## 1korea2020005 - EXECUTIVE SUMMARY AND RECOMMENDATIONS

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### Executive summary and key recommendations
- Non-financial private sector debt remains elevated in Korea and requires close monitoring.
- Aggregate findings:
  - Non-financial corporate leverage has remained stable since 2013 at around 100 percent of GDP.
  - Around 28 percent of total non-financial corporate debt (around 28 percent of GDP) is registered “at-risk”.
  - Non-SMEs hold around 60 percent of total core corporate debt; SME debt is around 38 percent of GDP.
  - Over half of non-financial corporate debt-at-risk resides with SMEs.
  - Household leverage as share of GDP in Korea is among the highest in the OECD and continues to grow.
  - Around 14 percent of GDP in household debt is classified as debt-at-risk; this is equivalent to around one-third of Korea’s total stock of non-financial debt-at-risk.
  - Around one-quarter of household debt-at-risk is held by retiree households.
- Stress-test and risk aggregation conclusions:
  - Under a downside macro-financial stress test scenario, non-SME credit losses would total a touch over 2 percent of GDP.
  - Bank stress tests indicate maximum cumulative bank losses from distressed SME loans in a stress scenario would total around 2 percent of GDP.
  - Household credit losses for the banking system under adverse scenarios will remain manageable at around 2.5 percent of GDP.
- Stylized policy implications:
  - Close monitoring of SME balance sheet vulnerability given concentration of debt-at-risk and bank exposure.
  - Prepare for corporate sector sensitivity to interest rate hikes combined with profit shocks.
  - Monitor household exposure to floating-rate and bullet-payment loan structures and demographic shifts increasing retiree-held debt.

*Author: Sohrab Rafiq (IMF), member of the FSAP 2019 team.*

### Non-financial corporate balance sheets — scope, structure, and stylized facts
- Objective:
  - Document evolution of Korean non-financial corporate debt since the Global Financial Crisis, analyze riskiness and allocation quality, uncover heterogeneity in balance sheet vulnerabilities, and assess implications for debt sustainability and financial stability.
- Empirical approach:
  - Firm-level panel regressions on a large sample of large, medium and small-cap Korean firms to assess determinants of debt-at-risk, Korea-specific capital structure factors, and models for macro-financial stress test scenarios.
- Leverage and composition:
  - Consolidated debt of non-financial corporations has remained relatively stable at around 100 percent of GDP since 2013.
  - Non-SMEs account for around 60 percent of core corporate debt; SMEs account for around 38 percent of GDP and are almost entirely composed of bank loans.
  - The majority (80 percent) of non-SME debt is concentrated in less than 50, mainly conglomerate-affiliated, firms.
  - The average debt-to-asset ratio: SME firms around 60 percent; non-SMEs around 30 percent.
  - Around 80 percent of the stock of corporate debt is linked to floating rates.
- Market vs bank financing:
  - Non-SME financing is market based (rise in bond financing, much of which is investment grade).
  - SME financing is bank based; banks’ exposure to SMEs is around 32 percent of GDP versus banks’ exposure to non-SMEs around 6 percent of GDP.
- Foreign-currency exposure:
  - Share of FX corporate bank lending is around 4 percent of total non-financial corporate lending.
  - Market-based FX corporate debt is around 11 percent of total market-based corporate debt; much of FX debt is hedged naturally or through swaps.
- Firm performance and heterogeneity:
  - Since 2014 conglomerate-affiliated firms saw rising profit margins and ROAs; non-conglomerate firms experienced weakening profitability.
  - The number of non-SME non-conglomerate firms reporting negative ROA and/or two consecutive years of negative operating income growth has increased.
  - Asset turnover and sales-to-asset ratios have fallen.
  - Aggregate corporate liquidity buffers (current ratio and solvency ratio) remained stable, but the share of non-SME firms with current ratio below 1 has risen markedly since 2011.

### Empirical determinants of corporate debt-at-risk and profitability
- Definition:
  - Debt-at-risk is defined as debt of firms with an interest coverage ratio (ICR) below 1.
- Main empirical findings:
  - Around 28 percent of non-financial corporate debt is at risk.
  - Over 50 percent of corporate debt-at-risk is held by SMEs; around half of total SME debt is estimated to be at-risk.
  - Non-SME debt-at-risk has fallen partially due to restructuring in the ship building industry.
  - The low-for-long interest rate environment has reduced defaults among weaker firms but may have supported inefficient allocation of corporate debt.
  - Macro-financial conditions affect average firm profitability and the likelihood of firms belonging to the left tail (debt-at-risk) after controlling for firm characteristics.
  - Firms that are more indebted, less profitable, smaller, and have lower turnover are more likely to have debt-at-risk.
- Marginal effects and regression evidence (selected coefficients and diagnostics):
  - Corporate Debt-at-Risk Probit (dependent ICR<1) — selected signs and significance:
    - ROA: -0.31***; -0.30***; -0.03***; -0.02**
    - Debt-to-assets: 0.51***; 0.50***; 0.53***; 0.61***
    - Current ratio: -0.04*
    - Turnover to assets: -0.39**
    - Total assets: -0.38***
    - FCI: 0.02**
    - Pseudo-R2: 0.31–0.38
  - Corporate Profitability Regressions (dependent ROA) — selected coefficients:
    - Debt-to-assets: -2.86***; -2.62***; -2.85***; -2.01***
    - Operating revenue: 1.84***; 1.90***; 1.84***; 3.35***
    - ROA (lag): 0.18**; 0.03*
    - Total assets: 1.30**; 2.53***
    - Profit margin: 0.07**; 0.03**
    - FCI: 0.72***; 0.59***; 0.55***
    - Growth-at-risk: -0.21**; -0.07*; 0.12**
    - Observations: 20540; Pseudo-R2 range: 0.20–0.22 (cols 1–3), 0.21 (col 4)
- Sample coverage:
  - Sample covers 2749 firms from 2008 to 2018 (probit/profitability analysis).
  - Separate stress-testing sample covers 2748 firms from 1992 to 2018 for the bottom-up non-SME stress test.
  - The Orbis database sample captures around 60 percent of total core corporate debt stock (almost the entire total stock of non-SME corporate debt).

### Corporate liquidity, FX exposure, and vulnerability to shocks
- Liquidity and cash buffers:
  - Conglomerate-affiliated firm leverage has been driven by bond issuances and mitigated by increased cash holdings.
  - Falling interest rates have helped contain debt service ratios across corporates.
- FX risk:
  - Corporate FX debt has been falling and remains low; natural FX hedges and swaps mitigate FX shocks.
  - Share of FX corporate bank lending is around 4 percent of total non-financial corporate lending.
- Vulnerability to interest rate and profit shocks:
  - Static stress tests show corporate balance sheets are vulnerable to a sudden hike in interest rates.
  - Combined interest rate and profit shocks could significantly increase corporate debt-at-risk.
  - Exchange rate shocks appear manageable given low FX corporate debt and natural FX hedges.

