency (EDF) average estimate; Bayesian Model Averaging (BMA) used to address model uncertainty; LGD kept constant.

- Interest income/expense:
  - Effective interest rates of various interest-bearing assets and liabilities projected considering scenario paths and bank-specific repricing/maturity profiles computed from IRRBB reporting, maturity ladder and securities holdings.

- Net fee and commissions income (NFCI):
  - Stressed using historical variance of non-interest income by activity.
  - Under adverse scenario, profits from each business activity projected equal to latest income minus one standard deviation of historical variability.
  - Asset management and payment net income: apply a flat increase by 25 percent of related cost and a decrease by 25 percent of related income.
  - For one bank with insured loan exposures, insurance-related fees assumed to increase by 50 percent.

- Trading income and realized losses on securities (FVTPL, FVTOCI):
  - Market risk assessed via modified duration approach on fixed-income securities holdings, using granular securities data across three classes of fixed-income securities and two maturity buckets.
  - Exclusions: amortized cost positions in hedge-accounting relationship, hedge accounting derivatives, and floating rate bonds.

- Tax rate: set at the effective tax rate in 2021 of 20.6 percent for the whole horizon in case of positive net income and zero otherwise.
- Extraordinary items and minority interest: assumed equal to zero.
- Accumulated other comprehensive income: updated for unrealized losses on FVTOCI securities.

### Key balance-sheet, RWA, dividend and capital assumptions
- Balance-sheet growth: semi-static — growth equal to nominal GDP growth of the scenario when positive, and null otherwise.
- RWAs: three components estimated — IRB credit RWAs (Basel IRB formulas used), market RWAs and operational RWAs; market and operational RWAs kept constant.
  - Basel IRB formulas use point-in-time default projections to obtain through-the-cycle PDs.
  - Risk-weight floors considered: 25 percent on mortgage loans and loans to residential CRE; 35 percent on commercial CRE, used to estimate changes in “additional RWA”.
- Capital issuance and dividends:
  - Banks assumed not to issue new shares or repurchase during the horizon.
  - Dividends payable out of current year’s profit per Basel III capital conservation rule. Maximum allowed dividend payout equal to 2021 dividend payout ratio, capped at 40 percent. No dividend if net income is negative.
- Hurdle rates (CET1): reflect CRR and phased-in buffers — CET1 hurdle rate ranging from 7.0 to 10 percent (Common Equity Tier 1 minimum 4.5 percent + Capital Conservation Buffer 2.5 percent + phased-in buffer of 3 percent for SIBs). Baseline considers phase-in of countercyclical capital buffer starting from 2023.

### Results — scenario outcomes and contributors to capital change
- Baseline scenario:
  - Banks’ capital ratios trend slightly upwards: CET1 from 18.7 percent to 19.5 percent.
  - RWAs increase in the first year causing a mild decline in CET1 ratio due to higher PDs as interest rates rise.

- Adverse scenario:
  - On aggregate, CET1 ratio declines by about 6.2 percentage points by the 3rd year.
  - No bank’s capital ratio falls below the hurdle rate.
  - Major contributors to decline in capital ratios (system level, cumulative over three years):
    - Credit risk provisioning: about 8.2 percentage points decline.
    - Risk-weighted assets: 2.5 percentage points.
    - Losses from trading portfolio: 0.2 percentage points.
  - Drivers of capital deterioration also include decline in Net Interest Income (NII) from abrupt rise in interest rates and risk premia (limited pass-through), and decline in Net Fee and Commission Income per stress assumptions.
  - Provision charges largely stem from high loss rates on CRE exposures; RWA changes also amplified by CRE risk weight floors. Some banks experience material losses from consumer loan portfolios.

### Sensitivity analysis — CRE flowback risk and collateral capacity
- Motivation: CRE sector’s heavy reliance on market funding could lead to flowback risk if market funding dries up and CRE firms seek bank financing.
- Determination of pledgeable collateral:
  - CRE property value 2020: SEK 2.11 trillion.
  - Estimated 2021 property value applying 10 percent property growth rate: SEK 2.32 trillion.
  - Apply decline of 20 percent (average housing decline rate from adverse scenario) → stressed property value: SEK 1.85 trillion.
  - Apply haircut of 30 percent → total pledgeable collateral: SEK 1.3 trillion.
- Deduction of existing collateral usage:
  - CRE existing bank funding: 768 billion SEK (backed by collateral).
  - Existing credit facilities with banks: SEK 200 billion (backed by collateral).
  - Remaining available collateral: 337 billion SEK.
- Market funding coverage:
  - Outstanding CRE bonds: 660 billion SEK.
  - Market financing maturing over next three years: SEK 230 billion.
  - The SEK 230 billion maturing over next three years is fully covered by remaining available pledgeable collateral (SEK 337 billion) and could be financed if banks choose to.
- Impact if banks absorb SEK 230 billion maturing market financing:
  - Baseline scenario: CET1 capital will deplete by 1 percentage point.
  - Adverse scenario: CET1 capital will be depleted between 2.8 and 3.9 percentage points (range reflects uncertainty in pass-through between PD PiT and PD TTC).

