Singapore
Financial Sector Assessment Program; Technical Note-Financial Stability Analysis and Stress Testing

This technical note Financial Stability Analysis and Stress Testing on Singapore contributes to the 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, which simulate the health of banks, insurers, nonfinancial 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. The 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. The stress tests reveal that the financial system is broadly resilient to severe adverse shocks; however, foreign exchange liquidity is a key vulnerability. The analyses suggest that Monetary Authority of Singapore should continue strengthening its surveillance by closing data gaps and developing its analytical tools. Further data collection on domestic interlinkages, household mortgage debt at the borrower level, insurers’ balance sheets would enhance surveillance.

Abstract

This technical note Financial Stability Analysis and Stress Testing on Singapore contributes to the 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, which simulate the health of banks, insurers, nonfinancial 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. The 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. The stress tests reveal that the financial system is broadly resilient to severe adverse shocks; however, foreign exchange liquidity is a key vulnerability. The analyses suggest that Monetary Authority of Singapore should continue strengthening its surveillance by closing data gaps and developing its analytical tools. Further data collection on domestic interlinkages, household mortgage debt at the borrower level, insurers’ balance sheets would enhance surveillance.

Executive Summary1

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. It boasts a highly developed and sophisticated financial sector, 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.

Singapore’s 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. Financial firms are increasingly exposed to cyber risk, and the authorities have actively enhanced the regulatory framework in this area.

This 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, which simulate 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. The 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.

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, however, could occur through cross-border interbank exposures and financial market volatility. Spillovers to and from Singapore largely mirrors a strong foreign bank presence headquartered in advanced economies (e.g., Japan, the U.K., and the U.S.) as well as the 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.

Going forward, the authorities should continue to strengthen U.S. dollar liquidity among D-SIBs. The authorities have the capacity to provide U.S. dollars to banks through money market operations and the FX swap market has historically been resilient to periods of stress. Nevertheless, it is important for banks to self-insure more of their foreign currency liquidity risk. The Monetary Authority of Singapore (MAS) has chosen to use the supervisory process to encourage banks to improve their foreign currency liquidity positions. This approach seems feasible, given that the supervisory process has been successful in reducing banks’ reliance on the foreign currency swap market for funding normal U.S. dollar lending activity. Other jurisdictions have found it useful to introduce minimum requirements for specific foreign currency Liquidity Coverage Ratios. MAS should therefore keep this option open if improvement is not achieved through the supervisory process.

MAS should continue strengthening its surveillance by closing data gaps and developing its analytical tools. Further data collection on domestic interlinkages, household mortgage debt at the borrower-level, insurers’ balance sheets would enhance surveillance. MAS should consider alternative approaches to estimate credit gap, recognizing changes in credit cycles, which would better support timely macroprudential policy actions. Solvency stress testing could be improved by adopting more elements of the Singapore Financial Reporting Standards 109 and liquidity stress testing could be improved by revising cashflow reporting templates. Cyber risk surveillance would benefit from developing a cyber network map that accounts for both financial linkages and Information and Communication Technology (ICT) connections.

Table 1.

Recommendations on Financial Stability Analysis and Stress Testing

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“I-Immediate” is within one year; “N-Near term” is within 1–3 years; “M-Medium term” is 3–5 years.

Background

1. Since the last FSAP, the Singaporean economy has navigated economic and financial cycles well. The fall in commodity and house prices affected oil-related sectors and construction in recent years, but overall activity has held up reasonably well. Singapore’s financial markets have weathered recent bouts of market volatility with asset prices showing no lasting impact. There is no sign of a build-up of excessive credit as of now (Figure 1). Macroeconomic and financial stability has been preserved thanks to strong policy frameworks, sound policies, and a large net foreign asset position. After a period of subdued economic activity, growth accelerated in 2017 benefitted from the global expansion and easy financial conditions, but growth has started to moderate. To strengthen long-term growth prospects amid population aging, the government is encouraging the economy to adopt emerging digital technologies. This strategy has put Singapore at the forefront in fintech but makes cyber risk as a growing risk to financial stability.

Figure 1.
Figure 1.

Assessment of Credit Cycle

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: MAS; Haver Analytics; and IMF staff calculation.Note: Green and red colors show low and high vulnerable periods in credit cycle, respectively. The first four indicators are standardized with its mean and standard deviation. The red color in the last row (“overall assessment”) shows a strong signal of a build-up of systemic risk, with (i) either the change in the credit-to-GDP ratio above 5 percent (y-o-y) or (ii) the credit-to-GDP gap greater than 1.5 times its standard deviation and the annual growth rate of the credit-to-GDP ratio above 10 percent (y-o-y), while the orange color denotes a modest signal of systemic risk build-up with the change in the credit-to-GDP ratio between 3 and 5 percentage points.

2. Singapore is an important regional financial center with high quality supervisory and regulatory framework but is exposed to external risks. Singapore is a small and very open economy, and its large financial system has significant cross-border linkages and lending exposures, especially to China and ASEAN countries. The financial system is exposed to external macrofinancial shocks through extensive trade and financial channels. A growth slowdown in China or disorderly monetary policy normalization in advanced economies would deeply impact the financial system in Singapore and also in the region. The impact can be amplified by domestic vulnerabilities, like corporate sector leverage and households’ sensitivity to property prices.

3. The objective of the FSAP systemic risk analysis is to assess the resilience of the financial system to adverse shocks. The assessment relies on simulations of the capacity of the financial system to withstand severe but plausible macrofinancial shocks. It assesses risks and vulnerabilities in the system and the channels through which adverse shocks are transmitted and amplified. Adverse scenarios are devices for exploring risks and should not be interpreted as macroeconomic forecasts.

4. The remainder of this technical note is structured as follows. The background section describes the macrofinancial environment, the scope of systemic risk assessment, and macrofinancial scenarios used in the analyses. The section on financial stability analyses includes analyses of contagion and cyber risk, and analyses of the resilience of households, non-financial corporations, banks and insurers. The concluding section provides an overall assessment and recommends policy actions to enhance financial stability.

A. Financial System Landscape

5. Singapore’s financial system is large and dominated by banks (Table 2). The financial system mostly comprises banks (with assets equal to about 600 percent of GDP) and asset management firms (with 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 the three local banking groups (DBS, OCBC, and UOB) and four foreign banking groups (Citibank, Maybank, Standard Chartered, and HSBC).2,3 All but one of the remaining non-DSIBs are foreign branches.4 The insurance sector holds assets of 55 percent of GDP and Singapore is an important regional center for reinsurance. The asset management industry caters mainly to foreign investors, invests primarily outside of Singapore, and is not a significant source of funding for the banking system.

Table 2.

Financial Sector Structure (2013–2018Q2)

(In billions of Singapore dollars)

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Sources: MAS; Haver; and IMF staff calculations.Notes:

Data from the Central Provident Fund.

Data for 2017.

Based on annual Singapore Asset Management Survey for 2013 and 2017. Financial Institutions surveyed and responded include Banks, Capital Markets Services licensees and other financial sector entities conducting asset management activities.

AUM = Assets under management.

As at March 31, 2013.

Registered and licensed fund managers.

Other holders of CMS license comprise real estate investment trust managers, credit rating agencies, and corporate finance advisers.

The MAS has designated three local banking groups and four foreign banking groups as D-SIBs in April 2015, which comprise twelve individual D-SIB entities.

Foreign banks include foreign D-SIBs.

Data not available.

Not reported.

6. The three local D-SIBs play a key role. They provide a full range of services in retail and institutional banking as well as wealth management, which accounts for a growing share of these banks’ total income (Figure 2). Local D-SIBs account for about 60 percent of domestic loans to private sectors and over 80 percent of mortgage loans in Singapore. They also have significant cross-border lending to China and ASEAN countries (about a quarter of total lending), an activity that picked up strongly in the last two years (Figure 2).

Figure 2.
Figure 2.

Cross-border Linkages of the Banking System

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

7. Foreign banks have a significant presence. They account for a half of total financial sector assets. Except for the four foreign D-SIB subsidiaries and one small locally incorporated wholesale bank, the rest operate as branches. Foreign D-SIB subsidiaries focus on local retail banking, while foreign branches use corporate deposits and intragroup funding to lend non-financial corporates and provide financial services like treasury and wealth management across the region. Particularly, non-DSIB foreign branches focus on cross-border banking services with their non-resident loans and liabilities accounting for over a two-third of total lending and liabilities to private sectors, respectively.5

8. Though less systemically important than banks, insurance activity has grown in recent years. Life insurance is primarily written domestically while general insurance is written across the region. Life insurance covers protection products (mainly against mortality, morbidity and accident risks) and savings products. Only a small amount of annuity products is underwritten, given the dominance of the Central Provident Fund’s (CPF) Lifelong Income for the Elderly scheme—a life annuity scheme administered by the government and associated with the compulsory pension scheme. The assets of the insurance sector are concentrated in the four largest direct life insurers by assets: AIA, Great Eastern, NTUC Income and Prudential. Reinsurance activity is growing, but only accounts for about 7 percent of the assets of the sector.