### Stress testing and loss estimates for corporates and banks
- Stress scenarios (Box 1 highlights):
  - Scenario 1: Interest rate shock — severe 300 basis point shock to the baseline interest rate on debt.
  - Scenario 2: Interest and profit shock — 300 basis point interest increase and a 35 percent decline in operating income (EBIT).
  - Scenario 3: Interest, profit and KRW-US dollar exchange rate shock — 300 basis point interest increase, 35 percent decline in EBIT, and exchange rate depreciation of 20 percent against the dollar.
- Bottom-up dynamic non-SME corporate stress testing (Box 5):
  - Purpose: project PDs for non-SME firms under macroeconomic scenarios over a three-year horizon using micro balance sheet data and economy-wide and firm-specific risk factors.
  - Procedure: scenario specification; forecast common and firm-specific risk factors (including liquidity, profitability, size); project firm PDs using forward intensity model; use projected PDs in a credit portfolio module (Vasicek single factor) to calculate expected credit loss distribution.
  - Modeling notes include distance-to-default measures and mixed-frequency regressions for risk factor forecasts.
- Historical benchmarks:
  - Asian crisis: 12 (24) month PD registered 623 basis points (1200bps) → implied 12 (24) month PD of 6.2 percent (12 percent).
  - Global Financial Crisis (GFC): 12-month (24 month) PD registered 83 basis points.
- Stress-test outcomes:
  - Static simulations show that in a hypothetical macroeconomic scenario where financial conditions tighten, aggregate debt at risk of non-SME firms would rise by 3 percent of total debt.
  - Under the hypothetical adverse stress scenario: 12-month implied PD rises to around 50 basis points.
  - Estimated credit losses from stress scenario:
    - Cumulative expected credit loss after three years: around 1.3 percent of GDP.
    - Corresponding unexpected credit losses: 2.1 percent of GDP.
  - Drivers of low non-SME corporate credit losses in the stress scenario:
    - (i) Very large share of debt being market-based and investment grade.
    - (ii) Over 90 percent of non-SME corporate debt held by less than 100 firms (mainly conglomerate-affiliated) which since 2013 have seen resilient ROAs, ICRs and liquidity ratios.
    - (iii) These firms have relatively low leverage ratios and carry a large amount of cash and equity.
- SMEs, banks, and data gaps:
  - SME debt is predominantly bank-based; banks’ exposure to SMEs is around 32 percent of GDP.
  - Estimates from bank stress tests suggest maximum cumulative bank losses from distressed SME loans in a stress scenario would total around 2 percent of GDP.
  - Around 70 percent of banks’ corporate loans are to SMEs, totaling around 20 percent of GDP and mostly secured against commercial and residential real estate.
  - Limits remain in fully understanding corporate-sector risk to financial stability due to lack of SME balance sheet data.
- System-wide assessment:
  - Together, estimated losses from corporate and household stress scenarios would be broadly manageable for the financial system to absorb, though bank balance sheets face risks given co-risk between large corporate solvency and SME performance.

### Summary conclusions for corporate sector
- Since 2013 non-financial corporate debt has been stable at around 100 percent of GDP, with significant heterogeneity: conglomerate-affiliated firms maintaining leverage and smaller/non-conglomerate firms deleveraging.
- Around 28 percent of corporate debt is at-risk, with SMEs bearing a disproportionate share of this risk.
- Low interest rates have masked vulnerabilities; rapid increases in rates or profit shocks could materially raise debt-at-risk and credit losses.
- Policy recommendation: ensure that credit market frictions to viable SME firms remain contained and closely monitor SME balance sheet vulnerabilities given significant bank exposure.

### Household balance sheets — stylized facts, vulnerabilities, and stress tests
- Objectives:
  - Document evolution of Korean household debt, analyze riskiness and heterogeneity by demographics, and develop household stress testing to characterize risks to financial stability.
- Stylized facts and composition:
  - Since 2013 household debt rose from around 85 to 100 percent of GDP → corresponds to a debt to disposable income ratio of 180 percent.
  - Household debt was 93 percent of GDP as of the second quarter of 2019, one of the highest levels in the OECD.
  - Approximately 53 percent of household debt is secured against real estate, and 70 percent of household wealth is linked to real estate.
  - Around half of household debt is linked to floating interest rates and is structured as a bullet payment.
  - Around 15 percent of the total stock of household debt (14 percent of GDP) is registered at-risk; reported also as 14.3 percent of GDP in Box 6.
  - Around 12 percent of household debt is held with households whose balance sheets are upside down (debt-to-asset ratio above 100 percent).
  - Over 70 percent of bank loans have been channeled to those households with the highest credit scores.
  - Median household debt-to-income and loan-to-value ratios have remained stable at around 55 percent.
  - Share of mortgages with maturity greater than 10 years: currently standing at around 50 percent.
  - Over 30 percent of household debt is held by households with a debt service ratio above the prudential ceiling of 40 percent (bank average).
  - Around a quarter of total household debt is held by households who are retired or close to retirement.
  - A significant share of household debt is secured against a non-primary residence.
- Household debt-at-risk and composition (Box 6):
  - As of 2018: Around 15 percent (14 percent of GDP) of the total stock of household debt is ‘at-risk’.
  - Composition of household debt-at-risk:
    - Around 85 percent is tied to a household with a debt service ratio above the bank average prudential ceiling of 40 percent.
    - 45 percent with households aged 50 and above.
    - Over 50 percent of HH debt-at-risk is due to unsecured borrowing for living and property expenses.
    - Around 12 percent (11 percent of GDP) is held with households at solvency risk (debt-to-asset ratio above 100).
      - About half this amount is held with households who have a debt service ratio above 40 percent.
      - 46 percent with households headed by a retiree or near-retiree individual.
      - A quarter have a bullet payment structured loan.
    - The ratio of household debt-to-GDP held by households at liquidity and solvency risk stands at 4 percent.
- Methodology and definitions:
  - Financial margin (FM) defined as FM = (consumption + debt service payment + monthly rent) / (disposable income + 0.7 ∙ savings).
  - A household with FM < 0 is considered borrower-at-risk; debt-at-risk if such a household carries debt.
  - Household stress tests use Monte-Carlo simulations of shocks to house prices, interest rates, and household income; behavioral assumptions include consumption adjustments and liquidation of 70 percent of savings (excluding insurance and pension).
  - Stress-test shock sizes (Box 9 highlights):
    - Income -15 percent
    - House price -16 percent
    - Interest rate +200 basis points
  - Model assumptions: around 70 percent of household loans are linked to a floating rate.
- Stress scenario findings (selected exact outcomes):
  - Baseline proportion of household debt-at-risk: 13.7 percent of GDP.
  - Income shock: A 15 percent decline in income increases household debt-at-risk by 6 percent of GDP from 13.7 to 19.6 percent of GDP.
    - Around 60 percent of the increase is from households in their 40s and retirees.
  - Interest rate shock: A 200-basis point increase increases household debt-at-risk from 13.7 to 17.0 percent of GDP.
    - Retiree households have an almost equal impact from an interest rate shock as from an income shock (around 1.6 percent of GDP).
  - Baseline proportion of household debt held with insolvent households: around 4 percent of GDP.
    - Income shock: increases by around 1.2 percent of GDP to 5.3 percent of GDP.
    - House price shock: increases by around 2.5 percent of GDP to 6.6 percent of GDP.
  - Household credit losses for the banking system under adverse scenarios are estimated to be around 2.5 percent of GDP.
  - Interpretation: The low-for-long interest rate environment in Korea has been helpful for containing household balance sheet vulnerabilities.
- Household heterogeneity and demographics:
  - Retiree households:
    - Hold around 20 percent of the total stock of Korean household debt.
    - Are the most levered as measured by financial debt-to-asset ratio.
    - Have suffered the largest fall in net worth of all age groups since 2010.
    - Official retirement age in Korea is 55 years old.
    - Older households are more at insolvency risk from a house price shock given higher wealth concentration in real estate.
  - Demographic projections:
    - Based on current trends over 50 percent of HH debt will be held by HHs aged 50 and above, around 10 percent higher than presently.

