## 1sgpea2019005 ## Source details **Canonical URL:** [1sgpea2019005](https://www.imf.org/-/media/files/publications/cr/2019/1sgpea2019005.pdf) ## Other formats - [Markdown version](/-/media/files/publications/cr/2019/1sgpea2019005.pdf.md) - [Structured JSON version](/-/media/files/publications/cr/2019/1sgpea2019005.pdf.json) --- ### Executive summary — key findings - Singapore is a large financial center with a strong regulatory framework and significant external exposures. - Singapore is a small and very open economy with a high saving rate and a significant foreign asset position. - The financial sector is highly developed and sophisticated, with many foreign branches intermediating funds throughout the region. - Regulations are closely aligned to international standards; since the last FSAP, bank solvency and liquidity improved, and the authorities adopted Basel III capital and liquidity requirements and the new International Financial Reporting Standards. - Financial markets have weathered recent bouts of market volatility but are exposed to external shocks and cyber events. - The financial sector is significantly exposed to regional economic activities, especially in China, and to global financial conditions. - Non-financial corporates have high gross debt; households are exposed to volatility in property prices. - Singapore is becoming a hub for new financial technologies, increasing cyber risk for financial firms. ### Scope, methodology, and scenarios - Technical note contributes to FSAP assessment of financial stability using risk indicators and stress tests across banks, insurers, non-financial corporates, and households. - Scenarios include global financial market turmoil, a major slowdown in China, cyber-attacks, and extreme flooding. - Stress tests simulate contagion within international banking network, domestic banking system, and across financial institutions. - Two severe adverse scenarios used (five-and-a-half year horizon starting 2018Q2; downside events from 2019 onward): - Adverse Scenario 1: large-scale global financial market turmoil; equity and house prices drop by 40-45 percent; short-term rates rise by 250 basis points in first two years. - Adverse Scenario 2: major slowdown in China with regional spillovers; interbank spread rises 250 basis points over the stress horizon; large unemployment rise. - Baseline and scenario GDP and macro paths (selected figures for 2019–2023): - Real GDP growth baseline: 2.9, 2.7, 2.7, 2.6, 2.6 (2019–2023). - Adverse 1: 2.9, -3.1, -0.8, 4.3, 5.2, 4.1. - Adverse 2: 2.9, -2.5, -3.3, 0.5, 6.8, 6.4. - Property prices (% change) baseline: 8.0, 0.0, 0.0, 0.0, 0.0 (2019–2023). - Adverse 1 property prices: 8.0, -30.0, -15.0, 5.0, 5.0, 5.0. - Adverse 2 property prices: 8.0, -20.0, -25.0, 0.0, 3.0, 5.0. ### Stress-test and systemic resilience results — summary - Financial system broadly resilient to severe adverse shocks; foreign exchange (U.S. dollar) liquidity is a key vulnerability. - Households resilient overall; small segment of highly-leveraged, low-income, young private residential borrowers could face repayment difficulties. - Corporates have healthy debt servicing capacity and significant cash buffers; corporate debt-at-risk rises significantly under stress but cash and foreign currency revenues provide a buffer. - D-SIBs maintain risk-based capital ratios above regulatory requirements under adverse scenarios, though exposures to property price volatility, legacy transportation loans, and name concentration risk warrant caution. - Liquidity Coverage Ratios (LCRs) and liquidity stress tests show broadly adequate all-currency liquidity for D-SIBs, but: - D-SIBs’ U.S. dollar LCR (asset-weighted) = 48 percent (entity-level); group-level consolidation LCR = 38 percent. - Shortfalls of liquid U.S. dollar assets equivalent to some 20 percent of GDP implied by USD LCR; using group consolidation yields shortfall of 17 percent of GDP. - Cashflow-based stress tests find U.S. dollar shortfalls of 8-11 percent of GDP under short-term scenarios and 11 percent after three months in long-term scenarios. - Direct general insurers’ profitability declined but they remain resilient under stress; direct life insurers’ solvency vulnerable to falling equity and corporate bond prices but capital shortfalls under stress are small and not systemic. - Direct domestic contagion through balance-sheet interlinkages is limited; significant contagion channels are cross-border interbank exposures and financial market volatility. - Financial institutions estimate potential cyber-attack losses and insurance claims would be manageable. ### Major vulnerabilities emphasized - Foreign exchange (U.S. dollar) liquidity for D-SIBs. - Exposure to property price volatility affecting households and bank credit. - High gross corporate debt and legacy loans to transportation sector. - Name concentration risk at some banks. - Increased cyber risk from fintech adoption and ICT linkages. ### Policy recommendations (timing and responsibility; timing codes: I-Immediate (within one year), N-Near term (1–3 years), M-Medium term (3–5 years)) - MAS should strengthen U.S. dollar liquidity among D-SIBs. Time: M. Responsibility: MAS. - Explore alternative approaches to estimate credit-to-GDP gap, recognizing changes in credit cycles. Time: I. Responsibility: MAS. - Enhance data collection to assess domestic financial interlinkages. Time: N. Responsibility: MAS. - Collect total stock of household mortgage debt at the borrower-level over time. Time: M. Responsibility: MAS. - Improve the bank solvency stress testing model by adopting the full aspects of the Singapore Financial Reporting Standards 109. Time: N. Responsibility: MAS. - Revise contractual and behavioral cashflow reporting templates for liquidity stress testing. Time: N. Responsibility: MAS. - Update and expand liquidity guidance for banks issued in 2013. Time: N. Responsibility: MAS. - Collect data on direct life insurers’ liability cashflow projections and on underlying asset allocation of collective investment schemes. Time: N. Responsibility: MAS. - Model insurers’ corporate bond losses using a cashflow projection method. Time: N. Responsibility: MAS. - Develop a cyber network map incorporating financial linkages and ICT connections for cyber risk surveillance. Time: M. Responsibility: MAS. - Explore extending bilateral swap arrangements with other central banks to strengthen external liquidity arrangements. ### Banking sector: size, structure, capitalization, asset quality, and liquidity - Banks’ assets ≈ 600 percent of GDP. - Asset management firms AUM = 701 percent of GDP (2017); AUM grew 15 percent per year to reach 701 percent of GDP in 2017; 78 percent of AUM sourced from abroad; 50 percent of AUM invested outside Singapore in Asia-Pacific. - Seven banking groups designated D-SIBs (April 2015): three local (DBS, OCBC, UOB) and four foreign (Citibank, Maybank, Standard Chartered, HSBC). - Local D-SIBs account for about 60 percent of domestic loans to private sectors and over 80 percent of mortgage loans. - Banking soundness metrics (2018): - Risk-weighted capitalization = 16.9 percent (provides a buffer of 7 percentage points). - D-SIBs’ leverage ratio = 7.2 percent (vs. 6.0 percent global G-SIB average). - NPL ratios: D-SIBs = 1.9 percent; Non-DSIBs = 2.1 percent. - Liquidity and prudential metrics: - Narrowly-defined liquid assets = 12 percent of assets and 15 percent of short-term liabilities. - Loans ≈ 99 percent of deposits. - Loans growing faster than deposits, driven by foreign currency activity. - D-SIBs’ asset-weighted LCRs: 132 percent (Singapore dollars) and 287 percent (other currencies as reported). - D-SIBs’ NSFR = 110 percent (10-percentage point buffer over 100 percent requirement). - Foreign currency and residency composition of D-SIB liabilities: - 61 percent denominated in foreign currency. - 71 percent of liabilities are non-resident. - 37 percent denominated in U.S. dollars; 27 percent in other foreign currencies. - 73 percent of D-SIB liabilities come from deposits; 20 percent of deposit liabilities are deposits of non-financial firms. ### Foreign-exchange (FX) swap market and USD liquidity specifics - Singapore is third-largest foreign currency market (BIS, 2016). - Daily average turnover in OTC FX instruments = US$517 billion; daily turnover October 2018 = US$508 billion. - Turnover by instrument: Spot US$122 billion; Outright forwards US$105 billion; FX swaps US$248 billion; Currency swaps US$6 billion; FX options US$37 billion. - FX swap activity concentrated in FX swaps (maturities < one year); about three-quarters of FX activity in forwards and swaps; 88 percent of trading with counterparties outside Singapore. - D-SIBs reduced reliance on FX swap market for funding normal foreign currency lending since last FSAP; D-SIBs’ USD LCR = 48 percent (asset-weighted entity-level), implying USD liquid asset shortfall ~20 percent of GDP under adverse assumptions; group consolidation yields USD LCR = 38 percent and shortfall ~17 percent of GDP. - Quantified USD liquidity shortfalls (Table 4 summary): - USD LCR (percent, asset-weighted entity): All D-SIBs 48; Domestic D-SIBs 40; Foreign D-SIBs 74. - HQLA needed for 100% LCR (SGD millions): All D-SIBs 88,329; Domestic 40,093; Foreign 48,236. - HQLA needed (percent of GDP): All D-SIBs 20; Domestic 9; Foreign 11. - HQLA needed to meet same requirements as all-currency LCR (SGD millions, assuming 100% for domestic banks and 50% for foreign banks): total 53,203 (Domestic 40,093; Foreign 13,110) = total 12 percent of GDP (Domestic 9; Foreign 3). ### Bank solvency stress tests — key quantitative outcomes - System starting NPL ratio = 1.6 percent. - Under Adverse Scenario 1 and 2, system-wide NPL ratios increase 4 and 4.4 times starting level in 2020 and 2021 respectively. - Local D-SIBs’ point-in-time PD: - Starting: 0.8 percent (2018). - Peaks: 3.0 percent in 2020 under Adverse Scenario 1; 3.3 percent in 2021 under Adverse Scenario 2. - Local D-SIBs’ LGDs: - 27 percent in 2018; rise to 40 percent in 2020–2021 under adverse scenarios. - Credit and market losses: - Baseline annual loan loss ≈ 0.3 percent of system-wide RWAs (2019-2021). - Accumulated credit losses: 2.6 percent of RWAs (Adverse Scenario 1, first two years); 4.1 percent of RWAs (Adverse Scenario 2, first three years). - Market revaluation losses on debt securities: S$6 billion under Adverse 1 (0.7 percent of RWAs); S$13 billion under Adverse 2 (1.4 percent of RWAs). - Equity price shock equivalent ≈ 0.2 percent of RWAs. - Expected loss on interest income under Adverse 2 = 0.9 percent of RWAs (2019-21). - Top-line stress-test results: - Baseline system CAR and CET1 = 17.0 and 14.1 percent end-2018; would increase to 18.3 and 15.5 percent by 2021 under baseline. - Adverse Scenario 1: D-SIBs’ total CAR declines to 11.2 percent in 2020; recovers to 17 percent as markets rebound. - Adverse Scenario 2: D-SIBs’ total and CET1 ratios decline to 10.9 percent and 8.6 percent in 2021; no D-SIB fails to meet hurdle rates. - Hurdle rates used in adverse scenarios: Total capital ratio 10.0 percent; Tier 1 8.0 percent; CET1 6.5 percent; Leverage 3.0 percent. - Leverage ratio remains >6.0 percent in adverse scenarios. ### Sensitivity and concentration risks - Interest rate shock: 300 basis point increase causes Tier 1 ratios to decline by 2.6 percentage points from market losses; additional credit losses S$4.3 billion; lower net interest income S$1.4 billion; total cost = 3.4 percentage points of Tier 1 ratio. - FX shock: 40 percent depreciation causes market losses S$12 billion and Tier 1 decline of 1.6 percentage points. - House price shock: 50 percent drop increases PDs (from 1.3 percent to 4 percent), aggregate provisions from S$3 billion to S$15 billion, Tier 1 capital loss ~2 percentage points. - Name concentration (largest common borrowers): - Top one largest borrower: 2.5 percent of Tier 1 capital; top 20 largest common exposures = 24.6 percent of Tier 1 capital and 2.1 percent of total assets. - Banks withstand simultaneous defaults up to top-10 largest borrowers; failure only occurs with simultaneous top-15 defaults. - Fintech disruption scenario: loss of 75 percent of fee/commission income from affected segments reduces CAR by 2.0 percentage points. ### Liquidity stress tests (LCR-based and cashflow-based) - Narrow liquid assets = 12 percent of assets; 15 percent of short-term liabilities. - D-SIBs’ asset-weighted LCRs: SGD LCR = 132 percent; other-currency LCR = 287 percent (as reported). - LCR-based stress-test findings: - Liquid asset buffers over all-currency LCR minima deplete under adverse scenarios. - Domestic banks main source of all-currency shortfalls due to higher all-currency LCR minima. - To restore all-currency LCRs to minima, D-SIBs would need additional liquid assets equivalent to 11 and 14 percent of GDP under retail and wholesale scenarios respectively. - Volatility adjustment: adding a 12 percentage point buffer to starting LCR halves shortfalls. - Cashflow-based results by currency: - All currencies: D-SIBs can generally withstand 1-week and 1-month stress; 1–2 banks lack sufficient liquid assets for 1–3 month stress; shortfalls < 1.3 percent of GDP. - Singapore dollars: six of seven D-SIBs sufficient for 1 week to 3 months; one D-SIB fails causing shortfalls 2.2 and 2.9 percent of GDP (short- and long-term scenarios). - U.S. dollars: 4–7 D-SIBs fail 1-week to 3-month stresses; system-wide shortfalls 8–11 percent of GDP. - Cashflow tests assume internal group transferability of liquidity; cross-subsidization can mask entity-level shortfalls. ### Deposit concentration, NSFR, and funding structure - 73 percent of D-SIB liabilities are deposits; 20 percent of deposits from non-financial firms. - Loans ≈ 99 percent of deposits; foreign-currency loan-to-deposit ratio rose ~12 percentage points over two years to 128 percent (driven by non-D-SIBs); D-SIBs reduced foreign-currency loan-to-deposit to 90 percent. - NSFR = 110 percent (10-point buffer). - Deposit concentration: - Largest single corporate deposit and largest five corporate deposits average 1.9 and 5.8 percent of liabilities respectively. - Corporate deposits more concentrated than retail; Herfindahl indices: foreign D-SIBs 14 basis points, domestic 11 basis points. - Stress simulation of largest depositor withdrawals indicates largest deposits covered by liquid assets; two domestic banks have five largest corporate deposits within Singapore operations exceeding high-quality liquid assets. ### Insurance sector — structure, APNGB, and stress results - Insurance sector assets = 55 percent of GDP. - Life insurance written primarily domestically; general insurance written regionally; reinsurance ≈ 7 percent of insurance assets. - Four largest direct life insurers: AIA, Great Eastern, NTUC Income, Prudential (assets concentrated). - Insurers’ regulatory capital = 247 percent of risk-based regulatory capital requirements (2018). - Participating policies ≈ 80 percent of insurers’ liabilities; APNGB (Allowance for the Provision of Non-Guaranteed Benefits) sizable: - About three-quarters of regulatory capital of four largest life/composite insurers comes from APNGB. - Under current safeguards, only 50 percent of future discretionary benefits can be recognized toward regulatory capital of participating policies. - Under RBC 2 (expected 2020), the 50 percent limit will be removed and new safeguards (minimum CET1 and Tier 1) will be introduced. - Top-down insurer stress-test results: - Two of four largest direct life/composite insurers breached minimum risk-based capital requirements in staff top-down test; capital shortfalls = S$33 billion of asset losses vs. S$36 billion regulatory capital (shortfalls moderate = 1.3 