9. The asset management industry serves as a gateway to the region for funds from around the world. As of 2017 there are 715 licensed and registered fund managers in Singapore. Assets under management of financial institutions have grown by 15 percent per year to reach some 701 percent of GDP in 2017 (Table 2).6 Even though it is large, its links to the domestic economy are limited because much of its activity is cross-border. Some 78 percent of assets under management are sourced from abroad, and 50 percent are invested outside Singapore in the Asia-Pacific region. Resident collective investment schemes, which intermediate savings of Singaporean individuals, are significantly smaller at 11 percent of GDP.7 Asset managers comprise about 23 percent of all securities financing activity in Singapore, against which banks are the primary counterparty (MAS, 2018). In addition to the Government of Singapore Investment Corporation (GIC) and Temasek, seven global public investors maintain offices in Singapore.8

10. Singapore’s two sovereign wealth funds and a public mandatory saving scheme are important components of the financial landscape but somewhat insulated from the financial system. Temasek Holdings and the GIC are responsible for managing government savings. As they have diverse portfolios focusing on foreign assets, they are not tightly connected with the financial system. But Temasek is a major shareholder of two D-SIBs, DBS and SCB. The CPF invests its assets (80 percent of GDP) mostly in the Special Singapore Government Securities, whose proceeds are managed by GIC, without connection to other financial institutions.9

11. The foreign currency (FX) swap market is a global center for exchanging major currencies. Singapore boasts the third-largest foreign currency market in the world (BIS, 2016), which is linked to the economy’s role in international trade and as a gateway to Asia.10 Some three-quarters of foreign currency exchange activity takes place in the form of derivatives, including forwards and swaps.11 In turn, the foreign currency swap activity is concentrated in FX swaps, mostly with a maturity of less than one year, rather than in cross-currency basis swaps. Typically, foreign banks provide foreign currency to the swap market and domestic banks provide Singapore dollars (MAS, 2017). The largest participants are European banks, followed by U.S. banks, and then Singaporean and Japanese banks. MAS participates in the FX swap market as part of its money market operations. Notably, MAS is currently a significant provider of foreign currency to the swap market.12 Since the last FSAP, D-SIBs have significantly reduced their reliance on the FX swap market for funding normal foreign currency lending. Nevertheless, domestic banks’ value still seems to be associated with the price of borrowing U.S. dollars from this market (Box 1). The U.S. dollar plays a special role in the FX swap market as an intermediate currency between other pairs of currencies. The largest volumes of transactions are against the Japanese yen, followed by the euro, and some 88 percent of trading is with counterparties outside Singapore. The commercial banks that play the largest role in the FX swap market are foreign branches (Figure 3). Price and quantity measures show some resilience of the FX swap market under past stress episodes.13

Figure 3.
Figure 3.

Structure of the FX Swap Market in Singapore

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Banks in Foreign Currency Swap Markets

Since the global financial crisis (GFC), the spread on the long-term cross-currency basis swap has deviated from zero and become negative for many major currencies against the U.S. dollar. The existence of non-zero cross-currency basis reflects deviation from the covered interest parity and presents an opportunity for banks to profit from participating in the FX swap market. In theory, with a negative spread for the SGD/USD cross-currency swap, banks in Singapore can earn a risk-less profit by exchanging U.S. dollars for Singapore dollars and lend the Singapore dollars received. Recent studies have found that a main reason for the presence of a persistent negative currency basis in recent years is that bank capital has become costlier since the GFC (Du and others, 2018). In other words, the negative cross-currency basis reflects the dislocation in the FX swap market and capital constraints of participants in the market. While Singaporean banks generally do not rely on the FX derivative market for their structural funding needs, dislocation in the market can still pose risk to banks, especially in stress conditions.

The SGD/USD cross-currency basis appears to be a systematic risk factor that prices equity returns of Singaporean banks, although the magnitude is small (text figure) and causality is not straightforward. For daily returns of Singaporean banks since 2010, the five-year SGD/USD cross-currency basis appears to be a systematic risk factor that prices these daily equity returns.1/ Based on an augmented Fama-French four-factor model (with ex-Japan Asian factors based on Kenneth French’s website), a more negative value of the basis is associated with, on average, a contemporaneous lower return for Singaporean banks.2/ The statistical significance and economic magnitude of this relationship appear to be small overall and slightly different across the banks.

Note: 1/ As longer-term contracts are not frequently used, the measured basis may be influenced by liquidity and other market-wide factors. The empirical analysis includes standard market risk factors (such as Fama-French factors based on data from Kenneth French’s website) to exclude the potential confounding effects from these factors.2/ The augmented Fama-French four-factor model is the standard Fama-French three-factor model with the addition of a cross-currency basis factor. The Fama-French three factors include the market factor in Singapore and the SMB and HML factors based on daily returns of ex-Japan Asian stocks from Kenneth French’s website. The cross-currency basis factor is the spread for the SGD/USD cross-currency swap (deviation from the annual mean).

B. Current Situation in the Financial System

Banking Sector Soundness

12. The financial health of Singapore’s banks compares well to those of other financial centers. Risk-weighted capitalization is strong (16.9 percent in 2018) and provides a buffer of 7 percentage points that can be used in a stress scenario (Table 3).14 Although capital ratios are lower than ones in other similar-sized financial centers, they compare well with neighbors and other international banks and also reflect conservative risk-weighted asset calculation. D-SIBs’ leverage ratio stands at 7.2 percent, higher than the 6.0 percent average for global systemically important banks (Figure 4).

Table 3.

Financial Soundness Indicators (2013–2018)

(In percent)

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Source: MAS.Note: The capital adequacy ratio is defined under Singaporean risk-based capital regulations to be the ratio of available capital to risk-weighted assets (i.e., required capital).
Figure 4.
Figure 4.

Singapore and Peer Countries: Financial Soundness Indicators

(In percent, latest)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Sources: IMF Financial Soundness Indicators; national authorities’ websites; and Haver Analytics.Note: Yellow bars refer to Singapore’s peer group of financial centers and economies with financial sectors that account for a large share of GDP.

13. Profitability is strong and asset quality is broadly sound, but some risks remain. Banks have enjoyed solid profits with stable net interest margins, higher than those in other financial centers, and increasing fee and commission income (Figure 4). Nonperforming loans (NPLs) are low―especially for D-SIBs (1.9 percent of total loans) relative to non-DSIBs (2.1 percent of total loans)―and are adequately provisioned. However, there exist legacy exposures to transportation (including in oil-related sectors) and manufacturing firms. Also, “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. However, if China growth slows down, regional spillovers could be considerable and NPLs could rise. Loans directed towards the property sector, including residential mortgages and loans to building and construction sector, accounted for about 30 percent of total loans, exposing banks to property price movements.

14. The banking system has adequate liquidity overall. Narrowly-defined liquid assets are 12 percent of assets and 15 percent of short-term liabilities (Figure 5).15 Loans are about 99 percent of deposits, which is high amongst advanced economy peers and reflects the significant presence of foreign branches with intragroup funding. However, loans have been growing faster than deposits, mainly as a result of foreign currency activity (Figure 5). D-SIBs have healthy buffers over minimum regulatory LCR requirements in all currencies and in Singapore dollars. Their (asset-weighted) LCRs in these currencies are 132 percent and 287 percent respectively. In the LCR, most liquid assets are high quality, and LCR requirements are driven by derivatives and margin calls (Figure 6). D-SIBs’ Net Stable Funding Ratio (NSFR) is 110 percent. For domestic banks, this NSFR provides a narrow margin over the 100 percent requirement, but MAS assesses that the 10-percentage point buffer is sufficient given the low volatility of these ratios.

Figure 5.
Figure 5.

Singapore and Selected Countries: Bank Liquidity Indicators

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Figure 6.
Figure 6.

Composition of D-SIBs’ Liquidity Coverage Ratios (LCRs)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Sources: MAS; and IMF staff calculations.Notes: Liquid assets refer to the HQLA numerator in the LCR and outflows refer to the gross outflows in the denominator of the LCR. The aggregation across D-SIBs is achieved by summation across individual entities, using submissions at the country-level group where available. Each outflow is calculated as an outstanding balance multiplied by its LCR weight under MAS Notice 649. Each color shows a category of cash outflow, according to the categorization in that notice. For example, “additional requirements” includes derivatives and margin calls.

15. However, the foreign currency liquidity position is vulnerable to stress. The banking system’s loan-to-deposit ratio in foreign currency has risen some 12 percentage points over the past two years, to 128 percent, which is driven by non-DSIBs. Since the last FSAP, D-SIBs successfully reduced their foreign currency loan-to-deposit ratios from these levels to 90 percent, which means that they do not rely on FX swaps to fund normal foreign currency lending. D-SIBs’ U.S. dollar LCR is 48 percent, which 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 (Table 4).16 The adverse scenario implicit in the computation of the U.S. dollar LCR includes assumptions of stress in the FX swap market. This shortfall is split relatively evenly between domestic and foreign D-SIBs, and for foreign D-SIBs is driven by their branches (rather than subsidiaries). Analysis of banks’ liquidity planning processes reveals that banks count on the FX swap market for foreign currency funding in the event of liquidity stress. There are safety nets in U.S. dollars, including the fact that the 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 its daily money market operations. MAS’ foreign exchange reserves are significant (at 80 percent of GDP), but the primary purpose of these reserves is to implement monetary policy.

Table 4.

U.S. Dollar Liquidity Shortfalls at D-SIBs. 1/

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Sources: MAS; and IMF staff calculations.Notes:

This table shows the additional high-quality liquid assets that would be needed to meet hypothetical requirements for projected one-month liquidity in USD.

Average of USD LCRs at the entity level, weighted by total assets. If we use the country-group level of consolidation for those banks that report it, we obtain a USD LCR of 38 percent for all D-SIBs.

This assumes an LCR requirement of 100% for domestic banks and 50% for foreign banks. The requirements are imposed at the same level of consolidation as those for the all-currency LCR.

16. Banks’ funding structure relies primarily on deposits, but also reflects the openness and sophistication of the economy. Some 73 percent of D-SIBs’ liabilities come from deposits, of which 20 percent are deposits of non-financial firms. Local D-SIBs rely relatively more on retail deposit funding and foreign D-SIBs rely relatively more on interbank funding, which is mostly intragroup (Figure 7). The significant reliance of branches on intragroup funding results in loan-to-deposit ratios of 145 percent. The openness of Singapore also drives high shares of foreign currency (61 percent) and nonresident (71 percent) liabilities. Some 37 percent of D-SIBs’ liabilities are denominated in U.S. dollars and 27 percent are denominated in other foreign currencies. The complexity of banks’ funding is evident from the fact that “other” liabilities, including derivatives, make up 12 percent of liabilities.17

Figure 7.
Figure 7.

D-SIBs’ Funding

(In percent of liabilities)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: MAS.Notes: Bank deposits include funding from repurchase agrements. Other liabilities include derivatives.

17. Since the last FSAP, MAS has strengthened the regulatory framework for bank capital and liquidity. It amended regulatory capital requirements (Notice 637) to introduce the minimum leverage ratio for locally incorporated banks (3 percent) and loan loss provisioning rules (Notice 612), known as the SFRS 109, to encourage timely recognition of credit losses. MAS has also introduced minimum LCR and NSFR requirements for D-SIBs in all currencies and in Singapore dollars (Notice 649 and Notice 652). 18 It also monitors LCRs in significant foreign currencies, but there is no minimum requirement for foreign currency liquidity specifically. Other banks remain subject to the minimum liquid asset requirement. It conducts regular stress tests of bank solvency and liquidity.