### Household policy responses and macroprudential recommendations
- Macroprudential responses and lending standards already implemented or recommended:
  - Stricter standards for calculating debt-to-income ratios, eliminating anomalies for second mortgages, and improving income documentation.
  - Incentives for borrowers to take safer mortgages given high share of bullet and floating rate mortgages.
  - Policy measures to encourage fixed interest rate amortizing loans (e.g., easing LTV limits for such mortgages).
- Additional macroprudential recommendations:
  - Strengthen macroprudential policies, including the introduction of a sectoral countercyclical buffer (SCCyB) covering secured and unsecured household lending.
    - The SCCyB would build resilience, allow banks to build up and release capital as household risks fluctuate, and help mute pro-cyclicality in bank lending.
  - Continue and complement borrower-based measures such as limits on loan-to-value, debt-to-income, and debt service-to-income ratios.
  - Consider heterogeneity across households and specificities of the mortgage market in Korea, particularly the large share of floating rate and bullet payment debt.
  - Apply macroprudential policies to banks including internet-only banks and other depository institutions heavily exposed to households.

### Summary of cross-sector risks and policy priorities
- Corporate sector:
  - Around 28 percent of corporate debt is at-risk, with SMEs bearing a disproportionate share.
  - Non-SME stress tests indicate expected and unexpected credit losses for large firms are manageable (cumulative expected credit loss after three years around 1.3 percent of GDP; unexpected losses 2.1 percent of GDP), but SME-linked bank losses could be around 2 percent of GDP.
  - Policy priority: close monitoring and targeted measures for SME balance sheet vulnerabilities; contingency planning for corporate sector sensitivity to interest rate hikes and profit shocks.
- Household sector:
  - Household leverage is high by OECD standards and rising; household debt-at-risk equals around 14 percent of GDP and is concentrated with some exposure among retiree households.
  - Policy priority: measures to strengthen household balance sheet resilience, monitor loan structures with floating rates and bullet payments, and address demographic-driven vulnerabilities.
- System-wide assessment:
  - Combined corporate and household stress losses would be broadly manageable for the financial system to absorb, though bank balance sheets face risks given co-risk between large corporate solvency and SME performance and concentration of household vulnerabilities in real estate and floating-rate debt.

*Source: IMF staff note — EXECUTIVE SUMMARY AND RECOMMENDATIONS (1korea2020005).*

### EXECUTIVE SUMMARY AND RECOMMENDATIONS ___________________________________________ 5

### 1korea2020005 - EXECUTIVE SUMMARY AND RECOMMENDATIONS

### Executive summary and key recommendations
- Non-financial private sector debt remains elevated in Korea and requires close monitoring.
- Aggregate findings:
  - Non-financial corporate leverage has remained stable since 2013 at around 100 percent of GDP.
  - Around 28 percent of total non-financial corporate debt (around 28 percent of GDP) is registered “at-risk”.
  - Non-SMEs hold around 60 percent of total core corporate debt; SME debt is around 38 percent of GDP.
  - Over half of non-financial corporate debt-at-risk resides with SMEs.
  - Household leverage as share of GDP in Korea is among the highest in the OECD and continues to grow.
  - Around 14 percent of GDP in household debt is classified as debt-at-risk; this is equivalent to around one-third of Korea’s total stock of non-financial debt-at-risk.
  - Around one-quarter of household debt-at-risk is held by retiree households.
- Stress-test and risk aggregation conclusions:
  - Under a downside macro-financial stress test scenario, non-SME credit losses would total a touch over 2 percent of GDP.
  - Bank stress tests indicate maximum cumulative bank losses from distressed SME loans in a stress scenario would total around 2 percent of GDP.
  - Household credit losses for the banking system under adverse scenarios will remain manageable at around 2.5 percent of GDP.
- Stylized policy implications (summarized from analysis):
  - Close monitoring of SME balance sheet vulnerability given concentration of debt-at-risk and bank exposure.
  - Prepare for corporate sector sensitivity to interest rate hikes combined with profit shocks.
  - Monitor household exposure to floating-rate and bullet-payment loan structures and demographic shifts increasing retiree-held debt.

*Author: Sohrab Rafiq (IMF), member of the FSAP 2019 team.*

### Non-financial corporate balance sheets — Introduction and scope
- Objective:
  - Document evolution of Korean non-financial corporate debt since the Global Financial Crisis, analyze riskiness and allocation quality, uncover heterogeneity in balance sheet vulnerabilities, and assess implications for debt sustainability and financial stability.
- Empirical approach:
  - Firm-level panel regressions on a large sample of large, medium and small-cap Korean firms to assess:
    - Determinants of debt-at-risk (including macro-financial conditions).
    - Korea-specific factors affecting capital structure.
    - Empirical models for macro-financial stress test scenarios and characterization of financial stability risks.

