Republic of Korea
Financial Sector Assessment Program-Technical Note-Systemic Risk Analysis, Financial Sector Stress Testing, and an Assessment of Demographic Shift in Korea
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International Monetary Fund. Monetary and Capital Markets Department
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Publication Date:
18 Sep 2020
eISBN:
9781513557052
Language:
English
Keywords:
ISCR; CR; financial system; sensitivity analysis; balance sheet; banking system; central bank; credit risk; housing market; return on assets; sovereign bond; bond yield; real GDP; market structure; solvency analysis; Insurance; Loans; Stress testing; Commercial banks; Insurance companies; Global
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This note presents the systemic risk analysis conducted for the Republic of Korea in the course of the 2019 Korea FSAP. It comprises a forward-looking solvency analysis for banks, insurers, and pension funds, a liquidity stress test for banks, and an assessment of network and interconnectedness for a wide range of financial sector entities and their ties to the real economy. Various structural characteristics of Korea’s economy and its financial system informed the features and focus for its forward-looking risk analysis. They include Korea’s strong export orientation, limited diversification, and its key role as a node in regional and international supply chains. Korea’s financial system has grown by 40 percentage points of GDP since 2013, enhancing the importance of a deep financial sector analysis as conducted through the FSAP. Mortgage insurance schemes are widely used—which was reflected in the way the risk assessment for banks was conducted. Korea’s life and non-life insurance sector is large, highly concentrated and saturated. Fintech developments keep accelerating, in terms of its Open Banking system and e-money providers. Demographic developments in Korea are among the most adverse world-wide, implying a continuous drag on demand, downward pressure on interest rates, financial firms’ income, and hence their capitalization unless they will be altering their business models.

Abstract

This note presents the systemic risk analysis conducted for the Republic of Korea in the course of the 2019 Korea FSAP. It comprises a forward-looking solvency analysis for banks, insurers, and pension funds, a liquidity stress test for banks, and an assessment of network and interconnectedness for a wide range of financial sector entities and their ties to the real economy. Various structural characteristics of Korea’s economy and its financial system informed the features and focus for its forward-looking risk analysis. They include Korea’s strong export orientation, limited diversification, and its key role as a node in regional and international supply chains. Korea’s financial system has grown by 40 percentage points of GDP since 2013, enhancing the importance of a deep financial sector analysis as conducted through the FSAP. Mortgage insurance schemes are widely used—which was reflected in the way the risk assessment for banks was conducted. Korea’s life and non-life insurance sector is large, highly concentrated and saturated. Fintech developments keep accelerating, in terms of its Open Banking system and e-money providers. Demographic developments in Korea are among the most adverse world-wide, implying a continuous drag on demand, downward pressure on interest rates, financial firms’ income, and hence their capitalization unless they will be altering their business models.

Executive Summary 1

This note presents the systemic risk analysis conducted for the Republic of Korea in the course of the 2019 Korea FSAP. It comprises a forward-looking solvency analysis for banks, insurers, and pension funds, a liquidity stress test for banks, and an assessment of network and interconnectedness for a wide range of financial sector entities and their ties to the real economy.

Various structural characteristics of Korea’s economy and its financial system informed the features and focus for its forward-looking risk analysis. They include Korea’s strong export orientation, limited diversification, and its key role as a node in regional and international supply chains. Korea’s financial system has grown by 40 percentage points of GDP since 2013, enhancing the importance of a deep financial sector analysis as conducted through the FSAP. Mortgage insurance schemes are widely used—which was reflected in the way the risk assessment for banks was conducted. Korea’s life and non-life insurance sector is large, highly concentrated and saturated. Fintech developments keep accelerating, in terms of its Open Banking system and e-money providers. Demographic developments in Korea are among the most adverse world-wide, implying a continuous drag on demand, downward pressure on interest rates, financial firms’ income, and hence their capitalization unless they will be altering their business models.

The bank solvency risk analysis concludes that the Korean banking system is broadly resilient to a severe economic downturn scenario. The performance of banks under a macro-financial downturn scenario would be to an extent heterogeneous, with the drop in specialized banks’ capital ratios being most pronounced, and those of ODIs, including Mutual Savings Banks and Credit Cooperatives, being most dispersed. None of the nation-wide, regional, and specialized banks are found to fall short of regulatory capital minima. Fintech-implied rising competition in the banking system can have notable implications in terms of systemic liquidity, implied solvency as well as system-wide operational risks.

The FSAP team’s assessment is that the adverse FSAP macro-financial scenario is severe enough to encapsulate a COVID-19 implied fallout on economic activity. This assessment stands both in terms of the expected depth and duration of the shock (the FSAP’s scenario’s deep downturn spans over two full years before normalizing).

Measures have been taken to strengthen the liquidity of the Korean banking system and banks have ample room to withstand liquidity shocks. Overall, the banking system is likely to maintain adequate liquidity, both in terms of domestic and foreign currency, following hypothetical asset price falls, retail funding and wholesale funding shocks. In general, this is the assessment for all types of banks in Korea. However, state-owned banks’ reliance on unsecured wholesale funding would lead to a fall in FX liquidity coverage from 112 percent to 85 percent.

Insurance firms’ capital would decline substantially under the stress scenario, while all firms would stay above regulatory thresholds. The top-down solvency stress test for life and non-life insurance firms covered about 75 percent of the market. The same narrative and severity of the scenario from the banking sector solvency analysis were adopted. Under the current accounting regime, held-to-maturity designation is still allowed—and widely used among life insurers—hence a significant portion of insurers’ investments were shielded from market price changes. Life insurers specifically would experience a significant decline in income, pointing to the need for them to restructure their business to restore underwriting profitability, and shifting further from guaranteed savings products into lower guarantees and protection business.

The interconnectedness analysis for the Korean financial system suggests that specialized banks are as systemic as nation-wide commercial banks. Nation-wide banks are net lenders to specialized banks, and hence are more vulnerable to specialized banks than the other way around. Insurers are not as systemic but vulnerable to stress, should it arise, in the banking system; insurers are net lenders to all other sub-segments of the financial system in Korea.

The recommendations derived from the systemic risk analysis pertain in many respects to enhancing the Korean authorities’ quantitative assessment frameworks (Table 1). They include the urgent need for a detailed quantitative impact analysis of Fintech developments in terms of systemic liquidity and system-wide operational risks which the Open Banking and e-money developments may imply—despite their positive intent and expected benefits. Real estate market structures in Korea, including the Jeonse leasehold system, ought to be better reflected and the related risks be assessed, amid a slowing economy due to adverse demographic developments.

Table 1.

Korea: Main Recommendations

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Source: IMF staff. * I = immediate, ST = short-term, MT = medium-term.

Macro-Financial Environ Structure, and Scope of the Systemic Risk Analysis

A. Financial System Structure and Trends

1. As of 2018Q4, total assets of financial institutions in Korea reached about 300 percent of GDP. Since the previous 2013 FSAP, Korea’s financial system has grown by about 40 percentage points of GDP. Real estate is the central asset class where leverage is high. The asset management industry and second-pillar pension funds are less developed than other segments of the financial system but are becoming increasingly important amid the material demographic transition observed and further expected in Korea. The rapid growth of Fintech is also adding to such changing dynamics. Growth in the onshore FX derivatives market lags cross-border investment flows while similarly with other emerging currency markets, the offshore NDF market in Korean won remains large and growing, most likely reflecting electronification in NDF markets and global legal and regulatory reforms for derivatives markets which incentivized greater central clearing of NDFs. Selected indicators of the Korean financial system structure are presented in Figure 1.

Figure 1.
Figure 1.

Korea: Financial System Structure

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Bloomberg, IMF Financial Development Index, IMF World Economic Outlook, FSB Global Monitoring Report on Nonbank Financial Intermediation 2018, FSS.1/ For more details about the financial development index and its financial institutions and markets subcomponents see IMF SDN/15/08 and IMF WP/16/5.

Financial Institutions’ Assets

(in percent of total)

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: FSS/FISIS and IMF staff calculations.

2. The banking sector comprises a vast spectrum of entities. It is composed of nation-wide and regional commercial banks, specialized (policy) banks and Other Depository Institutions (ODIs); see Table 2. The state-owned financial firms are a prominent part of this landscape. The KHFC provides mortgages insurance and issues fully guaranteed mortgage-backed securities composed of “conforming” loans2 for which it sets the maximum amount, maturity and interest rates in advance.3 KAMCO has played an instrumental role after the Asian crisis when it acquired about USD 100bn of bad loans from the banking system and has also bought substantial amounts of NPLs in the wake of the credit card crisis (2003), global financial crisis (2008), and household debt crisis (2013). The National Pension Service (NPS) is the third largest in the world with USD 600bn in assets and is the largest investor in Korea. The Korean Investment Corporation (KIC) is a sovereign wealth fund with about USD 130bn assets under management.

Table 2.

Korea: Financial System Structure

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Sources: FSS/FISIS; IMF, World Economic Outlook database; and IMF staff calculations. Note: 2018 as of 2018Q3. Excludes community credit cooperatives.

3. Financial holding companies (FHCs) play a systemically important role. As of end-2018, nine FHCs held about 40 percent of total financial institutions’ assets (114 percent of GDP) through complex networks of subsidiaries with operations across all segments of the financial system; four FHCs have been identified as D-SIBs. The financial firms belonging to the bank holding companies operate across a broad cross-section of the financial sector, with subsidiaries being engaged in insurance, capital markets and asset management business.

4. The business models of Korea’s banks are broadly conventional (Figure 2). Banks, including ODIs, are primarily funded by retail deposits. Assets are concentrated in loans, mostly related to real estate. Bank loans are split about equally across lending to households and firms, with 80 percent of the stock of corporate bank loans outstanding to SMEs. Most SME loans are collateralized against real estate. Commercial banks have a relatively diversified loan portfolio and securities’ holdings. State-owned banks are focused on lending to SMEs, which is reflective of Korea’s economic policy priorities.

Figure 2.
Figure 2.

Korea: Depository Institutions’ Business Models

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Source: FSS/FISIS and IMF staff calculations.

5. The insurance sector is large, highly concentrated, and saturated (Figure 3). The sector comprises 24 life insurance firms and 30 non-life insurers, managing KRW 1170 trillion in total. Insurance penetration (premiums to GDP) is one of the highest in the world, exceeded only by Taiwan Province of China and Hong Kong SAR. This reflects, in part, the central role that life insurance has played as a conduit of savings in Korea, where life insurance assets account for more than 20 percent of household financial assets, well above the OECD average. Cross-sectoral linkages exist as insurers typically also offer financial consulting services and are involved in asset management. The bancassurance market is well developed and 50 percent of new business in life is sold via banks. The market is dominated by large firms owned by FHCs and Korea’s largest conglomerates (chaebols).

Figure 3.
Figure 3.

Korea: Structure of the Insurance Sector

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: FSS, IMF World Economic Outlook Database, and IMF staff calculations.

6. The Korean capital market is one of the most active markets in Asia. The equity market has a market capitalization of around USD 1.8 trillion with foreigners holding around 35 percent of the listed Korean stocks. The largest five conglomerates account for over 50 percent of market capitalization. Compared with major advanced and emerging market economies, the price-to-book and price-to-earnings ratios of Korean companies are notably lower, which has been coined the “Korea discount” and attributed to various factors, such as in relation to North Korea, and the complicated ownership and governance structure of many Korean corporations and especially large firm conglomerates.

7. The Korean bond market is dominated by government and other public debt, while ‘green’ or ESG bonds’ importance is rising. Korea’s asset management industry has experienced robust growth over the past few years reflecting changing saving patterns but also a search for yield by households and other investors. Assets under the management of privately placed funds, derivatives-linked products such as equity-linked securities, etc., all grew at double digit rates over the past year and now amount to about KRW 500 trillion; roughly 30 percent of GDP.