percent of GDP). - Authorities’ bottom-up tests: three of four largest direct life/composite insurers and four of five smaller direct life/composite insurers breached minimums. - Bottom-up general insurer tests: all 15 direct general insurers passed both adverse scenarios. - Flood catastrophe scenario (average depth 600 millimeters): CARs fall by 8 percentage points; no breach of minimum CAR. - Supervisory conclusions: - Insurers’ buffers erode under stress but capital shortfalls modest and not systemic. - MAS recommends modelling corporate bond losses via cashflow projection methods to better capture term-structure effects; current modified-duration approach is conservative but less precise for large yield movements. ### Cyber risk — exposures, survey findings, and recommendations - MAS conducted bottom-up cyber survey covering 18 banks and 17 direct general/composite insurers. - Financial institutions allocate 0.4-2.0 percent of staff and about 4-5 percent of IT budgets to cybersecurity; CSA recommends ≥8 percent of IT budgets to cybersecurity. - Historical incidents: mostly DDoS and website attacks; some ransomware and third-party provider attacks; no significant losses to date and trend improving since 2015. - International evidence: financial firms’ stock prices fall 39–45 basis points on days of cyber-attacks causing data breach or business disruption. - Banks’ survey quantitative estimates (18 banks): potential losses ≈ [50] percent of quarterly profits; CAR and LCR reductions of 0.4 percent and 16 percent respectively (survey-reported impacts). - Insurers’ cyber survey (17 insurers): exposures S$600 million (affirmative cyber) and S$3.4 billion (silent cyber); resulting claims S$1.8 billion; net losses reduce aggregate CAR by 3 and 2 percentage points for affirmative and silent coverages respectively. - Policy recommendations: - Develop a cyber network map integrating financial exposures and ICT interdependencies. - Enhance monitoring of third-party provider vulnerabilities and sector-wide interdependencies. - Continue industry briefings and supervisory scrutiny of cyber preparedness. ### Cross-border interconnectedness and spillovers - Singapore banks heavily cross-border engaged; cross-border lending ≈ 60 percent of total loans with strong East Asia exposure (China, Hong Kong SAR, Japan, Korea, Taiwan PoC, ASEAN). - Cross-border network findings: - Singapore banks tightly connected with Japan, and vulnerable to credit events in U.S., U.K., Hong Kong SAR, China, and Malaysia. - For most other Asian countries, Singapore banks produce more outward than inward spillovers. - Market-data connectedness (Diebold–Yilmaz): Singapore among most connected Asian banking systems with strong inward linkages to U.S. and U.K.; regional connectedness to China and Hong Kong SAR stronger in market data than balance-sheet data. - SGD/USD cross-currency basis is predictive of downside risk to bank equity returns; more negative basis associated with lower left-tail residual returns. - Domestic interbank contagion analysis: - Gross bilateral exposures within domestic financial system = 29 percent of GDP (2018Q2); three quarters between banks. - Interbank contagion limited: a hypothetical solvency event could induce domino failures of up to three additional banking groups and reduce system-wide total capital ratio by up to 0.8 percentage points in conservative case. - Small foreign branches with high interbank asset shares are on average four times more vulnerable than other banks. ### Household sector resilience and mortgage stress tests - Total household assets = 446 percent of GDP (end-2017). - Household debt ≈ 70 percent of GDP since 2013. - Average debt-to-income ratio = 2.1. - Debt outstanding / total assets = 15.6 percent (end-2017). - Liquid financial assets ≈ twice total household debt. - Household NPL ratio < 0.5 percent (2018Q2). - Private residential assets ≈ 44 percent of household assets. - Mortgage stress-test (Adverse Scenario 2) results: - Many borrowers resilient; public housing owners particularly resilient due to lower initial DSRs and MSR. - Private housing: small segment of highly-leveraged borrowers with income < S$7,500 ( < 10 percent of mortgage borrowers in 2018) could face repayment difficulties. - Younger borrowers’ TDSR may exceed 60 percent in stress but are often PMETs with higher income growth potential and smaller loans. - July 2018 mortgage LTV reductions will improve servicing risk profiles. ### Corporate sector resilience - Corporate debt rose from 104 percent of GDP (2010) to over 148 percent in 2015Q3 and was 148 percent of GDP as of 2018Q2. - Median debt-to-equity ratio < 40 percent; median cash-to-debt ratio = 50 percent. - Interest coverage ratio fell from about 12 to below 4 post-GFC; stabilized ≈ 3 since 2016. - 90 percent of domestic corporate debt financed by bank loans; foreign-currency denominated debt = 64 percent (2018Q2). - Corporate PD and debt-at-risk projections: - PDs increase sharply in first two years under adverse scenarios; 75th percentile PDs exceed GFC levels, especially under Adverse Scenario 2. - Debt-at-risk rises significantly under adverse scenarios; corporate cash buffers and foreign revenue provide mitigation. ### Conclusions, caveats, and surveillance priorities - Overall: financial system broadly resilient but with notable USD liquidity vulnerability and sensitivity to external shocks (China slowdown; global tightening). - Caveats: - Results subject to methodology, coverage, data constraints; second-round macrofinancial effects not fully captured. - Stress tests use supervisory and market data at specific points in time; credit risk models estimated from post-2004 data. - Surveillance and data priorities for MAS: - Close data gaps on domestic interlinkages and borrower-level mortgage debt time series. - Collect life insurers’ liability cashflow projections and asset allocation of collective investment schemes. - Adopt full aspects of SFRS 109 in solvency monitoring. - Revise contractual and behavioral cashflow reporting templates to support liquidity stress testing. - Keep option of minimum foreign-currency LCRs under review if supervisory actions do not sufficiently improve USD liquidity. *Source: EXECUTIVE SUMMARY (Technical Note) from the IMF FSAP material.* ### EXECUTIVE SUMMARY______________________________________________________________________________________ 6 ### EXECUTIVE SUMMARY ### Key findings - Singapore is a large financial center with a strong regulatory framework and significant external exposures. - Singapore is a small and very open economy with a high saving rate and a significant foreign asset position. - The financial sector is highly developed and sophisticated, with many foreign branches intermediating funds throughout the region. - Regulations are closely aligned to international standards. Since the last FSAP, bank solvency and liquidity has improved, and the authorities have adopted Basel III capital and liquidity requirements and the new International Financial Reporting Standards. - Financial markets have weathered recent bouts of market volatility but are exposed to external shocks and cyber events. - The financial sector is significantly exposed to regional economic activities, especially in China, and to global financial conditions. - Non-financial corporates have high gross debt, and households are exposed to volatility in property prices. - Singapore is becoming a hub for applications of new technologies in the provision of financial services, increasing financial firms’ exposure to cyber risk. ### Scope and methodology - The technical note contributes to the FSAP’s assessment of the stability and soundness of the financial sector with a comprehensive set of risk analyses. - The work combines an examination of key risk indicators with detailed stress tests simulating the health of banks, insurers, non-financial corporates and households under severe yet plausible (counterfactual) adverse scenarios. - Scenarios include global financial market turmoil, a major slowdown of economic activity in China, cyber-attacks and extreme flooding. - Analyses include simulations of contagion within the international banking network, within the domestic banking system and between different types of financial institutions in the financial system. ### Stress test and systemic resilience results - The stress tests reveal that the financial system is broadly resilient to severe adverse shocks, but foreign exchange liquidity is a key vulnerability. - Households remain resilient under stress, although a small segment of highly-leveraged, low-income and young borrowers in private residential properties could face repayment difficulties. - Corporates have a healthy debt servicing capacity and significant cash buffers. Corporate debt-at-risk rises significantly under stress, but cash and foreign currency revenues provide a buffer. - Domestic systemically important banks (D-SIBs) maintain risk-based capital ratios above regulatory requirements under the adverse scenarios. However, banks’ exposures to property price volatility, legacy loans to transportation sector, and name concentration risk suggest some caution is needed. - Liquidity Coverage Ratios (LCRs) and liquidity stress tests reveal broadly adequate all-currency liquidity of D-SIBs. However: - D-SIBs’ U.S. dollar LCRs (which are a monitoring tool but not a regulatory requirement) and stress tests suggest shortfalls of liquid U.S. dollar assets of up to 20 percent of GDP in adverse scenarios and many D-SIBs do not pass the liquidity stress tests in U.S. dollars. - The adverse scenarios for these risk analyses include assumptions about severe declines in liquidity in the FX swap market. - Direct general insurers’ profitability has declined in a competitive environment, but they remain resilient under stress scenarios. - Direct life insurers’ solvency positions are vulnerable to falling equity and corporate bond prices, but they do not pose a systemic risk because capital shortfalls under stress are small. - Despite its size, complexity, and the level of development, domestic contagion through direct financial interlinkages between banks and non-bank financial institutions, within the interbank market, and common exposures to large borrowers, is limited. - Contagion could occur through cross-border interbank exposures and financial market volatility. Spillovers to and from Singapore largely mirror a strong foreign bank presence headquartered in advanced economies (e.g., Japan, the U.K., and the U.S.) as well as strong regional linkages to China and Hong Kong SAR. - Banks in Singapore have more outward rather than inward spillover effects for its other Asian neighbors. - Financial institutions estimate that the potential losses from cyber-attacks as well as explicit and implicit claims from cyber insurance policy coverage would be manageable. ### Vulnerabilities emphasized - Foreign exchange (U.S. dollar) liquidity is a key vulnerability for D-SIBs. - Exposure to property price volatility affecting households. - High gross corporate debt and legacy loans to the transportation sector. - Name concentration risk at some banks. - Increased cyber risk from fintech adoption and ICT linkages. ### Policy recommendations (timing and responsibility) - MAS should strengthen U.S. dollar liquidity among D-SIBs. Time: M. Responsibility: MAS. - Explore alternative approaches to estimate credit-to-GDP gap, recognizing changes in credit cycles. Time: I. Responsibility: MAS. - Enhance data collection to assess domestic financial interlinkages. Time: N. Responsibility: MAS. - Collect total stock of household mortgage debt at the borrower-level over time. Time: M. Responsibility: MAS. - Improve the bank solvency stress testing model by adopting the full aspects of the Singapore Financial Reporting Standards 109. Time: N. Responsibility: MAS. - Revise the contractual and behavioral cashflow reporting templates to make them useful for liquidity stress testing. Time: N. Responsibility: MAS. - Update and expand on the liquidity guidance for banks issued in 2013. Time: N. Responsibility: MAS. - Collect data on direct life insurers’ liability cashflow projections and on the underlying asset allocation of their collective investment schemes. Time: N. Responsibility: MAS. - Model insurers’ corporate bond losses using a cashflow projection method. Time: N. Responsibility: MAS. - Develop a cyber network map that takes into account both financial linkages and Information and Communications Technology connections and use it for cyber risk surveillance. Time: M. Responsibility: MAS. (Note: “I-Immediate” is within one year; “N-Near term” is within 1–3 years; “M-Medium term” is 3-5 years.) ### Background highlights - Since the last FSAP, Singapore navigated economic and financial cycles well; falls in commodity and house prices affected oil-related sectors and construction, but overall activity held up reasonably well. - There is no sign of a build-up of excessive credit as of now. - After subdued activity, growth accelerated in 2017 due to global expansion and easy financial conditions, but growth has started to moderate. - The government is encouraging adoption of emerging digital technologies to strengthen long-term growth prospects amid population aging, placing Singapore at the forefront in fintech and increasing cyber risk as a growing risk to financial stability. - Singapore’s large financial system has significant cross-border linkages and lending exposures, especially to China and ASEAN countries. A growth slowdown in China or disorderly monetary policy normalization in advanced economies would deeply impact the financial system in Singapore and the region, potentially amplified by domestic vulnerabilities like corporate sector leverage and household sensitivity to property prices. - The FSAP systemic risk analysis assesses resilience by simulating the capacity of the financial system to withstand severe but plausible macrofinancial shocks; adverse scenarios are devices for exploring risks and should not be interpreted as macroeconomic forecasts. *Source: EXECUTIVE SUMMARY (Technical Note) from the IMF FSAP material.