### Non-financial corporate balance sheets — Stylized facts and structure
- Leverage and composition:
  - Consolidated debt of non-financial corporations has remained relatively stable at around 100 percent of GDP since 2013.
  - Non-SMEs account for around 60 percent of core corporate debt; SMEs account for around 38 percent of GDP and are almost entirely composed of bank loans.
  - The majority (80 percent) of non-SME debt is concentrated in less than 50, mainly conglomerate-affiliated, firms.
  - The average debt-to-asset ratio: SME firms around 60 percent; non-SMEs around 30 percent.
  - Around 80 percent of the stock of corporate debt is linked to floating rates.
- Market vs bank financing:
  - Non-SME financing is market based (rise in bond financing, much of which is investment grade).
  - SME financing is bank based; banks’ exposure to SMEs is around 32 percent of GDP versus banks’ exposure to non-SMEs around 6 percent of GDP.
- Foreign-currency exposure:
  - Share of FX corporate bank lending is around 4 percent of total non-financial corporate lending.
  - Market-based FX corporate debt is around 11 percent of total market-based corporate debt; much of FX debt is hedged naturally or through swaps.
- Firm performance and heterogeneity:
  - Since 2014 conglomerate-affiliated firms saw rising profit margins and ROAs; non-conglomerate firms experienced weakening profitability.
  - The number of non-SME non-conglomerate firms reporting negative ROA and/or two consecutive years of negative operating income growth has increased.
  - Asset turnover and sales-to-asset ratios have fallen, indicating limited improvement in firm efficiency.
  - Aggregate corporate liquidity buffers (current ratio and solvency ratio) remained stable, but the share of non-SME firms with current ratio below 1 has risen markedly since 2011.

### Empirical determinants of corporate debt-at-risk and profitability
- Definition:
  - Debt-at-risk is defined as debt of firms with an interest coverage ratio (ICR) below 1.
- Main empirical findings:
  - Around 28 percent of non-financial corporate debt is at risk.
  - Over 50 percent of corporate debt-at-risk is held by SMEs; around half of total SME debt is estimated to be at-risk.
  - Non-SME debt-at-risk has fallen partially due to restructuring in the ship building industry.
  - The low-for-long interest rate environment has reduced defaults among weaker firms but may have supported inefficient allocation of corporate debt.
  - Macro-financial conditions affect average firm profitability and the likelihood of firms belonging to the left tail (debt-at-risk) after controlling for firm characteristics.
  - Empirical regressions find firms that are more indebted, less profitable, smaller, and have lower turnover are more likely to have debt-at-risk.

### Corporate liquidity, FX, and vulnerability to shocks
- Liquidity and cash buffers:
  - Conglomerate-affiliated firm leverage has been driven by bond issuances and mitigated by increased cash holdings.
  - Falling interest rates have helped contain debt service ratios across corporates.
- FX risk:
  - Corporate FX debt has been falling and remains low; natural FX hedges and swaps mitigate FX shocks.
- Vulnerability to interest rate and profit shocks:
  - Static stress tests show corporate balance sheets are vulnerable to a sudden hike in interest rates.
  - Combined interest rate and profit shocks could significantly increase corporate debt-at-risk.
  - Exchange rate shocks appear manageable given low FX corporate debt and natural FX hedges.

### Stress testing and loss estimates
- Non-SME stress scenario and losses:
  - A stress scenario based on Korea’s typical historical crisis episodes suggests default risks for non-SMEs would rise but remain lower than during the Asian and Global Financial Crises.
  - Expected and unexpected credit losses for non-SMEs in the scenario are around 2 percent of GDP (a touch over 2 percent of GDP reported as non-SME credit losses).
- Bank losses from SME distress:
  - Maximum cumulative bank losses from distressed SME loans in a stress scenario would total around 2 percent of GDP.
- Household sector stress summary (linked to corporate risks):
  - Household debt-at-risk around 14 percent of GDP; adverse income shocks would increase household debt-at-risk by around 40 percent.
  - Retiree households would be most affected by an interest rate shock due to greater use of floating rate loans, higher leverage and less stable income flows.
  - Risk of household balance sheet insolvencies from adverse house price shocks rises with age; younger households are more at risk from adverse income shocks.
  - Combined household credit losses for the banking system under adverse scenarios are estimated to be around 2.5 percent of GDP.
- System-wide assessment:
  - Together, estimated losses from corporate and household stress scenarios would be broadly manageable for the financial system to absorb, though bank balance sheets face risks given co-risk between large corporate solvency and SME performance.

### Summary conclusions
- Corporate sector:
  - Since 2013 non-financial corporate debt has been stable at around 100 percent of GDP, with significant heterogeneity: conglomerate-affiliated firms maintaining leverage and smaller/non-conglomerate firms deleveraging.
  - Around 28 percent of corporate debt is at-risk, with SMEs bearing a disproportionate share of this risk.
  - Low interest rates have masked vulnerabilities; rapid increases in rates or profit shocks could materially raise debt-at-risk and credit losses.
- Household sector:
  - Household leverage is high by OECD standards and rising; structural features (floating-rate exposure, bullet payments, concentration in real estate, and aging demographics) increase vulnerability.
  - Household debt-at-risk equals around 14 percent of GDP and is concentrated with some exposure among retiree households.
- Policy priorities implied by analysis:
  - Close monitoring and targeted measures for SME balance sheet vulnerabilities given significant bank exposure.
  - Contingency planning for corporate sector sensitivity to interest rate hikes and profit shocks.
  - Measures to strengthen household balance sheet resilience, monitor loan structures with floating rates and bullet payments, and address demographic-driven vulnerabilities.

*Source: IMF staff note — EXECUTIVE SUMMARY AND RECOMMENDATIONS (1korea2020005).*

### 13.     For non-SMEs the low-for-long interest rate environment has been conducive for

### 13.     For non-SMEs the low-for-long interest rate environment has been conducive for

### Key findings on corporate debt-at-risk and interest rates
- Low-for-long interest rates have helped contain corporate balance sheet vulnerabilities for non-SME firms.
- Static simulations show that in a hypothetical macroeconomic scenario where financial conditions tighten, characterized by a sharp rise in the interest rate, the aggregate debt at risk of non-SME firms would rise by 3 percent of total debt.
- A corporate profitability shock coupled with a rise in interest rates would have an even larger impact on the proportion of non-SME debt-at-risk.
- A sizeable aggregate amount of corporate debt-at-risk would impact the cost of risk in capital markets and the banking system.

### Stress scenarios applied to corporate balance sheets (Box 1)
- Scenario 1: Interest rate shock
  - Apply a severe 300 basis point shock to the baseline interest rate on debt as defined by interest expense in year t relative to last year’s debt.
  - Shock representation: interest_expense_ishock,t = i_rate,t + 3 100 * debt_t−1
- Scenario 2: Interest and profit shock
  - Combined shock: 300 basis point interest increase and a 35 percent decline in operating income (EBIT).
  - EBIT_ishock,t = EBIT_t * (1 − 0.35)
  - ICR_profitshock,t = EBIT_ishock,t / interest_expense_ishock,t
- Scenario 3: Interest, profit and KRW-US dollar exchange rate shock
  - Simultaneous: 300 basis point increase in interest expense, 35 percent decline in EBIT, and exchange rate depreciation of 20 percent against the dollar.
  - Shock includes adjustment term: Share of External Debt x Borrowing Cost x Total Debt x [(Share of USD Debt x Nominal Exch.Rate Depreciation vs USD)]

### Corporate liquidity, solvency, and empirical coverage
- Aggregate short-term liquidity buffer indicators remain above 1 for conglomerates and non-conglomerates, but the number of non-SME firms at short-term liquidity risk has risen since 2010.
- Firm solvency ratios have remained flat since 2013 while the aggregate cash-to-short debt ratio has risen.
- Sample and data coverage:
  - Sample covers 2749 firms from 2008 to 2018 (for probit/profitability analysis).
  - Separate stress-testing sample covers 2748 firms from 1992 to 2018 for the bottom-up non-SME stress test.
  - The Orbis database sample captures around 60 percent of total core corporate debt stock (almost the entire total stock of non-SME corporate debt).