### Liquidity stress tests
- Approach: complement structural liquidity ratios (Basel III LCR and NSFR) with cashflow liquidity stress tests.
- Focus: cashflow-based stress tests rather than stressed structural LCR/NSFR.
- Data and horizons: supervisory contractual cash flows by maturity buckets; scenarios of increasing severity over horizons of 5 days, 4 weeks, and 3 months with varying assumptions on liquidity buffers and shocks to cash inflows and outflows.

*Source: IMF analysis contained in the provided content unit.*

### 37. To  deal  with  parameter  uncertainty,  the  cash  flow  tests  were  conducted  over  a  wide

### 37. To  deal  with  parameter  uncertainty,  the  cash  flow  tests  were  conducted  over  a  wide

### Liquidity stress test calibration and sample findings
- The calibration of the liquidity stress test drew on the assumptions built into the solvency stress test to ensure consistency among both tests (for example, stressed market values of securities or markets’ reaction towards banks’ ability to raise funding after drop in their capital ratios).
- All banks in the sample meet the 100 percent minimum LCR requirement.
  - Weighted average LCR: SEK about 119 percent, EUR 163 percent, USD 145 percent.
  - All banks have liquidity above the regulatory minimum of 100 percent.
- Extraordinary monetary support and increased retail deposits contributed to buttressing banks’ liquidity buffers.
- The share of wholesale funding in three major banks remains high at 63 percent.
- Retail funding in the sample of banks is 34 percent.
  - Deposit insurance in the sample amounts to SEK 2.6 trillion (an implied contingent liability).
- All banks are compliant with the 100 percent NSFR requirement.
  - Aggregate stable funding needs are around SEK 5 trillion, mainly driven by the loan portfolio.
  - Retail deposits contribute SEK 2.2 trillion to stable funding; wholesale funding contributes SEK 1.85 trillion.
- Asset encumbrance (AE) ratios:
  - Average AE for the 5 banks in the sample is 23 percent, down from 25 in 2016.
  - AE reaches 58 percent for one bank.
  - A decline in collateral value by 30 percent would lead to an additional encumbrance of 4 percent of assets, yet all banks would have enough additional liquidity to cover such need.
- Encumbrance risks are particularly relevant for banks issuing covered bonds to finance mortgage portfolios; high shares of low risk-weighted assets may heighten liquidity risks if losses in the mortgage segment lead to sharp capital ratio drops at times of significant refinancing needs.

### Cash-flow based liquidity stress testing: approach and scenarios
- Cash-flow based liquidity stress tests transform reported cash-flow data into stressed cash-flows and security flow data based on a matrix of scenario dependent stress factors.
- Two key indicators:
  - Liquidity risk exposure: difference between cash-inflows and cash-outflows in each time bucket (the net-funding gap) and the cumulated net-funding gap across buckets.
  - Liquidity risk bearing capacity: the CBC (counterbalancing capacity), defined as the sum of cash inflows banks can generate under stress at reasonable prices in the respective bucket after considering securities flows.
- Analysis builds on data collected within the Additional Maturity Mismatch Template (Additional Maturity Ladder, Corep C66.00).
- Five scenarios considered to test resilience (Figure 12); scenarios formulated in terms of roll-on/roll-off rates and haircuts to CBC and designed to capture:
  - Net outflows of retail deposits;
  - Increase in use of committed credit lines by corporates;
  - Significant increase in risk aversion with higher haircuts on counterbalancing capacity assets due to financial market stress, in line with the macro scenario.
- Scenario definitions:
  - Baseline: business as usual outflows.
  - Contractual: contractual flows.
  - Macroeconomic: linked to the macro scenario, assuming haircuts on liquid assets, closure of wholesale unsecured funding markets.
  - Idiosyncratic: institution specific shocks.
  - Idiosyncratic (no inflow): institution specific shocks and full provision of credit to the customers (i.e., no inflows from maturing loan portfolio).
- Scenario calibration references and empirical run-off rates:
  - Retail deposit outflows observed in event studies: Banes to (ES, 1994) 11 percent; Banesto (ES, 1994) 8 percent; IndyMac (USA, June 2008) 7.5 percent; Washington Mutual (USA, September 2008) 8.5 percent in 10 days; DSB Bank (NL, 2009) 30 percent in 12 days (Schmieder et al. 2012, Table 3).
  - Severe scenario comparable to Lehman: retail term deposits 10 percent over 30 days and 20 percent for demand deposits.
  - Unsecured short-term wholesale funding run-off rates: 100 percent.
  - Secured wholesale funding outflow rate: 20 percent.
  - EBA Severe Market Scenario run-off rates: 5 percent (retail deposits), 10 percent (NFC deposits), 20 percent (nonbank financial institutions), 100 percent (financial institutions), 0 percent (government/public entities).
  - Halal, Laliotis (2017, Severely adverse scenario) run-off rates: 10 percent stable deposits, 20 percent non-stable deposits, 100 percent net unsecured interbank funding, 50 percent net secured interbank funding, 100 percent other wholesale funding (except ABS 50 percent).