Insurance Sector Soundness

18. 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 in falling asset prices in 2018 (Table 3). Competition in the direct general insurance sector has driven down its profits recently. Some three-quarters of the regulatory capital of the four largest life/composite insurers comes from yet undeclared discretionary benefits (Box 2).

19. Market risks in the life insurance sector have trended up in the last decade. Given their large portfolios of marketable securities, the main risk to life insurers is of falls in asset prices. Life insurers have increased their share of assets allocated to equity securities over time (Figure 8), as their participating policies have shifted to those with greater emphasis placed on terminal (rather than reversionary) bonuses. Life insurers are also notably exposed to corporate bonds, making them particularly vulnerable to widening credit spreads. Insurers in Singapore need corporate bonds to match their liability cashflows because life insurance liabilities are almost as large as the market for bonds issued by the Singapore government and statutory boards. However, more than 90 percent of rated corporate bonds are investment grade. The share of assets allocated to corporate bonds has remained constant over time, but the credit quality has shifted very slightly toward lower-rated investment grade bonds.

Figure 8.
Figure 8.

Life Insurers’ Asset Allocation

(In percent of total assets)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: MAS.

Insurance: The Allowance for the Provision of Non-Guaranteed Benefits (APNGB)

The loss absorbing capacity of undeclared discretionary benefits from participating policies is difficult to determine and comprise a large share of regulatory capital in Singapore. Participating policies are popular in Singapore, representing some 80 percent of insurers’ liabilities. The assets of these policies exceed their guaranteed liabilities. In principle, this excess will need to be shared in future between the insurer and its policyholders, the latter in the form of discretionary benefits (bonuses) to be declared. A portion of these excess assets is recognized as regulatory capital. The amount of capital from such discretionary benefits that insurers are eligible to recognize under the regulatory capital calculation is known as the allowance for the provision of non-guaranteed benefits (APNGB). About three-quarters of regulatory capital of the four largest life/composite insurers comes from this source, given their sizable portfolios of participating policies.

The current capital regulations therefore include safeguards to prevent insurers from over-relying on this source of capital. Given the need to share these excess assets with policyholders, some of this excess would not be available to absorb losses in an adverse scenario. In principle, the undeclared discretionary benefits are not guaranteed, and insurers can refuse to declare them if necessary. Furthermore, the APNGB applies two limits on the extent to which undeclared discretionary benefits can be recognized as eligible capital for the purposes of meeting minimum capital requirements. First, the capital of participating policies is also measured separately from other capital, and only 50 percent of the value of future discretionary benefits can be recognized toward regulatory capital of participating policies. This turns out to be the binding condition in most cases. It can be interpreted as allowing insurers to take credit for a hypothetical cut to future bonuses by one-half. Second, the capital of participating policies is ringfenced in the sense that it is not allowed to be used to offset any capital shortfalls among other insurance policies. These safeguards were assessed to be in observance of the Insurance Core Principles (IMF, 2013).

The safeguards seem reasonable given historical experience and the stress tests. The first safeguard in the APNGB credits insurers with a cut of discretionary benefits by 50 percent. This was not exceeded under the GFC—insurers needed to cut discretionary benefits by only 24 percent in 2008. It is also not exceeded in the bottom-up stress tests discussed below, where selected insurers report that they would need to cut discretionary benefits by up to 40 percent. The extent of reduction to discretionary benefits depends on the severity of the stressed situation.

These safeguards will be revised as part of the enhanced risk-based capital framework (RBC 2). The revised framework will introduce new safeguards (against overreliance on the APNGB) in the form of minimum requirements for CET 1 and Tier 1 capital. These minimum requirements will ensure that capital requirements will be met by high quality capital. The revised framework will also remove the 50 percent limit, which authorities view as consistent with the lower risk appetite (1-in-200 years) that is built into the revised framework.

20. Since the last FSAP, regulations have evolved to enhance the soundness of the sector. Requirements came into force in 2014 for insurers to adopt formal capital planning and risk assessment processes. The authorities have recently revised the risk-based capital regime (RBC 2) to make several technical improvements and this is expected to come into force in 2020. Large insurers that staff met are prepared for this new regime and do not expect it to change their capital positions materially. International Financial Reporting Standard (IFRS) 17 was recently adopted by the Singaporean accounting standard-setting body, to come into force at the same time proposed by the International Accounting Standards Board. Insurers are evaluating its implications group-wide, which could be significant. Larger insurers that are part of internationally active insurance groups are also involved in field-testing of the Insurance Capital Standards being developed by the International Association of Insurance Supervisors.

Interconnectedness

21. Singapore’s financial sector has extensive direct cross-border linkages. Singaporean banks have large cross-border exposures amounting to about 60 percent of total loans, as they heavily engage in cross-border lending especially to East Asia (China, Hong Kong SAR, Japan, Korea, Taiwan PoC, and ASEAN). Being a financial hub, there is also a large presence of foreign banks, some of which are systemically important to domestic system. On the funding side, this contributes to the fact that interbank funding is mostly driven by cross-border flow, especially intragroup funding which functioned as a stabilizing factor for some foreign branches and subsidiaries during the past crises (MAS, 2015; Figure 9). As noted above, there can be other sources of cross-border connections including common exposures and ownership links, which are beyond the scope of this note.

Figure 9.
Figure 9.

Banking System’s Funding

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

22. Financial institutions within Singapore, however, have limited direct interlinkages among each other.19 The gross bilateral exposures within the domestic financial system amounted only to 29 percent of GDP at 2018Q2 (Table 5), very small relative to total assets of financial institutions and those in other countries (e.g., 230 percent of GDP in Ireland at 2015Q2). Three quarters of the exposures are between banks. As shown in the 2013 FSAP, bank liabilities to nonbank financial institutions (NBFIs), including asset management firms, are small at about 4 percent of GDP (1.1 percent of D-SIBs’ liabilities) and banks also have very small lending exposures to NBFIs (only 1 percent of GDP, 0.3 percent of D-SIBs’ assets). Furthermore, excluding intra-group and cross-border interbank exposures, interbank exposures among the 118 banking groups stood only at 0.8 percent of total banking system assets and about 5 percent of GDP.

Table 5.

Financial Network Matrix within the Domestic Financial System

(Gross Bilateral Position, as of 2018Q2)

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Sources: MAS; and IMF staff calculation.

C. Developments in the Private Sectors

Household Sector Soundness

23. Households have a strong financial position. Total household assets amount to 446 percent of GDP at end-2017 (Figure 10). The household sector’s balance sheet has strengthened further thanks to rising financial asset and housing asset amid the recent increase 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 and the average debt-to-income ratio is low at 2.1. The ratio of debt outstanding to total assets stood at only 15.6 percent at end-2017. Liquid financial assets (including currency, deposits, shares, and securities) are almost twice the size of total household debt, reflecting the resilience of household balance sheets. NPL ratio of household loans is low at less than 0.5 percent as of 2018Q2.

Figure 10.
Figure 10.

Total Assets and Liabilities of Households as of end-2017

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

24. Households are sensitive to house price fluctuations. House prices have contributed the most to developments in household assets, with about 44 percent of assets comprising residential property. A panel data analysis with 17 countries from Asia and other advanced economies by MAS (2017) found that property prices have contributed to about half of the change in household debt-to-GDP ratio after the GFC. Other factors, such as stock prices, interest rates, income, and macroprudential policies, have also played a role in driving the changes in household debt.

25. House prices are expected to remain stable, but the upcoming housing supply could pose a downside risk to the prices. Real estate prices recovered strongly since the second half of 2017, but the growth rates have since stabilized with the latest tightening of property market measures.20 Private residential property prices experienced a strong turnaround (+8.3 percent, y-o-y) between 2017Q3 and 2018Q2 after a gradual decline of about 12 percent between 2013Q3 and 2017Q2. Following the tightening of macroprudential measures in July 2018, however, the pace of house price inflation has started to moderate. An upcoming increase in the supply of new housing units may pose a downside risk to property prices (see Technical Note on Macroprudential Policy).

26. Sound household sector financial conditions and stable property price developments are crucial for financial stability. Households allocate their funds into 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. Thus, household sector financial vulnerabilities, which can lead to higher NPLs and deposit withdrawal, have important implications on banks’ solvency and liquidity and financial stability overall. Also, as mentioned above, property-related loans accounts for about 30 percent of total bank lending, exposing banks to risks stemming from property price movements (see the bank solvency stress tests below).

Non-Financial Corporate Sector Soundness

27. Corporate leverage is high, but corporates maintain ample cash buffers. Corporate debt increased steeply from 104 percent of GDP in 2010 to over 148 percent in 2015Q3 in a persistently low interest environment post-GFC (Figure 11). It has stabilized since then and stood at 148 percent of GDP as of 2018Q2. Such high level of debt relative to the size of domestic economy to some extent reflects the presence of large foreign owned companies and Singapore corporate’s large overseas activities in general. The balance sheet leverage ratios do not stand out as much, with the median debt to equity ratio falling in recent years to below 40 percent. In addition, corporates hold significant amount of liquid assets with a median cash-to-debt ratio of 50 percent. The leverage on a net basis is much lower.

Figure 11.
Figure 11.

Financial Indicators of Non-Financial Corporates

(In percent)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Sources: Monetary Authority of Singapore, Singapore Department of Statistics, IMF staff calculations.

28. Corporate debt service capacity appears healthy at the aggregate level. Corporate profitability, return on assets, gradually declined post-GFC until 2016. Deteriorating profitability together with the increasing leverage weighted on healthy corporate debt service capacities, and ICR (EBIT divided by interest expense) fell from the high level of 12 to below 4. Since 2016, however, profitability, as well as the leverage, have started to level off, stabilizing the debt service capacity at a still-healthy level of about 3.

29. Corporate sector health is important for financial stability. Corporates in Singapore rely heavily on bank loans for financing (90 percent of domestic corporate debt). Deposits from corporates are also important source of bank funding. Corporate sector soundness thus has important implications on banks’ asset quality and financial stability overall. A potential area for risks is the large and increasing share of foreign-currency denominated corporate debts (64 percent as of 2018Q2, Figure 12). While this may reflect the international nature of Singapore’s corporates as mentioned earlier, given the potential currency risks, the authorities monitor hedging strategies of large companies based on their financial reports and notes that most companies employ natural hedging and/or foreign exchange derivatives (MAS, 2018).