* ### 4. The remainder of this technical note is structured as follows. The background section ### 1sgpea2019005 - 4. The remainder of this technical note is structured as follows. The background section ### Background and structure - The technical note describes: the macrofinancial environment, the scope of systemic risk assessment, and macrofinancial scenarios used in the analyses. - Financial stability analyses include: contagion and cyber risk, and resilience analyses of households, non-financial corporations, banks and insurers. - The concluding section provides an overall assessment and recommends policy actions to enhance financial stability. ### Financial system landscape — key features and structure - The financial system is large and dominated by banks. - Banks’ assets equal to about 600 percent of GDP. - Asset management firms have assets under management equal to 701 percent of GDP. - In April 2015, MAS designated seven banking groups as domestic systemically important banks (D-SIBs), including three local banking groups (DBS, OCBC, and UOB) and four foreign banking groups (Citibank, Maybank, Standard Chartered, and HSBC). - Local D-SIBs: - Account for about 60 percent of domestic loans to private sectors. - Account for over 80 percent of mortgage loans in Singapore. - Have significant cross-border lending to China and ASEAN countries (about a quarter of total lending). - Foreign banks: - Account for a half of total financial sector assets. - Except for four foreign D-SIB subsidiaries and one small locally incorporated wholesale bank, most foreign banks operate as branches. - Non-DSIB foreign branches: non-resident loans and liabilities account for over a two-third of total lending and liabilities to private sectors, respectively. - Insurance sector: - Holds assets of 55 percent of GDP. - Life insurance written primarily domestically; general insurance written across the region. - Reinsurance accounts for about 7 percent of insurance sector assets. - Assets concentrated in four largest direct life insurers: AIA, Great Eastern, NTUC Income and Prudential. - Asset management industry: - As of 2017 there are 715 licensed and registered fund managers in Singapore. - Assets under management of financial institutions grew by 15 percent per year to reach 701 percent of GDP in 2017. - Some 78 percent of assets under management are sourced from abroad. - 50 percent of assets under management are invested outside Singapore in the Asia-Pacific region. - Resident collective investment schemes equal 11 percent of GDP. - Asset managers comprise about 23 percent of all securities financing activity in Singapore. - Government-related investors present: GIC, Temasek, and seven global public investors maintain offices in Singapore. ### Public savings, sovereign wealth, and CPF - Temasek Holdings and the GIC manage government savings and have diverse portfolios focusing on foreign assets; they are not tightly connected with the financial system. - Temasek is a major shareholder of two D-SIBs: DBS and SCB. - The Central Provident Fund (CPF): - Invests its assets (80 percent of GDP) mostly in the Special Singapore Government Securities; proceeds are managed by GIC. ### Foreign exchange (FX) swap market - Singapore is the third-largest foreign currency market in the world (BIS, 2016). - Daily average turnover in OTC foreign exchange instruments: US$517 billion. - Daily turnover in October 2018: US$508 billion. - Turnover by instrument: - Spot transactions: US$122 billion. - Outright forwards: US$105 billion. - Foreign exchange swaps: US$248 billion. - Currency swaps: US$6 billion. - Foreign exchange options: US$37 billion. - FX swap activity: - Concentrated in FX swaps, mostly with maturity of less than one year. - About three-quarters of foreign currency exchange activity takes place in the form of derivatives, including forwards and swaps. - Some 88 percent of trading is with counterparties outside Singapore. - Largest participants are European banks, followed by U.S. banks, then Singaporean and Japanese banks. - Commercial banks that play the largest role in the FX swap market are foreign branches. - MAS participates in the FX swap market as part of its money market operations and is currently a significant provider of foreign currency to the swap market. - Since the last FSAP, D-SIBs have significantly reduced reliance on the FX swap market for funding normal foreign currency lending, but domestic banks’ value still seems tied to the price of borrowing U.S. dollars from this market. Box: Banks in foreign currency swap markets (summary of findings) - The long-term cross-currency basis has deviated from zero and become negative for many major currencies against the U.S. dollar since the global financial crisis. - A persistent negative cross-currency basis reflects deviation from covered interest parity and presents profit opportunities for banks participating in the FX swap market. - A main reason for persistent negative currency basis is that bank capital has become costlier since the GFC (Du and others, 2018); negative basis reflects dislocation in FX swap market and capital constraints of participants. - The SGD/USD cross-currency basis appears to be a systematic risk factor that prices equity returns of Singaporean banks; more negative basis is associated with, on average, contemporaneously lower returns for Singaporean banks (statistical significance and economic magnitude are small). ### Current situation — banking sector soundness and vulnerabilities - Capitalization and leverage: - Risk-weighted capitalization: 16.9 percent in 2018. - Provides a buffer of 7 percentage points that can be used in a stress scenario. - D-SIBs’ leverage ratio stands at 7.2 percent, higher than the 6.0 percent average for global systemically important banks. - Profitability and asset quality: - Banks have enjoyed solid profits with stable net interest margins, higher than those in other financial centers, and increasing fee and commission income. - Nonperforming loans (NPLs) are low: - D-SIBs: 1.9 percent of total loans. - Non-DSIBs: 2.1 percent of total loans. - NPLs are adequately provisioned. - Remaining risks: - Legacy exposures to transportation (including oil-related sectors) and manufacturing firms persist. - “Problem loans” (NPLs plus special mention loans and restructured loans) remain relatively high in a few foreign D-SIBs. - Cross-border asset quality has stabilized as NPL ratios for loans to Malaysia and Indonesia have fallen over the past year. - Risk if China growth slows: regional spillovers could be considerable and NPLs could rise. - Loans to the property sector (residential mortgages and building and construction) account for about 30 percent of total loans, exposing banks to property price movements. *Source: IMF staff compilation from the technical note (1sgpea2019005).* ### 14. The banking system has adequate liquidity overall. Narrowly-defined liquid assets are 12 ### 14. The banking system has adequate liquidity overall. Narrowly-defined liquid assets are 12 percent of assets and 15 percent of short-term liabilities. ### Banking system liquidity and prudential metrics - Narrowly-defined liquid assets are 12 percent of assets and 15 percent of short-term liabilities. - Loans are about 99 percent of deposits. - Loans have been growing faster than deposits, mainly due to foreign currency activity. - D-SIBs have asset-weighted LCRs of 132 percent (in Singapore dollars) and 287 percent (in other currencies as reported). - In the LCR, most liquid assets are high quality; LCR requirements are driven by derivatives and margin calls. - D-SIBs’ Net Stable Funding Ratio (NSFR) is 110 percent. - For domestic banks, this NSFR provides a 10-percentage point buffer over the 100 percent requirement. - MAS assesses that the 10-percentage point buffer is sufficient given the low volatility of these ratios. ### Foreign currency liquidity vulnerabilities - The banking system’s loan-to-deposit ratio in foreign currency rose by some 12 percentage points over the past two years, to 128 percent; this rise is driven by non-D-SIBs. - D-SIBs reduced their foreign currency loan-to-deposit ratios to 90 percent since the last FSAP, indicating they do not rely on FX swaps to fund normal foreign currency lending. - D-SIBs’ U.S. dollar LCR is 48 percent (asset-weighted average across D-SIBs at the entity level). - This suggests a shortfall (relative to a 100 percent LCR) of liquid U.S. dollar assets of some 20 percent of GDP in an adverse scenario. - The adverse scenario implicit in the U.S. dollar LCR computation includes assumptions of stress in the FX swap market. - The shortfall is split relatively evenly between domestic and foreign D-SIBs; for foreign D-SIBs it is driven by their branches (rather than subsidiaries). - Using group-level consolidation for LCR compliance yields a lower asset-weighted USD LCR of 38 percent and a slightly lower liquid asset shortfall of 17 percent of GDP. - Banks’ liquidity planning shows reliance on the FX swap market for foreign currency funding during liquidity stress. - Safety nets in U.S. dollars: - Parent banks of large foreign bank branches are largely located in countries with access to U.S. dollar credit facilities. - MAS provides U.S. dollar funding through the FX swap market as part of daily money market operations. - MAS’ foreign exchange reserves are significant at 80 percent of GDP; the primary purpose of these reserves is to implement monetary policy. ### Quantified U.S. dollar liquidity shortfalls at D-SIBs (Table 4 summary) - USD LCR (In percent, asset-weighted average at the entity level): All D-SIBs 48; Domestic D-SIBs 40; Foreign D-SIBs 74. - HQLA needed for 100% LCR (In millions of Singapore dollars): All D-SIBs 88,329; Domestic D-SIBs 40,093; Foreign D-SIBs 48,236. - In percent of GDP: All D-SIBs 20; Domestic D-SIBs 9; Foreign D-SIBs 11. - HQLA needed to meet same requirements as all-currency LCR (In millions of Singapore dollars) assuming 100% for domestic banks and 50% for foreign banks: 53,203 total (Domestic 40,093; Foreign 13,110). - In percent of GDP: total 12; Domestic 9; Foreign 3. - Notes: Table shows additional HQLA needed to meet hypothetical projected one-month USD liquidity requirements. The USD LCR of 48 percent is the entity-level asset-weighted average; group-level consolidation yields 38 percent. ### Funding structure and composition of D-SIB liabilities - Some 73 percent of D-SIBs’ liabilities come from deposits. - Of deposit liabilities, 20 percent are deposits of non-financial firms. - Local D-SIBs rely relatively more on retail deposit funding; foreign D-SIBs rely relatively more on interbank funding, mostly intragroup. - Branches’ reliance on intragroup funding results in loan-to-deposit ratios of 145 percent. - Currency and residency composition: - 61 percent of D-SIBs’ liabilities are in foreign currency. - 71 percent of D-SIBs’ liabilities are non-resident. - 37 percent of D-SIBs’ liabilities are denominated in U.S. dollars. - 27 percent are denominated in other foreign currencies. - “Other” liabilities, including derivatives, make up 12 percent of liabilities. ### Regulatory and supervisory developments for banks - MAS amended regulatory capital and liquidity framework since the last FSAP: - Introduced minimum leverage ratio for locally incorporated banks of 3 percent (Notice 637). - Introduced loan loss provisioning rules (Notice 612, SFRS 109). - Introduced minimum LCR and NSFR requirements for D-SIBs in all currencies and in Singapore dollars (Notice 649 and Notice 652). - Minimum LCR requirement: 100 percent in Singapore dollars; for all currencies, 100 percent for the three domestic banks and 50 percent for foreign banks. - Minimum all-currency NSFR requirement: 100 percent for domestic banks and 50 percent for foreign banks. - MAS monitors LCRs in significant foreign currencies but there is no minimum requirement specifically for foreign currency liquidity for non-D-SIBs. - Other banks remain subject to the minimum liquid asset requirement (MLA). - MAS conducts regular stress tests of bank solvency and liquidity. ### Insurance sector soundness and risks - Insurers have strong buffers over minimum capital requirements: regulatory capital amounted to 247 percent of risk-based regulatory capital requirements in 2018. - Profits have been strong but were weighed down by falling asset prices in 2018. - Competition in direct general insurance has driven down profits recently. - About three-quarters of the regulatory capital of the four largest life/composite insurers comes from undeclared discretionary benefits (APNGB). - Market risks in life insurance have trended up over the last decade: - Life insurers have increased equity allocations over time as participating policies shifted toward terminal bonuses. - Life insurers are notably exposed to corporate bonds, exposing them to widening credit spreads. - Life insurance liabilities are almost as large as the market for Singapore government and statutory board bonds; insurers need corporate bonds to match liability cashflows. - More than 90 percent of rated corporate bonds are investment grade; share of assets in corporate bonds has remained constant, with slight shift toward lower-rated investment grade bonds. ### APNGB (Allowance for the Provision of Non-Guaranteed Benefits) observations - Participating policies represent some 80 percent of insurers’ liabilities; assets of these policies exceed guaranteed liabilities. - APNGB allows recognizing a portion of future discretionary benefits as regulatory capital; about three-quarters of regulatory capital of the four largest life/composite insurers comes from APNGB. - Safeguards in current capital regulations: - Only 50 percent of the value of future discretionary benefits can be recognized toward regulatory capital of participating policies (this is binding in most cases). - Capital of participating policies is ringfenced and cannot offset shortfalls in other insurance policies. - These safeguards were assessed to be in observance of the Insurance Core Principles. - Historical/stress context: - The 50 percent recognition limit implies a hypothetical cut to future bonuses by one-half. - During the GFC insurers needed a cut of discretionary benefits by only 24 percent in 2008. - Bottom-up stress tests show insurers would need to cut discretionary benefits by up to 40 percent in some cases. - Planned changes under RBC 2: - Revised framework will introduce new safeguards against overreliance on APNGB via minimum CET 1 and Tier 1 capital requirements. - The revised framework will remove the 50 percent limit; authorities view this as consistent with the lower risk appetite (1-in-200 years) built into the revised framework. - RBC 2 expected to come into force in 2020. ### Insurance regulatory developments and accounting standards - In 2014, requirements for insurers to adopt formal capital planning and risk assessment processes came into force. - RBC 2 revised risk-based capital regime will come into force in 2020. - Large insurers met by staff are prepared and do not expect material changes to capital positions. - Singaporean accounting standard-setter adopted IFRS 17, to come into force at the same time proposed by the IASB; insurers are evaluating group-wide implications. - Larger insurers in internationally active groups are participating in field-testing of Insurance Capital Standards by IAIS. ### Interconnectedness within Singapore’s financial sector - Singaporean banks have large cross-border exposures amounting to about 60 percent of total loans, with heavy engagement in cross-border lending to East Asia (China, Hong Kong SAR, Japan, Korea, Taiwan PoC, and ASEAN). - Presence of foreign banks is large; some are systemically important to the domestic system. - Intragroup funding has functioned as a stabilizing factor for some foreign branches and subsidiaries during past crises. - Direct domestic interlinkages are limited: - Gross bilateral exposures within the domestic financial system amounted to 29 percent of GDP at 2018Q2. - Three quarters of these exposures are between banks. - Bank liabilities to nonbank financial institutions (NBFIs) are about 4 percent of GDP (1.1 percent of D-SIBs’ liabilities). - Banks’ lending exposures to NBFIs are about 1 percent of GDP (0.3 percent of D-SIBs’ assets). - Excluding intra-group and cross-border interbank exposures, interbank exposures among the 118 banking groups are 0.8 percent of total banking system assets and about 5 percent of GDP. *Source: IMF staff summary of chapter "The banking system has adequate liquidity overall" from 1sgpea2019005.