### Cash-flow stress test results and gaps
- Cashflow-based stress test suggests potential liquidity gaps when extending the horizon beyond 30-days.
  - In general, banks can withstand mild and medium liquidity outflows with existing counterbalancing capacities, but liquidity position becomes weaker beyond one month.
  - Some banks are prone to liquidity shortfalls even in the short-term (below 30 days), due to derivatives positions or large off-balance sheet exposures.
  - Shock impact is high: even in the baseline scenario CBC declines by 10 p.p. of total assets, yet banks can withstand significant outflows also when considering high haircuts.
- Liquidity stress tests can be enhanced by using more granular data from derivative reporting and corporate loans:
  - FI already receives granular data on derivatives trading by Swedish banks in the context of the European Market Infrastructure Regulation.
  - FI can use those data to develop an infrastructure to monitor liquidity needs stemming from margin calls on derivatives portfolios, at least for banks with sizeable positions.
  - Supervisory data give only limited information on such outflows, which can be material in case of market volatility.
  - FI has been collecting ad-hoc microdata on loans to CRE and corporates which can provide deeper knowledge of committed credit lines to corporates, especially CRE.

### Conclusions and recommended actions for banking sector liquidity
- Authorities need to use structural models in combination with existing stress testing frameworks:
  - Use CRE stress tests to inform banks solvency assessment.
  - Consider alternative modelling approaches to capture spillover to consumption and corporates profitability while more granular data on households become available.
  - Develop infrastructure to assess contingent liquidity risk from derivatives exposures for the largest banks.

---

### Investment funds: objective, scope, methodology
- Objective of liquidity stress testing for open-end investment funds:
  - (i) assess ability of investment funds to withstand severe but plausible shocks;
  - (ii) identify types of funds potentially more vulnerable to liquidity risk;
  - (iii) estimate the sector’s capacity to transmit shocks to the rest of the financial system.
- Emphasis on fixed income and mixed funds investing into assets with different degrees of liquidity and maturity.
- Sample based on end of 2021 supervisory data:
  - Sample consists of 171 funds divided in 6 categories for a total Net Asset Value (NAV) of SEK 1.223 bn (EUR 120 bn).
  - Categories: alternative funds, mixed funds, government, corporate, HY and short-term bond funds.
  - Funds-of-fund, ETFs and Equity funds are not considered in the stress test exercise.
  - The alternative fund category includes generic open-end funds investing in hedge-fund like strategies (e.g., market-neutral, macro trading, systematic trend).

### Investment funds: calibration and liquidity metrics
- Redemption shocks calibration:
  - Homogeneity assumption: funds within same category face same redemption shock calibrated on average of worst 3 percent net flows observed by funds in each category.
  - Resulting levels of redemption shocks (in percent of NAV) range from around 8 percent for mixed funds to 12 percent for short-term bond funds.
  - Heterogeneity assumption: historical redemption shocks calibrated at fund-level using fund net outflow (median outflow indicated under heterogeneity approach).
- HQLA and Redemption Coverage Ratio (RCR):
  - Investment funds’ holdings of High-Quality Liquid Assets (HQLA) compared with redemption requests.
  - RCR = Highly Liquid Assets / Redemption shock (expressed in percent of NAV).
  - Liquidity shortfall computed as difference between redemption shock and available highly liquid assets when a fund presents an RCR below one.
  - Highly liquid assets estimated at fund-level using portfolio composition and liquidity weights derived from Basel III framework for HQLA calculation.
- Liquidation strategies after shock:
  - Pro-rata (vertical slicing): sell assets proportionally to their weight in portfolio.
  - Waterfall (horizontal slicing): sell most liquid securities first.
  - Price impact of sales estimated by comparing market depth to volumes of sales.

### Investment funds: results and vulnerabilities
- Portfolio composition:
  - Overall, funds have similar portfolio structures across and within categories: mix of covered and corporate bonds, with shares of sovereign and Money Market Instruments (MMIs); limited share in other collective investment undertaking (CIUs).
  - Mixed funds present more diversification via equities and foreign assets.
- Ability to withstand severe redemption shocks:
  - Around 75 percent of funds would have enough highly liquid assets to meet investors’ redemptions.
  - Up to 25 percent of funds in the analysis could experience a liquidity shortfall under the severe but plausible assumptions.
  - Funds more vulnerable: portfolios not benefiting from high diversification or heavily exposed to unrated or poorly rated debt securities.
  - Results sensitive to assumptions over trading volumes in fixed income markets.
- Magnitude of shortfalls:
  - Some funds’ liquidity shortfall (difference between redemption shock and liquid assets, in percent of NAV) would be above 10 percent, the maximum temporary borrowing limit foreseen by the European UCITS Directive.
  - Funds holding domestic corporate bonds and pursuing long-term buy-and-hold strategies are more likely to have a shortfall, reflecting limited market depth and structural limitations in assessing credit risk and repricing as risks change.
- Table 4 results (Historical approach):
  - Total funds with RCR < 1: Homogeneity (ES 3%): 45 funds (26% of funds) representing 8% of NAV; Heterogeneity (ES 3%): 33 funds (19% of funds) representing 8% of NAV.
  - Category breakdown (Funds with RCR < 1, % Funds with RCR < 1, % NAV with RCR < 1):
    - Alternative: 6, 4%, 5% (Homogeneity); 5, 3%, 5% (Heterogeneity).
    - Corporate bond: 10, 6%, 7% (Homogeneity); 6, 4%, 5% (Heterogeneity).
    - Government: 0, 0%, 0% (both approaches).
    - HY: 23, 13%, 85% (Homogeneity); 14, 8%, 59% (Heterogeneity).
    - Short-term: 3, 2%, 1% (Homogeneity); 2, 1%, 1% (Heterogeneity).
    - Mixed: 3, 2%, 0% (Homogeneity); 6, 4%, 7% (Heterogeneity).
- Market and supervisory findings:
  - Following Spring 2020 market events, FI conducted supervisory activities; found some fund managers may not perform thorough liquidity risk assessments and may assume assets can be liquidated quickly, including large volumes of unrated corporate bonds.
  - Many fund managers do not sufficiently consider redemption requirements arising in periods of market stress and their impact on portfolio composition.
  - In June 2021 FI published a supervisory report including recommendations to fund managers on how to improve liquidity risk management and is following up on implementation.
  - FI is working to operationalize a stress test framework to monitor liquidity risks in the Swedish fund sector.