8. Fintech innovations are rapidly taking root in the retail payments service sector and altering the market structure. The ongoing push to foster Fintech innovation by the Government is facilitating this. Defining features of the Korean retail banking sector include great financial institution depth and a saturated credit card market, high smartphone penetration, and customer eagerness to embrace cutting-edge technologies—providing a fertile environment for mobile payment services to thrive. The entry of large technology companies in the payment services market (as providers of stored value payment products and of payments-initiation interfaces) is beginning to alter the market structure. These developments affect the degree of concentration and competition, catalyzed by the authorities’ work plan to facilitate innovation, specifically the introduction of an “Open Banking” system, in parallel with loosening of legal restrictions for electronic financial transactions and use of personal data.

9. Korean banks have improved their capital position and asset quality since the 2013 FSAP (Table 3, Figures 4 and 5). Banks’ aggregate capital ratio at about 16 percent of risk-weighted assets does not stand out compared to other banks in the region. NPL ratios are structurally low but must be interpreted cautiously because banks sell NPLs swiftly into an active market for distressed assets4. Korean banks underperform their regional peers in terms of profitability. The share of foreign business in total assets remains relatively small at about 6 percent, up from 4 percent in 20135. Nation-wide banks’ asset exposures are diversified, regional banks’ and mutual savings banks’ exposures are concentrated in SME lending, and ODIs’ lending is concentrated in consumer credit (Figure 6).

Table 3.

Korea: Core Financial Soundness Indicators, 2012–17

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Sources: IMF Financial Soundness Indicators.
Figure 4.
Figure 4.

Korea: Financial System Performance

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: FSS, Haver, and IMF staff calculations.
Figure 5.
Figure 5.

Korea: Bank Profitability (Significant Institutions):1 Key Trends

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Haver, FISIS, IFS.1 Includes commercial and specialized banks that are currently active.2 Includes internet-only banks.
Figure 6.
Figure 6.

Korean Banks’ Asset Portfolio Structure, Liability Structure, and P&L Components

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.Notes: Based on data for underlying 8 nation-wide banks, 5 regional banks, 6 specialized banks, and 2,316 ODIs (888 Credit Unions, 79 Mutual Savings Banks, 1,122 Agricultural Cooperatives, 90 Fisheries Cooperatives, 137 Forestry Cooperatives).

10. Insurers are currently well capitalized but low interest rates are weighing on profitability (Figure 7). The median life insurers recorded a coverage of its risk-based capital (RBC) at end-2018 of 239 percent, well above the regulatory minimum of 100 percent and the recommended level of 150 percent. In the non-life sector, the RBC coverage was even higher, at 263 which is the highest level since 2012. Increasing difficulties to match higher interest rate guarantees with dwindling returns have led most insurers to extend asset durations (such as through foreign investments) and to offer more ‘protection’ instead of savings products. Still, profitability is low; the sector’s return on assets stood at 0.3 percent in 2018, reflecting decreasing investment yields which declined continuously from 5.1 percent in 2012 to 3.4 percent in 2018. With unfavorable demographics and competition from other financial institutions, growth prospects are limited, triggering foreign expansion and cooperation with fintech providers. Non-life companies, too, have material exposure to long-term saving and protection business. Profitability is impacted by underwriting losses, especially in health insurance where the government aims for lower cost of private coverage. For the non-life sector, the combined ratio hovered slightly above 100 percent in recent years, indicating that claims and operating costs exceed premium income. Nevertheless, the return on assets is still higher than in the life sector, with 1.3 percent for the median company in 2018.

Figure 7.
Figure 7.

Korea: Solvency and Profitability of the Insurance Sector

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Source: IMF staff calculations based on FSS data.

11. While well capitalized under the current regime, additional capital needs are expected under the new regime, which requires close supervisory attention. The new solvency regime, which is going to be aligned with IFRS 17, will introduce a market-consistent valuation of assets and liabilities by 2022. An initial quantitative impact study in 2018, based on the IAIS’ Insurance Capital Standards and Solvency II, showed a notable capital shortfall in the life insurance sector. A more recent study in 2019 implies substantially smaller capital needs as several parameters were adjusted to local market conditions6. The transition to the new regime has already triggered an increased issuance of subordinated debt. An increased re-classification of fixed-income assets from available-for-sale to held-to-maturity, observed recently, would need to be reverted when transitioning to a fully market-consistent asset valuation.

B. Macro-Financial Environment

12. Macro-financial stability in Korea remains vulnerable to external shocks. Korea’s economy is very open and closely integrated with international supply chains. The export-oriented manufacturing sector, accounting for about a quarter of GDP, comprises Korea’s steel, telecommunications equipment, electronics, auto, and shipping industries. Production is concentrated, with the top five conglomerates dominating the Korean stock index.

13. The macroeconomy has performed well since the last FSAP but faces headwinds. GDP growth was robust at around 3 percent in 2017 and 2018, reflecting strong export growth and business and construction investment. The semiconductor industry, a stable driver of South Korea’s growth, has experienced a cyclical slowdown. Growth is expected to remain sluggish (Table 4) due to ongoing trade tensions and low business confidence, notwithstanding a boost from monetary and fiscal policy easing. Cross-border financial flows have been volatile, while the Korean won has depreciated partly on the back of trade tensions between the United States and China.

Table 4.

Korea: Selected Economic Indicators, 2017–24

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Sources: Korean authorities; and IMF staff estimates and projections.

Contribution to GDP growth.

Excludes gold.

Debt service on medium- and long-term debt in percent of exports of goods and services.

14. The financial cycle has reached an advanced phase with household debt among the highest for OECD countries (Figure 8). Financial conditions in mid-2019 were close to historical average after several years in easy territory, while the recent monetary policy loosening should counter the impact of rising market risks somewhat. The ratio of total non-financial private sector debt to GDP has reached an elevated level—standing close to 200 percent—and core debt (debt of the non-financial sector owed to banks) amounts to about 130 percent of GDP, which is high in international comparison. Although the trend has been upward, an accelerating trend is not evident. The credit-to-GDP gap is close to zero. After some deleveraging, corporate credit growth has picked up again, particularly to the SME sector driven by sole proprietors and often secured by lending against real estate.

Figure 8.
Figure 8.

Korea: Macrofinancial Environment

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Bloomberg, IIF, BIS, Haver, IMF staff calculations.

15. The upward trend in house prices has moderated, but household leverage in real estate remains high (Figure 9). Housing supply has expanded over recent years and large sections of the financial sector are exposed to the housing market. Household lending growth, primarily related to housing, has slowed but remains above nominal GDP growth while household debt as a ratio of disposable income stands at about 180 percent, which is among the highest across the OECD countries.

Figure 9.
Figure 9.

Korea: Housing Market

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Haver, KOSIS, OECD, CEIC, and IMF staff calculations.Notes: The disposable income series in the denominator of the median house price and apartment price to income ratios in sub-figure 3 are trailing 4-quarter sums of quarterly disposable income flows.

16. Demographic shifts are posing a long-term challenge for the financial sector (Figure 10). By 2050 almost 40 percent of the population is expected to be older than 65, up from 13 percent now, while the working-age population will shrink significantly given Korea’s low birth rate. One implication is that the proportion of debt held by older households will increase, also reflecting reverse mortgages promoted by the KFHC, and the DSTI may rise significantly. Adverse demographic developments are having an impact on capital flows as a rising share of growing retirement savings is invested abroad by pension funds, insurance firms, and asset management companies. This demographic shift, combined with competitive pressures from China, has raised concerns that Korea might be destined for a prolonged period of low growth and inflation with an erosion of its financial buffers. Structural changes and long-term implications that these demographic shifts are expected to exert on banks, insurers and other economic agents will be discussed later in this note.

Figure 10.
Figure 10.

Korea: Demographics Shift and Household Debt

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Haver, KOSIS, and IMF staff calculations.

17. The rapid rise in COVID-19 virus cases in South Korea since February 2020 likely implies a significant drag on economic activity, which is being tempered by a proactive policy response of Korea’s government. The Korean authorities have taken bold steps to contain the COVID-19 outbreak and mitigate its impact on public health and the economy, including through large-scale testing of the population to rapidly identify, isolate, and treat infected patients. The Korean government is also using its fiscal space to mitigate the macroeconomic impact of the outbreak, including through a proposed KRW 11.7trn (0.6 percent of GDP) supplementary budget that provides resources to step up disease control efforts, support small merchants and SMEs, provide transfers to sustain consumption and employment, and support local economies hit hardest by the spread of COVID-197.

C. Korean Housing Market Structure and Dynamics

18. House prices appear to have developed in line with fundamentals at the national level, but regional heterogeneity can be observed (Figure 9). Korean nominal house price growth has moderated since the GFC. Nation-wide median house price and apartment price to disposable income ratios have trended upward to an extent since 2014 (sub-figure 3 in Figure 9). Debt-to-income ratios have been rising steadily since 2010 for younger age cohorts (up to age 50), and also for people at 60 and above. A model-based valuation, based on regional data, points to overvaluations in the Seoul market and the larger capital region of 10 percent and 5 percent respectively. More rural areas have seen house prices increase less than fundamentals in relation to income and mortgage interest rates would have predicted8.

19. The leasehold deposit market (Jeonse) is a unique feature of the Korean financial system and implies potential vulnerabilities for the Korean real estate market. Jeonse tenants give landlords a deposit equivalent to on average about 50–70 percent of the house price that is to be paid back to the tenant at the end of the two-year contract, if the lease contract is not extended9. To finance these deposits, tenants often turn to banks for Jeonse loans which doubled to KRW 72.2trn since 2014, while loan-to-deposit ratios have increased to 42 percent in 2018. In lieu of rental income, Jeonse landlords then rely on house price increases and return on any financial investments made using the Jeonse deposit. Given that landlords do not accrue a yield on Jeonse properties, in the cases where the Jeonse deposit is used to fund the property purchase, the total return on such an investment is entirely dependent on house price increases. Consequently, the investment in Jeonse properties is not sustainable in an environment in which house prices would fall over an extended period.

20. The rollover risk of Jeonse contracts could amplify negative shocks to house prices and the prices of financial assets. In a severe adverse scenario in which Jeonse deposit prices would fall, landlords may have to cover the resulting capital shortfall using their own funds. If this is done by selling financial assets, this would put pressure on asset prices. In an extreme case, landlords may need to sell their properties, which would further amplify the fall in house prices. An assessment conducted by the BOK shows that following a 20 percent fall in the Jeonse deposit price, about 78 percent of landlords could cover total leasehold deposits with their own financial assets, while a remaining 22 percent would need an additional loan10.

21. The authorities need to be vigilant on the following aspects of the Jeonse market structure: (i) who the ultimate holder of risk is and potential claims on the public exchequer; (ii) a rise in rollover risk of Jeonse contracts; and (iii) negative shocks to house prices and the economy more broadly could be amplified through adverse conditions in the Jeonse market. The potential vulnerability associated with household leverage with Jeonse leasing of their properties, possibly in addition to bank debt, could be mitigated by a policy tool that can consider and capture various factors, including the combined value of mortgage and Jeonse deposits, dynamics between house prices and Jeonse prices, and Jeonse loans held by tenants.

22. The public sector has a large footprint in the housing market. The Korean Housing-Finance Corporation (HF) offers a number of products with both a social function and a risk-sharing objective. The HF engages in direct funding of mortgages with funding from the Ministry of Land, Infrastructure and Transportation. It also offers subsidized mortgage loans to lower income households via banks, which are then securitized by the HF11. The HF securitizes non-subsidized long-term mortgages and total MBSs issued and guaranteed by the HF amounts to 116tr KRW, a large portion of which are held by insurance companies. The HF also guarantees other housing-related loans, including loans using Jeonse deposits as collateral and loans by landlords needed to cover falls in Jeonse deposit prices12. The HF offers reverse mortgages to the rapidly growing group of low-income pensioners. There is lack of transparency regarding the public sector’s total contingent liabilities related to the housing market and whether the capital buffers held are adequate. While housing market subsidies and guarantees may have a social function, it is not clear to what extent these distort the market price of risk.