Figure 12.
Figure 12.

Corporate Debt Vulnerabilities

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

30. Corporate debt vulnerabilities have improved in recent quarters. The share of debt-at-risk (i.e., the share of debt owed by firms with ICR below one) peaked recently in 2015, as the low oil prices and slow global trade put a strain on companies especially in marine and offshore engineering (captured in TSC in Figure 12) and manufacturing sector. It has improved since then in light of strong external demands and the recovery of oil prices, especially among large corporates in manufacturing sector. Corporate NPL ratio also fell to 2.4 percent in 2018Q3 from the latest peak of 2.8 in 2016Q4. Currently, while some weakness continues in construction and TSC sector especially among medium-sized corporates, the overall debt-at-risk is at a moderate level (e.g., below the average levels seen among emerging market firms (GFSR, 2016)).

31. The riskiness of credit allocation does not seem to have risen significantly despite easy credit conditions in 2017. The riskiness of credit allocation index captures the evolution of relative vulnerabilities among those firms that are leveraging up fast; that is, the average vulnerabilities (e.g., high debt ratios) among the firms that are accumulating debt fastest, compared to those reducing debt fastest (GFSR, April 2018). If it rises over time, it indicates the relative vulnerabilities among the top issuers are rising, thus credit allocation is becoming riskier. In Singapore, it rose during the periods of easy financial conditions, for instance, during the run-up to the GFC and recently in 2010–11. They are then followed by de-risking periods as financing condition subsequently tightens (Figure 13). Despite easy financing conditions in 2017, the indices do not yet show a significant uptick in the riskiness of credit allocation.

Figure 13.
Figure 13.

The Riskiness of Credit Allocation

(Index, two-year moving average)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Sources: WorldscopeJMF staff esiiiriates.

D. Scenarios and Scope of Financial Stability Analyses

Risks and Vulnerabilities

32. The main macrofinancial vulnerability relates to Singapore’s extensive linkages to the region and the rest of the world (Risk Assessment Matrix, Appendix I). The financial system is exposed to global and regional macrofinancial shocks through significant trade and financial channels. Particularly, a sharp economic slowdown in China would have a large impact via the extensive trade, investment, and financial linkages, and via regional second-round spillover effects. A disorderly normalization of monetary policies in advanced economies would also have an important impact transmitted through financial and property markets. A sharp fall in house prices and a widening of credit spreads would impair balance sheets of financial institutions as well as private sectors.

33. Cyber risk is a growing risk to financial stability and the expansion of fintech poses challenges to financial oversight. Financial innovation has increased the exposure to cyber-attacks, which could adversely affect banks and nonbank financial institutions through disruption of business, damages to data and systems, explicit and implicit cyber exposures, and loss of confidence within the financial system.

Macrofinancial Scenarios

34. Two tail scenarios underpin the systemic risk assessment. Against the background of the macrofinancial developments and structural features of the financial system described above, MAS and the FSAP mission designed two severe but plausible scenarios as the basis for the financial stability analyses (Table 6).21 The scenarios are driven mainly by external shocks and their initial impacts are amplified by existing vulnerabilities (e.g., legacy loans to transportation firms, vulnerable foreign currency liquidity, households’ sensitivity to property prices, high corporate leverage). The scenarios have a duration of five-and-a-half years and start at the reference date of 2018Q2. The first half-year of the adverse scenarios matches the baseline scenario, so their downside events only occur from 2019 onward. These scenarios are mainly used in the bank and insurer solvency stress tests22 and corporate sector analyses. Bank liquidity stress tests use tailor-made scenarios to capture short-term dynamics. The following narratives underpin the scenarios:

  • Baseline scenario is based on the July 2018 World Economic Outlook (WEO) projections. Some of these variables, such as house prices and equity prices, are not part of the WEO projections; as a result, these variables are projected by the FSAP team using a simple VAR model with real GDP growth rate and the Singapore Interbank Offer Rate as additional endogenous variables.

  • Adverse scenario 1 features large-scale global financial market turmoil, precipitating financial cycle downturns in Singapore (text figure). It causes falling asset prices, which then propagate to the real economy. Equity and house prices drop by 40–45 percent, and short-term interest rates rise by 250 basis points in the first two years.

  • Adverse scenario 2 involves a major slowdown and macrofinancial stress in China with a persistent impact on Singapore via its extensive linkages to the ASEAN region (Figure 14). It would lead to rising NPLs, a decline in investor sentiment, and pullback of funding from the region. The interbank spread rises 250 basis points over the stress horizon. Unemployment rise dramatically. The economy would recover only in the fourth and fifth years, but unemployment remains high at the end of the stress test horizon.

Figure 14.
Figure 14.

Transmission Channels of Adverse Shock

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: IMF staff.
Table 6.

Detailed Stress-Test Scenarios

article image
Sources: MAS; and IMF staff estimates.

35. The adverse scenarios are severe enough to ensure that stress tests are rigorous (Figure 15). The output gap opens to -12.3 percent and cumulative declines of real GDP growth peak at 2.3 standard deviations in 2021 under Adverse Scenario 2, more severe than both the Asian Crisis and the GFC. Detailed path of key variables under the scenarios can be found in Table 6.

Figure 15.
Figure 15.

Economic Growth under Baseline and Adverse Scenarios

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Sources: MAS; IMF WEO database; and IMF staff calculation.

Scope of Financial Stability Analyses

36. Staff simulated the impact of these and other scenarios to assess contagion, solvency, liquidity, and cyber risks as well as private sector financial soundness. The impact of the scenarios is assessed with a wide-ranging set of analyses (Figure 16). For banks, the exercise covers the seven D-SIB groups (a total of 10 banks, including both branches and subsidiaries of foreign D-SIBs), which account for about 75 percent of non-bank loans to private sectors.23 For insurers, the exercise focuses on the big four insurers, which represent 80 percent of total assets of the sector. The contagion analyses cover domestic interbank exposures, interlinkages within the domestic financial system, and cross-border bank networks. For private sectors, the FSAP team assesses the impact of adverse shocks on their debt servicing burden. Detailed information of each analysis can be found in Appendix II.

  • Domestic and cross-border Interconnectedness analyses. Following Espinosa-Vega and Solé (2010), the FSAP simulates the impact of a hypothetical bank failure on the domestic interbank market. This model is also applied to cross-border interbank network. A complementary analysis is conducted based on the methodology by Diebold and Yilmaz (2014) using the volatility of bank equity return indices as proxy for bank stress. Further, the FSAP explores how stress in the dollar funding market may signal downside risk to the banks’ health, using the arbitrage pricing theory and an efficient market hypothesis.

  • Private sector financial soundness analyses. MAS, in close collaboration with the FSAP team, simulates mortgage debt servicing ratio under Adverse Scenario 2 and identifies segments of households that are particularly susceptible to negative shocks. The FSAP team’s corporate vulnerability exercise quantifies the share of firms under financial distress (those with inadequate debt servicing capacity, with ICR below one or two) and the debt owed by these firms (i.e., debt-at-risk).

  • Bank stress tests. The tests project the solvency positions of banks under the scenarios explained above. The FSAP performs top-down stress tests that are cross-checked against bottom-up stress tests performed by banks (and administered by MAS). They are complemented by sensitivity analyses, which project banks’ solvency positions under simpler scenarios of single-variable changes in the economic environment, including the impact of fintech on fee and commission income. Concentration risk is assessed by assuming default of the largest private borrowers in the banking system. The bank liquidity stress tests project the liquidity positions of banks under adverse scenarios. Complementary stress tests assess liquidity positions in absolute terms and relative to minimum regulatory requirements. They are complemented with analyses of the drivers of stable funding and concentration of deposit funding.

  • Insurer stress tests. The assessment of insurers’ solvency draws on bottom-up stress tests by insurers and top-down stress tests undertaken by MAS and the FSAP team. In addition, the bottom-up tests include scenarios for catastrophe risk from extreme flooding.24 Given their importance for insurers, term structures of interest rates are modelled, in greater detail than in the bank solvency stress tests.

  • Cyber risk analysis. It involved a bottom-up survey of 18 banks and 17 direct general and composite insurers regarding their exposures to cyber-attacks. Banks are asked to describe cyber events that they would be most vulnerable to and mitigating measures, to conduct qualitative analysis of transmission channels of cyber events, and to provide quantitative estimates of potential losses. Insurers on the other hand are asked to calculate their exposures arising from claims on insurance policies that (explicitly or implicitly) cover such incidents.

Figure 16.
Figure 16.

FSAP Systemic Risk Assessment Framework

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: IMF staff.

Financial Stability Analyses

A. Contagion Risk

Cross-border Banking System Contagion

37. Banks in Singapore are most connected, through direct balance sheet exposures, with banks in major advanced economies like Japan, the U.K., and the U.S. and regionally with Hong Kong SAR, China and Malaysia. The analysis simulates the cross-border transmission of credit and funding shocks based on the methodology by Espinosa-Vega and Solé (2010), using the country-level interbank exposure data from the BIS Locational Banking Statistics (Figure 17). It shows banks in Singapore, including foreign branches and subsidiaries, are tightly connected with banks in Japan, reflecting the exposures through Japanese banks in Singapore. In addition, credit events in banks in major advanced economies such as 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.

Figure 17.
Figure 17.

Spillover of Credit and Funding Shocks through Cross-border Interbank Exposures

(Locational Banking Statistics, unconsolidated)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Sources: BIS Locational Banking Statistics; MAS; and IMF staff estimates.Notes: The analysis measures spillover through aggregate, unconsolidated cross-border bank flows using the BIS Locational Banking Statistics, based on the methodology by Espinosa-Vega and Sole (2010). As a benchmark case, this assumes that banks need to absorb 50 percent of loss given defaults of another banking system and unable to source 50 percent of lost funding thus leading to fire sale of assets at 50 percent discount. The size of spillover measured by capital impairment varies depending on assumed parameters, but relative order across countries remain largely the same.1/ The average over individually triggered failure of all other countries.