* ### 23. Households have a strong financial position. Total household assets amount to 446 ### 1sgpea2019005 - 23. Households have a strong financial position. Total household assets amount to 446 ### Household sector balance-sheet strength - Total household assets amount to 446 percent of GDP at end-2017. - Household sector’s balance sheet strengthened due to rising financial assets and housing assets amid recent increases in real estate prices, with the growth of housing assets outpacing the growth of household debt. - Household debt has stabilized around 70 percent of GDP since 2013. - Average debt-to-income ratio is 2.1. - Ratio of debt outstanding to total assets stood at 15.6 percent at end-2017. - Liquid financial assets (including currency, deposits, shares, and securities) are almost twice the size of total household debt. - NPL ratio of household loans is less than 0.5 percent as of 2018Q2. ### Sensitivity to property prices and asset composition - About 44 percent of household assets comprise residential property. - MAS (2017) panel data analysis with 17 countries found property prices contributed to about half of the change in household debt-to-GDP ratio after the GFC. - Other drivers of household debt changes include stock prices, interest rates, income, and macroprudential policies. ### Recent house price developments and risks - Private residential property prices experienced a turnaround of +8.3 percent, y-o-y, between 2017Q3 and 2018Q2 after a gradual decline of about 12 percent between 2013Q3 and 2017Q2. - Following tightening of macroprudential measures in July 2018, the pace of house price inflation began to moderate. - An upcoming increase in the supply of new housing units may pose a downside risk to property prices. ### Financial stability implications of household sector - Households allocate funds to deposits, insurance products, equities or debt securities, and borrow from financial institutions to finance consumption and investment. - Household loans, including residential mortgages and loans to professional and private individuals, account for about 30 percent of total bank lending. - Property-related loans account for about 30 percent of total bank lending, exposing banks to risks from property price movements. - Household sector financial vulnerabilities can lead to higher NPLs and deposit withdrawal, which have important implications for banks’ solvency and liquidity and financial stability overall. ### Non-Financial Corporate Sector soundness (summary of linked section) - Corporate debt rose from 104 percent of GDP in 2010 to over 148 percent in 2015Q3 and stood at 148 percent of GDP as of 2018Q2. - Median debt-to-equity ratio fell to below 40 percent in recent years. - Median cash-to-debt ratio is 50 percent. - Interest coverage ratio (ICR, EBIT divided by interest expense) fell from about 12 to below 4 post-GFC, stabilizing around 3 since 2016. - 90 percent of domestic corporate debt is financed by bank loans. - Share of foreign-currency denominated corporate debt was 64 percent as of 2018Q2. - Corporate NPL ratio fell to 2.4 percent in 2018Q3 from a peak of 2.8 percent in 2016Q4. - Debt-at-risk peaked in 2015 and improved thereafter; some weakness remains in construction and TSC sectors among medium-sized corporates. ### Macrofinancial vulnerabilities and systemic risk drivers - Main macrofinancial vulnerability: extensive linkages to the region and the rest of the world (trade and financial channels), with a sharp slowdown in China or disorderly normalization of advanced-economy monetary policies posing large risks. - A sharp fall in house prices and a widening of credit spreads could impair balance sheets of financial institutions and private sectors. - Cyber risk and expansion of fintech increase exposure to cyber-attacks and pose challenges to financial oversight. ### Tail scenarios for financial stability analyses - Two severe but plausible adverse scenarios designed for stress tests; duration is five-and-a-half years starting at reference date 2018Q2, with downside events from 2019 onward. - Scenarios amplify external shocks by existing vulnerabilities (legacy loans to transportation firms, vulnerable foreign currency liquidity, households’ sensitivity to property prices, high corporate leverage). - Adverse Scenario 1: large-scale global financial market turmoil; equity and house prices drop by 40-45 percent; short-term interest rates rise by 250 basis points in the first two years. - Adverse Scenario 2: major slowdown and macrofinancial stress in China with regional spillovers; interbank spread rises 250 basis points over the stress horizon; unemployment rises dramatically with slow recovery. - Scenarios are used in bank and insurer solvency stress tests and corporate sector analyses; insurer solvency tests use the first two years of the five-year adverse scenario. ### Quantitative paths under baseline and adverse scenarios (selected figures from Table 6) - Real GDP growth rate (2019–2023): - Baseline scenario: 2.9, 2.7, 2.7, 2.6, 2.6, 2.6 (years 2019–2023) - Adverse scenario 1: 2.9, -3.1, -0.8, 4.3, 5.2, 4.1 - Adverse scenario 2: 2.9, -2.5, -3.3, 0.5, 6.8, 6.4 - Output gap (percent of potential output, 2019–2023): - Baseline: 0.9, 0.7, 0.6, 0.4, 0.2, 0.0 - Adverse 1: 0.9, -5.0, -8.4, -7.1, -5.0, -3.8 - Adverse 2: 0.9, -4.4, -10.2, -12.3, -8.8, -5.7 - Unemployment rate (2019–2023): - Baseline: 2.9, 2.9, 2.9, 2.9, 2.9, 2.9 - Adverse 1: 2.9, 3.5, 5.8, 6.2, 4.9, 3.9 - Adverse 2: 2.9, 3.2, 5.8, 8.9, 8.8, 6.5 - Interest rate: 3-month SIBOR (2019–2023): - Baseline: 1.8, 2.3, 2.8, 2.6, 2.6, 2.6 - Adverse 1: 1.8, 3.8, 4.3, 3.6, 2.8, 2.3 - Adverse 2: 1.8, 2.8, 3.8, 4.3, 4.3, 4.3 - Property prices (change in percent, 2019–2023): - Baseline: 8.0, 0.0, 0.0, 0.0, 0.0, 0.0 - Adverse 1: 8.0, -30.0, -15.0, 5.0, 5.0, 5.0 - Adverse 2: 8.0, -20.0, -25.0, 0.0, 3.0, 5.0 - Exchange rate (SGD/USD, change in percent, 2019–2023): - Baseline: 2.0, 2.0, 1.0, 0.0, -1.0, 0.0 - Adverse 1: 2.0, 20.0, 5.0, -10.0, -10.0, -5.0 - Adverse 2: 2.0, 10.0, 10.0, 5.0, -10.0, -10.0 - Commodity prices (change in percent, 2019–2023): - Baseline: 31.4, -2.7, -5.9, -4.0, -2.8, -1.9 - Adverse 1: 31.4, -40.0, -5.0, 20.0, 20.0, 5.0 - Adverse 2: 31.4, -20.0, -20.0, -5.0, 20.0, 20.0 - Equity prices (change in percent, 2019–2023): - Baseline: 1.0, 3.0, 2.0, 2.0, 2.0, 2.0 - Adverse 1: 1.0, -35.0, -5.0, 20.0, 15.0, 6.7 - Adverse 2: 1.0, -20.0, -15.0, -5.0, 20.0, 15.0 ### Use of scenarios in financial stability analyses - Stress tests and analyses cover banks, insurers, contagion, liquidity, cyber risks, and private sector soundness. - Bank coverage: seven D-SIB groups (10 banks including branches and subsidiaries), accounting for about 75 percent of non-bank loans to private sectors. - Insurer coverage: big four insurers representing 80 percent of total assets of the sector. - Contagion analyses include domestic interbank exposures, interlinkages within the domestic financial system, and cross-border bank networks. - Private sector analyses include mortgage debt servicing simulations under Adverse Scenario 2 and corporate debt-at-risk and ICR-based vulnerability assessments. - Bank stress tests combine top-down and bottom-up approaches, include sensitivity analyses (e.g., FX risk, house price risk), liquidity stress tests, and concentration risk assessments. - Insurer stress tests combine bottom-up and top-down tests and include catastrophe risk scenarios (extreme flooding); term structures of interest rates are modelled in detail. - Cyber risk analysis involved a bottom-up survey of 18 banks and 17 direct general and composite insurers to assess exposures, transmission channels, and quantitative loss estimates. *Source: IMF staff and Monetary Authority of Singapore (content as provided).* ### 37. Banks in Singapore are most connected, through direct balance sheet exposures, with ### 1sgpea2019005 - 37. Banks in Singapore are most connected, through direct balance sheet exposures, with ### Cross-border interbank exposures — balance sheet analysis - Methodology: Simulates cross-border transmission of credit and funding shocks using Espinosa-Vega and Solé (2010) with BIS Locational Banking Statistics (unconsolidated). - Key findings: - Banks in Singapore, including foreign branches and subsidiaries, are tightly connected with banks in Japan via exposures through Japanese banks in Singapore. - Credit events in banks in the U.S. and the U.K. and regionally in Hong Kong SAR, China and Malaysia would have significant impact on banks in Singapore. - For the rest of countries in Asia, Singapore banks have more outward rather than inward spillover effects. - Benchmark shock assumptions (as noted in figure notes): - Banks absorb 50 percent of loss given defaults of another banking system. - Banks unable to source 50 percent of lost funding leading to fire sale of assets at 50 percent discount. ### Financial-market evidence — equity-return volatility analysis - Methodology: Diebold and Yilmaz (2014) using volatility of bank equity return indices (January 2016–December 2018). - Key findings: - Singapore is one of the most connected banking systems in Asia, together with China and Hong Kong SAR. - Market data show Singaporean banks’ inward connection to the U.S. and the U.K. - Regional connectedness (China and Hong Kong SAR) is stronger when using market data than balance sheet data. - Connectedness with Japan appears much smaller in market-data analysis. - Singaporean banks’ outward spillover is felt mostly by countries in Asia. - Metric: Pair-wise spillover measures the percent of a country's forecast error variance explained by shocks to another country. ### Cross-border spillover from U.S. dollar funding — FX derivative market signals - Empirical approach: Quantile regressions linking SGD/USD five-year cross-currency basis to lower tail of bank equity returns; residual return computed from Fama-French three factor model. - Key findings: - The SGD/USD cross-currency basis is strongly associated with the lower tail of the distribution of bank equity returns, although the magnitude is small. - A more negative five-year SGD/USD cross-currency basis is associated with a lower value of the left tail of residual returns of Singaporean banks (idiosyncratic component). - The basis is predictive about near-term (five days ahead) downside risk to bank equity value. - Dislocations in FX derivative markets are correlated with downside risk to bank value and can forecast larger near-term bank return volatility, especially downside risk. - Caveats: - Causality is not straightforward; dollar funding stress indicator may be influenced by liquidity and other market factors. - Analysis includes Fama-French factors as well as VIX and Libor-OIS spread to alleviate confounding factors. - Bank usage: Singaporean banks generally do not rely on FX derivative markets for structural funding needs but use them to close US$ funding gaps and for funding-cost management. ### Domestic interbank contagion — network stress results - Methodology: Domestic interbank network analysis following Espinosa-Vega and Solé (2010) using gross interbank exposures among all 118 banking groups in Singapore as of 2018Q2. - Shock and parameter assumptions (middle conservative case reported): - Hypothetical solvency event defined as failing to meet total capital adequacy ratio hurdle of 10 percent of RWAs. - LGD ratio for interbank lending (휆) = 0.45; loss of funding ratio (휌) = 0.5; discount rate (훿) = 1 in the conservative charts. - Key findings: - Contagion risks stemming from interbank exposures are limited. - Even in the most conservative case, a hypothetical solvency event of a banking group would induce domino solvency events of up to three more banking groups and reduce system-wide total capital ratio by 0.8 percentage points at most. - Three local banking groups are not vulnerable to the solvency event of foreign banks in Singapore, but their solvency events would make a few small non-DSIB foreign banks face solvency problems. - Twelve small foreign branches have a significant share (more than 10 percent) of assets in the form of interbank lending; four small foreign branches have a significant share (more than 10 percent) of liabilities in the form of interbank borrowing. - On average, these small foreign branches are four times more vulnerable to the event than other banks in Singapore. - Notes on contagion metric: - Index of contagion = percent of total capital losses in the system due to the failure of each bank. - Index of vulnerability = average of capital losses of each bank due to the failure of all other banks. - Analysis assesses direct and indirect contagion: direct credit losses and subsequent fire sales caused by funding losses; cascade effects computed across rounds. ### Household sector resilience — mortgage debt-service simulations - Data and scope: - Account-level information of new housing loans granted in 2018 by the 12 largest mortgage lending banks in Singapore. - Includes loan profile, borrower profile, and property profile. - Stress scenario and shock assumptions: - Adverse scenario 2: interbank interest rate increases by 250 basis points and nominal GDP drops by more than 20 percent cumulatively over the stress horizon. - Associated drop in disposable income = 10 percent (computed via elasticity of household income to nominal GDP using two household income definitions and meta-analysis averaging). - Simulations shock TDSR income and interest rate for each individual loan account and assess adjusted monthly mortgage payment as share of adjusted TDSR income. - Conservative assumptions in simulations: - Loans assumed fully disbursed despite partial disbursements being common. - Income used includes only main borrower’s income; for joint applications (about one-third of mortgage loans) only one borrower’s income is considered. - Rise in SIBOR assumed fully and immediately passed through to actual mortgage interest rate for all loan accounts. - Higher likelihood of default assumed if debt servicing ratio breaches 60 percent. - Data limitations and considerations: - Using newly issued mortgage loans provides a conservative estimate of outstanding debt but can underestimate risks because outstanding loan amounts of existing loans decrease over time. - Mortgage refinancing in 2018 represented about 15 percent of outstanding housing loans; anecdotal evidence suggests almost all housing loan portfolios are replaced within 7 to 8 years. - Results: - A significant proportion of borrowers remains resilient under the severe stress scenario, especially public housing owners due to lower initial debt service-to-income ratios and the 30-percent MSR on top of TDSR. - On the private housing market, a small segment of highly-leveraged households with income below S$7,500 (comprising less than 10 percent of the mortgage borrowers from financial institutions in 2018) could face repayment difficulties. - Some younger borrowers would see their TDSR increase beyond 60 percent; these younger borrowers are likely to be PMETs with higher potential income growth and tend to take up smaller loans (thus posing less systemic risk). - The reduction in mortgage LTVs across the board in July 2018 will further improve mortgage servicing risk profiles. - Behavioral/contextual notes: - The TDSR income excludes employer CPF contributions (which account for 17 percent of income for the majority of working adults) and includes a 30-percent haircut on variable income. - While a threshold of 100 percent would indicate technical default, simulations conservatively assume increased likelihood of default at 60 percent. ### Corporate sector resilience — debt-at-risk sensitivity - Scenarios used to recalculate ICRs and assess debt-at-risk: - Baseline scenario: - Increase in borrowing costs by 1 percentage points. - Currency depreciation of 2.5 percent. - No earnings shocks. - Adverse scenario: - Increase in borrowing costs by 2.5 percentage points. - Currency depreciation of 20 percent (consistent with peak interest and currency impact under bank stress tests adverse scenarios). - Fall of earnings by 20 percent (commensurate with magnitude seen during the GFC). - Assumptions on exposure shares: - Interest shock applies to 45 percent of total debt being rolled over (55 percent are on fixed rates). - Currency shock applies to 65 percent of total debt (referencing the share of FX debt). - Key findings: - Debt-at-risk would rise significantly under the adverse scenarios, and would rise from the current level already in the baseline scenario due to expected tightening of financing conditions. - The adverse scenario’s earnings shock would have a sizable impact on firms’ balance sheets and significantly raise debt-at-risk beyond levels seen in the global financial crisis, in line with projected NPL ratios in bank solvency stress tests, before considering mitigating factors. - Corporates’ significant revenue from foreign sales provides natural hedging mitigating currency shocks. - Corporates’ ample cash reserves can mitigate adverse impacts, even under assumed limits on cash use for debt service. *Source: IMF staff analysis as presented in the supplied chapter content.