### Empirical determinants of debt-at-risk (sections C, Box 3, Box 4)
- Model objective: explain the tail of the distribution of firm-level debt servicing capacity and predicted debt-at-risk under baseline macro-financial scenarios.
- Key firm-level determinants:
  - Higher indebtedness, lower profitability, smaller firm size, and lower turnover increase the likelihood of debt servicing difficulties.
  - Profitability is negatively associated with capital structures relying more on debt, and positively associated with size, turnover, and the share of fixed assets in total assets.
- Macro-financial influences:
  - Tighter financial conditions directly reduce firms’ servicing capacity and profitability.
  - A rise in growth-at-risk affects debt servicing capacity directly and via profitability.

### Marginal effects and regression evidence
- Marginal analysis: a lower ROA significantly increases the probability of debt-at-risk more than a low liquidity ratio (current ratio).
- Table 1 (Corporate Debt-at-Risk Probit Regressions, dependent ICR<1) — selected coefficient signs and significance:
  - ROA: -0.31*** (column 1); -0.30*** (column 2); -0.03*** (column 3); -0.02** (column 4)
  - Debt-to-assets: 0.51*** (col 1); 0.50*** (col 2); 0.53*** (col 3); 0.61*** (col 4)
  - Current ratio: -0.04* (column with current ratio)
  - Turnover to assets: -0.39** (column with turnover)
  - Total assets: -0.38*** (column with total assets)
  - FCI: 0.02** (appears across columns)
  - Pseudo-R2: 0.31 (cols 1–2), 0.32 (col 3), 0.38 (col 4)
  - Note: *** (**) (*) denotes 1 (5) (10) percent significance level.
- Table 2 (Corporate Profitability Regressions, dependent ROA) — selected coefficients:
  - Debt-to-assets: -2.86*** (col 1); -2.62*** (col 2); -2.85*** (col 3); -2.01*** (col 4)
  - Operating revenue: 1.84*** (col 1); 1.90*** (col 2); 1.84*** (col 3); 3.35*** (col 4)
  - ROA (lag): 0.18** (cols 1–3); 0.03* (col 4)
  - Total assets: 1.30** (cols 1–3); 2.53*** (col 4)
  - Profit margin: 0.07** (cols 1–3); 0.03** (col 4)
  - FCI: 0.72*** (col 1); 0.59*** (col 3); 0.55*** (col 4)
  - Growth-at-risk: -0.21** (col 2); -0.07* (col 3); 0.12** (col 4)
  - Observations: 20540; Pseudo-R2 range: 0.20–0.22 (cols 1–3), 0.21 (col 4)

### Bottom-up dynamic non-SME corporate stress testing (Box 5 and sections 18–20)
- Purpose: project PDs for non-SME firms under macroeconomic scenarios over a three-year horizon using micro balance sheet data and economy-wide and firm-specific risk factors.
- Stress scenario design: motivated by structural and external considerations, including trade protectionism, a material drop in China’s economic activity, a worldwide equity sell-off, significant capital outflows, KRW depreciation, supply-chain disruptions, higher unemployment, and depressed confidence reducing consumption and investment.
- Stress-test procedure (four-step conceptual outline):
  1. Scenario specification: decline in Korean GDP growth, tightening domestic financial conditions (rise in short and long-term interest rates), asset price declines (real house price adjustment), temporary fall in China growth, global financial tightening.
  2. Forecast common and firm-specific risk factors:
     - Common risk factors include return of domestic stock market index, short-term domestic interest rate, distance-to-default, market misvaluation, idiosyncratic volatility.
     - Firm risk factors include liquidity (cash + short-term investments/total assets), profitability (net income/total assets), size (market capitalization relative to median), idiosyncratic volatility.
     - Liquidity defined as cash + short-term investments to total assets; profitability as net income to total assets.
  3. Project firm PDs using forward intensity model with firm-specific and common risk factor evolution.
  4. Use projected PDs in a credit portfolio module to calculate expected credit loss distribution for the corporate portfolio.
- Modeling notes:
  - Distance-to-default uses a volatility-adjusted leverage measure based on Merton (1974) and Crosbie and Bohn (2000), correcting for higher leverage in financial firms (Duan, Sun, and Wang, 2012).
  - Mixed-frequency regressions forecast economy-wide risk factor changes as functions of their lagged values and lagged exogenous scenario variables.
  - Firm-specific variables modeled with a two-stage regression forecasting industry-average values and firm-level deviations.

### Policy-relevant implications and recommendations (from Box 2 and analysis)
- Persistent low interest rates have been more beneficial to larger firms, contributing to greater market concentration and divergence in firm performance.
- Evidence suggests misallocation of borrowing across non-financial corporates: larger chaebol-affiliated firms maintained or increased leverage and saw rising performance, while non-chaebol and smaller firms deleveraged and experienced declining ROA.
- Risks from SME weakness:
  - A large share of SME debt is estimated at-risk, and over 40 percent of SME debt is considered at-risk (figure/textual emphasis).
  - Smaller firms are a significant source of employment in Korea; further small-firm balance sheet weakness would pose indirect risks to household balance sheets.
- Policy recommendation: ensure that credit market frictions to viable SME firms remain contained.

_Italic: Source — IMF staff analysis from the Republic of Korea chapter (excerpts provided)._

### Box 5. Bottom Up Korean Non-Financial Corporate Stress Testing (Concluded)

### Box 5. Bottom Up Korean Non-Financial Corporate Stress Testing (Concluded)

### Methodology: firm-level projection and credit loss modelling
- Stage: modeling distance-to-industry-average
  - Distance defined as d(Y_i,j,t^k, Ȳ_i,j,t) = Y_i,j,t^k − Ȳ_i,j,t, where for firm k in sector i the j-th firm-specific factor is Y_i,j,t^k and the industry average is Ȳ_i,j,t.
- Project corporate solvency
  - One-year ahead projected PDs for individual Korean firms are obtained using a forward intensity model.
  - PDs are projected using a variation of Duan, Sun, and Wang (2012) and Duffie, Saita and Wan (2007) (DSW) to accommodate competing risks of default and exit for other reasons (mergers and acquisitions).
- Expected corporate credit losses
  - One-year projected PDs feed a credit portfolio module based on Vasicek’s (1987, 2002) single factor credit risk portfolio model.
  - Default criterion: obligor asset value A_i follows standard normal; default if and only if A_i ≤ d_i ≡ N^−1(PD_i), where N^−1 is the inverse standard normal.
  - One-factor asset model: A_i = ρS + √(1−ρ^2) ε_i, with ε_i and S uncorrelated standard normal variables and a common ρ for all loans.
  - Loss distribution estimated by simulation: draw S, draw ε_i for each loan, compute A_i, determine defaults, sum losses for each simulation realization.