### Investment funds: recommendations
- Monitor and address vulnerabilities in funds with low portfolio diversification or high exposure to poorly rated or unrated debt securities.
- Consider limitations of market depth and trading volume assumptions when assessing fund liquidity and potential for fire sales.
- Strengthen supervisory follow-up on fund managers’ liquidity risk management practices and implementation of FI recommendations.
- Operationalize a stress test framework to monitor liquidity risks across the fund sector.

*Source: IMF staff analysis in the provided content unit.*

### 61. FI  and  MoF  should  ensure  that  funds  exposed  to  potential  liquidity  mismatches  have

### 61. FI and MoF should ensure that funds exposed to potential liquidity mismatches have adequate LMTs in place and assess their effectiveness

### Liquidity Management Tools (LMTs) and Redemption Terms
- Recommendation: Funds investing in less liquid assets that, based on their risk profiles, are at risk of presenting liquidity shortfalls under market stress should move to redemption terms that are more closely aligned with the liquidity profile of their portfolio and have access to a broad and adequate set of LMTs.
- LMTs mentioned:
  - Notice periods (to address externalities associated with individual and collective large sales by funds and under stress scenarios).
  - Price tools (e.g., swing pricing).
  - Quantity tools (e.g., redemption gates).
- Objective: LMTs should be targeted at reducing the first-mover advantage and ensuring fair treatment of investors.
- Authorities’ role: Follow discussions and developments in international fora and within the European Union (ESMA, FSB (Financial Stability Board), IOSCO) on the effectiveness of LMTs and provide guidance to asset managers on their implementation and application.
- Observed adoption: Only a very limited number of funds have so far adopted these tools following the COVID-19 market stress.

### Liquidity Stress Testing Guidance for Funds
- Recommendation: FI should provide industry guidance on liquidity stress tests for funds with relevant exposure to asset classes with limited market depth.
- Requirement: Authorities should require asset managers to perform regular liquidity stress tests for different market scenarios, and review and challenge such liquidity stress tests, given the risk associated with abrupt changes in market conditions.
- Supervisory objectives: FI should aim at comparability of results and assessment of the liquidity management practices.
- Priority segments: Important especially for funds exposed to less liquid asset classes and short-dated bonds carrying credit and interest rate risk that are used like cash-management vehicles.

### Stress Test Framework and Monitoring Tools
- Recommendation: FI should further develop and adapt their stress test framework and monitoring tools for conducting market wide liquidity risk analysis.
- Rationale: Given the prominent role acquired by the investment fund industry in the Swedish financial system, authorities should complete their analytical framework to assess market impact of collective funds’ reaction to episodes of stress and analyse possible risks arising from interlinkages between investment funds and other financial institutions.

### Stress Testing and Analytical Context (selected numeric and procedural details from the Appendix I matrix)
- Investment Funds Liqudity Stress Testing:
  - Institutional perimeter: 171 Fixed-income, alternative and mixed funds.
- Banking Sector: Solvency Stress Test:
  - Institutional perimeter: The 5 largest Swedish banks (Swedbank, SEB, Handelsbanken, SBAB Bank and Länsförsäkringar Bank), to cover about 75 percent of banking system assets.
  - Cut-off date: December 2021.
  - Tax rate: 20.6 – effective rate in 2021.
  - Dividend policy: Maximum allowed dividend payout cap at 40 percent (dividend payout ratio in 2021 used as reference).
  - Hurdle rates: CET1 hurdle rate ranging from 7.0 to 10 percent (Common Equity Tier (CET1) regulatory minimum of a 4.5 percent Pillar 1 requirement, a fully phased Capital Conservation Buffer (CCB) of 2.5 percent, and a phased-in buffer of 3 percent for SIBs).
- Banking Sector: Liquidity Stress Test:
  - Institutional perimeter: The largest 5 Swedish banks (Swedbank, SEB, Handelsbanken, SBAB Bank and Länsförsäkringar Bank).
  - Portfolio reporting date: Dec 31, 2021, or later.
  - Flows data: Sourced from Morningstar, time interval 2008-2021.
  - Assets’ characteristics: Sourced from EIKON.

### Implementation priorities for authorities
- Provide guidance and require:
  - Adoption of LMTs aligned to funds’ liquidity profiles.
  - Regular, comparable liquidity stress testing for asset managers.
  - Supervisory review and challenge of fund liquidity stress tests.
- Enhance FI’s analytical capacity to:
  - Complete frameworks to assess market impact of collective fund actions under stress.
  - Analyse risks from interlinkages between investment funds and other financial institutions.