* ### 49. Corporate probability of defaults ### 49. Corporate probability of defaults ### Corporate PD projections and scenario outcomes - Projections based on the Bottom-up Default Analysis (Duan, Chan-Lau, and NUS CRI team, 2018) under a baseline and two adverse scenarios. - PDs gradually increase under the baseline scenario given expected tightening of financial conditions and economic slowdown. - Under adverse scenarios, default risk rises steeply in the first two years and weighs on banks and insurers. - Stress is more severe and persistent under Adverse Scenario 2, broadly mirroring the pace and severity of assumed stress test scenarios. - PDs among firms under higher distress (75 percentile) are expected to rise beyond the level seen during the GFC, especially in Adverse Scenario 2. ### Bank solvency stress test methodology (high-level) - Top-down solvency stress test follows a passive balance sheet assumption; gross exposures grow with overall credit growth (nominal GDP growth when positive, zero otherwise). - At the start of adverse scenarios, 5 percent of off-balance sheet exposures are drawn. - No new capital issuance allowed during the stress test horizon; dividends allowed subject to MAS payout limits. - Transmission of shocks to NPL ratios, point-in-time PDs, and point-in-time LGDs uses satellite credit risk models across Singapore, China, Malaysia, Indonesia, and Thailand. - Historical NPL ratios used as a proxy for PDs; non-linear effects included via square of real GDP growth. - LGDs for residential mortgages and corporate income producing real estate are scaled up with property price deflation in adverse scenarios (-45 percent in 2020-2021); full pass-through to LGDs assumed. - LGDs also increase by 2.15 percentage points as PDs rise by 1 percentage point (Schmieder and others, 2011). - Market losses measured via changes in interest rates, FX rates, and equity prices; debt security valuation changes via modified duration and yield-curve shifts. - RWAs evolve with balance sheet growth, standardized approach scaling, and Bas el II translation for IRBA exposures with conservative conversions from point-in-time to through-the-cycle PDs (∆PPPTT =0.8*∆PPPPP_TT for retail; ∆PPPTT =0.5*∆PPPPP_TT for other exposures). ### Key baseline and adverse scenario credit-risk projections - System starting NPL ratio: 1.6 percent. - In Adverse Scenario 1 and 2, system-wide NPL ratios increase 4 and 4.4 times as high as the starting level (1.6 percent) in 2020 and 2021, respectively. - Local D-SIBs’ point-in-time PD: - Starting level: 0.8 percent in 2018. - Peaks at 3.0 percent in 2020 under Adverse Scenario 1. - Peaks at 3.3 percent in 2021 under Adverse Scenario 2. - Local D-SIBs’ LGDs: - 27 percent in 2018. - Rise to 40 percent in 2020 and 2021 under Adverse Scenario 1 and Adverse Scenario 2, driven by falling property prices and rising PDs. - Under the baseline scenario, NPL ratios remain flat. ### Credit losses, market losses, and RWAs - Baseline: annual loan loss about 0.3 percent of system-wide RWAs during 2019-2021. - Adverse Scenario 1 and 2: accumulated credit losses amount to 2.6 percent and 4.1 percent of RWAs during the first two and three years, respectively. - Market valuation losses on debt securities in available-for-sale and held-for-trading: - S$6 billion under Adverse Scenario 1 in 2019-21, equal to 0.7 percent of system-wide RWAs. - S$13 billion under Adverse Scenario 2 in 2019-21, equal to 1.4 percent of system-wide RWAs. - Equity price shock equivalent to 0.2 percent of RWAs under adverse scenarios. - Expected loss on interest income (time-to-repricing gap × interest rate changes): - 0.9 percent of system-wide RWAs in 2019-21 under Adverse Scenario 2. - Losses on net foreign exchange positions are small: D-SIBs’ net open positions in U.S. dollar amount to S$5 billion as of end-2018, equivalent to about 0.3 percent of total assets. - RWAs projected growth (first three years): - About 6 percent under Adverse Scenario 1. - About 10 percent under Adverse Scenario 2. - About 2 percent under the baseline scenario. ### Hurdle rates and capital framework used in stress tests - Hurdle rates in adverse scenarios: Total capital ratio 10.0 percent; Tier 1 capital ratio 8.0 percent; Common Equity Tier 1 capital ratio 6.5 percent; Leverage ratio 3.0 percent. - Baseline scenario hurdle rates: Total capital ratio 12.5+CCyB; Tier 1 capital ratio 10.5+CCyB; Common Equity Tier 1 capital ratio 9+CCyB; Leverage ratio 3.0. - Adverse-scenario hurdles equal minimum regulatory requirements plus D-SIB capital surcharges: 10 percent total capital, 8 percent Tier 1, and 6.5 percent CET1. - Stress test based on Pillar I minimum capital ratios; does not consider individual Pillar II requirements. - MAS does not require foreign branches to calculate RWAs and capital in Singapore; RWAs and impact on capitalization calculated only for locally incorporated D-SIBs. ### Top-line top-down stress-test results and drivers - D-SIBs are resilient to severe adverse scenario shocks; no D-SIBs have capital shortfalls relative to hurdle rates. - Resilience driven by large initial capital buffers, high initial asset quality, and strong profitability. - Existing capital buffers above total regulatory requirements equal, on average, about 7 percentage points of RWAs. - Baseline scenario result: - System-wide total capital adequacy ratio (CAR) and CET 1 ratio: 17.0 and 14.1 percent at end-2018. - Would increase to 18.3 and 15.5 percent by 2021, respectively. - Adverse Scenario 1: - Large-scale global market turmoil causes significant market losses in the first two years (2.3 percent of RWAs). - D-SIBs’ total CAR would decline to 11.2 percent in 2020. - As markets recover sharply from 2021, total CAR would return to the starting level (17 percent). - Adverse Scenario 2: - NPL ratios increase from 1.6 percent to nearly 7 percent. - D-SIBs’ total and CET 1 capital ratios decline to 10.9 percent and 8.6 percent in 2021. - No bank fails to meet the hurdle. - Main drivers of capitalization change over the first three years: - Loan loss provisions: -4.1 percentage points of RWAs. - Market losses related to financial market prices: -2.5 percentage points of RWAs. - Change in RWAs: -4.1 percentage points of RWAs. ### Leverage ratio and risk-weight density - D-SIBs maintain leverage ratio above the minimum in adverse scenarios: remains above 6.0 percent (comfortably above 3.0 percent minimum). - Risk-weight density of IRBA assets averages 36 percent among local D-SIBs. ### Sensitivity analyses (static shocks as of end-2018) - Interest rate risk: sudden increase in interest rates of 300 basis points; channels: market losses from bond yields, reduction in net interest income from re-pricing, and credit losses from reduced debt-servicing capacity. - Foreign exchange rate risk: foreign currency depreciation against U.S. dollar of 40 percent, affecting market losses via net open FX positions. - House price risk: decline in property prices of 50 percent increases credit losses via higher PDs and LGDs. - Name concentration risk: default of largest private borrowers, incorporating a haircut on collateral values due to a 30 percent decline of property prices; loans to one and five largest private borrowers account for 4-19 percent and 11-70 percent of Tier 1 capital of individual D-SIBs, respectively. - Risk of common exposures: defaults of largest (top one, five, ten, and fifteen) private borrowers; the single largest common exposure equals 2.5 percent of total capital and 0.2 percent of total assets as of 2018Q2; the twenty largest common exposures amount to 25 percent of total capital and 2.1 percent of total assets. - Potential impact of fintech: banks could lose part of non-interest income due to fintech expansion, particularly in segments responsible for 75 percent of fee and commission income (transaction and payment services, wealth management, investment banking, and trade finance). *Source: IMF staff calculation and text from the IMF FSAP chapter "49. Corporate probability of defaults".* ### 60. A sharp movement in interest rates would have considerable effects on D-SIBs’ ### 1sgpea2019005 - 60. A sharp movement in interest rates would have considerable effects on D-SIBs’ ### Interest rate risk and overall impact on D-SIB capitalization - Market losses from holdings of debt securities to a 300 bps increase in interest rates lead D-SIBs’ Tier 1 ratios to decline by 2.6 percentage points. - Higher interest rates could generate: - Additional credit losses of S$4.3 billion by deteriorating borrowers' repayment capacity. - Lower net interest income by S$1.4 billion due to banks’ maturity mismatch. - Total costs of the interest rate shock amount to 3.4 percentage points of Tier 1 capital ratio. - This shock would not cause undercapitalization in any of D-SIBs because of their sizable capital buffers. ### Foreign exchange risk - A 40 percent depreciation of foreign currencies against US$ would cause market losses of S$12 billion, so that Tier 1 ratio of D-SIBs drops by 1.6 percentage points. ### House price risk and credit losses - A 50 percent drop in property prices results in: - A rise of D-SIBs’ PDs by three times (from 1.3 percent to 4 percent). - A sharp increase in LGD. - Aggregate loss provisions increase from S$3 billion to S$15 billion. - The loss of Tier 1 capital ratio among D-SIBs would be equivalent to 2 percentage points. ### Name concentration risk and largest borrower defaults (as presented) - Table/figure excerpts (preserving source formatting): - Top one largest borrower2.50.2-0.3 - Top 5 largest borrowers10.60.9-1.2 - Top 10 largest borrowers15.31.3-1.7 - Top 20 largest borrowers24.62.1-2.7 - Findings: - The value of an exposure of D-SIBs to any single largest private counterparty does not exceed 25 percent of Tier 1 capital. - All D-SIBs have enough Tier 1 capital buffers to withstand simultaneous defaults of top-10 largest borrowers; no bank becomes undercapitalized until top-15 largest borrowers default simultaneously. - A foreign subsidiary is relatively more exposed: defaults of five largest borrowers would cause its Tier 1 capital to drop by 6 percentage points, compared to 2 percentage points for other D-SIBs on average. - The simultaneous default of the top twenty largest common borrowers would cause D-SIBs’ Tier 1 ratio to drop by 2.7 percentage points; no D-SIB fails to meet the FSAP hurdle given large capital buffers (about 7 percentage points of RWAs). ### FinTech disruption scenario - Hypothetical scenario assumed in FSAP: - Banks lose 75 percent of fee and commission income from affected business segments (transaction and payment services, wealth management, investment banking, trade finance). - Result: D-SIBs experience a decline of total capital adequacy ratio by 2.0 percentage point of CAR. - Behavioral/systemic risks: - Fintech as pure solution providers can improve banks’ efficiency and risk management. - If fintech acquires large retail customer bases, intensified competition can lower incumbents’ franchise value and increase risk-taking. - Uncertainty about benefits of innovation can produce “winners-take-all” dynamics and an ex-ante increase in banks’ risk appetite. ### Solvency–funding cost interaction - A decline in capital adequacy ratio during the stress test horizon raises funding costs, which further weakens solvency via reduced profits. - Second-round impact computed via a linear panel regression: - The second-round impact resulted in a drop of the aggregated CAR by 0.3 percentage points in 2021 (from 10.9 percent to 10.6 percent) under Adverse Scenario 2 and caused failure of a D-SIB to meet the hurdle rate. - Capital shortfalls were small at 0.1 percent of GDP. - Estimation results (panel regression with bank fixed effects) (coefficients and p-values preserved from source): - Interest expense / Total assets (t) coefficient: 0.8568, P-value: 0.027 - SSSS (t) coefficient: 0.0479, P-value: 0.012 - SSRRRR GPPPP Fggc gcch ggrRcrc (t) coefficient: 0.0108, P-value: 0.039 - TtccccR RRR CCCC S (t−1) coefficient: -0.0069, P-value: 0.060 - NNNN ggRccFfc (t−1) coefficient: 0.0719, P-value: 0.074 - Constant coefficient: 0.0923, P-value: 0.562 - Note: Within SS2 =0.7397, Between SS2 =0.7397, and Overall SS2 =0.7397. ### Comparison with MAS’ top‑down and D‑SIBs’ bottom‑up stress tests - MAS’ top-down tests confirm FSAP top-down findings: decline in total CAR mainly driven by increases in credit RWAs and loan loss allowances. - In Adverse Scenario 2, MAS’ top-down tests showed one D-SIB failed to meet hurdle rates; capital shortfalls were small at 0.5 percent of GDP. - Two top-down stress test results were more conservative than D-SIBs’ bottom-up results; differences mainly driven by D-SIBs’ higher profit projections. ### Liquidity resilience and cashflow-based stress test results - Overall conclusions: - Cashflow-based stress tests confirm D-SIBs’ liquidity is broadly adequate overall, but reveal vulnerabilities in U.S. dollars. - D-SIBs have broadly adequate liquidity in all currencies and in Singapore dollars, with some weaknesses at specific banks and at longer maturities. - U.S. dollar liquidity is vulnerable to stress conditions, suggesting all-currency liquidity of D-SIBs is vulnerable to depreciations of the Singapore dollar against the U.S. dollar. - Specific outcomes: - All currencies: - In an environment of no significant exchange rate movements, D-SIBs have sufficient liquid assets to withstand significant stress over one week and one month, but 1-2 banks do not have sufficient liquid assets to withstand stress between one and three months. - Shortfalls are limited at below 1.3 percent of GDP (specifically, 1.3 percent of GDP using data as of Q2 and 0.1 percent of GDP using data as of Q3). - Singapore dollars: - Six of seven D-SIBs have sufficient liquid assets in Singapore dollars to withstand significant stress over one week and up to three months. - One failing D-SIB generates shortfalls of 2.2 and 2.9 percent of GDP under the short- and long-term scenarios respectively. Staff estimate these shortfalls could be 1.5 percentage points lower after adjustments for intragroup liabilities and depreciation reporting. - U.S. dollars: - A significant number of D-SIBs (between four and seven) have insufficient liquid assets in U.S. dollars to withstand one-week and three-month stresses. - Failures lead to system-wide shortfalls of 8-10 percent of GDP under the short-term scenario and 11 percent of GDP after the first three months of the long-term scenario. - Specifically, 4-5 banks fail the short-term scenario and 4-7 banks fail within the first three months of the long-term scenario (ranges reflect Q2 and Q3 data). - Cashflow-based tests reveal reliance on transferability of liquid assets within groups: - Example: In the first three months of the six-month stress scenario, one entity within a banking group shows insufficient liquid assets even though the group as a whole has adequate liquid assets (in all currencies). This cross-subsidization is evident in all currencies combined, but less clear in Singapore and U.S. dollars individually. *Source: IMF staff estimates.