D. Scope of the Systemic Risk Analysis

23. The FSAP risk analysis covers the banking and insurance sector. The analysis as presented in this note was based on public and supervisory data for 24 banks, covering 95 percent of banking system assets at end-2018. Five banks of the 24 are sub-aggregates, forming the ODI category (Credit Unions, Mutual Savings Banks, credit cooperative banks). Table 5 lists the banks in-scope of the banking system stress test, including information about their D-SIB status.

Table 5.

Korean Banks In-Scope of the Systemic Risk Analysis and Stress Test

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Source: FSS and IMF staff. Note: See Glossary for abbreviations used in this table. The last column (loan to deposit reg.) denotes whether the loan to deposit ratio-based regulatory constraints are relevant or not for a given bank (see FSS Handbook for details about the regulation). The FSS/STARS and BOK/SAMP models are the FSS’ and BOK’s top-down stress test model frameworks. All information provided in the table refers to end-2018.

24. Seven life and six non-life insurers were included in the insurance sector stress test. In terms of end-2018 balance sheet assets, the analysis covers 73 percent and 76 percent of the life and non-life sector, respectively. Table 6 presents the main characteristics of the insurance undertakings included in the analysis.

Table 6.

Korea: Insurance Firms Included in the Stress Test

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Source: FSS and company data, and IMF staff calculations.

Risks, Vulnerabilities, and Macro-Financial Scenarios

A. Risks, Vulnerabilities, and Macro-Financial Scenarios13

25. The risks to consider for the Korean economy stem primarily from escalating trade tensions. Adverse macro-financial developments may be triggered by escalating global trade tensions, involving the US, China, the EU, and Japan. Geopolitical risks could, moreover, unfold despite North Korea’s stated commitment to denuclearization, with the related diplomatic process appearing to be repeatedly prone to breakdown; albeit the news related to North Korea have recently abated.

26. The structural macro-financial conditions against which such broad-based risks are judged to have an impact upon materialization include Korea’s export orientation and notable Fintech developments. Relevant structural features include Korea’s strong reliance on macroeconomic terms is to be material for the Korean economy, for the risk (trigger) to be considered as a basis for a scenario narrative. The probability (of a trigger) will remain subjective.

27. The adverse macro-financial scenario narrative is rooted in the escalation of global trade tensions. Upon it being triggered, the scenario is assumed to entail a broad-based, worldwide sell-off in equity markets, reflecting a general fall in investors’ risk appetite. Korea would face significant capital outflows, coupled with strong currency depreciation, due to its close ties with China in terms of trade, and hence being close to the epicenter of the escalating trade tensions and very directly hit by a global slow-down in trade through falling export and import flows. 14,15

28. The scenario would imply pressure on export-oriented Korean firms and adverse spillover effects to domestic demand through various channels (higher unemployment, drop in income, depressed confidence, etc.). The activities of large firm conglomerates in Korea would be significantly disrupted due to their cross-border supply-chain interconnectedness16. Firms’ and households’ expectations would turn adversely and imply for investment activity and private consumption to drop in anticipation of a slow-down of activity in the medium-term future, thereby further pressuring real activity in the short-term (expectation channel). Interest rate-based monetary policy would have less room to stimulate demand (through the credit channel) since interest rates are structurally lower at the outset of the scenario horizon, compared to the monetary policy stance ahead of the GFC.

29. Table 7 presents the baseline and adverse scenario calibration for a set of core macro-financial variables17. The baseline scenario is aligned with the IMF’s October 2019 WEO. The adverse scenario was assumed to start unfolding from the first quarter of 2019 onward. The scenario horizon covers the 20-quarter period from 2019Q1–2023Q418. A chart collection corresponding to the variables in Table 7 is presented in Figure 11. The adverse scenario has been calibrated such that the first year’s real GDP growth matches an estimate implied by the Growth-at-Risk (GaR) framework that the IMF FSAP team has implemented for Korea. The GaR estimate corresponding to a 5 percent tail probability equals about -3.1 percent year-on-year for the year 2019. The real GDP shock severity corresponds to an approximate 2.1x standard deviation multiple (2-year cumulative) in historical perspective.

Table 7.

Korea: Baseline and Adverse Macro-Financial Scenario—Main Features

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Sources: IMF WEO and IMF staff calculations. Note: URX = unemployment rate, STN = short-term money market rate (call rate), LTN = long-term interest rate (long-term sovereign bond yield), TS = term spread (LTN-STN), ESX = nominal stock prices, USDKRW = 1 USD in KRW (up means depreciation of KRW), RPP = residential property prices. MSCI World is the MSCI World Index. NFC credit = nonfinancial corporate credit stock. HH credit = household credit, including mortgages, Jeonse loans and consumer credit.
Figure 11.
Figure 11.

Korea: Baseline and Adverse Macro-Financial Scenario

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF WEO and IMF staff calculations.Note: The scenario is developed at quarterly frequency. The scenario horizon covers the 20-quarter period from 2019Q1–2023Q4.

30. The IMF WEO as of April 2020 which reflects the expected impact of COVID-19 is less adverse than the adverse scenario profile considered for the FSAP. The April 2020 IMF WEO’s baseline outlook for 2020 expects real GDP in 2020 to grow at -1.2 percent year-on-year, compared to a drop of -3.1 percent in the FSAP’s adverse scenario. The unemployment rate is expected to move to 4.5 percent according to the April 2020 WEO, comparing to 4.8 percent in the FSAP’s adverse scenario. Hence, the judgment is that the FSAP adverse scenario is sufficiently conservative conditional on the current information set as of end-April 2020 to encompass the adverse economic consequences of the COVID-19 pandemic.

31. Measures of inflation (deflator- and consumer price index-based) move up visibly in the first year of the adverse scenario. This reflects the impact of the depreciating currency through the implied rise in the costs of imports. After the value of the currency would be expected to normalize from 2020 onward, the inflation measures level off, moving temporarily into negative territory, before they recover throughout the second half of the scenario horizon.

32. Asset prices in Korea, including for real estate, would drop markedly. Asset prices would drop due to the slowdown in private sector demand for housing, inter alia for the assumed reason that loan interest rates would not fall sufficiently (to stimulate demand) because of banks being under solvency and liquidity stress and thereby implying for their funding costs to stretch to higher levels. Nation-wide nominal house prices would drop by -16 percent in cumulative terms between 2018Q4 and 2021Q2. Equity prices in Korea would drop by -39 percent from 2018Q4 to 2020Q2 (trough), before they start to recover.

33. Figure 12 shows the real GDP flow trajectories under the scenarios, along with their historical evolution during the AFC, the GFC, and the FSAP 2013 adverse scenarios. The baseline chart—involving the estimated pre-crisis trends for the AFC and GFC profiles in the figure— suggests that the FSAP 2019 baseline scenario is the weakest in comparison to the AFC, GFC pre-crisis trends as well as the FSAP 2013’s baseline. The FSAP scenarios—both in 2013 and 2019—imply a more protracted path compared to the shape of the GDP flows as observed during the AFC and GFC (more frontloading and faster recovery). This shape has been considered for the sake of conservatism.

Figure 12.
Figure 12.

Korea: Baseline and Adverse Scenario Paths for Real GDP in Historical Perspective

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF WEO and IMF staff calculations.Note: AFC = Asian Financial Crisis (1997/38). GFC = Global Financial Crisis (2007–09). The origin, “t0”, in the charts denote the years [1997Q4, 2008Q3, 2012Q4, 2018Q4] for the [AFC, GFC, FSAP 2013, FSAP 2019].

34. Figure 13 shows the cumulative real GDP deviations along different horizons, in historical perspective. The FSAP scenario’s GDP losses relative to the baseline are less negative compared to the FSAP 2013 deviations. This deviation profile is judged to be justified against the background of the weaker baseline (resulting in comparable level trajectories under the adverse scenario, see Figure 12) as well as from the perspective of the GaR benchmark estimates that take account of the cyclical position of the Korean economy and its downside risks at a 5 percent probability level.

Figure 13.
Figure 13.

Korea: Cumulative Real GDP Deviations (Adverse Relative to Baseline)

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF WEO and staff calculations.Note: T1:X denotes the sum of quarterly GDP flows (real) over X=[4,8,12] quarters. The deviation measures are computed as the sum of adverse scenario’s GDP flows minus the sum of baseline GDP flows, divided by the sum of baseline GDP flows; along 4, 8, and 12 quarters. For the AFC and GFC, the respective pre-crisis trends (average growth) over the preceding 24 quarters of the two recession episodes were used to project a ‘baseline’ that is assumed to have ensued if the AFC and GFC would not have happened. For the FSAP 2013 and FSAP 2019 calculations, the respective baseline scenarios were involved.

35. Real estate is ‘pervasive’ in the Korean economy, for the financial system’s resilience to real estate price fluctuations therefore deserving emphasis. The assessment about the valuation of housing prices and their endogenous response in the scenario is surrounded by significant uncertainty, hence implying that sensitivity analyses around the adverse scenario with a special focus on house price shocks will be considered and presented later in this note.

B. Fintech Developments in Korea

36. The possible implications of Fintech developments as observed in Korea can be seen in three dimensions. Both the Open Banking initiative and the e-money service provider developments imply changing liquidity dynamics in the retail payments and money market in Korea19. These have implications for solvency of financial institutions and system-wide operational risks (Figure 14).

Figure 14.
Figure 14.

Korea: Implications of Open Banking and E-Money Developments in Korea

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff.

37. Notwithstanding its various expected positive implications, the Open Banking initiative could unfold its impact through rising competition and imply potential risks; in three dimensions. First, bank retail customers would be more inclined to swiftly transfer deposits across banks to where deposit rates are most competitive; using mobile phone applications that conveniently display them the pricing conditions across banks and allowing the transfer in the same place. This in turn may pressure banks’ interest expense. Second, bank customers may learn to more actively seek the most competitive offers in terms of loan interest rates, as a result of being more open-minded to where to deposit their funds, which would pressure banks’ interest income; third, rising financial literacy as a result of significant Fintech developments such as through Open Banking may enhance customers’ willingness and ability to behave as outlined above.

38. The result of such changing customer behavior may be that banks with currently lower retail deposit rates may raise them, and those with currently higher loan interest rates may lower them. Such behavior is captured in the Fintech Overlay analysis which will be presented in the solvency analysis section of this note.

39. The growing popularity of e-money providers could in the future exacerbate such rising competition through at least two channels. First, they may compete with commercial bank deposits by offering remuneration resembling interest to attract customers20, enabled by the Korean authorities’ gradual de-regulation initiative; second, banks might offer more sizable deposit rates to the e-money providers to attract their—possibly sizable in the future—re-deposited customer funds. Both aspects could imply further upward pressure on banks’ interest expense.

40. Open Banking and the growing popularity of e-money providers can in the future imply risks in terms of higher-frequency liquidity dynamics. First, the Open Banking system would imply that “stickiness” of bank deposits will be reduced. Bank customers could more likely react to ‘bad news’—whether substantiated or not—by swiftly and sizably moving funds to other banks. This would represent an ‘electronic deposit run’ and could exacerbate a liquidity squeeze for banks beyond what would be justified by banks’ fundamental solvency risk perception by the market21. Second, with e-money providers re-depositing customer funds in one or at most a few banks can imply concentration risk, i) from a system-wide operational risk viewpoint, and ii) in terms of a structural shortage of liquid funds (alongside reserves) for those banks with whom the service providers do not hold their funds.