### Historical benchmarks for corporate default risk
- Asian crisis:
  - 12 (24) month PD registered 623 basis points (1200bps) → implied 12 (24) month PD of 6.2 percent (12 percent).
  - Interpreted as composite of individual firm PDs; implies half of corporate obligors had PD greater than 6.2 percent (12 percent) over next 12 (24) months.
  - Equivalent implied credit rating: CCC.
- Global Financial Crisis (GFC):
  - 12-month (24 month) PD registered 83 basis points → implied credit rating of BB+ (one notch below investment grade).

### Baseline versus hypothetical stress scenario: corporate default risk
- Under the baseline scenario:
  - Corporate default risks remain contained.
  - Mid- and small-sized firms have steadily deleveraged.
  - Proportion of debt held by weaker corporates has fallen since 2013.
  - Corporate cash balances have risen considerably.
- Under the hypothetical adverse stress scenario:
  - Tightening financial conditions and external slowdown reduce corporate distance-to-default (default barrier defined as face value of short-term liabilities plus half of face value of long-term liabilities).
  - 12-month implied PD rises to around 50 basis points.
    - This is around one-quarter of the maximum registered during the Asian crisis and around 60 percent of that registered during the GFC.

### Estimated credit losses from stress scenario
- Potential losses from corporate defaults in stress scenario:
  - Cumulative expected credit loss after three years: around 1.3 percent of GDP.
  - Corresponding unexpected credit losses: 2.1 percent of GDP.
- Drivers of low non-SME corporate credit losses in the stress scenario:
  - (i) Very large share of debt being market-based and investment grade.
  - (ii) Over 90 percent of non-SME corporate debt held by less than 100 firms (mainly conglomerate-affiliated) which since 2013 have seen resilient ROAs, ICRs and liquidity ratios, unlike smaller corporates.
  - (iii) These firms have relatively low leverage ratios (debt-to-asset-ratios) and carry a large amount of cash and equity on their balance sheets.

### SMEs, banks, and data gaps
- Limits due to absence of micro balance sheet data on SMEs:
  - Korean SMEs are tightly integrated in supply chains of larger corporates; combined with balance sheet vulnerabilities, SMEs likely suffer harder in stress.
  - SME debt is predominantly bank-based; bank balance sheets are likely at significant risk.
  - Estimates from bank stress tests suggest maximum cumulative bank losses from distressed SME loans in a stress scenario would total around 2 percent of GDP.
- Sectoral exposure and systemic links:
  - Non-SME firms remain a systemic source of risk for banks due to interconnectedness with SMEs → strong correlation between bank and non-SME firm default risks.

### Summary and conclusions (corporate sector)
- Corporate leverage and distribution:
  - Corporate leverage in Korea is slightly elevated compared to peer countries and relatively spread between economic sectors, but more elevated in small-medium size enterprises.
  - Mid- and small-sized firms have steadily deleveraged; leverage remains high for SME firms.
- Rising vulnerabilities in SMEs pose risks to bank balance sheets:
  - Growing disparity in performance between large and small corporates.
  - Rising number of firms reporting a negative ROA or a liquidity coverage rate below 1.
  - Profit margins have fallen for weakest firms; interest coverage ratios have declined for medium and smaller firms.
  - Low-for-long interest rates have helped contain corporate balance sheet vulnerabilities.
- Debt-at-risk and exposures:
  - Around one-quarter (28 percent of GDP) of corporate debt is considered at risk.
  - Around 50 percent of corporate debt-at-risk is held by SMEs, all of which is owed to banks and linked to floating rates.
  - Around 70 percent of banks’ corporate loans are to SMEs, totaling around 20 percent of GDP and mostly secured against commercial and residential real estate.
  - Despite low immediate exposure, non-SME firms remain systemic sources of risk to banks via interconnectedness.
- If risks materialize:
  - Non-financial corporate debt-at-risk would rise in scenarios of a large unexpected hike in interest rates.
  - Corporate default risks would rise for large (systemically important) corporates but remain below defaults registered during the Asian and Global Financial Crises.
  - Expected and unexpected credit losses from large firms (holding around 60 percent of corporate debt) are expected to be manageable.
  - Limits remain in fully understanding corporate-sector risk to financial stability due to lack of SME balance sheet data.

### Household balance sheets: key findings (from continuation of the chapter)
- Objectives of household analysis:
  - Document evolution of Korean household debt, analyze riskiness and heterogeneity by demographics, and develop household stress testing to characterize risks to financial stability.
- Main findings (selected bullets preserved exactly as in source):
  - Since 2013 the household leverage cycle in Korea has grown quickly. Household debt was 93 percent of GDP as of the second quarter of 2019, one of the highest levels in the OECD.
  - Approximately 53 percent of household debt is secured against real estate, and 70 percent of household wealth is linked to real estate.
  - Around half of household debt is linked to floating interest rates and is structured as a bullet payment.
  - Around 15 percent of the total stock of household debt (14 percent of GDP) is registered at-risk, defined as a household unable to meet debt servicing and consumption needs from disposable income. This equates to around one-third of Korea’s total non-financial debt at-risk.
  - Around 12 percent of household debt is held with households whose balance sheets are upside down, characterized by a debt-to-asset ratio above 100 percent.
  - Empirical model: debt-at-risk is positively correlated with debt-to-income and loan-to-value ratios (the ‘double trigger’ of household default).
  - Household stress tests indicate debt-at-risk and insolvencies would rise to income, interest rate and house price shocks, with heterogeneity across households:
    - Retiree households are equally vulnerable to income and interest rate shocks; younger households are more exposed to income shocks.
    - Older households are more at insolvency risk from a house price shock given higher wealth concentration in real estate.
  - Policy implication: reinforce macroprudential regulations accounting for heterogeneity across households and mortgage-market specificities (large share of floating rate and bullet payment debt).