* ### 72. The LCR-based stress tests reveal that ### The LCR-based stress tests reveal that ### Liquidity Coverage Ratio (LCR) stress-test findings - Demand for high quality liquid assets would increase substantially over the medium term under an adverse scenario. - The LCR-based stress tests reveal that the liquid asset buffers over the minimum all-currency LCR requirements would fully deplete under the adverse scenarios (Table 11). - An apparently healthy 30 percentage point buffer (over regulatory requirements) among domestic banks can quickly erode under stress. - These buffers have shrunk over the years as minimum requirements have been phased in. - By contrast, the buffers in Singapore dollars are preserved under stress, indicating that shortfalls in all-currency liquid assets are driven by foreign currency activity; exchange rate effects seem small. - Domestic banks are the main source of these shortfalls, to a large extent because they face higher minimum all-currency LCR requirements. - D-SIBs appear equally exposed to the retail and wholesale scenarios. - To restore all-currency LCRs to regulatory minima, D-SIBs would need to raise liquid assets equivalent to an additional 11 and 14 percent of GDP under the retail and wholesale scenarios respectively. - The LCR is volatile, and the shortfalls are reduced by one-half if a moderately higher starting LCR is used. - Under the wholesale scenario’s exchange rate depreciation of 10 percent, the all-currency LCRs fall by a modest 3 percentage points (asset-weighted average across D-SIBs). - The volatility of year-on-year changes in the LCR of D-SIBs is 12 percentage points; staff added a buffer of 12 percentage points to each D-SIB’s LCR before calculating shortfalls to check the effect of volatility. ### FX swap market funding and internal limits - Banks tend to rely on the FX swap market for foreign currency funding in stress episodes, but set prudent internal limits on recourse to FX swap market funding. - These internal limits are expressed as a share of the lowest FX swap market turnover observed in recent years; the share is chosen by each bank to be similar to or smaller than its typical share of turnover. ### Net Stable Funding Ratio (NSFR) and maturity structure - Domestic banks draw more of their stable funding at shorter maturities than foreign banks. - Analysis of NSFR components by maturity reveals: - A surplus of stable funding with up to six months’ maturity and a corresponding shortage of stable funding with greater than one year’s maturity. - This pattern is consistent with normal maturity transformation and is less pronounced for foreign D-SIBs than for domestic banks. - Dashed lines show the minimum NSFRs for domestic and foreign D-SIBs of 100 and 50 percent respectively. ### Deposit concentration and stress implications - D-SIBs’ corporate deposits are more concentrated than their deposits from individuals. - The largest single corporate deposit and the largest five corporate deposits make up on average 1.9 and 5.8 percent of liabilities respectively; the same numbers are 1 and 3 tenths of a percent for deposits of individuals. - D-SIBs’ largest twenty corporate deposits closely follow a power law, with exponent 0.9, suggesting excess concentration. - Deposits are slightly more concentrated in foreign D-SIBs than in domestic D-SIBs, with Herfindahl indices of 14 and 11 basis points, respectively. - A simple stress test simulating the withdrawal of each D-SIB’s largest depositors suggests that deposit concentration would not cause distress, because the largest deposits are covered by liquid assets. - There is some concentration risk within the Singapore operations of domestic banks: for two domestic banks, the five largest corporate deposits within their Singapore operations exceed those operations’ high-quality liquid assets. - From a systemic risk perspective, concentrations of corporate deposits should be read alongside the reliance of corporates on their liquid asset buffer to offset their gross debt. - Liquid assets are defined as high quality liquid assets under the LCR reporting standard. ### Insurer solvency resilience — methodology (summary) - The section assesses insurers’ solvency positions by modelling regulatory capital and capital requirements under adverse scenarios using top-down and bottom-up stress tests. - Top-down stress tests are undertaken by IMF staff and MAS; bottom-up stress tests are administered by MAS. - Both tests use financial data on insurers as of 2018Q2. - The top-down stress test covers the four largest direct life/composite insurers, which hold 80 percent of the assets of the direct life insurance sector; tests cover all life insurance activities but exclude investment-linked business. - Insurers pass the test if regulatory capital exceeds minimum risk-based capital requirements under the adverse scenario (CAR remains above 100 percent); otherwise the amount breached is the capital shortfall. - Bottom-up tests include single-factor stress tests for natural catastrophe and cyber-attacks on clients. - The top-down stress test uses a static regulatory balance sheet approach under the first adverse scenario of the bank solvency stress tests, calibrated to the cumulative effects of years 2019 and 2020 under Scenario 1. - Asset revaluation: haircuts on government and corporate bonds are calibrated by revaluing by maturity, coupon and country and by a modified duration approach; staff averaged the two methods. - Government bond yield curves for selected countries appear in Table 6 and were extended to other countries by least squares; yield curves interpolated to semi-annual maturities by cubic splines. - Corporate bond yield curves derived assuming an initial global credit spread of 2 percent in 2018Q2 and applying year-on-year credit spread changes in Table 6. - Liability revaluation: technical reserves revalued by discounting projected cashflows at statutory yield curves; all liabilities not denominated in SGD or USD are treated as if denominated in the Australian dollar. - Insurers provided monthly projected liability cashflows, net of reinsurance recoveries; monthly cashflows improved accuracy of liability revaluation by about one-half of a percent. - Authorities’ top-down and insurers’ bottom-up tests differ in scenario and methodology and in coverage; bottom-up tests cover the largest nine direct life/composite insurers and the largest fifteen direct general insurers and consider both adverse scenarios of the bank solvency stress tests. - Scenario 1 simulates financial stress and is used for insurer stress testing. ### Insurer solvency resilience — results and implications - The top-down stress test shows vulnerability of solvency positions, but does not indicate systemic risk. - Two of the four largest direct life/composite insurers breached their minimum risk-based capital adequacy requirements in the top-down solvency stress test due to mark-to-market losses within both participating and non-participating funds. - The capital shortfalls amount to a moderate 1.3 percent of GDP, which does not suggest a macroprudential concern. - Losses are driven by market risk given sizeable equity and corporate bond holdings. - The top-down stress tests reveal losses of S$33bn due to asset price falls over the two-year scenario horizon, relative to regulatory capital (adjusted financial resources) of S$36bn. - Asset losses are concentrated in equities and corporate bonds; most bonds are held to maturity to meet long-term liabilities, so corporate bond devaluations would have only a temporary effect on solvency if bonds remain fully performing. - The four largest life/composite insurers tend to gain from a depreciation of the Singapore dollar, given holdings of foreign currency assets and limited foreign currency liabilities, though profits from depreciations are limited because most insurers hedge currency risk in foreign bond holdings. - The authorities’ bottom-up stress tests showed that three of the four largest direct life/composite insurers tested, and four of the five smaller direct life/composite insurers tested, breached their minimum capital adequacy requirements. - The authorities’ top-down results aligned closely with bottom-up results and were slightly weaker than staff results due to a more conservative modified duration method and inclusion of losses from floating rate bonds. - In bottom-up stress tests insurers prepared capital recovery plans deliberated by executive boards and senior management; management actions included, in order of size: capital injection from the parent, cuts to bonuses of participating policies, reinsurance arrangements, and other actions like portfolio de-risking. - MAS assesses that the parent would be willing and able to provide the capital injection; MAS assesses each capital injection is small relative to the insurer’s turnover, its regulatory capital and its parent’s capital. - The extent of bonus cuts in bottom-up stress tests are similar to those observed in 2008 if the credit spread widening in the adverse scenario is adjusted to match that observed in 2008. _International Monetary Fund staff and MAS stress-test analysis as presented in the chapter._ ### 80. The authorities’ stress tests showed the resilience of general insurers to market risk ### 80. The authorities’ stress tests showed the resilience of general insurers to market risk ### Stress-test results for general insurers - All 15 direct general insurers passed the bottom-up stress tests under the first and second adverse scenarios. - The extreme flooding scenario required direct general insurers to estimate the impact of flooding in Singapore at an average depth of 600 millimeters. - Under the flood scenario, CARs fall by a small 8 percentage points and no insurers breach their minimum CAR requirement. - The bottom-up stress tests included management actions to restore capital, which MAS assessed to be systemically feasible. ### Large insurers’ exposures and concentration risk - Staff analyzed the concentration of the exposures of the four largest life/composite insurers. - For participating and non-participating funds, insurers’ top five bank exposures are less than 60 percent of capital buffers. - More than half of large bank exposures take the form of equity holdings; only 21 percent of these exposures take the form of deposits. The remainder take the form of unsecured and subordinated bonds. - Concentration by bank: the largest exposure to any single bank, across all participating and non-participating funds, is equivalent to 20 percent of the fund’s capital buffer. - Insurers’ largest exposures to banks are less concentrated than their largest exposures to non-banks. - In one insurer, large exposures to non-banks exceed capital buffers for the non-participating fund; those exposures take the form of unsecured and subordinated bonds. ### Riskiness of asset allocation and supervisory responses - Insurers have buffers greatly in excess of capital requirements, but these buffers erode under a severe but plausible adverse scenario; capital shortfalls are modest. - Some of this outcome is an artifact of the capital regime: assets are valued at market value, but liabilities are stable. - Under the revised risk-based capital framework, the matching and illiquidity premium adjustments will make liabilities more sensitive to market conditions, so that the reduction in liability value from higher discount rates will offset some mark-to-market losses on fixed income assets. - Signs of search for yield among life insurers should be monitored closely; MAS plans to include this topic in its briefings to the industry on the results of the bottom-up stress tests. - MAS could enhance its measurement of market risk from corporate bonds by modelling the revaluation effects of an adverse scenario using a cashflow projection method. Such a method models the timing of bond cashflows and accounts for the effects of the full shape of the yield curve on bond prices. - The current approach is based on modified duration, which is more conservative, but less precise, for the large movements in yields typical of stress tests. ### Cyber risk — exposures, preparedness, and impacts - Cyber risk is an emerging risk to financial stability and exposures grow with the digitalization of the financial system. - MAS conducted a bottom-up cyber risk survey and collected responses from 18 banks and 17 direct general and composite insurers as part of the 2019 FSAP. - The Cybersecurity Act of 2018 imposes responsibilities on owners of designated critical information infrastructures and defines rules for licensing cybersecurity service providers. The Cyber Security Agency (CSA) coordinates national policy and responds to incidents. MAS oversees cybersecurity of the financial sector and monitors threats through its 24/7 financial sector security operations center. - Financial institutions allocate some 0.4-2.0 percent of their staff and about 4-5 percent of their information technology budgets to cybersecurity. The CSA has recommended that firms allocate at least 8 percent of their information technology budgets to cybersecurity. - Successful cyber-attacks in the Singapore financial sector have not caused significant losses to date; confidential data reviewed by staff show an improving trend since 2015. Most attacks were distributed denial of service attacks and other website-targeted behavior; there have also been ransomware incidents and attacks on third-party providers. - Analysis of international data suggests financial firms’ stock prices fall by between 39 and 45 basis points on days of cyber-attacks leading to data breach or business disruption. Stock prices fall by on average 45 and 39 basis points on days of cyber-attacks leading to data breaches and business disruption respectively. - Incidents of cyber-related fraud have had much smaller effects; these effects are difficult to distinguish from normal stock market volatility. ### Banks’ bottom-up cyber survey findings - MAS’s bottom-up cyber risk survey collected answers from 18 banks on: (1) description of cyber-attacks they would be most vulnerable to; (2) qualitative analysis of transmission channels; (3) quantitative estimates of potential losses; and (4) description of mitigating measures. - Banks envisage they would be mostly affected by money theft and business disruption but can weather the impact with ample capital and liquidity buffers. - Survey quantitative estimates (from 18 banks): potential losses from cyber-attacks would amount to about [50] percent of their quarterly profits and cause the CAR and the LCR to drop by 0.4 and 16 percent, respectively. - Banks report adequate mitigating measures including multiple layers of security controls, strong data encryption and access controls, regular cyber security simulations, and disaster recovery sites. - Survey responses suggest some banks do not fully consider negative impacts arising from loss of confidence and spillover contagion within the financial system. ### Insurers’ cyber exposures and expected losses - MAS surveyed 17 direct general/composite insurers on claims arising if their ten largest clients of affirmative cyber coverage and their ten largest clients of Property & Casualty insurance were victims of cyber-attacks. - Insurers report exposures of S$600 million for affirmative cyber coverage and S$3.4 billion for silent cyber coverage. - Claims arising from these exposures amount to S$1.8 billion, shared between direct insurers and reinsurers and may be offset against a release of technical reserves. - The net losses reduce the aggregate CAR