Financial Market Evidence for Cross-border Spillover

38. The analysis using equity return data further highlights Singaporean banks’ regional interconnectedness especially with China and Hong Kong SAR. In order to capture the interconnectedness of bank stress beyond balance sheet exposures, a complementary analysis is conducted based on the methodology by Diebold and Yilmaz (2014) using the volatility of bank equity return indices as proxy for bank stress (see Appendix III for methodological details and Figure 18 for results). It shows that Singapore is one of the most connected banking systems in Asia, together with China and Hong Kong SAR. The market data also show that Singaporean banks’ inward connection to the U.S. and the U.K. However, regional connectedness comes out more strongly when using market data than balance sheet data. Singapore banks are tightly connected with China and Hong Kong SAR, while the connectedness with Japan appears much smaller. It also reaffirms that Singaporean banks’ outward spillover is felt mostly by countries in Asia.

Figure 18.
Figure 18.

Cross-border Spillover of Bank Equity Return Volatility

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: Worldscope, IMF staff estimatesNotes: Full sample interconnectedness during January 2016 and December 2018 based on the methodology by Diebold and Yilmaz (2014). Pair-wise spillover measures the percent of a country’s forecast error variance explained by shocks to another country.1/ Percent of Singapore banks’ forecast error variance explained by shocks from another country2/ Percent of the forecast error variance of the country explained by shocks from Singapore3/ Total directional interconnectedness is the sum of inward spillover to the country from all other countries (inward) or outward spillover from the country to all other countries (outward).

Cross-border Spillover from U.S. Dollar Funding

39. Dislocations in FX derivative markets can disrupt bank business activities and signal risk to bank value. Quantile regressions show that 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. Specifically, 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, the idiosyncratic component of bank return (Figure 19).25 This suggests that dislocation in the FX derivative market is correlated with the downside risk to bank value. Moreover, the basis is also predictive about near-term (five days ahead) downside risk to the value of bank equity. This indicates that FX derivative market dislocation can forecast a larger near-term bank return volatility, especially the downside risk to bank value.

Figure 19.
Figure 19.

Dollar Funding-at-Risk Analysis Results

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

40. Stress conditions in FX derivative markets can provide useful insights on bank riskiness, although causality is not straightforward. While Singaporean banks generally do not rely on these markets for structural funding needs, FX derivative markets are used by banks to close US$ funding gaps and are highly relevant for banks’ funding cost in general. It is possible that the measured dollar funding stress indicator is influenced by liquidity and other market factors. This concern is alleviated by the analysis including standard known (Fama-French) factors as well as VIX and Libor-OIS spread as additional factors. The goal of this analysis is to understand how stress conditions in dollar funding market can provide useful insights on bank riskiness and signal downside risk to bank value.

Domestic Interbank Contagion

41. The domestic interbank network analysis, following Espinosa-Vega and Solé (2010), reveals that contagion risks stemming from interbank exposures are limited (Figure 20). Even in the most conservative case, the hypothetical solvency event of a banking group―failing to meet the minimum regulatory requirement (10 percent of RWAs for total capital adequacy ratio)―would have a limited impact on other banking groups, inducing domino solvency events of up to three more banking groups and reducing system-wide total capital ratio by 0.8 percentage points at most.26 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 to face a solvency problem. On contrary, twelve and four small foreign branches have a significant share (more than 10 percent) of assets and liabilities in the form of interbank lending and borrowing, respectively. On average, they are four times more vulnerable to the event than other banks in Singapore.

Figure 20.
Figure 20.

Interbank Network Analysis Results1/

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: IMF staff calculation.Notes: 1/ The size of each vertex represents its degree (the sum of in-degree and out-degree). The thickness of an arrow depicts the volume of a relative bilateral exposure from a bank to another.2/ Index of contagion is the percent of total capital losses in the system due to the failure of each bank, while index of vulnerability shows the average of capitla losses of each bank due to the failure of all other banks.3/ There are three parameters: LGD ratio for interbank lending (λ), loss of funding ratio (ρ), and the discount rate (δ). In the middle two charts, these parameters are set at the most conservative level, λ =0.45, ρ =0.5, and δ =1, while the bottom two charts show the system-wide average of the index of vulnerability and contagion for all possible ρ and δ value.

B. Household Sector Resilience27

42. Household sector soundness analysis is based on simulations of the effect of interest rate and income shocks on household debt service ratios. Mortgage debt service simulations are carried out by shocking the Total Debt Servicing Ratio (TDSR) income and the interest rate for each individual loan account, and by assessing the corresponding increase in borrowers’ mortgage debt servicing ratio (based on adjusted monthly mortgage payment as a share of the total adjusted TDSR income). Shocks correspond to those of Adverse scenario 2, in which the interbank interest rate increases by 250 basis points and nominal GDP drops by more than 20 percent in cumulative terms over the stress horizon. The associated drop in disposable income (10 percent) is computed by estimating the elasticity of the growth rate of household income to nominal GDP growth, using two different definitions of household income (the household gross income and the earnings per worker) in the absence of a national accounting variable of household income that would perfectly match the definition of the TDSR income, and computing the average elasticity according to a meta-analysis principle.

43. Simulations use account-level information of new housing loans granted in 2018 by the 12 largest mortgage lending banks in Singapore, which collectively provide the vast majority of outstanding housing loans by financial institutions. The account-level data includes information on the loan profile (the period of borrowing, the mortgage loan amount, the monthly mortgage repayments, the loan tenure, and the interest rate), the borrower profile (the age and the total debt-servicing ratio), and the property profile (the value of transaction and the type of property).

44. The simulations entail a set of conservative assumptions. Loans are assumed to have been fully disbursed, even though a significant share of borrowers get only partial disbursement at the time the loan is granted.28 Second, the income used in the simulation and in the computation of the TDSR include only income of the main borrowers, so that for joint applications (about one-third of mortgage loans), only one of the borrowers’ income is considered though the loan account would be serviced by a dual- or potentially triple-income household.29 Third, the rise in the SIBOR interest rate is assumed to be fully and immediately passed through to the actual mortgage interest rate for all loan accounts, despite the fact that the majority of borrowers are on non-floating rate packages (and could take further steps to mitigate risks from rising mortgage rates by refinancing to non-floating mortgage rates). Finally, simulations assume a higher likelihood of default should the debt servicing ratio breach a 60-percent threshold, while according to the latest household expenditure survey, the average household typically spends 40 to 45 percent of its income on non-mortgage living expenses and households purchasing private property are likely to have lower expenditure-to-income ratios.30

45. Using newly issued mortgage loans provides a conservative estimate of the actual outstanding amount of debt of borrowers but can also lead to underestimate risks. Given that the outstanding loan amount of existing loans decreases as mortgages are paid down over time, the debt service ratio at the point of loan inception is higher than the one of the average borrower. Conversely, as the credit risk profile of borrowers improved over time thanks to the implementation of credit-based macroprudential measures, using new loans rather than the total stock of existing loans could possibly exclude riskier loans of the simulation sample.31 This latter concern is however somewhat attenuated by refinancing practices that progressively eliminate older loans of the current stock of household debt. Mortgage refinancing represented in 2018 about 15 percent of the outstanding amount of housing loans, in line with anecdotal understanding from banks suggesting that almost all of their housing loan portfolio is replaced within 7 to 8 years.

46. Results indicate that a significant proportion of borrowers remains resilient under severe stress scenario, although a small segment of highly-leveraged, low-income households as well as younger borrowers could face repayment difficulties (Figure 21). Simulations on bank-extended mortgage loans are carried out for two types of borrowers based on whether the latter purchased public or private residential properties. A significant proportion of households remains resilient under the stress scenario, especially public housing owners as the latter have low debt service-to-income ratios initially, reflecting lower prices of public housing and the imposition of the 30-percent Mortgage Servicing Ratio (MSR) on top of the TDSR limit. However, 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. Likewise, some younger borrowers would see their TDSR increasing beyond 60 percent. These younger borrowers are however likely to be professionals, managers, executives, and technicians enjoying higher potential income growth, improving their debt servicing capacity in the years following the shocks. Also, they tend to take up smaller loans, so their individual defaults would pose less risk to the banking system as compared to other loans. The reduction in mortgage LTVs across the board in July 2018 will also further improve the mortgage servicing risk profiles of borrowers.

Figure 21.
Figure 21.

Households’ Mortgage Servicing Ratio under Baseline and Adverse Scenarios

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: MAS.Notes: This figure shows median debt servicing ratios under baseline scenario and Adverse Scenario 2 for borrowers purchasing public and/or private properties, by income and age ranges. The stress test covers new property loans granted in 2018.

C. Corporate Sector Resilience

47. Financial resilience of non-financial corporates is assessed based on the sensitivity analysis of debt-at-risk under adverse macroeconomic scenarios. The debt-at-risk level is assessed based on the ICRs recalculated under two different scenarios, which are chosen with reference to the stress test scenarios above.

  • Baseline scenario assumes the increase in borrowing costs by 1 percentage points and currency depreciation of 2.5 percent, without earnings shocks.

  • Adverse scenario assumes the increase in borrowing costs by 2.5 percentage points and currency depreciation of 20 percent, consistent with the peak interest and currency impact under bank stress tests adverse scenarios. It also assumes the fall of earnings by 20 percent, commensurate to the magnitude seen during the GFC.

In both scenarios, it is assumed that the interest shock applies to 45 percent of total debt that are being rolled over (55 percent are on fixed rates), and the currency shock applies to 65 percent (referencing the share of FX debt) of total debt.

48. Corporate debt-at-risk would rise significantly under the adverse scenarios, but cash reserves can provide some buffer. The debt-at-risk is expected to rise from the current level already in baseline scenario due to the expected tightening of financing condition. The further severe interest and currency shocks, and most of all the earnings shock, under the adverse scenario would have a sizable impact on firms’ balance sheet and significantly raise the debt-at-risk level beyond the levels seen in the global financial crisis in line with the projected NPL ratios in the bank solvency stress tests, before considering mitigating factors (Figure 22). However, the fact that corporates generate significant revenue from foreign sales provide natural hedging mitigating currency shocks. Further, corporates’ ample cash reserve can mitigate the adverse impacts, even as we assume limits on cash use for debt service in consideration of honoring other current liabilities. (see bank liquidity analysis below for deposit concentration risk).