*Source: Excerpt from IMF staff report content unit 1sweea2023010*

### 2. Channels of

### 2. Channels of risk propagation

### Methodology and stress-testing framework
- Various levels of redemptions shock compared level of highly liquid assets at the fund level.
- Redemption shocks based on historical fund flow data using VaR and Expected Shortfall methodologies with multiple thresholds.
- Stress test horizon: Weekly data frequency, instantaneous shocks.

### Tail shocks and scenario analysis
- Pure redemption shock: severe outflows based on historical distribution of fund flows.

### Risks assessed and buffers
- Positions/risk factors assessed:
  - Liquidity risk: severe redemption shock.
- Buffers:
  - Level of highly liquid assets.

### Reporting format for results
- Output presentation:
  - Number of funds with a redemption coverage ratio (ratio of highly liquid assets to redemptions) below one.
  - Liquidity shortfall amount for individual funds after redemptions.

### Risk Assessment Matrix (selected entries)
- Russia’s invasion of Ukraine leads to escalation of sanctions and other disruptions:
  - Likelihood: H
  - Impact: Medium
  - Key impacts listed: negative shock to exports, dampened exports and investment, higher funding costs, reduced credit availability including from non-bank financial intermediaries.
- De-anchoring of inflation expectations in the U.S. and/or advanced European economies:
  - Likelihood: M/L
  - Impact: High
  - Key impacts listed: Market losses in banks’ unhedged fair value portfolios, potential significant liquidity impact on banking sector, higher funding costs impacting corporate borrowers and households, pressures on banks’ capital adequacy.
- Significant property price decline in Sweden due structural changes:
  - Likelihood: L
  - Impact: Medium
  - Key impacts listed: Investment and collateral values undermined, loan quality impacted, potential curtailing of lending.
- Geopolitical tensions and deglobalization:
  - Likelihood: H
  - Impact: Medium
  - Key impacts listed: Higher disruptions and barriers to trade damping exports and investment.
- Cyberthreats:
  - Likelihood: M
  - Impact: Medium
  - Key impacts listed: Widespread disruption to supply of essential goods, payments systems, and financial market infrastructure.

*Appendix III. Projections of Probability of Default by Segment — Corporates*

### Corporate PD projections methodology
- Tool: Bayesian Model Averaging (BMA) by Gross and Población, J. 2019.
- Scope: Project PDs of corporates for Sweden and the Nordic-Baltic region.
- Model setup:
  - Independent variables chosen from the pool in Table 3 for the relevant geography.
  - Tool run with a maximum four and up to three lags of right-hand variables per model.
  - For Swedish corporates, an autoregressive term was added.
  - A logit transformation was applied to the dependent variable, and sign constraints were imposed on all variables.
- Sweden: Variables for BMA (as listed)
  - Unemployment - levels
  - GDP growth QoQ
  - Term spread
  - CPI (Consumer Price Index) YoY
  - Unemployment - QoQ
  - GDP growth YoY
  - Short-Term risk-free rate
  - CPI QoQ
  - Unemployment - YoY
  - First difference CPI YoY
  - Sign constraint row shown as: + - + +

*Figure and long-run multipliers: Top panels show the baseline and adverse paths for the two macro variables which have largest long-run multipliers. The paths are obtained as weighted average of the paths for the countries in the region, when available from the GFM model employed for the scenario design.*