41. Systemic liquidity risks may arise from e-money providers if their activities are not subject to adequate and formal regulation. Certain baseline requirements for e-money providers have been stipulated by EFTA (e.g., obligations to redeem the e-money they issue and to maintain dedicated accounts segregated for each line of business), but redemption risks may arise. E-money providers mitigate this risk in current practice by holding funds underlying their e-money on a 1:1 basis. Given the growing popularity of e-money, risk controls and mitigants would be strengthened by enshrining in law or regulation clear requirements as to a service providers’ treatment of funds underlying their e-money, e.g., to avoid that e-money providers would in the future deviate from their 1:1 holding practice.

42. The FSAP recommends that the Korean oversight institutions conduct a detailed Fintech and competition-related impact assessment for the banking sector and other components of the financial system. As argued above, such an assessment would not only consider medium-term solvency implications through changing competition but focus as well (primarily) on systemic liquidity and system-wide operational risk implications (Figure 14).

Forward-Looking Bank Solvency Analysis

A. Methodology

43. The schematic in Figure 15 illustrates selected elements of the modular Solvency Analysis Tool Suite for banks. It consists of several connected modules that have been set up for the Korea FSAP. The modules’ inputs, functioning and outputs are described in this section.

Figure 15.
Figure 15.

Korea: Dependencies Captured in Top-Down Solvency Model Suite for Korean Banks

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff.

44. Four defining features of the tool suite deserve emphasis. They include i) an account for IFRS 9 loan loss provision principles; ii) a nonlinear solvency-funding cost feedback mechanism; iii) dynamic balance sheets; and iv) a “Fintech Overlay”. Figure 15 indicates which elements the Fintech Overlay relates to from a methodological perspective. Details will be explained later in this section. Figure 16 shows the P&L structure adopted for the Korea bank solvency analysis along with a summary of the model approach adopted for each line.

Figure 16.
Figure 16.

Korea: P&L Structure and Model Approaches Adopted for the Korea Solvency Analysis

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff.

Credit Risk (CR) Module

45. Figure 17 shows the structure of the CR module22. It summarizes the “hybrid” PD-satellite-Z-factor methodology developed for Korea, related to item 2 in Figure 16. Appendix IV shows a table which maps the line items from the banks’ loan and trading books to their treatment under either the credit risk or market risk methodology.

Figure 17.
Figure 17.

Korea: Credit Risk Module Structure

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff. see Glossary for abbreviations.

46. Default rate satellite models were estimated based on confidential bank data provided by the FSS (Figure 17, Block A). They were structured and estimated based on a Bayesian Model Averaging (BMA) Methodology subject to sign constraints on the long-run multipliers of the equations23. The default rate models were developed for four portfolio segments: large nonfinancial corporates, SMEs, household mortgage loans (including Jeonse leasehold loans), and consumer credit. The default rate models were used to derive forecasts conditional on the baseline and the adverse scenario for Korea (Block B in Figure 17)24,25. The PDs at the outset of the scenario horizon and the maxima under the adverse scenario are shown in Figure 18. The model structure is sketched in Appendix V. Household mortgage portfolio PDs were found to be very reactive to the adverse scenario in terms of multiples to their starting points; the latter being very small in comparison to the other portfolios’ PDs as of 2018. Their levels under the adverse scenario were deemed sufficiently conservative in historical perspective.

Figure 18.
Figure 18.

Korea: Baseline and Adverse Scenario-Conditional PDs

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Korean banks’ and FSS data and IMF staff calculations. Annual horizon PDs. The NFC category comprises both large nonfinancial corporate exposures as well as SME exposures. The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying distribution across banks. The error bars extending to the up- and downside denote the maxima and minima. White lines denote the median. Under the baseline (adverse) scenario, the distribution reflects the average (maximum) of the PDs along the 20-quarter scenario horizon.

47. Historical transition data in accordance with IFRS 9 classification criteria have been compiled by the FSS from the Korean banks, under FSAP guidance. The Korean regulatory asset classification scheme was mapped into the Stage 1–2-3 classification under IFRS 9: the prudential categories “normal” and “precautionary” were mapped to the accounting Stages 1 and 2, respectively. The prudential asset classes “substandard”, “doubtful” and “loss” were jointly mapped to Stage 3. The historical flows across the resulting proxy accounting stages were sourced from the banks, including the additional flow categories for new business, repayment, write-offs and asset sales. They were collected at bank-level for three portfolio segments: nonfinancial corporate (large firms and SMEs combined), household mortgages, and consumer credit. Historical transition flow data with quarterly frequency were available from 2010Q1–2018Q4 for the majority of banks and portfolios.

48. Historical Z-factors and additional related parameters were estimated based on the historical bank-portfolio-specific transition flow data (Figure 17, Block C). The Z-factor methodology is summarized in Box 1.

The Z-Score Methodology

The Z-score methodology (Belkin et al., 1998) aims to reduce the information contained in a time series of transition matrices down to one number per point in time. It was originally developed for rating transition matrices; involving letter-based grids of ratings spanning up to 10–15 classes (AAA-D). The methodology is applicable to matrices of any size and irrespective of the criteria set behind the classes in the matrix, and hence applicable to a time series of IFRS 9 transition matrices. Figure B1.1 combines Figure 1 from Belkin et al. (1998) (on the left, for an initial BBB exposure) with a modified version thereof that is based on an IFRS 9 staging structure (on the right, for an initial S2 exposure in this example). Both refer to a long-term average transition matrix.

Figure B1.1:
Figure B1.1:

Probability Density for BBB Rating (Left) vs. S2 Exposure Under IFRS 9 (Right)

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

The Z-score method builds on the assumption that the probability density X is a function of an idiosyncratic driver Y and a systematic economy-wide driver Z. Both are independent unit normal random variables by assumption. The parameter ρ captures the correlation between Z and X, with Z explaining a fraction ρ of the variance of X.

( 1 ) X t = 1 − ρ Y t + ρ Z t

The fitted transition probabilities, ∆t, are expressed as:

( 2 ) Δ t ( x g + 1 G , x g G , Z t , ρ ) = Φ ( x g + 1 G − ρ Z t 1 − ρ ) − Φ ( x g G − ρ Z t 1 − ρ )

Φ is a standard normal cumulative distribution function. The terms xgG are the “bin boundaries” (referring to the vertical lines in Figure B1.1) which are computed based on an inverse of a standard normal cumulative distribution function, with reference to a long-term average transition matrix. The historical deviation between an observed and fitted transition matrix can be computed as:

( 3 ) min Z t Σ G Σ g w t g ( P t ( G , g ) ︸ o b s ,   t r a n s i t i o n   r a t e s − Δ ( x g + 1 G , x g G , Z t , ρ ) ︸ f i t t e d   t r a n s i t i o n   r a t e s ) 2

where the two sums in this equation indicate a summation over all elements in a transition matrix. Conditional on a ρ and the bin boundaries from the long-term average transition matrix, a Zt can be found for each point in time that minimizes eq. (3). The constant ρ and the time series Zt were estimated jointly subject to the constraint that the variance of the resulting Zt be equal one, for each bank-portfolio. A +1/-1 value for Z denotes a “1-standard deviation from normal (long-run average)” conditions. The estimated parameters ρ (bank-portfolio specific) for the Korean banks are depicted in Figure B1.2.

Figure B1.2:
Figure B1.2:

Estimated ρ for Korean Banks’ Loan Portfolios

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Note: The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying distribution of estimated ρ across banks. The error bars extending to the up- and downside denote the maxima and minima. White lines denote the median. NFC abbreviates Nonfinancial corporate.

49. A “hybrid” PD-satellite-Z-factor methodology has been developed for Korea (Figure 17, Blocks A-G). In principle, one could use the estimated historical Z-factor times series at portfolio level—and either at bank- or aggregated banking-system level—to develop satellite models, to derive scenario-conditional paths of all Z’s into the future, and in this case not requiring the separate estimation of default rate satellite models. Default rates would be part of the transition matrix and implied by the projected Z factor paths26. This approach was not chosen for Korea because the historical transition matrix data—albeit rich from a cross-bank-portfolio perspective— was still deemed too short from a time series perspective, not capturing a full economic cycle (as starting in 2010). Default rates were longer, starting at the end of the 1990s for most portfolios. The “hybrid” methodology is summarized in Box 2.

“Hybrid” PD-Satellite-Z-factor Methodology and Dynamic Balance Sheets

Figure B2.1 depicts how the previous step (Box 1, first row in Figure B2.1) compares to the translation of a Z-score path back to a transition matrix (this box, second row in Figure B2.1).

Figure B2.1:
Figure B2.1:

From Transition Matrices to the Z-Score and Vice-versa

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

To accomplish what is indicated in the second row of this figure, a transition matrix forecast can be obtained from a Z-score forecast path in the same way as historical fit is produced at the Z-estimation stage (Box 1). The formula required to that end has the same structure as eq. (2) in Box 1:

( 4 ) Δ t ( x g + 1 G , x g G , Z t , ρ ) = Φ ( x g + 1 G − ρ Z t ˜ 1 − ρ ) − Φ ( x g G − ρ Z t ˜ 1 − ρ )

The parameter ρ and the bin boundaries are given at this stage (previously estimated, Box 1) and do not change. Only Z is an input that varies conditional on different scenarios, now denoted as Z˜t implying the transition probabilities across the transition matrix and along the scenario horizon.

The formulas that were used to imply the S1, S2, and S3 stocks are the following:

( 5 ) S 2 t = S 2 t − 1 + T R t 12 S 1 t − 1 + T R t 32 S 3 t − 1 ︸ I n f l o w s   t o   S 2 − T R t 12 S 2 t − 1 + T R t 23 S 2 t − 1 − M t S 2 S 2 t − 1 ︸ O u t f l o w s   a w a y ​ f r o m   S 2 S 3 t = S 3 t − 1 + T R t 13 S 1 t − 1 + T R t 23 S 2 t − 1 ︸ I n f l o w s   t o   S 3 − T R t 31 S 3 t − 1 + T R t 32 S 3 t − 1 − W R O t S 3 t − 1 ︸ O u t f l o w s   a w a y ​ f r o m   S 3 S t = S 1 t + S 2 t + S 3 t = ( 1 + g t ) S t − 1 S 1 t = max ( 0 , S t − S 2 t − S 3 t )

The portfolio-specific gross loan growth is under explicit control (gt), pre-determined by the scenario. The repayment percentage for S2 exposures as well as the write-off/asset-sale parameter (MtS2 and WROt) were held constant at observed end-sample position for all portfolios and banks.

Based on the above set of equations, the “hybrid Z-score-PD-Satellite” methodology was designed as follows: the bank-portfolio-specific Z path for a given bank-portfolio along the 20-quarter forward horizon was set such that the exposure-weighted PD would match a path implied by the PD satellite models. The PD paths from the PD satellite models were attached to bank-portfolio-specific PD starting points using a distance-to-default transformation. The exposure-weighted PDs based on the transition matrices were computed, period by period, as follows:

( 6 ) P D t + 1 | t P i T = S 1 t × T R 13 t + 1 | t + S 2 t × T R 23 t + 1 | t S 1 t + S 2 t

50. A “perfect foresight” assumption was employed (Figure 17, Block E). “Perfect foresight” means that the multiple probability-weighted scenario requirement as stipulated under IFRS 9 for accounting provision purposes is ignored for what concerns the solvency stress test analysis. Two concrete scenarios are instead considered, with 100 percent weight set for each of them separately (baseline, adverse).

51. ECL provisioning under IFRS 9 requires forming an expectation as to how the underlying risk parameters behave until the end of the lifetime of the relevant financial assets. This horizon extends beyond the 5-year horizon of the FSAP scenarios for portfolios with residual maturities larger than 5 years; that is, in particular for mortgage portfolios. The baseline risk parameters (PDs, LGDs) as of the last quarter of year 5 (2023Q4) were held constant; the adverse scenario’s parameters at 2023Q4 were assumed to decay back to baseline over an 8-year period (Figure 19)27.

Figure 19.
Figure 19.