Figure 22.
Figure 22.

Sensitivity Analysis, Debt at Risk

(In percent, share of debt owed by firms with low ICR)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: Worlriscope and IUF staff «legations.Note: Etaeline scenario assumes increasing bornwing costs by 1 ppt and currency depreciation by ? patient. Interst shock assumes increasing borrwing costs by by 3–5 ppt on non-fined rate debts (50 percent of total); FX shock assumes currency depreciation of 2S percent, increasing interst burden by 25 percent on FX debts (50 percent of total) while increasing the earnings from foreign sales by 25 percent in local currency (natural hedging); Earnings shock assumes the ‘all of EBIT by 20 percent. Use of cash is limited to keep quick ratios above 1 somewbeat conservative assumption limiting the depleting c-f liquid assets.

49. Corporate probability of defaults projections point to higher stress on corporates as the economy slows down. The analysis projects the corporate probability of defaults (PD) based on the Bottom-up Default Analysis (Duan, Chan-Lau, and NUS CRI team, 2018), under the baseline and two adverse scenarios (Figure 23). Given the already expected tightening of financial conditions and slowdown of the economy, the model projects gradually increasing PDs even under the baseline scenario. Under adverse scenarios, default risk is expected to rise steeply in the first two years and weigh on banks and insurers as shown in the stress tests below. Broadly mirroring the pace and the severity of assumed stress test scenarios, stress is more severe and persistent under the second adverse scenario. The PDs among the firms under higher distress (75 percentile) are expected to rise beyond the level seen during the GFC, especially in Adverse Scenario 2, which is in line with the projected NPL ratios in the bank solvency stress tests.32

Figure 23.
Figure 23.

Corporate Probability of Defaults

(In basis points, 75 percentile, 12-month horizon)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Sources: Haver Analytics, MAS, Risk Management Institute at National University of Singapore, IMF staff estimates.Note: Estimates based on Bottom-up Default Analysis by Duan, Chan-Lauand NUS CRI team (2018).The data base covers 412 NFCs that ara listed and domiciled in Singapore, drawn from Bloomberg and Thomson Reuters Datastream and compiled by Credit Research Initiative at the Risk Management Institute, Nations University of Singapore.

D. Bank Solvency Resilience

IMF’s Top-down Solvency Stress Test

50. The solvency stress test follows a balance sheet approach broadly aligned with the MAS regulatory framework. Based on the Basel III capital standard in Singapore (MAS Notice 637), which is now fully phased in, the solvency test assesses if banks would have adequate capital buffers (existing capital and forthcoming after-tax net income) to absorb potential credit and market losses if economic conditions deteriorate from the beginning of 2019 (Box 3).

Methodology of Bank Solvency Stress Tests

The bank solvency stress test is carried out under a passive balance sheet assumption. Growth of gross exposures, such as total loans and gross holding of debt securities, is identical to overall credit growth, with the balance sheet composition unchanged. Overall credit growth equals to nominal GDP growth when the GDP growth rate is positive and zero otherwise. This assumption prevents banks from deleveraging under the adverse scenario, which imposes some conservatism and avoids the need to model second-round effects on the economy. At the beginning of the adverse scenarios, total assets increase as a small portion (5 percent) of off-balance sheet exposures (non-cancelable credit guarantees and commitments) are drawn. Banks are not allowed to raise new capital during the stress test horizon. Given the evolution of total assets and equity, total liabilities are adjusted accordingly, with banks raising additional funding, as needed (see Appendix VI for other assumptions for balance sheet projections). Banks are allowed to pay dividends if their net income after taxes are positive, with limits on dividend payout rate as per the MAS regulatory requirement.

The transmission of macrofinancial shocks to NPL ratios, point-in-time PDs, and point-in-time LGDs is assessed by estimating satellite credit risk models. The models use macrofinancial scenarios of five countries where D-SIBs have significant credit exposures, including Singapore, China, Malaysia, Indonesia, and Thailand. In the models, the logit-transformed NPL ratios are determined by real GDP growth rates, short-term interest rates, house prices, equity prices, and nominal (bilateral) exchange rates in a non-linear fashion. Because of the unavailability of historical point-in-time PDs, the FSAP uses historical NPL ratios as a PD proxy. Besides the logit transformation, non-linear effects are introduced with the square of real GDP growth rate as an explanatory variable, so that NPLs rise at an increasing rate as economic conditions exacerbate further. The FSAP team estimates several credit risk models with different panel estimation methods and combinations of explanatory variables to test their robustness, projects the NPL path by averaging results across the models, and then applies the path to the latest point-in-time PD estimates to produce the PD dynamics over the stress test horizon. LGDs for residential mortgages and corporate income producing real estate assets are scaled up with property price deflation in the adverse scenarios (-45 percent in 2020–2021) through a decline of collateral value. The FSAP team conservatively assumes a full pass-through of the price changes to LGDs (see Appendix VII for the detailed calculation). Following Schmieder and others (2011), LGDs also increase by 2.15 percentage points as PDs rise by 1 percentage point. Not only point-in-time LGDs but also regulatory LGDs are calculated in this way, imposing another conservatism.

Market losses correspond to changes in key financial variables, such as interest rates, foreign exchange rates, and equity prices. These losses (or gains) are due to the existence of “open positions” in banks’ balance sheets (e.g., currency, maturity, time-to-repricing mismatches between assets and liabilities). The valuation changes corresponding to holdings of debt securities are measured through changes in yields leading to re-pricing based on a modified duration approach. By tracking the shifts in sovereign and corporate bond yield curves over time, changes in yields are obtained for any given (modified) duration and are applied to calculate haircuts and re-price bond portfolios in available-for-sale and held-for-trading accounts. Risk related to equity investments is considered for completeness, even if equity positions make up a small part of assets.

The changes in RWAs reflect the evolution of balance sheets, credit risk, and foreign exchange risk. First, the size of total assets increases as a portion of non-cancellable off-balance sheet exposures: second, the RWAs for credit risk under the standardized approach increase proportionately with balance sheet growth for all D-SIBs; third, for the exposures under the internal ratings-based approach, the stress test uses the Basel II formula to translate credit risk parameters (e.g., through-the-cycle PDs, LGDs, correlation, maturity, and scaling factors) into stressed RWAs. Conservative parameters are used to convert point-in-time PDs to through-the-cycle PDs: ΔPDTTC = 0.8 * ΔPDPIT for retail exposures and ΔPDTTC = 0.5 * ΔPDPIT for all other exposures; and lastly, RWAs are adjusted to reflect changes in the value of exposures in foreign currencies.

51. Credit risk constitutes the largest risk factor for D-SIBs on the asset side. Total loan portfolios constitute 67 percent of total assets, while debt securities (sovereign bonds) account for 13 (8) percent. Three local banks use both the standardized approach and the internal ratings-based approach to measure the credit risk of loan portfolios, while foreign D-SIBs use only the standardized approach. Regardless of the approach, the FSAP calculates loan-loss provisions of all D-SIBs according to the SFRS 109, which became effective in January 2018 (Appendix IV). It allows the top-down stress tests to be easily compared with the bottom-up stress tests based on the SFRS 109. Write-back of provisions are not allowed during the stress test horizon.

52. Estimates from the credit risk models suggest that NPL ratios, point-in-time PDs, and point-in-time LGDs would rise sharply under adverse scenarios (Figure 24). NPL ratios are sensitive to both economic developments and financial conditions, such as interest rate spikes, a sharp house price decline, and exchange rate depreciation, in the countries that D-SIBs are exposed to (Appendix V). At the beginning of the stress test horizon, the negative effects pass mostly through financial channels. But, as the economic conditions deteriorate further, GDP growth slowdown affects debt service capacity of borrowers and increases credit losses for banks. 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. In line with corporate sector analysis above, corporate sector would make a significant contribution to the rise of NPL ratios,33 In contrast, the NPL ratios remain flat under the baseline scenario. In line with the NPL path, local D-SIBs’ point-in-time PD increases fourfold from the starting level (0.8 percent in 2018), peaking at 3.0 percent and 3.3 percent in 2020 and 2021 under Adverse Scenario 1 and 2, respectively. Local D-SIBs’ LGDs rise from 27 percent in 2018 to 40 percent in 2020 and 2021 under Adverse Scenario 1 and 2, respectively, driven by falling property prices and rising PDs.

Figure 24.
Figure 24.

Projection of Credit Risk Parameters under Baseline and Adverse Scenarios

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: IMF staff calculation.

53. Credit losses significantly affect D-SIBs’ solvency under adverse scenarios. On average, the annual loan loss stays at about 0.3 percent of system-wide risk-weighted assets (RWAs) during 2019–2021 under the baseline scenario, as economic conditions moderate and domestic interest rates gradually rise. However, in Adverse Scenario 1 and 2, the accumulated credit losses amount to 2.6 percent and 4.1 percent of RWAs during the first two and three years, respectively. The results revealed that credit loss allowance is more preemptive under the SFRS 109 than the old standards (the incurred credit loss model), due to the cliff-effect on loan loss provisioning between Stage 1 and 2 (Figure 25).

Figure 25.
Figure 25.

Loan Loss Provision of Local D-SIBs: Adverse Scenario 2

(In billions of Singapore dollar)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: IMF staff calculation.