of these insurers by only 3 and 2 percentage points for affirmative and silent cyber coverage, respectively. - Moving forward, insurers with exposures to silent cyber coverage intend to include appropriate exclusion clauses in their contracts. ### Policy recommendations and supervisory actions on cyber - The authorities should develop a cyber network map and continue to monitor cybersecurity risks from third party service providers. - MAS has been monitoring interconnectedness via financial exposures and cyber-interdependences separately; a holistic approach combining financial and ICT connections would help identify interconnectedness, potential risk concentrations and common dependencies (including financial market infrastructures and third-party service providers). - The authorities already supervise third party providers that are financial firms and have a framework for engaging with non-financial third-party providers that are separately regulated; MAS should enhance monitoring of banks’ vulnerability to third-party cyber risk as revealed by stress tests. *Source: 1sgpea2019005 - 80. The authorities’ stress tests showed the resilience of general insurers to market risk* ### CONCLUSIONS AND RECOMMENDATIONS ### CONCLUSIONS AND RECOMMENDATIONS ### Systemic risk assessment: key findings - The financial system is "broadly resilient to severe adverse shocks", but foreign exchange liquidity is a key weakness. - D-SIBs do not show any shortages of capital in the adverse macroeconomic scenario, although some banks are relatively more exposed to legacy loans, property price volatility, and concentration risk. - Many banks do not pass the liquidity stress tests in U.S. dollars, suggesting overall liquidity is reliant on a stable value of the domestic currency. - Insurers show vulnerability to asset prices, but do not suggest a systemic risk. - Contagion analyses indicate severe distress in major advanced economies could inflict significant losses on banks in Singapore and Singapore could cause significant outward spillovers to regional neighbors. - Households remain resilient under stress except a small segment of highly leveraged, low income, and younger borrowers. - Non-financial corporates remain resilient in stress tests thanks to large cash reserves that offset their high gross debt. - Cyber-attacks have not led to significant losses to financial firms in Singapore to date, but institutions should fully consider negative impacts from loss of confidence and spillovers within the financial system. ### Interpretation and caveats - Systemic risk assessment results should be interpreted with caution due to methodology, coverage, and data constraints. - Analyses do not fully capture macrofinancial second-round effects. - Bank stress tests estimate satellite credit risk models with historical data only after 2004 (excluding the Asian Financial Crisis) and are subject to estimation uncertainty. - Bank and insurer stress tests cover D-SIBs and the four largest direct life/composite insurers. - FSAP risk analyses are based on supervisory and market data collected at a specific point in time. - The economy is affected by a combination of external and domestic shocks that (ex-ante) have a low probability of realization. ### Strengthening U.S. dollar liquidity - Staff find banks’ U.S. dollar liquidity positions to be vulnerable: - The U.S. dollar LCR suggests shortfalls of liquid U.S. dollar assets of some 20 percent of GDP (Table 4). - Stress tests reveal shortfalls of 8-11 percent of GDP. - Adverse scenarios for liquidity risk assume deteriorating liquidity conditions in foreign currency swap markets. - The authorities can provide U.S. dollars to banks through money market operations, but banks should self-insure more of their foreign currency liquidity risk. - Possible policy approaches: - MAS has chosen to use the supervisory process to encourage banks to improve foreign currency liquidity positions; this has reduced banks’ reliance on the FX swap market for funding normal U.S. dollar lending. - Other jurisdictions have introduced minimum requirements for foreign currency LCRs (jurisdictions cited: Sweden, Norway, Korea and Iceland). - MAS should keep the option of minimum foreign currency LCRs open if improvement is not achieved through supervision. - Any actions should be undertaken gradually to avoid disrupting banking or foreign exchange activity. ### Surveillance, data gaps, and analytical tools - MAS should strengthen surveillance by closing data gaps and developing additional analytical tools. - Recommended data collection and enhancements: - Domestic interlinkages. - Total stock of household mortgage debt at the borrower-level over time. - Direct life insurers’ liability cashflow projections and the underlying asset allocation of the collective investment schemes in which they invest. - MAS needs to explore alternative approaches to estimate credit gap, recognizing changes in credit cycles, to support timely macroprudential policy actions. - MAS should continue to enhance bank solvency monitoring by adopting the full aspects of the SFRS 109. - MAS should revise contractual and behavioral cashflow-reporting templates to make them more useful for stress testing. - MAS should develop a cyber network map that accounts for financial linkages and Information and Communications Technology connections and use it for cyber risk surveillance. ### Swap lines and external liquidity arrangements - It would be beneficial for Singapore to explore extending swap line arrangements with other central banks. - Rationale: - The Asian dollar market in Singapore intermediates funds from advanced economies to Asia, creating significant regional spillovers. - The authorities have established swap lines for financial stability purposes with the People’s Bank of China and the Bank of Japan, and Singapore participates in regional liquidity arrangements. - Extending Singapore’s network of bilateral swap arrangements would improve ability to withstand global liquidity events. ### Risk Assessment Matrix (selected entries) - Tighter global financial conditions and retreat from cross-border integration - Transmission: higher interest rates, tightening financial conditions, reduction of cross-border capital flows; higher debt service costs, rising delinquencies, deteriorating banks’ capital; market and liquidity risk with tighter dollar funding conditions. - Relative likelihood: Medium/High - Potential impact: High - Weaker-than-expected global growth, especially China - Transmission: large spillovers to Singapore as a financial center and trading hub; direct effects and indirect regional impacts; rising NPLs; pullback of funding; deterioration of regional exposures. - Relative likelihood: Medium - Potential impact: Medium/High - Cyber-attacks - Transmission: cyber-attacks on interconnected financial systems could trigger systemic financial instability or widely disrupt socio-economic activities; liquidity and operational risk are main channels. - Relative likelihood: Medium - Potential impact: Medium/High ### Stress Testing Matrix (STeM) — selected features and parameters - Banking sector: Solvency Stress Test (bottom-up and top-down) - Institutional perimeter: - "All seven D-SIBs (10 banks)." - Market share: "75 percent of total loans to private residents." - Data and starting position: "Starting position: 2018Q2 and projected to end-2018 under the baseline scenario." - Methodology: - Overall framework: Balance sheet approach; satellite models and stress testing methodology developed by MAS and FSAP team. - Stress test horizon: "3-years (2019-2021)." (bottom-up) and "5-years (2019-2023)." (top-down by authorities) - Scenarios: - Three macrofinancial scenarios agreed with the authorities, combining domestic and global variables. - Baseline scenario based on the July 2018 WEO projections. - Two adverse scenarios: - Scenario 1: Large-scale global financial market turmoil. - Scenario 2: Protracted recession centered on a major slowdown in China and trade tensions. - Under the two adverse scenarios: - The output gap is " -8.4 percent and -12.3 percent" (respectively). - The cumulative decline of real GDP growth is "1.7 and 2.3 standard deviations over the first two and three years, respectively." - The SGD/USD bilateral exchange rate would depreciate by "20 percent in 2019" under adverse scenario 1, equal to "3.7 standard deviation" of annual exchange rate movement (y-o-y). - Banking sector: Liquidity Stress Test (top-down by authorities and FSAP team jointly) - Institutional perimeter: - "All seven D-SIBs (10 banks)." - Market share: "76 percent of total deposits of private residents." - Starting position: "2018Q2. Robustness will be investigated using data for 2018Q3." - Methodology: - Two types of tests: (i) LCR-based stress test and (ii) cashflow-based test. - LCR-based test stresses all-currency and Singapore dollar LCRs; cashflow-based test projects liquid asset position under stress up to six months and is applied to all currencies, Singapore dollars and U.S. dollars separately. - The NSFR and U.S. dollar LCR will be inspected without being stressed. "There is no minimum requirement for the U.S. dollar LCR." - Scenarios and calibration: - LCR-based tests consider retail and wholesale scenarios, include delays in cash inflows and margin calls, and model effects of SGD depreciation on all-currency LCR. - Cashflow-based tests consider 1-week and 6-month stress scenarios. - Run-off rates on deposits calibrated to a two-standard deviation fall in historical supervisory data on deposit balances; run-off and roll-off rates on FX swaps calibrated to a two-standard deviation scenario using bid-ask spreads on FX forwards as a proxy. *Source: 1sgpea2019005 - CONCLUSIONS AND RECOMMENDATIONS* ### 1. Institutional ### 1. Institutional ### Perimeter and institutions included - 9 largest life/composite insurers and 15 largest • The 4 largest life/composite insurers • The 4 largest life/composite insurers - Market share: - At least 80 percent of total assets (life insurers) - At least 80 percent of gross written premiums (general insurers) - 80 percent of total assets • 80 percent of total assets - Data and starting position: - Starting position: 2018Q2 - Each insurer’s own data - Starting position: 2018Q2 • Supervisory data • Only life insurance activities of these insurers will be included • Investment-linked business is excluded for market risks • The liabilities of investment-linked business, non-participating term life and non-participating accident and health are not revalued (7 percent of liabilities in the sector, measured using guaranteed liabilities and a provision for uncertainty). - Starting position: 2018Q2 • Supervisory data • Only life insurance activities of these insurers will be included • Investment-linked business is excluded for market risks • The liabilities of investment-linked business, non-participating term life and non-participating accident and health are not revalued (7 percent of liabilities in the sector, measured using guaranteed liabilities and a provision for uncertainty). ### Methodology — overall framework - Projection of the regulatory capital position under several scenarios - Each insurer’s own internal methodology - Projection of the regulatory capital position under a severe yet plausible macroeconomic stress scenario - The capital position is modelled by revaluing assets and liabilities under the scenario - Static balance sheet approach, where the impact of the entire scenario is evaluated in one step - A satellite model is used to estimate the impact on government bond prices by maturity, for those currencies where yield curves are not projected under the bank solvency scenario ### Type of analyses and scenarios - Scenario analysis: - Macroeconomic and non-macroeconomic scenarios. Macroeconomic scenarios have horizons of 2.5 years and non-macroeconomic scenarios have horizons of one year. - Three macroeconomic scenarios, matching the baseline and two adverse scenarios used under the bank solvency stress tests. - One climate-related scenario tests the impact on general insurers of severe but plausible rainfall. - A cyber risk scenario tests the impact on general insurers of claims on direct (affirmative) and indirect (silent) cyber insurance policies. - For life-insurer/stress-test subsets: - There will be one adverse scenario only. - The scenario will have a two-year horizon, based on the combined impact of 2019 and 2020 under “Scenario 1” of the agreed bank solvency scenarios. - Risk-free discount rates under the adverse scenario will follow the Singapore regulatory regime, where longer-term discount rates are less sensitive to interest rate movements. This insensitivity will change under RBC 2. ### Risks and buffers - Risks: - Falls in asset prices, possibly due to fire sales (“market risk”) - Catastrophe risk - Cyber risk - Default by largest counterparties - Falls in asset prices will be modelled in more detail than in the bank solvency stress tests - Buffers: - Buffers are regulatory capital in excess of regulatory capital requirements. - Insurers may recognize future profits as they are earned (e.g., interest income from bonds) as buffers. - Insurers also model the effects of their recovery plans on their regulatory capital positions. - The fall in asset values will result in an automatic relaxation of capital requirements for market risk. - Falls in liability values, due to rising sovereign yields, will also improve the solvency position. ### Regulatory standards - Current RBC regulations (“RBC 1”). - Insurers specify hurdle rates according to their internal targets, which are higher than their regulatory requirement. - Insurers’ own regulatory requirements include industry-wide and firm-specific requirements. - The stress tests will use a hurdle Capital Adequacy Ratio based on confidential, insurer-specific capital requirements. - The stress tests will use a hurdle Capital Adequacy Ratio of 100 percent. - In addition, a higher Capital Adequacy Ratio will be considered, based on an indicative and confidential capital surcharge for high-impact insurers. Quantitative results will not be published, to maintain confidentiality. ### Reporting format for results - The FSAP team will have access to a summary of insurers’ stress test results. - Attribution analysis will assist in identifying the drivers of risk. - As much sector-wide detail as possible will be included in the technical note, while preserving insurer—supervisor confidentiality. - Sector-wide regulatory capital position and capital shortfall. --- ### Financial System: Interconnectedness Analysis ### Perimeter and institutions included - Interbank network: all banking groups (119). - Common exposure: All seven D-SIBs. - Intra-financial network: 8 groups of financial institutions. - Cross-border bank network: the banking system of selected countries. - Data and starting position: - Starting position: 2018Q2 - Supervisory and market data. - Scope of consolidation: - Interbank and common exposure: individual banks. - Intra-financial: groups of financial institutions. - Cross-border: banking system. ### Methodology — overall framework - Interbank: Espinosa-Vega and Solé (2010). - Common exposure: balance sheet approach. - Intra-financial network: Steady-state Markov Chain probability. - Cross-border network: Espinosa-Vega and Solé (2010) and Diebold and Yilmaz (2014). ### Risks and buffers - Risks: - Credit and funding losses related to interbank exposures, intra-financial exposures, and cross-border banking exposures. - Default of large common borrowers in the banking system. - Fire-sale of assets following sizeable withdrawals of deposits. - Buffers: - Interbank network: banks’ own capital and liquidity