Korea: Perfect Foresight and Risk Parameter Behavior after Initial 5-Year Horizon

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff. X is 5 years. Z is 8 years.

52. The solvency analysis was conducted based on a dynamic bank balance sheet assumption (Figure 17 Blocks E/F and Box 3). Gross credit growth paths for the banks’ corporate and household loan portfolios were set as part of the scenario design process, consistent with the assumed macro-financial developments in the baseline and adverse scenario, at economy-wide level. The portfolio-specific growth paths were assumed to be equal across banks. Write-offs and asset sales were allowed to be positive, which is important in Korea since banks make active use of NPL management firms for them to take care of the workout, seizure of collateral, etc. The relevant equations to account for write-offs and asset sales were shown in Box 2 in relation to the hybrid Z-factor methodologies.

53. A simple structural LGD model was employed to link housing-collateralized portfolios’ LGDs to the house price paths in the scenarios (Figure 17, Block H). The structural LGD model captures the dependence of the LGDs on the underlying housing collateral value28,29. For all other portfolios, LGDs were kept constant in the baseline and scaled by a factor of 1.15 under the adverse scenario. On top, for all portfolios in-scope of the CR Module, a conservative overlay has been applied, by taking the maximum of the model-implied (structural model or simple factor) LGDs and the bank-reported regulatory downturn LGDs30. Figure 20 shows the resulting distribution of LGDs for different portfolios under the baseline and adverse scenario.

Figure 20.
Figure 20.

Korea: Baseline and Adverse Scenario-Conditional LGDs

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Korean Banks’ and FSS data and IMF staff calculations. The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying LGD distribution across banks. The error bars extending to the up- and downside denote the maxima and minima. White lines denote the median.

54. Effective interest rate assumptions are required for discounting purposes when computing ECLs (Figure 17, Block I). The effective interest rates as of end-2018 were sourced from the banks at bank-portfolio level (Figure 21) and held constant for the purpose of discounting (Box 3 documents how).

Figure 21.
Figure 21.

Korea: Effective Interest Rates for ECL Discounting Purposes

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: FSS and IMF staff calculations. The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying distribution across banks. The error bars extending to the up- and downside denote the maxima and minima. White lines denote the median.

55. An IFRS 9-compatible ECL scheme was employed (Figure 17, Block J). Box 3 summarizes the methodology for computing 12-month ECLs for Stage 1 exposures, lifetime ECLs for Stage 2 and 3 exposures, as well as for implying the provision stocks and flows.

56. RWs for credit exposures were treated differently for STA and IRB portfolios. RWs for STA exposures were kept constant at their end-2018 levels. RWs for IRB exposures were allowed to move dynamically as a function of the underlying changes in regulatory TTC-PDs. Downturn LGDs were kept constant as deemed sufficiently conservative. The TTC PDs at bank-portfolio level were made a smooth function of PiT-PDs as used for accounting provisioning, using the formula PDt+1|tTTC=φ−1(φ(PDt|t−1TTC)+α×Δφ(PDt+1|tPiT)). The smoothing parameter α was set to 0.5 for the base set of results. A sensitivity analysis related to α will be presented in the result section. Under both the STA and IRB approach, the RWA evolution in volume-terms also reflects the allowance for dynamic balance sheets31.

Interest Income and Expense, incl. Nonlinear Solvency-funding Cost Feedback, Feedback from Credit Risk to Interest Income, and Fintech Overlay

57. The interest income and expense module—capturing interest rate risk in the banking book—is based on cross-bank panel econometric models. The banks’ historical effective interest income and interest expense rates form the basis for these models (Figure 22). The corresponding panel models were estimated for the groups of nation-wide banks, regional banks, and specialized banks separately. The regional banks’ models were employed for the group of ODIs (credit unions, credit cooperatives and mutual savings banks). The interest income and expense panel model equations are documented in Appendix V.

Figure 22.
Figure 22.

Korea: Banks’ Historical Effective Interest Rate Evolution for Korean Banks

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff.Notes: The bars show the distribution of the underlying individual-bank samples at a quarterly frequency since the beginning of the 2000s.

58. The interest expense model contains a feedback link from banks’ solvency position to their funding cost (Appendix V). From an economic perspective, this estimated feedback was deemed material enough to warrant its inclusion in the model for the Korean banks. From a methodological perspective, including that link meant that a sequential, iterative link from the end-period (quarter) capital ratios resulting from the overall stress test model suite to banks’ funding cost in any subsequent quarter was built in.

Lifetime (LT-) ECL and Provision Stock and Flow Calculations

The LT-ECL formula employed for the Korea solvency analysis has been structured as follows:

( 6 ) E C L t L T , S 2 = Σ s = t + 1 M T R s 2 − 3 , * × L G D s × S 2 s − 1 ( 1 + r ) s

with M denoting the average residual maturity of a portfolio. The lifetime ECL is here measured in monetary units. The right hand-side includes a point-in-time LGD and the relevant exposure, which is the S2 stock. The denominator of the formula involves an effective loan interest rate for discounting the ECL along the lifetime of a loan portfolio. The formula for the incremental PD in the numerator, denoted TRs2−3,*, is:

( 7 ) T R s 2 − 3 , * = T R s 2 − 3 × Π t = 1 s − 1 ( 1 − T R t 2 − 3 ) ︷ P i T   s u r v i v a l   p r o b a b i l i t y ︸ c u m u l a t i v e   s u r v i v a l   p r o b a b i l i t y

The term TRs2−3 is the unconditional transition probability for S2 stocks which links to the outcome of the transition matrix forecast path (Box 2). While this unconditional PD (TR2–3) moves over the lifetime of a loan portfolio in an ‘unrestricted’ manner and in relation to macro-financial conditions, the incremental PD measures the probability of default in period s conditional on not having defaulted up to period s-1 and approaches zero over time1. The exposure term in eq. (6), i.e. the S2 stock, is projected for simplicity using a linear principal repayment scheme2.

For S1 exposures under IFRS 9, the provision stocks are to equal the 12-month expected credit loss, i.e.:

( 8 ) P R O V t , S 1 = E C L t , S 1 = T R t + 1 | t 13 × L G D t + H | t × S 1 t

Any change in the underlying risk parameters implies a provision flow and hence an impact on capital through the P&L The term TRt+1|t13 is the expected default rate for S1 exposures conditional on end of period-t information for the following year. The LGDt+H|t term has a t+H to denote the fact that the LGD is meant to be forward-looking beyond a 1-year horizon if the expected time until collateral can be sold is more than 1 year. For S2, exposures, the lifetime ECL formula becomes relevant (eq. 1), with its provision stock is supposed to equal, that is, PROVt,S2=ECLtLT,S2. For S3 exposures, provision stocks are to cover the portion of the defaulted loan exposures that will likely not be recoverable, that is:

( 9 ) P R O V t , S 3 = E C L t , S 3 = L G D t + H | t × S 3 t

The total provision stock is to the sum of the stage-specific provision stocks:

( 10 ) P R O V t = P R O V t , S 1 + P R O V t , S 2 + P R O V t , S 3

The loan loss provision flow is then the change in the stock, adjusted for write-offs and asset sales:

( 11 ) P R O V F L O W t = Δ P R O V t + W R O t × L G D t × S 3 t − 1

The adjustment term related to the write-offs in eq. (11) accounts for the fact that exposures that are written off or sold to asset managers, whose provision stock is falling for that reason, and which should be residual net-equity neutral. The way the adjustment is designed involves the assumption that the LGD estimate based on which a provision stock had been set just before the write-off or asset sales equals the realized LGD upon the collateral sale for the exposures that are written off or sold.

1 If the maturity parameter M in eq. (6) would be set to one, then the lifetime ECL would be a 12-month ECL. In this case, the incremental PD would yet equal the unconditional PD according to eq. (7). If one may ignore in this case the discount factor in the denominator of eq. (6), then the ECL formula would attain the standard “PDxLGDxEAD” structure. 2 Alternatively, it could be projected into the future based on a nonlinear repayment schedule of fixed or variable rate loans. If a nonlinear repayment schedule was employed, for variable rate loan portfolios specifically, then an expectation about the loan interest rate would have to be considered as well. For portfolios that are “mixed”, i.e. contain fixed and variable loans, two corresponding repayment schedules could be considered which could then be added together for the two portfolio components. Modelling prepayments explicitly is an option as well. All of these model options were not deemed necessary for the Korea FSAP, as they would have added undue complexity that was not warranted given the target level of detail for the analysis.

59. In addition to being included as such, the solvency-funding cost feedback was allowed to be nonlinear. A notable nonlinearity has been found (Figure 23). The shape of the nonlinear relation implies that at low initial capital ratio levels of a bank, an increase in that ratio would compress its funding cost, while at higher initial capital ratios, a further rising capital ratio may increase its funding cost32. Loan interest rates in turn depend on banks’ funding cost, for the solvency feedback to funding costs to effectively also influence lending rates at the bank level (Appendix V) 33.

Figure 23.
Figure 23.

Korea: Nonlinear Solvency-to-Funding Cost Feedback for Korean Banks

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations. The estimated feedback from solvency to the banks’ cost of debt is significantly negative on average over the capital ratio range observed for the banks. The light grey (yellow) shaded area indicate the 5th-95th percentile range of the banks’ shareholder equity/TA ratios under the baseline (adverse) scenario across banks and the scenario horizon. Solid lines (dashed lines) depict the mean (10th and 90th percentiles) of the derivative of the cost of debt to the initial capital ratio. The interpretation for example for the regional banks: a 1p.p. shift to the capital ratio starting at 1 percent (4 percent) would induce a -0.25 p.p. (-0.1 p.p.) response of their cost of debt.

60. An additional feedback mechanism from materializing credit risk to interest income is captured. It is the structural link from the CR Module—as a result of which initially performing exposures (Stage 1 and 2) move to nonperforming status (Stage 3)—which implies no interest income by assumption in Stage 3. This is depicted by the connecting line between credit risk and interest income in Figure 15.

61. The interest income and expense models were used to implement a “Fintech Overlay” analysis. The methodology (Box 4) is embedded in the stress test model suite, implying that it can be used in “scenario-conditional” mode, and hence be set as an overlay to both the baseline and the adverse scenario. The Fintech Overlay concerns only one of the three dimensions (Fig. 14)—the medium-term solvency impact on banks—under the assumption of “no business model change”.

Market Risk (MR) Module

62. The methodologies applied to exposures subject to MtM revaluation depend on their exposure type (bonds, equity). For sovereign, nonfinancial and financial bond exposures under the FVOCI and FVPL category, a modified-duration formula was employed to revalue the exposures as a function of their reported residual duration and the relevant bond yield assumptions under the scenarios34.

63. Domestic and foreign equity exposures were revalued using a direct link to the assumed equity index paths under the scenarios. The KOSPI and the MSCI World index were used to revalue Korean banks’ domestic and foreign equity exposures under the FVPL category.

64. Korean banks’ domestic NFC exposures in the FVOCI category were subject to both the MR and CR Module. In line with the accounting standard, the CR revaluation component was reflected in the P&L (tax non-neutral), while the FVOCI differential beyond the CR impact was reflected through the OCI account (tax neutral).

Fintech Overlay – Methodology

The Fintech Overlay methodology is anchored in the bank-level panel econometric models for interest income and expenses (Appendix V) and focused on the medium-term solvency impact of rising competition. The panel models have bank-specific intercepts. These intercepts will be adjusted based on the following methodology.