54. D-SIBs are also exposed to market risk. Under Adverse Scenario 1 and 2, potential valuation losses due to a decline in the price of debt securities in the available-for-sale and held-for-trading portfolios amount to S$6 billion and S$13 billion, equal to 0.7 and 1.4 percent of system-wide RWAs in 2019–21. Two factors contribute to this result: first, the sizable exposure of D-SIBs to debt securities, with an average exposure of 13 percent of total assets; and the substantial increase in credit spreads for corporate bonds under the adverse scenarios (e.g., 350 basis points for Singapore manufacturing firms during 2019–21 under Adverse Scenario 2), resulting in large haircuts on bond prices. In addition, D-SIBs are exposed to a large equity price shock under adverse scenarios, equivalent to 0.2 percent of RWAs. However, the expected losses on interest income, computed as the product of the time-to-repricing gap and the changes in the interest rate, are projected to be 0.9 percent of system-wide RWAs in 2019–21 under Adverse Scenario 2. Losses on banks’ net foreign exchange positions are small, because 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.34

55. RWAs of D-SIBs are projected to grow considerably under the adverse scenarios. During the first three years, RWAs are expected to grow about 6 percent and 10 percent under Adverse Scenario 1 and 2, while they would grow only 2 percent under the baseline scenario. Note that MAS does not require foreign branches to calculate RWAs and capital in Singapore and thus RWAs and the impact on capitalization are calculated only for D-SIBs that are locally incorporated.35

56. The hurdle rates are set according to the minimum regulatory requirements (Table 7). In adverse scenarios, they are the sum of the minimum regulatory requirements and D-SIB capital surcharges: 10 percent for total capital, 8 percent for Tier 1 capital, and 6.5 percent for CET1 capital, respectively. In the baseline scenario, D-SIBs should maintain a capital conservation buffer of 2.5 percent and countercyclical capital buffers introduced by jurisdictions outside of Singapore. They can use the two buffers under the adverse scenarios. The 3 percent leverage ratio is also set as an additional hurdle for both baseline and adverse scenarios. The stress test is based on the minimum capital ratios under Pillar I and do not consider any individual requirement under Pillar II.

Table 7.

Hurdle Rates for Bank Solvency Stress Test under Adverse Scenarios

(In percent)

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Sources: MAS; and IMF staff calculation.Note:

Countercyclical Capital Buffer.

57. The top-down stress tests show that D-SIBs are resilient to severe shocks described in the adverse scenarios (Figure 26). Despite large credit losses and a significant increase in RWAs, there would be no D-SIBs with capital shortfalls relative to the hurdle rates.36 The resilience largely stems from their large initial capital buffers, high initial asset quality (e.g., low point-in-time PDs for IRBA assets) and strong profitability, which allow them to absorb sizeable credit and market losses. Existing capital buffers (above total regulatory requirements) equal, on average, about 7 percentage points of RWAs. The key results from the FSAP top-down solvency stress test are:

  • Baseline scenario. The system-wide total capital adequacy ratio (CAR) and CET 1 ratio (17.0 and 14.1 percent at the end of 2018) would go up to 18.3 and 15.5 percent by 2021, respectively. Profits would be more than enough to cover credit and market losses as well as the increase in RWAs and would provide room for D-SIBs to beef up their capital buffers.

  • Adverse scenario 1. The large-scale global financial market turmoil precipitates stresses in Singapore’s financial and property markets and results in 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 financial markets start recovering sharply from 2021, the total CAR would go back to the starting level (17 percent) rather quickly.

  • Adverse scenario 2. As the marked growth slowdown and macrofinancial stress in China spill over to Singapore and the Asian region, NPL ratios sharply increase from 1.6 percent to nearly 7 percent. D-SIBs’ total and CET 1 capital ratios would decline to 10.9 percent and 8.6 percent in 2021 (text figure). But, no bank would fail to meet the hurdle. The main drivers of the change in capitalization for the first three years are the following: (i) loan loss provisions (-4.1 percentage points of RWAs); (ii) market losses related to financial market prices (-2.5 percentage points); and (iv) the change in RWAs (-4.1 percentage points).

Figure 26.
Figure 26.

Bank Solvency Stress Test Results

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: IMF staff calculation.

58. D-SIBs can maintain the leverage ratio above the minimum requirement in adverse scenarios. In the adverse scenarios, the leverage ratio of D-SIBs, measured as Tier 1 capital to total exposures, remains above 6.0 percent, comfortably above the 3 percent minimum requirement. It shows that their RWA calculations based on internal ratings-based models are suitably conservative with risk-weight density of IRBA assets being 36 percent on average among local D-SIBs.

IMF’s Top-down Sensitivity Analyses

59. The sensitivity analyses consider three single risk factors—foreign exchange rate, interest rate, and house prices—as well as name concentration risk, risk of common exposures, and the impact of fintech. The sensitivity analysis is static unlike the scenario analysis: it assesses the instantaneous impact of the risk factors on capitalization of D-SIBs as of end-2018. Each shock is calibrated to be larger than the accumulated change of each risk factor in the most severe adverse scenario. The sensitivity analysis captures the following transmission channels:

  • Interest rate risk. The impact of a sudden increase in interest rates (300 basis points) encompasses three channels: market losses due to the increase in bond yields; a reduction of net interest income following the re-pricing of adjustable deposit and lending rates; and credit losses as a result of diminished debt-servicing capacity.

  • Foreign exchange rate risk. A sensitivity test assesses how banks would be affected by market losses with foreign currency depreciation against U.S. dollar (40 percent) due to existing net open foreign exchange positions.

  • House price risk. A decline in the property prices (50 percent) increases credit losses via higher PDs and LGDs.

  • Name concentration risk. This analysis calculates credit loss generated by default of the largest private borrowers and the impact on capitalization, incorporating the 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. It assesses the impact of defaults of the largest (top one, five, ten, and fifteen) private borrowers. The single largest common exposure is equivalent to 2.5 percent of total capital and 0.2 percent of total assets as of 2018Q2, while 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 the expansion of fintech firms, particularly in business segments related to transaction and payment services, wealth management, investment banking, and trade finance that are responsible for 75 percent of fee and commission income.

60. A sharp movement in interest rates would have considerable effects on D-SIBs’ capitalization, but the current capitalization provides them a buffer to avoid undercapitalization (Table 8). Market losses with 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 also generate additional credit losses of S$4.3 billion by deteriorating borrowers’ repayment capacity and lower net interest income by S$1.4 billion due to banks’ maturity mismatch. All in all, total costs of the interest rate shock amount to 3.4 percentage points of Tier 1 capital ratio. This shock, however, would not cause undercapitalization in any of D-SIBs, because of their sizable capital buffers.

Table 8.

Results of Sensitivity Analyses

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Source: IMF staff estimates.

61. The impact of sharp movements of foreign exchange rates would be moderate. 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.

62. A sharp decline in property prices would increase loan loss provisions sizably. 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) in addition to a sharp increase in LGD. This would increase aggregate loss provisions 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.

63. D-SIBs have well diversified loan portfolios in general, but the concentration risk of a foreign subsidiary warrants a close monitoring. The value of an exposure of D-SIBs to any single largest private counterparty does not exceed 25 percent of Tier 1 capital. All the D-SIBs have enough Tier 1 capital buffers to withstand simultaneous defaults of top-10 largest borrowers (Figure 27) and no bank become undercapitalized until top-15 largest borrowers default at the same time. However, relatively speaking, a foreign subsidiary is more severely exposed to concentration risk than other banks: the 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.

Figure 27.
Figure 27.

Tier 1 Ratio after Concentration Risk Analysis

(In percent of risk-weighted assets)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: IMF staff calculation.

64. The impact of the simultaneous default of large common borrowers is manageable (Table 9). Due to the large capital buffers in the banking system (about 7 percentage points of RWAs) and well diversified portfolios, the simultaneous default of the top twenty largest common borrowers would cause D-SIB’s Tier 1 ratio to drop by 2.7 percentage points but no D-SIBs fails to meet the FSAP hurdle.

Table 9.

Impact of Hypothetical Default of Largest Common Borrowers

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Source: IMF staff estimates.

65. At the current stage, financial innovation does not pose a significant risk to the banking sector. It is difficult to incorporate the impact of the expansion of fintech within a macrofinancial scenario because of high uncertainty at its embryonic stage. Fintech may not be disruptive because of the focus on partnership with existing financial institutions (“business-to-bank solutions”) in Singapore. However, there are no grounds for complacency. Banks could lose a substantial portion of their non-interest income due to fintech disruption, particularly in business segments relating to transaction and payment services, wealth management, investment banking, and trade finance. The FSAP assumes a hypothetical scenario in which banks lose all the income from these business segments (75 percent of fee and commission income) due to the fintech disruption over the stress test horizon. Then, D-SIBs would experience a decline of total capital adequacy ratio by 2.0 percentage point of CAR (Figure 28).

Figure 28.
Figure 28.

Impact of Fintech Disruption on Total Capital Adequacy Ratio

(In percent of risk-weighted assets)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: IMF staff calculation.

66. However, financial institutions’ risk appetite may change when fintech development changes the market structure of financial services and when there is uncertainty surrounding the benefits of financial innovation. When serving as pure solution providers to banks, fintech can improve banks’ efficiency and capacity to manage risk without changing the market structure of the banking system. However, if a fintech firm provides complementary services to those provided by incumbents and quickly and successfully acquires a large retail customer base, incumbents would need to compete with each other for business with the fintech firm. Intensified competition lowers the franchise value of incumbents and can lead to greater risk-taking behavior. Uncertainty surrounding the benefits of financial innovation could also change the market structure and result in a “winners-take-all” situation. Such uncertainty in technology will increase banks’ risk appetite ex-ante. This is because, with limited liability, banks only care about the upside—and disregard the costs of a potential failure—and will benefit more from the new technology if they buy more risky assets. This is tantamount to an ex-ante increase in risk appetite and higher systemic risk in the financial sector (see Technical Note on Fintech).

Solvency-Funding Cost Interaction

67. The interaction between solvency and funding costs is not acute, as banks relies primarily on deposits in Singapore. A decline in capital adequacy ratio during the stress test horizon translates into higher funding costs, which in turn weakens solvency further through reduced profits. The FSAP mission derived the relationship between solvency and funding costs from a linear panel regression model (Table 10) and computed the second-round impact on solvency via the funding cost channel. 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, however, were small at 0.1 percent of GDP.

Table 10.

Estimation Results of Interaction between Solvency and Funding Cost

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Source: IMF staff estimates.Note: Within R2=0.7397, Between R2=0.7397, and Overall R2=0.7397.

MAS’ Top-down and D-SIBs’ Bottom-up Solvency Stress Test

68. MAS’ top-down and D-SIBs’ bottom-up stress tests reveal similar resilience.37 The authorities’ top-down tests confirmed the findings from the FSAP top-down stress tests. Decline in total CAR was mainly driven by increases in credit RWAs and loan loss allowances. MAS projected that the impact of adverse shocks on pre-loss income would be relatively milder, while RWAs increased more than those of the IMF top-down stress test. Therefore, the overall solvency impact would be similar. The authorities’ tests showed that, in Adverse Scenario 2, one D-SIBs failed to meet the hurdle rates, but the capital shortfalls are small at 0.5 percent of GDP. Two top-down stress test results were more conservative than D-SIBs’ bottom-up results (Figure 29). The difference was mainly driven by D-SIBs’ higher profit projections than two top-down stress tests.