### Stylized facts on household balance sheets (selected exact figures and observations)
- Since 2013:
  - Household debt rose from around 85 to 100 percent of GDP → corresponds to a debt to disposable income ratio of 180 percent.
  - Around 70 percent of the increase in household debt has been to purchase assets, mainly real estate.
- Bank lending patterns and loan characteristics:
  - Over 70 percent of bank loans have been channeled to those households with the highest credit scores.
  - Around 70 percent of total household bank loans are secured, mostly against real estate.
  - Median household debt-to-income and loan-to-value ratios have remained stable at around 55 percent.
  - Share of mortgages with maturity greater than 10 years: currently standing at around 50 percent.
- Risks highlighted:
  - Approximately 50 percent of household debt is linked to floating interest rates and is structured as a bullet payment.
  - Over 30 percent of household debt is held by households with a debt service ratio above the prudential ceiling of 40 percent (bank average).
  - Around a quarter of total household debt is held by households who are retired or close to retirement.
  - A significant share of household debt is secured against a non-primary residence.

*Source: Box 5, Republic of Korea, INTERNATIONAL MONETARY FUND.*

### Box 6. Household Debt Sustainability and House Prices

### Box 6. Household Debt Sustainability and House Prices

### Methodology and dataset
- Analysis uses a common trends model (a structural VAR framework with cointegration vectors).
- Model variables: household debt (푐푟푡 − 푦푡), national residential house prices (푝푡ℎ), the policy rate (푖푡푝표푙푖푐푦), lending rate (푖푡푙푒푛푑푖푛푔), consumption and investment components of GDP (푦푡).
- Data runs from 1986Q1 to 2018Q3.
- Long-run restrictions follow Juselius and Drehmann (2015) and Juselius, et al. (2016).
- The 3 long-run relationship assumptions are statistically significant at the 10 percent level.

### Long-run equilibrium relationships (estimates)
- Leverage constraint (credit-to-GDP and house prices):
  - 푐푡 − 푦푡 = 2.89 + 1.4 ∙ 푝푡ℎ
- Debt-service constraint (credit-to-GDP and lending rate):
  - 푐푡 − 푦푡 = −0.6 − 10.1 ∙ 푖푡푙푒푛푑푖푛푔
  - Interpretation: A 1 percentage point reduction in the average lending rate allows borrowers to service an additional 10.1 percentage points of debt for the same income in the long run.
- Loan markup (policy rates and effective household lending rates):
  - 푖푡푝표푙푖푐푦 = 1.3 − 2.1 ∙ 푖푡푙푒푛푑푖푛푔
  - Finding: There has been a constant mark-up in the long run so that the average lending rate increases by more than policy rates.

### Key findings on drivers of household debt and leverage
- Higher asset prices support an elevated level of household debt: long-run coefficients show higher asset prices support higher credit-to-GDP ratios.
- Since 2013 the leverage gap has risen steeply while the debt service gap has remained contained (consistent with the low interest rate environment).
- Estimates suggest house prices have been the biggest factor explaining variation in household leverage; debt service burden has been less important, consistent with the 25-year persistent fall in lending rates in Korea.
- During booms asset prices may run ahead of the credit-to-GDP ratio, making borrowers appear deceptively solid in the boom phase.

### Household balance sheet resilience and demographics
- A household resilience index is constructed as the difference between the net-worth ratio and the debt ratio (index started declining in early 2015).
- The index decline implies households continued to accumulate financial assets but household credit grew at a faster pace.
- Retiree households:
  - Hold around 20 percent of the total stock of Korean household debt.
  - Are the most levered as measured by financial debt-to-asset ratio.
  - Have suffered the largest fall in net worth of all age groups since 2010.
  - Official retirement age in Korea is 55 years old.
- Household behavior: households actively manage balance sheets, increasing (reducing) liquidity during downturns (upturns).
- Demographic projections:
  - Based on current trends over 50 percent of HH debt will be held by HHs aged 50 and above, around 10 percent higher than presently.
  - Ongoing demographic shifts and a shallow social safety net imply an increasing share of household debt will be held by households with relatively more unstable income, raising risks to debt resilience and the probability of larger forced sell-offs during stress.

### Macroprudential responses and lending standards
- Authorities have tightened macroprudential policies to mitigate financial stability risks from household debt:
  - Stricter standards for calculating debt-to-income ratios, eliminating anomalies for second mortgages, and improving income documentation.
  - Incentives for borrowers to take safer mortgages given high share of bullet and floating rate mortgages.
  - Policy measures to encourage fixed interest rate amortizing loans (e.g., easing LTV limits for such mortgages).

### Household debt-at-risk: levels and composition
- Survey data: Korean Household Expenditure Survey ~18,000 households; 20 percent of the sample changes each year.
- As of 2018:
  - Around 15 percent (14 percent of GDP) of the total stock of household debt is ‘at-risk’.
  - The analysis also reports household debt-at-risk equates to around 14.3 percent of GDP.
- Composition of household debt-at-risk:
  - Around 85 percent is tied to a household with a debt service ratio above the recently introduced bank average prudential ceiling of 40 percent.
  - 45 percent with households aged 50 and above, more likely to have bullet payment structured loans.
  - Many such debts are secured on a second property and/or include unsecured credit for living and property expenses.
  - Over 50 percent of HH debt-at-risk is due to unsecured borrowing for living and property expenses.
  - Around 12 percent (11 percent of GDP) is held with households at solvency risk (debt-to-asset ratio above 100).
    - About half this amount is held with households who have a debt service ratio above 40 percent.
    - 46 percent with households headed by a retiree or near-retiree individual.
    - A quarter have a bullet payment structured loan.
  - The ratio of household debt-to-GDP held by households at liquidity and solvency risk stands at 4 percent.

### Determinants of borrowers-at-risk
- Empirical results (cross-section panel probit) indicate:
  - Probability that a household’s debt is at-risk is determined by loan-to-value (LTV), debt-to-income (DTI), and debt-service ratio (DSR).
  - Higher balance sheet leverage ratios increase the probability of debt being at-risk.
  - The closer DTI and LTV are to 1 (the “double trigger”), the higher the probability of debt-at-risk.
  - The probability rises with higher DSR.
  - Households with larger net assets and higher income have lower probability.
- The model specification:
  - 푃[푃퐷푖푡 = 1] = 훽0 + 훽1 DSR + 훽2 LTV + 훽3 DTI + 훽4 Net Assets + 훽5 income + 휀푖푡
  - 푃퐷푖푡 is a binary variable equal to 1 if the household has a negative financial margin (Box 7 definition).

### Definitions: financial margin and debt-at-risk
- Financial margin (FM):
  - FM = (consumption + debt service payment + monthly rent) / (disposable income + 0.7 ∗ savings)
  - Assumes 70 percent of savings (excluding insurance and pension) can be liquidated without significant cost.
  - A household with FM < 0 is considered borrower-at-risk; if such a household carries debt, that liability is registered as household ‘debt-at-risk’.
- Default probability from FM:
  - 푃퐷푖 = 0 if FM_i < 0; 푃퐷푖 = 1 if FM_i > 1 (per the box definitions).