A Fintech Overlay “strength” parameter is defined as α which can range between zero and one. The share of household loans in total loans of a bank is denoted as hbL; the share of household deposits in total liabilities as hbD. The initial bank-specific intercepts for the interest income rate (IIR) and interest expense rate (IER) models are denoted as cbLandcbD. The cross-bank percentiles over the initial intercepts are defined as:

( 1 ) c ^ L = p e r c ( ( c b = 1 L , ... , c b = B L ) , 1 − α ) a n d c ^ D = p e r c ( ( c b = 1 D , ... , c b = B D ) , α )

where α determines the percentile. The adjustment to the IIR models’ intercepts, denoted as c˜bL, is then done as follows:

( 2 ) c ˜ b L = min ⁡ ( c b L , c ^ L ( α ) + ( 1 − h b L ) ( c b L − c ^ L ( α ) ) )

For the IER models, the intercepts are adjusted as:

( 3 ) c ˜ b D = min ⁡ ( c b D , c ^ D ( α ) + ( 1 − h b D ) ( c b D − c ^ D ( α ) ) )

The underlying idea is that an adjustment of an intercept in, say, the IIR model, downward to a lower cross-bank percentile would be scaled by the share of household loans in total loans; likewise, the upward adjustment of an expense rate model’s intercept by the share of household deposits in total liabilities. Banks that do not have any household deposits (as for example some selected specialized banks) would not face any change in their funding cost, by assumption. The max and min operators in eqs. (2) and (3) imply that banks whose intercept would already stand below/above the required target intercept would not change.

Two additional assumptions are embedded in the methodology: first, a “no business model change” assumption, as fees and other sources of income are not modified; second, there is no consideration and account of caps on transactions fees which the Open Banking initiative in Korea implies, due to insufficient data in this respect.

The methodology is simplistic, yet useful for the purpose of deriving an approximate impact of heightened competition, in a scenario-conditional mode of the overall integrated stress test model suite. It can be further developed and refined in numerous dimensions; for example, by the Korean oversight institutions.

Regulatory Capital Ratio Thresholds

65. Regulatory capital thresholds are employed for nation-wide, regional, and specialized banks. Inclusive of the CCB (2.5 percent), the threshold for non-DSIBs amounts to 7 percent, 8.5 percent and 10.5 percent for CET1/RWA, Tier1/RWA and total capital (Tier 1 + Tier 2)/RWA ratios, respectively. For DSIBs, these three thresholds are 1 percentage point higher35.

B. Results

Headline Bank Solvency Stress Test Results

66. The Korean banking system appears resilient under the FSAP adverse macro-financial scenario. A few regional, specialized banks and ODIs face the most sizable capital losses across banks. Credit risk losses and diminished net interest income would be the most pronounced source of pressure on banks’ capital ratios (Figure 24). Specialized banks would experience a notable change in their capital ratios in both absolute and relative terms (Figure 25). The cross-bank heterogeneity in terms of capital responses is most visible in the ODI category.

Figure 24.
Figure 24.

Korea: Solvency Stress Test Results: CET1 Ratios, Sub-Sector Aggregates

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.Notes: The forecasts conditional on the baseline scenario are displayed in cumulative terms up until Year 5 (end-2023). The adverse scenario results are reported in cumulative terms up to the low point, as indicated in the title of the Figures (year 1–5 correspond to 2019–23). The capital ratios are defined as CET1/RWA for nation-wide, regional and specialized banks. For ODIs, the ratios are defined as accounting equity (net of loan loss provision stocks) over total assets. OCI carries an asterisk to indicate that its impact is not measured before tax but after tax, despite in the figures being positioned to the left of the tax impact.
Figure 25.
Figure 25.

Korea: Changes in CET1 Capital Ratios from Starting Point to Low Point Under the Adverse Scenario

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.Notes: The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying distribution across banks. The error bars extending to the up- and downside denote the maxima and minima. White lines denote the median. “Factors” are defined as the capital ratio at the banks’ respective low points divided by their initial capital ratios at end-2018.

67. The system-wide capital depletion from the starting point amounts to about 22 percent of initial capital levels (Figure 26). This represents about 2.9 percent of GDP as of 2018.36 All banks’ capital ratios would stay above regulatory minima when allowing the consumption of the capital conservation buffer (CCB).37 A subset of the specialized banks would consume between 0.3 and 1.4 percentage points of their CCB under the adverse scenario.

Figure 26.
Figure 26.

Korea: Capital Depletion Under the Adverse Scenario

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.

68. The banks’ asset exposure shares and their portfolio-loss-in-total-loss shares are not fully proportional, i.e. some portfolios are more sensitive under the adverse scenario than others. Consumer credit losses are about aligned with their portfolio shares while nonfinancial corporate loss shares are higher and household mortgage-related loss shares—including from Jeonse loans—lower relative to their portfolio shares (Figure 27). The latter finding reflects the widespread use of mortgage insurance schemes in Korea which protect the banks’ capital and provide relief in terms of risk weight densities for such portfolios under both the baseline and the adverse scenario38.

Figure 27.
Figure 27.
Figure 27.

Korea: Asset Exposure Shares vs. Credit and Market Loss Shares Under the Adverse Scenario

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.Notes: Each dot (24) is a bank (including five ODI aggregates). The “loss share” is based on the maximum cumulative loan loss per bank-portfolio from along the adverse scenario horizon (2019–23). The “exposure share” is measured as of end-2018.

Fintech Overlay

69. The pressure on capital ratios may grow if Fintech-induced competition would intensify. The “Fintech Overlay” has been used to gauge the effect of intensifying competition due to the “Open Banking” initiative as well as Fintech developments in the retail payment sector, suggesting potentially notable impacts on regional banks, ODIs and selected specialized banks. The analysis assumes that Open Banking and e-money may put upward pressure on retail deposit rates, coupled with downward pressure on loan interest rates, and not allowing banks to adjust their business models, e.g. by raising fees. The aim was to thereby examine how Open Banking may impact the solvency conditions of banks under some conservative assumptions (related system-wide liquidity aspects are discussed later in this note).

70. Under the baseline scenario, regional banks’ capital ratios may fall by 0.6 to 1.3 percentage points by the end of the five-year horizon under the Fintech Overlay. For nationwide and specialized banks, the effects are “nonlinear” in the sense that only once the strength of the overlay is set to high levels, the impact on the banks’ capital ratios would become notable. When activating the Fintech Overlay, the system-wide capital depletion would rise to 4 percent, from 3 percent without the overlay. In this case, several regional and specialized banks’ as well as ODIs’ capital ratios would be further stretched. In aggregate, nation-wide, regional, and specialized banks would consume about 0.4, 0.7, and 2.2 percentage points of their CCBs. Figure 28 shows the impact in terms of capital ratios and capital depletion from Year 0. Figure 29 shows the impact on NIMs, which are the main underlying channel through which stronger competition would influence the capital ratios. This effect is not a baseline forecast, as banks will likely change their business model to counteract such possible losses in income.

Figure 28.
Figure 28.

Korea: Fintech Overlay—Impact on Banks’ Capitalization Under Conservative Assumptions

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Source: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.
Figure 29.
Figure 29.

Korea: Fintech Overlay—Impact on NIMs

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.Notes: The figure shows the difference in NIMs compared to baseline scenario, on average per annum, in percentage points. The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying distribution across banks. The error bars extending to the up- and downside denote the maxima and minima. White lines denote the median.

71. The Fintech Overlay analysis suggests that various regional banks, a subset of the specialized banks and MSBs might be affected the strongest. MSBs, however, offer loans to customers which are deemed to be structurally riskier, for their loan interest rate spreads over MSBs’ funding cost to be structurally higher. Such spreads may not necessarily fall as much as a result of rising competition, hence lessoning a bit the effects for MSBs relative to other banks in Korea.

Bank Solvency-Funding Cost Feedback

72. The solvency-liquidity cost feedback is notable. Under the adverse scenario, for eight of the banks the additional feedback explains between -0.3 and -0.7 percentage points in the fall of their regulatory capital ratios (Figure 30).

Figure 30.
Figure 30.

Korea: Solvency-Liquidity Cost Feedback

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.

House Price Sensitivity Analysis

73. The rationale for conducting a house price sensitivity analysis stems from the special housing market structure in Korea. The housing market in Korea is of notable centrality and uniqueness, in terms of its leasehold rent system and the use of insurance in relation to leasehold and mortgage loans by state-run institutions. Over the past decades, Korea has not experienced any significant housing market downturn, except for its endogenous, yet limited, response in terms of price falls during the AFC and GFC. The adverse scenario reflects this past endogenous response of about -16 percent peak-to-trough.

74. The Korean banking system appears resilient to a hypothetical more severe house price decline of -40 percent, coupled with the adverse scenario. The additional burden on banks’ capital ratios appears limited (Figure 31). A caveat to this analysis is that it captured the impact of falling house prices on only the banks’ mortgage portfolios, i.e., not capturing commercial real estate portfolios39. The analysis was conducted in a way that affects only the mortgage portfolios’ LGDs, under the assumption that house price movements in isolation would not materially affect mortgage PDs. This is a reasonable assumption in jurisdictions such as Korea—a full recourse system—in which households have no notable incentives for strategic default in response specifically to falling house prices when they would face “negative equity”.

Figure 31.
Figure 31.

Korea: Capital Ratios’ Sensitivity to Stronger House Price Shock for Residential Mortgage Portfolios

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.Notes: The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying distribution across banks. The error bars extending to the up- and downside denote the maxima and minima. White lines denote the median.

Mortgage Insurance

75. Mortgage insurance schemes are used to a significant extent in Korea. They imply a shield to banks’ capital and relief in terms of risk weight densities for such portfolios. The insured portion of the mortgage portfolios amount to 25 percent, 45 percent, and 27 percent for nationwide, regional, and specialized banks respectively (medians) at end-2018 (Figure 32). The shares were assumed to stay constant along the horizon under both the baseline and the adverse scenario40.

Figure 32.
Figure 32.

Korea: Mortgage Insurance Shares Across Korean Banks

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.Notes: The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying distribution across banks. The error bars extending to the up- and downside denote the maxima and minima. White lines denote the median.

76. To quantify the impact of switching the insurance off, three effects need to be accounted for. They include the accounting provision stock difference at the end-sample position, the provision flow difference conditional on the scenario along the 5-year scenario horizon (focus on the adverse scenario), and the relief in terms of risk weights.

77. The effect of shifting the insured mortgage share to zero percent on RWAs has been captured based on an auxiliary regression relating mortgage risk weight densities to their drivers. The equation had the following structure: RWDb=α+βPDbTTC+γ(1−wbI)LGDbI is the bank-specific risk weight density for the IRB mortgage portfolio of all banks for which this share is non-zero. PDbTTC is the IRB mortgage portfolios’ TTC-PD, wbI is the share of the mortgage portfolio that is insured, and LGDbI is the regulatory downturn LGD estimated for the insured portion of the portfolio, computed as LGDbI=LGDb/(1−wbI), where LGDb is the weighted average DT-LGD for the portfolio. The equation structure assumes that the TTC-PD is the same for the insured and uninsured portion of the portfolio and that the LGD for the insured portion is zero percent. All variables are measured as of 2018. The equation has an R-square of 89 percent and both /? and y were found to be economically significant positive coefficients41.

78. The counterfactual calculation suggests a sizable role for risk weighted assets among the three drivers on capital ratios (Figure 33)42. For nation-wide, regional, and specialized banks, the weighted average impact per group of banks amounts to a combined 21, 28, and 7 basis points, respectively. About 70 percent of these impacts stem from the risk weighted asset effect. For selected banks in the three categories, the overall impact reaches a non-negligible 26, 46, and 32 basis points, respectively. The fact that regional banks are most sensitive to the presence of the insurance squares with them having the highest insurance shares in the banking system (Figure 32).

Figure 33.
Figure 33.

Korea: Impact on Accounting Provision Stocks, Flows, and Risk Weights when Assuming that Mortgage Insurance was Absent

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.Notes: The bars reflect the RWA-weighted impact on CET1/RWA ratios of the underlying individual banks in each category. The bar extending to the downside denotes the maximal impact for one bank in the respective category.