*Appendix III. Projections of Probability of Default by Segment — Households*

### Structural model for mortgage default projections — key assumptions and structure
- Assumptions to deploy the structural model:
  - A DSR/LTV joint distribution for the stock of existing mortgage exposures can be constructed assuming an average maturity of individual loans of 70 years at origination and an unchanged distribution for the years prior to 2015.
  - DSR and LTV ratios across all vintage distributions were adjusted using household income and house price historical time series as proxies for the impact on DSR and LTV, respectively.
- Structural component: probability of a household being under stress is a function of:
  - Change in the Debt Service Ratio (DSR) due to an interest rate change.
  - Change in the unemployment rate in the scenario.
- Structural distress probability (as provided):
  - 푃푃푃푃푃푃 푡푡 =푎푎0 ⋅퐷퐷 +푎푎1 ⋅퐷퐷푃푃 푅푅 푡푡 훽훽1 + 훼훼1 ⋅훥훥퐷퐷푃푃 푅푅 푡푡 훽훽2 +훼훼3 ⋅�훼훼4 ⋅푢푢 푡푡 +훼훼5 ⋅ ( 훥훥푢푢 푡푡 ) 훽훽3
  - Notation: 퐷퐷 denotes a demographic distress contribution component; 퐷퐷푃푃푅푅 푡푡 denotes the borrower’s DSR post stress; 훥훥퐷퐷푃푃푅푅 푡푡 the delta in DSR vs the cut-off date; 푢푢 푡푡 the unemployment rate; 훥훥푢푢 푡푡 the change in unemployment rate from the cut-off date.
- Default conditionality:
  - Default occurs only when the household is in distress, the household’s liquid wealth is not enough to cover servicing needs, and the value of the loan is higher than the value of the collateral (post-stress LTV > 1).
  - Under a positive house price assumption, an outright sale would be triggered by a borrower’s distress as opposed to a default event.
- Default probability formulation (as provided):
  - 푃푃퐷퐷 푡푡 =푃푃푃푃푃푃 푡푡 ⋅ � # |푉푉 � 푡푡 −퐶퐶 <퐿퐿 푎푎푎푎푎푎 퐵퐵 푡푡 ( 퐿퐿퐿퐿 0 ) =0 � ( #푖푖 푡푡푖푖푖푖푎푎푡푡 푖푖표표푎푎표표 )
  - Interpretation: first bracket term denotes probability that the property value after stress minus some liquidation discount R is lower than outstanding loan notional H; second term denotes failure of behavioral rule B_t(x) accounting for use of Liquid Wealth H_L0 at the cut-off to save the loan from default. Outcome assessed by Monte Carlo simulations.
- Conditional LGD:
  - 퐻퐻퐿퐿 퐷퐷 푡푡 =1− (1−훿훿) 푃푃 푡푡+푠푠 퐿퐿∗(1+푖푖 푡푡 +푐푐표표 푡푡 ) 푠푠
  - Sale occurs at time t+s (s denotes average time to realize the collateral); sale proceeds net of transaction costs discounted at a rate reflecting the scenario interest rate premium; foreclosure liquidation discount 훿훿 applied.
- Joint distribution partitioning and Monte Carlo:
  - LTV partitioning values used: 1.2, 1.1, 1. 0.9, 08, 0.7, 0.6, and 0.5.
  - DSR partitioning values used: 0.2, 0.3, 0.4, 0.5 and 0.6.
  - The first two dimensions of household data are mapped to a 5 by 8 partitioned space.
  - For each DSR/LTV partition a Monte Carlo simulation (on house price changes anchored to a central house price shock) is used to produce model-based projections on 3-year loss rates.
  - Portfolio average 3-year loss rate is the weighted average of projected loss rates per DSR/LTV density partition.
- Translation of 3-year loss rates to yearly projections:
  - The structural model produces 3-year scenario-dependent loss rates.
  - To produce yearly projections, the model is sequentially run using the 1-year, 2-year and 3-year scenario loss rate projection.
  - Each run uses the 1-year loss rate projected by the previous run to infer the annual loss rate that would correspond to a cumulative loss rate as projected by the model for the total number of years and the scenario corresponding to this point in time.
  - This procedure translates 3-year loss rate projections into yearly projections, anchoring cumulative impact to the original 3-year loss rate projection and end-horizon scenario.
- Bank-specific translation:
  - Model projections for both baseline and adverse scenarios are translated into bank-specific projections using the bank mortgage exposure starting points.
  - Translation performed using an absolute shift in the PD space: starting point adjustment brings baseline scenario projections closer to idiosyncratic default rates observed in the market under current conditions; adverse scenario loss rates projected as an additional delta impact versus the baseline one.
- Behavioral rule calibration note:
  - In the actual calibration for the solvency stress test a linear survival rule was implemented as the behavioral rule: the survival probability is linear between a wealth buffer of 8 and 36 months. Buffers below 8 months will not be sufficient to weather a default event and borrowers with wealth buffers exceeding 36 months would survive the distress event with probability 1.

*Source: IMF staff calculations and referenced model descriptions in the chapter.*

### Appendix IV . Data and Sample of Funds Used in Stress Tests

### Appendix IV . Data and Sample of Funds Used in Stress Tests

### Data
- Flows:
  - Daily data on flows and net asset value (NAV) are retrieved over the 2008–2021 period for each fund in the sample.
  - The sample of fund is based only on funds that were still alive as of end-2021.
  - Daily flows have been aggregated to obtain weekly fund flows.
  - Supervisory information on fund flows is available to the authorities only with a quarterly frequency.
- Computation of net flows:
  - Net flows in percent of NAV are computed using the formula provided in the source (symbolic representation present in the original text).
  - Net flows whose absolute value are above 50% were excluded as they are likely related to reporting mistakes.
  - When info on daily flows is not available, monthly flows are proportionally distributed over the number of weeks in each month.
- Portfolio composition and asset info:
  - Supervisory information on asset-level portfolio composition at the end of 2021 is used, including asset categories and ISINs, price and market value of instruments, portfolio weights, amount of cash held in portfolio, and remaining liabilities.
  - Ancillary information on asset characteristics, sectors, credit quality, maturities, yields, and durations are obtained by Refinitiv EIKON.
  - Metrics on liquidity and trading are derived using information collected under MiFID.
  - Total volumes traded for different asset classes are taken from SELMA (the Riksbank’s reporting of turnover statistics by counterparties for the money and bond markets).

### Calibration of the Redemption Shock
- Purpose and general approach:
  - Assess liquidity risk for open-end investment funds exposed to fixed income instruments by calibrating an instantaneous redemption shock and comparing it to a measure of highly liquid assets.
  - Calibration follows Bouveret and Yu (2021, IMF) and similar approaches in other FSAPs (IMF, 2015b; 2017; 2018).
- VaR and ES approaches:
  - Value-at-Risk (VaR) approach: VaR at the α level is given by Na i(α) = F−1(α), where F−1 is the inverse distribution function of net flows.
  - Expected Shortfall (ES) approach: ES(α) = E(Z | Z < Na i(α)), where Z represents the net flows; ES addresses drawbacks of VaR by averaging net flows below the VaR.
- Homogeneity assumption:
  - Each fund within the same investment style faces the same redemption shock.
  - Shock is based on the distribution of all individual fund net flows ascribable to the same investment style.
  - Calibration is based on the 3% ES.
  - Robustness checks: ES at 1% and 5%; VaR at worst 1%, 3%, and 5% net flows observed.
  - Main focus: homogeneity assumption calibrated at the 3% level, with redemption shocks ranging from 7.8% percent for Mixed funds to ~14% percent for HY bond funds (as stated in the source).
- Heterogeneity assumption:
  - Redemption shock calibrated separately for each fund based only on its own historical data.
  - Shock is based on the 3% ES.
  - Robustness checks: 1% and 5% ES levels and using percentiles.
  - Limitations: does not allow comparing outcomes across funds for a redemption shock of the same magnitude; shocks may be not meaningful if funds have not experienced large outflows.