### Household balance sheet stress testing
- Stress tests conducted jointly with BoK, FSC and FSS using Survey of Household Finances and Living Conditions.
- Three shocks simulated via Monte-Carlo distributions: (i) house prices; (ii) interest rates; and (iii) household income.
  - House price shock tests solvency risk (wrong way up debt-to-asset ratios).
  - Interest rate and income shocks test debt servicing resilience (financial margin and liquidity).
- The intensities of shocks on households are determined by distributions generated via Monte-Carlo simulation.

### Summary of key risks
- A large share of household debt is held by households with a debt service ratio above 40 percent.
- Leverage has risen across age groups (higher debt service and debt-to-income ratios) while net worth has declined.
- A large share of household debt is secured against real estate, concentrating household assets in a relatively illiquid asset class and exposing balance sheets to real estate price corrections.
- Household liabilities include a high share of bullet and floating-rate mortgages, increasing sensitivity to rate rises.
- Weakening corporate sector, particularly SMEs, elevates risks to household income and debt servicing capacity.

*Source: Box 6. Household Debt Sustainability and House Prices, Republic of Korea country report (text provided).*

### 46.      Household stress tests suggest that household debt-at-risk and insolvencies would rise

### 46. Household stress tests suggest that household debt-at-risk and insolvencies would rise

### Stress scenario findings: household debt-at-risk
- Baseline proportion of household debt-at-risk: 13.7 percent of GDP.
- Income shock: A 15 percent decline in income increases the proportion of household debt-at-risk by 6 percent of GDP from 13.7 to 19.6 percent of GDP.
  - Around 60 percent of the increase in household debt-at-risk is from a weakening in the balance sheets of households in their 40s and retirees.
  - These age cohorts have in recent years witnessed greatest income volatility.
- Interest rate shock: A 200-basis point increase in interest rates increases the proportion of household debt registered at-risk from 13.7 to 17.0 percent of GDP.
  - Retiree households are most at risk from a hike in interest rates, having an almost equal impact on their debt-at-risk as an income shock (around 1.6 percent of GDP).
  - Retiree households are much more likely to have a floating rate loan; younger households are much less sensitive to interest rate hikes and are more likely to have fixed rate mortgages.
- Interpretation: The low for long interest rate environment in Korea has been helpful for containing household balance sheet vulnerabilities.

### Stress scenario findings: household insolvency (solvency risk)
- Baseline proportion of household debt held with insolvent households: around 4 percent of GDP.
- Income shock: The proportion of household debt held with insolvent households increases by around 1.2 percent of GDP to 5.3 percent of GDP.
- House price shock: The proportion increases by around 2.5 percent of GDP to 6.6 percent of GDP.
- Age heterogeneity:
  - To a house price shock, retirees are at biggest risk of transitioning from debt-at-risk to insolvency due to more savings in real estate-related assets and higher prevalence of floating rate and/or bullet loans.
  - An interest rate hike has a larger impact on retiree household solvency than income shocks.
  - An income shock is the bigger risk for younger aged household insolvency, having an almost equal impact as a house price shock.

### Implications for the financial system and banks
- Bank stress tests: Household credit losses would amount to around 2.5 percent of GDP in the stress scenarios considered.
- Mortgage insurance: The widespread use of mortgage insurance schemes in Korea should also protect banks’ capital.
- Immediate systemic risk assessment: In a shock scenario immediate risks to the financial system from rising household insolvencies would be manageable.

### Macroprudential policy recommendations
- Strengthen macroprudential policies, including the introduction of a sectoral countercyclical buffer (SCCyB).
  - The SCCyB should cover secured and unsecured household lending and would build resilience and complement existing borrower-based measures.
  - A SCCyB targeting household exposures would allow banks to build up and release capital as risks from the household sector fluctuate over the credit cycle, provide a buffer that could be released should the risks in the stress scenario crystallize, and help mute pro-cyclicality in bank lending.
- Continue and complement borrower-based measures such as limits on loan-to-value, debt-to-income, and debt service-to-income ratios.
  - These measures, especially when used in concert, improve the resilience of household balance sheets but may suffer from diminishing effectiveness.
- Consider heterogeneity across households and specificities of the mortgage market in Korea, particularly the large share of floating rate and bullet payment debt.
- Apply macroprudential policies to banks including internet-only banks and other depository institutions heavily exposed to households.

### Box 9 — Household Balance Sheet Stress Testing: scenario and methodology highlights
- Specified conservative stress scenario with shock variables: income, interest rates, housing prices and stock prices.
- Shock sizes:
  - Income -15 percent
  - House price -16 percent
  - Interest rate +200 basis points
- Behavioral assumptions:
  - Consumption adjusts by 10 percent after an income shock and by 5 percent after an interest rate shock.
  - The model assumes that around 70 percent of household loans are linked to a floating rate.
  - The model assumes that 70 percent of savings (excluding insurance and pension) is liquidated following the shock.
- Interest payment shock formulation: interest payment after shock = initial interest payment + 0.7(financial debt) * interest rate shock to individual household.
- Household debt-at-risk definition:
  - A household is considered at-risk if the financial margin is greater than 1:
    - Financial margin = (consumption after shock + debt service payment after shock + monthly rent) / (disposable income after shock + 0.7 * savings after shock) > 1
- Household solvency risk definition:
  - Household is at solvency risk if financial margin > 1 and Debt-to-Asset > 100 percent.

### Summary and conclusions (selected key points)
- Rising household indebtedness has resulted in growing household balance sheet vulnerabilities; household debt in Korea is among the highest in the OECD.
- Households have continued to accumulate assets, mainly in real estate; approximately 53 percent of household debt is secured against real estate.
- Around 15 percent of the total stock of household debt (14 percent of GDP) is currently considered ‘at-risk’.
- Around 12 percent of household debt is held with households who has a debt-to-asset ratio above 100.
- In a stress scenario where household income declines, household debt-at-risk would increase by around 20 percent, and the share of debt held by households with insolvent balance sheets would increase by 1 percent of GDP.
- Retiree households would be hit most in stress scenarios given propensity for floating rate loans, higher leverage as measured by debt-to-income, and less secure and stable income.
- A materialization of downside risks could result in a balance sheet recession, affecting housing loan demand and bank profitability through direct and indirect exposures.
- Demographic shifts: As society ages, an ever-larger share of household debt will be held with households characterized by relatively more unstable income, likely increasing the proportion of debt held by borrowers-at-risk and the probability of larger forced sell-offs and larger real estate price adjustments.

*Source: 46. Household stress tests suggest that household debt-at-risk and insolvencies would rise (Republic of Korea — IMF).*

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_Source: https://www.imf.org/-/media/files/publications/cr/2020/english/1korea2020005.pdf_