Figure 29.
Figure 29.

Total Capital Adequacy Ratio

(In percent of risk-weighted assets)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Sources: MAS; and MFstaff calculation.

69. Some risks remain and warrant closer monitoring by further enhancing surveillance tools. While maintaining capital ratios above the regulatory minima, D-SIBs experience sizable credit losses and their capital buffers erode under adverse scenarios. It could lead to a credit crunch domestically if they were to deleverage to re-build capital buffers. To avoid negative consequences for the real economy, it would be important to continue monitoring exposures to property price volatility, legacy loans to transportation sector, and name concentration risk in the banking system.38 MAS has been improving the bank solvency surveillance tools. However, there is some room for further development. MAS should explore alternative approaches to estimate the credit-to-GDP gap, recognizing changes in credit cycles and improve the bank solvency stress testing model by adopting the full aspects of the Singapore Financial Reporting Standards 109 (e.g., projection of a transition matrix of Stage 1, 2, and 3 assets with granular data and calculation of loan loss provision of foreign D-SIBs by asset class). These efforts will support timely and appropriate macroprudential policy actions.

E. Bank Liquidity Resilience

70. This section analyzes the drivers of liquidity metrics and simulates them under severe yet plausible counterfactual scenarios (Appendix VIII).39 This section investigates the drivers of the NSFR and measures of deposit concentrations. It also simulates liquidity positions under severe yet plausible counterfactual (‘adverse’) scenarios using stress tests, as outlined in Box 4.

Methodology of Bank Liquidity Stress Tests1/

Staff conduct two types of complementary liquidity stress tests that simulate banks’ liquidity positions under adverse scenarios. Liquidity stress tests simulate banks’ liquidity positions under severe yet plausible counterfactual (‘adverse’) scenarios. There are two types of stress tests considered here: LCR-based and cashflow-based. In addition to the LCR for regulatory purposes that is reported above, the LCR-based stress tests assess the adequacy of the buffer of liquid assets relative to regulatory requirements by simulating the LCR under adverse scenarios.2/ The cashflow-based stress tests assess the adequacy of banks’ liquid assets in absolute terms (i.e., not relative to requirements) by simulating them under adverse scenarios.

The analyses cover D-SIBs at multiple levels of consolidation, in multiple currencies and at two reference dates. The analyses cover D-SIBs, which hold 76 percent of the deposits of resident individuals and firms. Domestic banks are analyzed at the level of their consolidated global operations and at the level of their Singapore activities only. Foreign D-SIBs are analyzed at the entity level, which separates the branch and subsidiary, and at the level of their combined Singapore operations (the so-called country-level group). Since they depend on regulatory minima, simulations of LCRs are conducted on contracts denominated in all currencies and separately on just those contracts that are denominated in Singapore dollars. By contrast, cashflow-based simulations of liquid assets are also conducted in U.S. dollars. The LCR- and cashflow-based stress tests are applied at multiple levels of consolidation, but the greatest emphasis is placed on the highest level of consolidation, given that banks have regulatory approval to manage liquidity at this level. Liquidity positions can change quickly over time, so simulations are run using data as of both 2018Q2 and 2018Q3 for robustness.

The LCR stress test simulates banks’ LCRs under two scenarios:

  • Retail scenario. This scenario is sparked by increased liquidity demand and risk aversion among individual and non-financial firms. The scenario manifests in widespread withdrawals of deposits of individuals and non-financial firms, repayment difficulties on loans (including increased customer demand for roll-over of maturing loans) and rising government bond yields. Parameters are calibrated to produce a withdrawal of up to 15 percent of deposits of individual and SMEs (an increase of 5 percentage points over the parameters used in domestic regulations), a refinancing by the bank of 65 percent of its customers’ maturity loans (an increase of 15 percentage points) and a 2 percent reduction in the value of government bonds that banks hold.

  • Wholesale scenario. This scenario is sparked by increased liquidity demand and risk aversion among financial firms and a depreciation of the Singapore dollar. The scenario manifests in widespread withdrawals of deposits of financial and non-financial firms (including some withdrawal of parent funding), delays in receiving payments from financial firms, margin calls on derivatives and delays in receiving payments on derivative contracts. In addition, a depreciation of the Singapore dollar increases the Singapore dollar value of foreign currency outflows. Parameters are calibrated to produce a withdrawal of up to 55 and 70 percent of operational and non-operational deposits respectively (an increase of 30 percentage points over parameters used in domestic regulatory requirements), calls for 30 percent more margin on derivatives contracts (an increase of 10 percentage points), and a refinancing by the bank of 50 percent of the maturing loans that it has extended to other financial institutions (an increase of 50 percentage points).

The cashflow-based stress tests consider short- and long-term stress scenarios. The short-term scenario concentrates the stress on liquid assets in the first week, while the long-term scenario applies stress over six months and concentrates its effects in the first three months. Parameters are calibrated, with reference to the first year of the first adverse scenario in the bank solvency stress tests, to a two-standard deviation historical episode. The criteria for passing the stress tests vary with the purpose of the tests. Banks were assessed to pass the LCR-based test under a given scenario if their LCR under that scenario remains above regulatory requirements.3/ For contracts denominated in Singapore dollars, the minimum LCR is 100 percent, and for contracts denominated in all currencies, the minimum LCRs are 100 and 50 percent for domestic and foreign banks respectively. Banks pass the cashflow-based test if their liquid assets are not depleted after three months. The amount by which failing banks fail the stress tests are aggregated to calculate liquidity shortfalls. Numbers of failing D-SIBs are counted out of the seven D-SIB groups. In the scenario horizon, banks are not allowed to take management actions like deleveraging.

One caveat of the cashflow-based stress test results is that the test uses repurposed data sources. The cashflow-based stress test relies on banks’ reports of their amounts contractually receivable and payable, by maturity and currency. The templates for these reports, which follow the structure of the tables of run-off and roll-off rate assumptions in Appendix VIII, were designed to analyze banks’ business models rather than to conduct liquidity stress tests. Therefore, they lack some detail for stress testing purposes, like a distinction between insured and uninsured deposits.

1/Appendix VIII explains the liquidity stress test methodology further.2/ The standard LCR is already a stress test. The purpose LCR-based stress test is to project the LCR under an adverse scenario, which indicates whether minimum LCR requirements would be breached under an adverse scenario. The purpose of this test is therefore to examine the adequacy of liquid asset buffers over minimum regulatory requirements. It is likely that the LCR would not be enforced for a period of time under an adverse scenario, so the interpretation of any shortfalls is at a longer-term horizon when the LCR would be enforced again.3/ The interpretation of this pass/fail criteria is given in footnote 2.

71. Cashflow-based stress tests confirm that 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. However, U.S. dollar liquidity is vulnerable to stress conditions. Taken together, these results suggest that the all-currency liquidity of D-SIBs is vulnerable to depreciations of the Singapore dollar against the U.S. dollar. These conclusions stem from the following stress test outcomes, which are shown in Figure 30.

  • All currencies. In an environment of no significant exchange rate movements (i.e., at prevailing exchange rates), 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.40 The shortfalls are, however, limited at below 1.3 percent of GDP.41

  • Singapore dollars. Six of seven D-SIBs have sufficient liquid assets in Singapore dollars to withstand significant stress over one week and to withstand significant stress over up to three months. The one failing D-SIB generates shortfalls of 2.2 and 2.9 percent of GDP under the short- and long-term scenarios respectively. The results for this bank are affected by the quality of the data inputs (Box 4). Staff estimate that these shortfalls could be 1.5 percentage points lower if some adjustments are made for how the bank reports its intragroup liabilities and depreciation.

  • 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.42 These 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.

  • The cashflow-based stress tests reveal a reliance on the transferability of liquid assets within groups (Figure 31). For 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 suggests that the entity relies on other entities in the same group for liquid assets in adverse scenarios. This kind of cross-subsidization is evident in all currencies combined, but less clear in Singapore and U.S. dollars individually.

Figure 30.
Figure 30.

Cashflow-based Stress Test Results

(In percent)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source: IMF staff calculations.Notes: W denotes weeks, M denotes months and 1 denotes years. The top two charts show the result of the cashflow-based stress tests applied to the aggregate data of all D-SIBs. Liquid assets represent the remaining value of liquid assets after receiving stressed contractual cash inflows and paying stressed contractual cash outflows, where the stress assumptions account for contract renewal. Negative liquidity denotes a shortfall. The bottom two charts show the results of the cashflow-based stress tests applied to data for each D-SIB individualls. Shortfalls are the sum, across all D-SIBs at their highest level of consolidation, of the amount by which cash outflows exceed the sum of liquid assets and cash inflows.
Figure 31.
Figure 31.

Cross-subsidized Liquidity

(Number of failures)

Citation: IMF Staff Country Reports 2019, 228; 10.5089/9781498325868.002.A001

Source; IMF staff calculations.Notes: The bars show the number of groups (out of 5 for which we have data) that pass the liquidity stress test at the group level but fail the test at the level of an entity withinthe group. Failures within a banking group that do not occur for the group as a whole suggest that some entities implicitly rely on others for liquidity support. Failures here refer to failure under the the first three months of the long-term adverse scenario of the cashflow-based stress test. The test is based on data as of 201SQ2. The unfilled bars indicate currencies wherea similarresult is not obtained using data as of 201SQ3.

72. The LCR-based stress tests reveal that 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). This shows that even 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. The resilience of buffers in Singapore dollars suggests that these (all-currency) shortfalls of liquid assets are driven by foreign currency activity. However, exchange rate effects seem small.43 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. However, the LCR is volatile, and the shortfalls are reduced by one-half if a moderately higher starting LCR is used.44

Table 11.

Results of the LCR-Based Liquidity Stress Test

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Source: MAS and IMF staff calculations.Notes:1/ Asset-weighted average.Numbers of failures and value of shortfalls are measured relative to regulatory requirements, which are at 100 percent for the Singapore dollar LCR, 100 percent for the all-currency LCR for domestic banks, and 50 percent for the all-currency LCR for foreign banks.