buffers. - Cross-border bank network: capital buffers of a banking system. ### Reporting format for results - Interbank network: a network chart, index of vulnerabilities. - Common exposure: system-wide capital shortfalls. - Intra-financial network: bilateral exposure matrices, a chart of steady state Markov Chain probability. - Cross-border network: index of vulnerabilities and contagion, a heatmap of bank distress, and spillover charts. --- ### Appendix III. Cross-border Network Analysis Using Market Data - Methodology: Diebold and Yilmaz (2014) using the volatility of bank equity return indices as proxy for bank stress; connectedness measures from a forecast error variance decomposition. - VAR setup: - CC(N) Yt + S(N) Xt = εt, where Yt denotes a vector of monthly log volatility of the FTSE bank equity return indices of 24 countries for January 2016 to December 2018. - Includes Volatility Index (VIX), Xt, to control for common global factor. - Connectedness: - Derived from H-step Generalized Variance Decomposition matrix (Pesaran and Shin, 1998): PH = [F i,j H ], where each entry F i,j H captures F i -th pairwise directional connectedness — the percent of 12-day-ahead forecast error variance of a country F due to shocks from country i. - Connectedness is directional: F i,j H ≠ F j,i H. - Sum of off-diagonal elements in row F measures total directional connectedness from others to F; sum in column i measures total directional connectedness to others from i. - Results summary: - Clear regional clustering. - Countries tend to be connected with countries in the same region. - European banks are very connected with each other and are the most interconnected within the network. - Outside Europe, US and Canadian banks are highly connected with each other and also with the rest of the system. - The pair with the strongest pair connection in the entire network is from China to Hong Kong SAR. - Singapore is also most connected to these two economies. --- ### Appendix IV. SFRS 109 - Background: - The incurred loss model in IAS 39 delayed recognition of credit losses and contributed to procyclicality. - In July 2014, IASB issued IFRS 9 to measure credit risk over the lifetime of financial assets as a product of lifetime PDs, LGD, and EAD. - Key features: - For performing assets (Stage 1), report a 12-month expected credit loss provisioning. - For assets with a significant increase in credit risk (Stage 2), set aside loan loss provisions for lifetime expected credit losses. - IFRS 9 is more preemptive than the old standards and introduces a cliff-effect between Stage 1 and Stage 2 provisioning. - Local adoption: - SFRS 109 introduced on January 1, 2018. - MAS requires D-SIBs to maintain the higher of a minimum loss allowance of 1 percent on “non-credit-impaired” exposures (Stage 1 and 2) net of collaterals and loss allowances estimated under SFRS 109. - Transition to SFRS 109 had only marginal effects on banks’ capital levels in January 2018, as pre-SFRS 109 general provisions were sufficient and economic conditions were benign. - Any excess allowance beyond the required amount in accordance with SFRS 109 needs to be maintained in a non-distributable regulatory loss allowance reserve account through an appropriation of retained earnings. --- ### Appendix V. Balance Sheet and Income Statement Projection - Projection — Balance sheet: - Exposures: Growth of gross exposures by types (total gross loans and gross holding of debt securities) identical to overall credit growth, with balance sheet composition unchanged. - Overall credit growth consistent with GDP growth for a positive GDP growth, and zero for a non-positive GDP growth. - Projection — Income statement: - Net interest income: Combination of interest income and interest expense. - Interest income: - Sum of interest income from lending activity and other activities. - Amount of lending (adjusted for balance sheet growth and existing NPLs) multiplied by adjusted effective lending rate. - Interest rate risk adjusted with time-to-repricing gaps and changes in interest rates. - Interest expense: - Sum of interest expense from deposit funding and other funding. - Amount of deposits (adjusted for balance sheet growth) multiplied by adjusted effective deposit rate or funding cost. - Average funding cost adjusted with banks’ solvency position and asset quality deterioration, incorporating solvency-liquidity nexus. - Net non-interest income: - Net fee and commission income projected as a function of the growth rate of real GDP and equity price, considering procyclicality and financial markets impact. - Other non-interest income growth consistent with GDP growth. - Pre-loss net income: Sum of net interest income and other pre-loss net income. - Provision/impairment: - Credit losses based on the SFRS 109 that reflects the increase of PDs and LGDs on the back of worsening macro-financial conditions. - Write-back of loan loss provisions is not allowed. - Market loss due to mark-to-market to bond yield movements for the trading account, to exchange rate, equity price, and commodity price movements for the net open foreign-exchange, equity and commodity position. - Net income before taxes: Sum of pre-loss net income and provision/impairment. - Taxes: - Based on applicable tax rates (17 percent) if net income before taxes is positive, and 0 otherwise. - Net income after taxes: Sum of net income before taxes and taxes. - Dividend payout: Banks pay dividends only if net income after taxes are positive, with limits on dividend payout rate as per the MAS regulatory requirement. - Profits attributed to capital: Sum of net income after taxes and dividend payout. - Capital: - Risk-weighted assets: Adjusted for balance sheet growth, changes in credit risk, and exchange rate movements (for foreign-currency exposures). - Capital: Affected by retained profits and unrealized gain/loss associated with the available-for-sale portfolio. - No capital injections during the stress test horizon. --- ### Appendix VI. Estimation of Credit Risk Models - Scope and data: - Satellite credit risk models estimated using supervisory data over 2004-2018. - NPL projection uses macrofinancial scenarios of five countries: Singapore, China, Malaysia, Indonesia, and Thailand. - Macrofinancial variables averaged with share of aggregated credit exposures to the five countries as weights. - Model specification: - Logit-transformed NPL ratios determined by real GDP growth rates, short-term interest rates, house prices, equity prices, and nominal (bilateral) exchange rates. - Model form: - Y i,t = ln( NPL N i,t / (1 − NPL N i,t )) = (α + μ i ) + β Y * Y i,t−1 + β X * X t−s + ε i,t - Y i,t is the logit transformed NPL for bank i at time t. - X t is vector of macrofinancial variables including real GDP growth rates, the square of real GDP growth rates, short-term interest rates, house prices, equity prices, and nominal exchange rates. - s and μ i denote time lags and bank-specific fixed effects; ε i,t is iid error term. - Non-linear effects via logistic transformation, autoregressive term, and square of real GDP growth. - Time lags determined based on cross-correlation. - Estimation and projection: - Several credit risk models estimated with different panel methods and variable combinations to test robustness. - NPL ratios under scenarios computed by inverse logit: - NPL N i,t = 1 / (1 + e^{−Y i,t}) *100 - Bank-specific NPL paths projected by averaging results across models and accounting for fixed effects, then applied to latest point-in-time PD estimates to produce PD dynamics for all asset classes over the stress test horizon. --- ### Appendix VII. Adjustment of LGDs to Housing Price Inflation - Numerical example and formula to adjust mortgage LGDs for declining house prices under the adverse scenario: - Case without house price adjustment: - House price inflation (∆HP) = -20 percent - Loan amount = S$100 - LGD rate (NGP1) = 30 percent - Recovery rate = 70 percent - Recovery value = S$70 - With the 20 percent drop of house prices, LGD rate increases to 44 percent; the formula: - (1 − NGP2) = (1 + ∆HP) * (1 − NGP1) = (1 − 0.2) * (1 − 0.3) = 0.56 *Source: 1sgpea2019005 - 1. Institutional* ### Appendix VIII. Parameters for Bank Liquidity Stress Tests ### Appendix VIII. Parameters for Bank Liquidity Stress Tests ### LCR-based stress tests: method and scenario design - Retail scenario: - A two percent fall in government bond prices is applied. - The two percent fall is based on the average maturity of government bonds held by D-SIBs and the first half of the increase in government bond yields in 2019 under the first adverse scenario of the bank solvency stress tests. - Wholesale scenario: - Intragroup funding is stressed through operational deposits. - Reduction in inflows from financial firms is calibrated to match the rate of withdrawal of operational deposits. - Currency depreciation assumption: half of the depreciation against the U.S. dollar in the first year of the first adverse scenario of the bank solvency stress tests. - Margin call assumption is calibrated in line with the exchange rate depreciation because foreign currency swaps are the largest category of derivatives. - Committed credit lines are not subject to stress (mostly unconditionally revocable). - Appendix Table VIII.1 provides detailed parameter assumptions (calibrated with reference to recent FSAPs in advanced economies and financial centers). ### Cashflow-based stress tests: model, calibration, and key formulas - Model overview: - Inventory model projecting liquid assets as cash inflows accumulate and cash outflows deplete assets. - Time index c = 0,1,2,... with irregular spacing: c = 1,2,3,4 denotes weeks 1-4; c = 5 denotes 1-3 months; c = 6 denotes 3-6 months; etc. - Liquid assets at date c: - N C t = N C t−1 + Σ φ i,t R i,t − Σ χ j,t R j,t − Σ ψ k,t N k - where R i,t is contractual receivable at date c; R j,t is contractual payable at date c; N k is value of liability k at start date; φ is roll-off rate of asset; χ is run-off rate of liability as fraction of flow; ψ is run-off rate of liability as fraction of initial value. - Initial liquid assets: N C 0 = Σ υ h C h, where C h is value of liquid asset h and 1−υ h is the haircut. - Calibration of deposit run-off rates: - For demand deposits, run-off rate ψ up to one month (as fraction of initial value) = 1−exp(−2σ), where σ is volatility of month-on-month changes in the natural logarithm of deposit balances. - Run-off rates in 1-3 month window: [1−exp(−2σ3)] − [1−exp(−2σ1)], where σ n is volatility of n-month changes in log deposit balances. - One-month volatility is estimated from F-month volatilities (F = 1,2,3,4) using σ n = σ1 √{F [1 + (F−1) ρ]}, where ρ is correlation between one-month changes in (log) deposit balances. - Run-off rates estimated on aggregate deposit balances for all D-SIBs and applied symmetrically to every D-SIB. - Foreign exchange derivatives: - Amounts contractually payable on FX derivatives (D-SIBs aggregate, all currencies): 11 percent of total assets within one week, 23 percent within one month, and 35 percent within three months. - Run-off rate on FX derivatives calibrated to two-standard deviation deterioration in liquidity conditions using 1−exp(−2σ) where σ is volatility of week-on-week changes in the natural logarithm of the bid-ask spread. - Resulting run-off rate: 70 percent (roll-off on receivables set at same value). - Assumptions on banks’ actions under stress: - Banks are allowed not to refinance about half of the loans that customers repay. - After haircuts, banks may count their entire portfolio of government bonds toward liquid assets, even if bonds mature beyond the horizon of the adverse scenario. - No assumption on the order of asset liquidation. - Reserve requirements cannot be separated from liquid assets due to data limitations; banks can use reserve requirements to meet obligations. ### Key parameter values and rate matrices (selected figures from Appendix Tables) - Retail stress: government bond price shock = two percent fall. - FX derivatives (aggregate contract amounts as percent of total assets): - within one week = 11 percent - within one month = 23 percent - within three months = 35 percent - Calibrated run-off rate on foreign exchange derivatives = 70 percent. - Representative roll-off rates on assets in the long-term scenario (percentage of contractual flow received by time buckets): - Interbank claims (% flow): 23, 23, 23, 23, 18, 20, 10, 5 - Intra-group claims (% flow): 23, 23, 23, 23, 23, 18, 20, 10, 5 - Securities of Banks (% flow): 80, 80, 80, 80, 60, 60, 50, 50 - Non-financial corporates (% flow): 100 across all time buckets shown - Reverse repurchase agreements (% flow): 0 across buckets shown - Negotiable certificates of deposits held (% flow): 80, 80, 80, 80, 60, 60, 50, 50 - Loans to Governments (% flow): 100 across buckets shown - Non-bank financial institutions (% flow): 100, 90, 90, 90, 60, 50, 50, 25 - Individuals (% flow): 50, 50, 50, 50, 30, 30, 10, 10 - Interest rate derivatives (% flow): 100 across buckets shown - Foreign exchange derivatives (% flow): 70, 70, 70, 70, 70, 0, 0, 0 - Equity and credit derivatives (% flow): 100 across buckets shown - Forward asset sales (% flow): 100 across buckets shown - Representative run-off rates on liabilities in the long-term scenario (selected lines, percent stock or percent flow by time bucket): - Demand deposits of Singapore govt and statutory boards (% stock): 23, 11, 9, 9, 20, 0, 0, 0 - Other governments (% stock): 16, 8, 6, 6, 3, 1, 0, 0 - Non-bank financial institutions (% stock): 7, 3, 3, 3, 2, 2, 0, 0 - Non-financial corporates (% stock): 21, 11, 12, 3, 0, 0, 0, 0 - Individuals (% stock): 21, 11, 5, 5, 0, 0, 0, 0 - Fixed deposits of Singapore govt and statutory boards (% flow): 100, 100, 100, 100, 87, 69, 35, 17 - Fixed deposits of Other governments (% flow): 100, 100, 100, 100, 66, 66, 33, 17 - Fixed deposits of Non-bank financial institutions (% flow): 23, 23, 23, 23, 18, 20, 10, 5 - Fixed deposits of Non-financial corporates (% flow): 11, 11, 11, 11, 10, 11, 6, 3 - Fixed deposits of Individuals (% flow): 10, 10, 10, 10, 12, 14, 7, 4 - Negotiable certificates of deposit issue (% flow): 80, 80, 80, 80, 60, 60, 50, 50 - Debt securities issued (% flow): 80, 80, 80, 80, 60, 60, 50, 50 - Interbank obligations (% stock): 10, 5, 5, 4, 4, 15, 9, 0 - Intragroup obligations (% stock): 7, 3, 3, 3, 11, 8, 0, 0 - Foreign exchange derivatives (% flow): 70, 70, 70, 70, 70, 0, 0, 0 - Other liabilities on balance sheet (% flow): 100 across buckets shown - Guarantees (% stock): 23, 22, 22, 2, 0, 0, 0, 0 - Bills for collection (% flow): 100 across buckets shown - Forward asset purchase (% flow): 100 across buckets shown - Underwriting commitments (% flow): 100 across buckets shown - Other liabilities off balance sheet (% flow): 100 across buckets shown ### Methodological notes and data limitations - Some run-off rates are explicitly estimated from a two-standard deviation episode in historical data; others use judgment informed by literature and anecdotal evidence. - Run-off rates for interbank and intragroup deposits use quarterly data from BIS Locational Banking Statistics to proxy volatilities; intragroup funding found to be less volatile than interbank funding. - FX swap OTC volume data not available; staff proxy liquidity using bid-ask spreads in USDSGD FX forward contracts (daily Bloomberg data). - Data limitations prevent separating reserve requirements from liquid assets; reserve requirements are counted toward liquid assets in tests. *Source: Appendix VIII. Parameters for Bank Liquidity Stress Tests (1sgpea2019005) — IMF staff calculations and assumptions.* --- _Source: https://www.imf.org/-/media/files/publications/cr/2019/1sgpea2019005.pdf_