- Overall:
  - Each fund is subject to 12 different redemption shocks (six ES/VaR shocks under homogeneity and six under heterogeneity).

- Table of calibrated shocks (excerpted values exactly as presented):
  - Level: 1%
    - Alternative: Homogeneous ES -11.9; Homogeneous VaR -5.4; Heterogeneous ES -12.2; Heterogeneous VaR -10.4
    - Corporate bond: Homogeneous ES -10.7; Homogeneous VaR -6.3; Heterogeneous ES -10.1; Heterogeneous VaR -7.8
    - Government: Homogeneous ES -13.3; Homogeneous VaR -10.1; Heterogeneous ES -13.1; Heterogeneous VaR -8.4
    - HY: Homogeneous ES -17.2; Homogeneous VaR -9.0; Heterogeneous ES -6.2; Heterogeneous VaR -5.0
    - Short-term bond: Homogeneous ES -12.7; Homogeneous VaR -8.7; Heterogeneous ES -4.8; Heterogeneous VaR -3.3
    - Mixed: Homogeneous ES -11.5; Homogeneous VaR -4.4; Heterogeneous ES -1.3; Heterogeneous VaR -0.5
  - Level: 3%
    - Alternative: Homogeneous ES -8.7; Homogeneous VaR -3.2; Heterogeneous ES -11.3; Heterogeneous VaR -4.7
    - Corporate bond: Homogeneous ES -9.8; Homogeneous VaR -4.6; Heterogeneous ES -9.9; Heterogeneous VaR -5.3
    - Government: Homogeneous ES -11.9; Homogeneous VaR -6.0; Heterogeneous ES -11.0; Heterogeneous VaR -5.9
    - HY: Homogeneous ES -14.1; Homogeneous VaR -6.4; Heterogeneous ES -5.8; Heterogeneous VaR -3.1
    - Short-term bond: Homogeneous ES -12.0; Homogeneous VaR -7.3; Heterogeneous ES -4.6; Heterogeneous VaR -3.3
    - Mixed: Homogeneous ES -7.8; Homogeneous VaR -2.1; Heterogeneous ES -0.9; Heterogeneous VaR -0.5
  - Level: 5%
    - Alternative: Homogeneous ES -6.3; Homogeneous VaR -2.1; Heterogeneous ES -8.3; Heterogeneous VaR -1.1
    - Corporate bond: Homogeneous ES -7.4; Homogeneous VaR -3.0; Heterogeneous ES -7.8; Heterogeneous VaR -3.6
    - Government: Homogeneous ES -9.3; Homogeneous VaR -4.7; Heterogeneous ES -8.7; Heterogeneous VaR -4.7
    - HY: Homogeneous ES -10.5; Homogeneous VaR -4.1; Heterogeneous ES -4.6; Heterogeneous VaR -2.4
    - Short-term bond: Homogeneous ES -9.7; Homogeneous VaR -5.4; Heterogeneous ES -4.1; Heterogeneous VaR -3.0
    - Mixed: Homogeneous ES -5.3; Homogeneous VaR -1.2; Heterogeneous ES -0.7; Heterogeneous VaR -0.4

### Investment Funds’ Resilience: The Liquidity Bucket Approach
- Metric:
  - Redemption Coverage Ratio (RCR) used to measure ability to withstand redemption shocks (as in Luxembourg and US FSAPs).
  - RCR = (Highly liquid assets) / (Redemption shock) (symbolic representation present in the original text).
  - When RCR < 1, the fund does not have enough highly liquid assets to cover redemptions with minimal disruption.
  - Liquidity shortfall = redemption shock − stock of highly liquid assets (symbolic representation present in the original text).
- Highly liquid assets calculation:
  - Highly liquid assets for fund i: HLA_i = sum over k of (ω_i,k × s_i,k), where ω_i,k are liquidity weights assigned to each security s_i,k in the fund portfolio.
- Liquidity weights:
  - Liquidity weights are defined based on the type of assets and, for fixed income instruments, the credit quality.
  - Weights are taken from the Basel Committee to allow comparability.
  - Table of weights (presented exactly as in the source):
    - Cash: 100%
    - Equities: 50%
    - Sovereign bonds: AAA-AA 100%; A 85%; BBB 50%; Below BBB 0%
    - Corporate bonds: AAA-AA 85%; A 50%; BBB 50%; Below BBB 0%
    - Covered bonds and Securitized: AAA-AA 80%; A 0%; BBB 0%; Below BBB 0%