Static vs. Dynamic Bank Balance Sheets

79. Assuming static instead of dynamic balance sheets would make the bank-solvency results more benign. The banks’ capital ratios would be higher than they are under the dynamic balance sheet assumption if write-offs and asset sales were disallowed (Figure 34), while otherwise still operating under a dynamic balance sheet mode. Gross loan growth and hence overall balance sheet growth would equal the dynamic balance sheet growth; yet, prohibiting write-offs and asset sales implies that less new business enters the loan portfolios (by design of that counterfactual simulation), which would generate more income as these loans are performing, and carry less sizable risk weights. The move to a full static balance sheet (zero gross loan growth, no write-offs and asset sales) would render the solvency ratios even more benign. The median difference to the full DBS outcome would amount to 28, 46, and 50 basis points for nation-wide banks, regional banks and ODIs, and specialized banks, respectively. For some selected nation-wide banks, the difference would equal a material 90 basis points.

Figure 34.
Figure 34.

Korea: Capital Ratio Differentials under Different Static and Dynamic Balance Sheet Variants (Adverse Scenario)

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations. WRO = write-offs. AS = assets sales. BS = balance sheet. The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying distribution across banks. The error bars extending to the up- and downside denote the maxima and minima. White lines denote the median.

Regulatory Loan Loss Provision Overlay

80. The regulatory provision coverage ratios across banks and portfolios tend to exceed the top-down accounting model-implied starting point coverage ratios based on the adverse scenario. Unlike an unbiased accounting provisioning, regulatory loan loss coverage ratios are expected to be set in a prudent, downturn scenario-conditional manner, akin to what an accounting-based loan loss estimates under an adverse scenario may suggest. The Korean banks’ regulatory coverage ratios indeed largely exceed the Year 0 adverse scenario-based accounting model-implied coverage ratios (Figure 35, focus on orange dots for the adverse scenario)43. If the regulatory provision scheme was switched off, the capital depletion of the banks would rise from the “headline” impact of 2.9 percent of GDP (Figure 26) to 3.7 percent (Figure 36).

Figure 35.
Figure 35.

Korea: Loan Loss Coverage as of End-2018, Regulatory Provision-based vs. Top-down Model-based (Adverse Scenario)

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations. The scatter plots (each dot is a bank) show loan loss coverage ratios (provision stock for S1/2/3 over gross exposure in S1/2/3) for different portfolios.
Figure 36.
Figure 36.

Korea: Impact of Regulatory Loan Loss Coverage on Capital Ratios and Depletion

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations.

RWA Pass-Through Strength

81. The pass-through parameter that links changes in PiT-PDs to TTC-PDs to obtain RW changes for IRB portfolios is subject to uncertainty and hence deserves a sensitivity analysis. The “headline” results presented before (Figures 24–26) were based on a pass-through strength of 0.5. Varying this pass-through parameter on a range from zero to one implies a notable range for the resulting CET1/RWA ratios under the adverse scenario (Figure 37, left). Under the strongest pass-through of 100 percent (arguably a too conservative assumption), still all nation-wide, regional and specialized banks would not fall short of their regulatory minima. Next to the specialized banks, two nation-wide banks would start consuming their CCB under this conservative assumption.

Figure 37.
Figure 37.

Korea: Sensitivity of Capital Ratios to RWA Pass-Through Strength under the Adverse Scenario

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations. “Y0” refers to 2018Q4. The bars are the risk-weighted asset weighted average across the underlying banks. The parameter α (right figure) denotes the RWA pass-through strength. The scatter and the fitted polynomial/trend-line on the right were based on the subset of 19 banks (nation-wide, regional, specialized banks) who do or may employ the IRB approach.

82. The higher sensitivity of nation-wide banks’ solvency results on the RWA pass-through strength stems from their higher IRB portfolio shares. Naturally, the impact of the pass-through relates to the IRB portfolio shares (Figure 37, right).

C. Recommendations

83. The top-down stress test frameworks by the FSS and BOK are well developed. They serve the purpose of conducting top-down risk assessments for the Korean banking system well. They also were instrumental for informing the very detailed bank-by-bank deep-dive analyses conducted during the 2019 FSAP.44

84. Fintech developments are significant in Korea and require a thorough risk analysis. As outlined earlier in this note, Fintech developments such as the Open Banking initiative and the e-money providers deserve special emphasis. A risk analysis should be conducted in particular in the area of systemic liquidity implications and system-wide operational risks, to then derive policy recommendations to help shield the Korean financial system from potential risks.

85. It is recommended that the FSS and BOK consider developing a lifetime ECL and hence IFRS 9-compatible credit risk model. This development is important to make sure that a top-down scenario analysis performs well in accurately predicting how banks’ capitalization behaves under hypothetical downturn scenarios, whose impact is more frontloaded than under an incurred loss accounting regime. It is also important to enable the supervisory authority to gauge whether banks’ provision stock coverage (portfolio-specific) is adequate—as observed at a point in time, and conditional on a baseline scenario.

86. It is recommended that the Korean authorities invest resources in back-testing various of their top-down model components. This recommendation concerns in particular the structural model components such as the authorities’ LGD models. Documented back-test results are instrumental for enhancing certainty and confidence with the models that the authorities’ risk analysis is based upon.

87. It is recommended that the authorities invest more resources into developing a dynamic balance sheet feature in general, and accounting for asset sales and write-offs in their models specifically. Allowing for realistic dynamic balance sheet dynamics instead of hypothetical static balance sheets is important and understanding and capturing the specific market structures in terms of the continuous and swift sale of banks’ nonperforming assets to an active NPL market is essential; in particular since the NPL market is so active in Korea. The FSS’ willingness to modify their stress test model to account for write-offs is positively acknowledged.

88. The stress test models for credit guarantee institutions (HF, SGI, HUG, others) should be further developed. The BOK already conducted some first risk assessments for guarantors, which is a positive development. The framework should be further developed for example in terms of linking to a common scenario, which would require developing satellite model systems of different kinds for the guarantors (currently the BOK’s approach is to stress the guarantors’ subrogation rates by self-set multiples of standard deviations, not linking to historical data nor scenarios). The stress test of the guarantors should ideally be integrated with those of banks and other non-bank FIs to account for the likely significantly nonlinear system behavior due to the presence of guarantors. The BOK and FSS are recommended to work jointly on such model developments going forward, in the interest of maximizing the synergies of pooling the specific expertise of both authorities.

89. Korea’s ODI sector is systemically relevant—constituting about 20 percent of the banking system—and related risk analyses should be kept under the header of the ‘banking system’. Financial institutions under the ODI category are banks, not non-banks, as suggested in various documents that the Korean oversight institutions publish on a regular basis. Placing the ODI’s in separate chapters under a non-bank FI category implies the risk of misleading an inference about risks in the banking system.

Forward-Looking Bank Liquidity Analysis

A. Overview

90. The FSAP conducted a top-down liquidity stress test based on 2018Q4 regulatory data. The analysis included 18 banks: six nation-wide commercial banks, two internet-only banks (KRW currency exposures only), six regional banks, and four specialized banks. In addition, foreign bank branches are included (KRW only). These correspond to approximately 90 percent of the banking system as of 2018Q4 in terms of total assets. In addition to the liquidity stress test, the FSAP also analyzed liability characteristics, liquidity buffers, and asset-liability-matching.

B. Current Liquidity Conditions and Banks’ Liquidity Profiles

91. Banks’ Liquidity Coverage Ratios (LCR) stand above the recently introduced requirement of 100 percent. The LCR requirements have been gradually introduced to levels of 100 percent for all currency (60 percent for foreign bank branches) and 80 percent for FX in 2019. Most banks already met these requirements in 2018 (Figure 38).

Figure 38.
Figure 38.

Korea: Liquidity and Stable Funding

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: FSS and IMF staff calculations.

92. The Net Stable Funding Ratios (NSFR) suggest that banks can meet funding requirement over a 12-month horizon. All banks subject to the NSFR met the 100 percent minimum regulatory threshold in 2019Q1.

93. Banks’ funding mix is diversified, and the FX funding structure has become more stable. Short-term FX funding now mostly comprises FX deposits by domestic nonfinancial corporations. Retail deposits still represent most KRW funding. The NSFR funding structure appears more diversified with funding from retail and SME deposits, corporates, other financial institutions and sovereigns being more balanced. Maturities of assets and liabilities are generally well matched, despite some short-term maturity mismatch being measured for selected regional banks and internet-only banks in KRW.

94. Contractual maturities of assets and liabilities appear well matched. The cumulative funding gap is measured as the sum of the net funding gaps for different maturity buckets, between 3 months and 10 years, expressed as a percentage of total assets45. There appears to be some short-term maturity mismatch for regional banks and internet-only banks in KRW. The maturity gaps are close to -8 percent for both bank categories for assets and liabilities maturing within 6 months. For other categories of banks, contractual maturities are well matched, and the FX funding gap is positive for all bank categories.

C. Methodology

95. The liquidity stress test (LST) assesses banks’ ability to withstand net-cash outflows amid shocks to asset prices, retail funding and wholesale funding. Four scenarios were considered, one for each of the three shocks and a more extreme scenario in which all the three shocks occur simultaneously (Appendix 3). For each scenario, the assessment was conducted for KRW currency only, FX only, and both combined. The hurdle rate was set to 100 percent, even in the cases where the regulatory LCR requirement is less than 100 percent to assess banks’ capacity to withstand the shocks outlined in the adverse scenarios.46

96. The liquidity shocks applied in the LST were more severe than the historical experience of Korea47. Since liquidity crises (including bank runs) are exceedingly rare, there is little in terms of historical precedents (even globally) from which to calibrate extreme shocks (tail risks); history-based calibrations would therefore fall increasingly short of the severe yet plausible criterion for scenario design. The preference for calibrating severe shocks (more severe than implied by history) is also driven by the notable Fintech developments in Korea, as well as the increased cross-border interconnectedness. Korea’s Open Banking structure implies that bank customers can in the future even more swiftly channel their deposits across banks; rendering deposits notably less sticky and increasing customers’ reactiveness to bad news, possibly triggering electronic deposit runs.

97. Liquidity shortfalls are calculated as a percentage of the banks’ counterbalancing capacity for different scenario severities. In total, 20 scenarios with increasingly severe shocks were considered for retail and wholesale funding (Appendix 3). Liquidity shortfalls are identified for all currencies, KRW and FX both in terms of type of shock and type of bank.

D. Results

98. Banks’ liquidity buffers appear to be adequate against significant drops in financial asset prices (Figure 39). Asset price shocks consistent with the bank solvency stress test would have little effect on liquidity with liquidity coverage decreasing from 121 percent to 120 percent. The shock has a similarly small impact on all categories of banks for both KRW and FX liquidity and results from banks’ HQLAs being mainly central bank reserves withdrawable in stress (15 percent) and securities with 0 percent risk-weight (73 percent).

Figure 39.
Figure 39.
Figure 39.
Figure 39.

Korea: Liquidity Stress Test

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Source: FSS and IMF staff calculations

99. Banks appear resilient to retail funding shocks. These would impact KRW liquidity, with the average liquidity coverage of nation-wide banks falling to 98 percent, implying a marginal liquidity shortfall. FX liquidity coverage would remain above 100 percent. Regional and specialized banks would carry sufficient FX liquidity to cover their implied shortfalls in KRW. The liquidity buffers in KRW of internet-only banks and foreign bank branches would cover the run-off in deposits. However, given the heavy reliance of internet-only banks on retail deposits, their coverage ratio would fall from 284 percent to 140 percent.

100. FX liquidity is sensitive to wholesale funding shocks. The banking system is likely to maintain liquidity following a wholesale funding shock. However, specialized banks’ reliance on unsecured wholesale funding would lead to a fall in FX liquidity coverage from 112 percent to 85 percent.