### Liquidation Strategies and Price Impact of Fund Sales
- Liquidation strategies described:
  - Vertical slicing (pro rata): manager sells each asset class in proportion to their weight in the fund’s portfolio; aims not to distort portfolio but may require selling less liquid assets, resulting in potential higher trading costs.
  - Waterfall: manager sells the most liquid assets first (order based on HQLA liquidity weights); may mitigate immediate price impact but leaves remaining investors with a less liquid portfolio.
- Cash treatment:
  - Funds may increase cash positions in stressed market situations; cash is not considered in the liquidation strategies (cash retained to face redemptions or margin calls).
- Market depth and price impact:
  - Market depth formula: MD(τ) = a × HDDN / σ × √τ (symbolic representation present in the original text; original used parameters a, HDDN, σ, and τ).
  - τ is the time horizon to sell assets; for the instantaneous shock τ is considered equal to 1 day.
  - Price impact by asset class: PPI(τ) = (Sales by asset class) / MD(τ) (symbolic representation present in the original text).
- Volatility and volumes:
  - Volatility estimated for asset classes by filtering market indices through a GARCH (1,1) and taking average values over February and March 2020.
  - Indices used for market impact calibration (exact labels from source):
    - S&P SWEDEN IG CORP BOND INDEX — Symbol: SPSEICR — Currency: Swedish Krona — Frequency: Daily — Full Name: S&P Sweden Investment Grade Corporate Bond Index
    - S&P SWEDEN SOV BOND INDEX — Symbol: SPSFISV — Currency: Swedish Krona — Frequency: Daily — Full Name: S&P Sweden Sovereign Bond Index
  - Sovereign and corporate bonds are pooled together given lack of information on daily traded volumes at instrument level.
  - Total daily traded volumes for asset classes are taken from SELMA and averaged over the period from 1 January and 31 May 2022.
  - Volumes reported in SELMA do not distinguish specifically for debt instruments issued by CRE companies; it has been assumed that half of the corporate bonds traded are from CRE companies.

### Investment Funds Stress Test Results
- Presentation:
  - Share of investment funds with RCR < 1 is reported for the 12 redemption shocks (ES and VaR at 1%, 3%, 5%) under homogeneous and heterogeneous assumptions.
- Results (exact shares as presented in the source):
  - Homogeneous shock — ES / VaR; Heterogeneous shock — ES / VaR (percent of funds with RCR < 1)
  - Level: 1%
    - Alternative: Homogeneous ES 21% ; Homogeneous VaR 3% ; Heterogeneous ES 4% ; Heterogeneous VaR 5%
    - Corporate bond: Homogeneous ES 7% ; Homogeneous VaR 3% ; Heterogeneous ES 5% ; Heterogeneous VaR 5%
    - Government: Homogeneous ES 0% ; Homogeneous VaR 0% ; Heterogeneous ES 0% ; Heterogeneous VaR 0%
    - HY: Homogeneous ES 85% ; Homogeneous VaR 60% ; Heterogeneous ES 59% ; Heterogeneous VaR 57%
    - Short-term bond: Homogeneous ES 11% ; Homogeneous VaR 1% ; Heterogeneous ES 11% ; Heterogeneous VaR 1%
    - Mixed: Homogeneous ES 2% ; Homogeneous VaR 0% ; Heterogeneous ES 7% ; Heterogeneous VaR 7%
  - Level: 3%
    - Alternative: Homogeneous ES 5% ; Homogeneous VaR 0% ; Heterogeneous ES 5% ; Heterogeneous VaR 0%
    - Corporate bond: Homogeneous ES 7% ; Homogeneous VaR 1% ; Heterogeneous ES 5% ; Heterogeneous VaR 2%
    - Government: Homogeneous ES 0% ; Homogeneous VaR 0% ; Heterogeneous ES 0% ; Heterogeneous VaR 0%
    - HY: Homogeneous ES 85% ; Homogeneous VaR 60% ; Heterogeneous ES 59% ; Heterogeneous VaR 57%
    - Short-term bond: Homogeneous ES 1% ; Homogeneous VaR 1% ; Heterogeneous ES 1% ; Heterogeneous VaR 1%
    - Mixed: Homogeneous ES 0% ; Homogeneous VaR 0% ; Heterogeneous ES 7% ; Heterogeneous VaR 7%
  - Level: 5%
    - Alternative: Homogeneous ES 5% ; Homogeneous VaR 0% ; Heterogeneous ES 2% ; Heterogeneous VaR 0%
    - Corporate bond: Homogeneous ES 3% ; Homogeneous VaR 0% ; Heterogeneous ES 2% ; Heterogeneous VaR 2%
    - Government: Homogeneous ES 0% ; Homogeneous VaR 0% ; Heterogeneous ES 0% ; Heterogeneous VaR 0%
    - HY: Homogeneous ES 73% ; Homogeneous VaR 40% ; Heterogeneous ES 59% ; Heterogeneous VaR 36%
    - Short-term bond: Homogeneous ES 1% ; Homogeneous VaR 0% ; Heterogeneous ES 1% ; Heterogeneous VaR 1%
    - Mixed: Homogeneous ES 0% ; Homogeneous VaR 0% ; Heterogeneous ES 7% ; Heterogeneous VaR 7%
- Note: Share of investment funds with RCR < 1.

*Source: Morningstar, IMF staff.*

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_Source: https://www.imf.org/-/media/files/publications/cr/2023/english/1sweea2023010.pdf_