101. The liquidity shortfall is largest for nation-wide banks. Using gradually more severe scenarios for the retail and wholesale funding shocks illustrates the resilience of the Korean banking system. The overall system shortfall in KRW would reach 2 percent of the initial counterbalancing capacity. But while foreign bank branches would have excess liquidity of approximately 8 percent of the banking systems counterbalancing capacity, nation-wide banks would face a shortfall of close to 9 percent. The banking system’s FX liquidity shortfall resulting from FX funding shocks would reach 4 percent of FX counterbalancing capacity, where nation-wide banks would have an 8 percent liquidity shortfall. Regional and specialized banks would have combined liquidity buffers of 4 percent.

102. The BOK has ample space to provide liquidity in extreme stress scenarios. Both KRW and FX liquidity could be provided through standard facilities or unconventional policy measures. In an extreme scenario where banks with liquidity shortfalls had no access to liquidity from banks with surpluses, the aggregate shortfalls would reach KRW 43tr in domestic currency and USD 3.5bn in FX. The latter is equivalent to less than 1 percent of the BOK’s FX reserves.

E. Recommendations

103. Measures have been taken to strengthen the liquidity of the Korean banking system. Several regulatory requirements have been introduced, including the liquidity coverage ratios and stable funding ratios for both domestic and foreign currency, loan to deposit ratios and limits on open FX positions. The authorities are monitoring FX liquidity closely with both the BOK and the FSS carrying out FX liquidity stress tests separately, in addition to having a joint working group responsible for monitoring liquidity. While efforts are being made to stress test KRW liquidity, this seems to be lagging behind the recent developments in the FX liquidity stress test.

104. The authorities shall continue to improve the bank LST for domestic currency and develop the FX LST by major currencies. It would also be useful to expand the methodology of the joint BoK and FSS FX liquidity stress test to include FX LCRs, add an NDF stress scenario in different types of stress tests; and periodically survey the resident NDF FX market participants for any potential macroprudential effects as global shifts take place in currency and interest rate markets.

Risk Analysis for the Insurance and Pension Fund Sector

A. Insurance Sector

105. The FSAP conducted a top-down solvency stress test for seven life and six non-life insurance firms covering about 75 percent of the sector’s assets. The stress test was conducted on a solo entity basis, using public and supervisory data as of end-2018. See Figures 40 and 41, and the Insurance Stress Test Matrix in Appendix II for more details. An additional analysis of profitability trends was used to explore the ability of the insurance sector to re-capitalize after the adverse stress scenario.

Figure 40.
Figure 40.

Korea: Insurance Stress Test Sample—Fixed-Income Portfolio

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff calculations based on FSS and company data.Notes: Breakdowns only include bonds designated as held for trading or fair value through profit and loss.
Figure 41.
Figure 41.

Korea: Insurance Stress Test—RBC Coverage

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff calculations based on FSS and company data.
Figure 42.
Figure 42.

Korea: Insurance Stress Test—Contribution of Individual Shocks

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff calculations based on FSS and company data.
Figure 43.
Figure 43.

Korea: Insurance Profitability Projections After Stress

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff calculations based on FSS and company data.

106. The adverse scenario as employed for the bank stress test was slightly adjusted and more focused on market shocks; but is comparable in terms of severity and narrative of the banking solvency stress test (Table 8). It included shocks to sovereign bond yields (up to +250 bps), equity prices (up to -39 percent), corporate credit spreads and defaults, as well as FX rates. The shock applied to mutual funds (-12.5 percent) reflects the rather conservative nature of indirect investments which are mostly fixed-income or alternative investments including infrastructure. No explicit shock was used for real estate, as direct holdings in real estate are only allowed for own use, but not for investments. All shocks were assumed to occur instantaneously and modelled as haircuts to the asset values. The value of liabilities was only marginally affected by the stress scenario as discount rates are based on historic cost accounting.

Table 8.

Korea: Insurance Stress Test Scenario

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Sources: IMF staff.

107. The current asset valuation regime mitigates the impact of the market price shocks. Under the current accounting regime, held-to-maturity designation is still allowed—and widely used among life insurers, but also by a few non-life insurers for large shares of their assets. Hence, around 23 percent of the sample’s investment assets were shielded from market price changes.

108. The impact in terms of RBC coverage is the main output of the stress test. Under the current risk-based solvency regime, reductions in the asset base will typically reduce the available capital. In the stress test model, the required capital was assumed to decline very marginally by 2 percent, as two opposing effects are likely to broadly cancel each other out: With a reduced asset base, less capital is required; e.g., for equity risk, while rating downgrades would result in higher capital requirements for credit risks.

109. In the stress scenario, life insurers’ available capital declines substantially while non-life insurers are marginally affected. All companies stay above regulatory thresholds (Figures 41 and 42). Starting from an RBC coverage of 239 percent, the median life company drops to 169 percent, and two insurers would see their coverage ratio slightly below the recommended level of 150 percent, though still well above the 100 percent regulatory threshold. The overall reduction in available capital amounts to KRW 27.4 trillion, corresponding to a 38.7 decline, or 4.3 percent of total assets. Non-life insurers, of which most in the sample follow a very similar business model, reveal a narrow range of RBC ratios before stress which narrows even further after stress. The RBC coverage of the median non-life company declines from 214 to 158 percent. Only for one company in the sample, the RBC coverage falls below the recommended level. Available capital of the six non-life insurers is reduced by KRW 8.9 trillion (-27.8 percent, or -3.9 percent of assets).

110. The shocks on corporate bond yields and equity prices cause the largest reduction in available capital (Figure 42). In the life insurance sector, 35 percent of the KRW 27.4 trn reduction in available capital stems from the equity shock, another 30 percent from the corporate bond shock. The sovereign yield shock and the mutual fund shock, too, have a notable impact (18 and 15 percent, respectively). In the non-life sector, the relative importance of the corporate bond shock is even more pronounced as it contributes 50 percent of the overall reduction in available capital. In both sectors, the FX shock affects only a minority of companies as most are almost fully hedged using currency swaps and forwards.

111. In the stress scenario, life insurers would experience an accelerated decline in profitability forcing, perhaps, further liability restructuring to regain profitability (Figure 43). Underwriting results of life companies have been under pressure for some years, mostly as a result of declining premium income, and have turned negative in 2018. While this downward trend was largely compensated by a slightly increasing investment income, the top-down stress test model assumes no recovery of asset prices after the materialization of the instantaneous shock. Consequently, investment income would be largely composed of interest, dividends and rental income, but exclude any gains on the sale of investment assets. Compared to 2018, investment income in 2019 would be 12 percent (KRW 2.2 trillion) lower. Net income before tax would drop to 2.3 trn (-42 percent) in 2019, the first year of the projection horizon, recovering afterwards mainly on the back of positive investment results. Still, the projected underwriting results remain muted which exemplifies the urgency with which life companies should restructure their business to increase underwriting profitability, shifting further from guaranteed savings products into lower guarantees and protection business.

B. Pension Fund Sector

112. The Korean pension system faces heterogenous challenges in all three pillars. While the first pillar still provides very decent returns as of now, its long-term sustainability over the next decades will depend on reforms of its contribution and benefit parameters. In the meantime, strengthening the second and third pillar as complementing mechanisms is urgently needed.

113. The National Pension Service (NPS) faces challenges from the combination of low interest rates and adverse demographics, but also in its day-to-day operations. NPS is still in an accumulation phase and expected to grow from currently KRW 700 trn to 1,778 trn in 2041 when benefit payments will start exceeding contributions. Leaving parameters unchanged, the fund will be depleted by 2057. Options to cope with these long-term sustainability concerns include reducing benefits, increasing the contribution rate, increasing the retirement age, and increasing the target for investment returns. Operational challenges result from recent staff fluctuation, especially among investment and risk managers, which has surged after having moved the headquarter out of Seoul— as a public entity, NPS is increasingly competing with the private sector for talent.

114. NPS is striving for a more globally diversified investment portfolio, while currently still more than 60 percent are invested in domestic assets (Figure 44). In the domestic market, NPS holds more than 7 percent of the stock market’s capitalization, in addition to 18 percent of Korean sovereign bonds. In particular, the domestic equity portfolio is heavily weighted towards electrical and electronic equipment (30 percent), while overseas assets are more evenly spread across economic sectors. Alternative investments, accounting for 12 percent of total assets, are almost equally split in real estate (37 percent), private equity (32 percent) and infrastructure (30 percent), with the remaining 2 percent invested in hedge funds. Both asset classes, overseas and alternative investments, have contributed to shielding the fund from more volatility in 2018 when the return was negative for the first time since 2008, at -0.9 percent—since its inception in 1988, the average annual performance was 5.6 percent. As more foreign assets are included in the portfolio, the NPS will be exposed to higher currency risk—which are currently largely unhedged—and will have to develop its risk management capacity accordingly. In the past however, the KRW/USD exchange rate tended to co-vary negatively with foreign asset prices, especially in the long term.

Figure 44.
Figure 44.

Korea: The National Pension Fund

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Notes: */ 2019 performance is for the first nine months only.Source: IMF staff calculations based on NPS data.

115. In the second pillar, risks to pension scheme members and beneficiaries are largely contained due to a conservative asset management process, which limits long-term performance, however. Products in the second pillar, the retirement pension, such as bank deposits, guaranteed investment contracts (financial products with principal and interest guaranteed) or funds (financial products whose earnings depend on their performance) are currently offered by around 50 pension service providers, incl. banks, insurers and securities firms. Assets managed by these pension service providers reached KRW 190 trillion at end-2018, of which banks managed 51 percent, followed by life insurers with 23 percent, investment firms with 19 percent, and non-life insurers with 6 percent. Around 90 percent of retirement pension funds were being invested in principal-protected products, and the remainder in funds. In 2018, the return averaged at 1.0 percent, below the average deposit rate; the principal-protected product yielded 1.6 percent, while the performance-based type was negative at -3.8 percent. Still, pension participants and sponsors have recently been showing more interest in diversified fund vehicles with better yields and actively seeking products without guarantees. As of now, retirement pensions cannot exploit their full potential yet, as necessary reforms are stuck in the parliamentary process. In particular, the possibility to offer pooled pension funds, covering multiple employers, has been discussed for several years. Such multi-employer schemes require a careful monitoring to ensure fair treatment of all cohorts and scheme members.

116. Private pensions of the third pillar are also offered by most financial sectors—certain products suffer from their inherent design, limiting the long-term performance. The third pillar includes savings-based annuities (the largest proportion with 78 percent), pension savings trust, and pension savings funds, all of which offer tax deduction benefits to investors. Pension savings trusts offered by banks have traditionally been showing a relatively weak performance. These can typically be redeemed annually without full market value adjustments, so that guaranteed amounts are effectively locked in every year, instead of providing the guarantee at retirement age. Accordingly, investments are to a large degree in sovereign bonds and highly rated corporate bonds. As a drawback, the performance of such products is not incentivizing potential customers to join those pension plans.

C. Recommendations

117. The FSS shall further develop its insurance stress tests and include adverse macro-financial scenarios, based on a medium-term risk outlook. Currently the FSS uses single-factor sensitivity analyses which potentially underestimate the effect of a widespread financial crisis on the balance sheets and solvency ratios of insurers. Combining risk factors in a consistent adverse scenario should consider the dependence across risk factors and could be used both for top-down and bottom-up stress tests. After the implementation of the new insurance capital standard (K-ICS), stress test results could inform about the need for potential capital add-ons, and should, as a minimum, determine the level of supervisory intensity.

Network and Contagion Analysis

A. Analysis

118. Korea’s financial institutions are linked through significant direct balance sheet and interbank market exposures, as well as their significant exposures to the real estate sector. The FSAP’s analysis employed a network approach based on cross-exposures for 64 large financial institutions across financial sectors (banks, insurance, investment firms, and credit-specialized firms). A network visualization based on raw exposure data as well as based on the outcome of a default simulation48 confirm the significant balance sheet interlinkages (Figure 45). Figure 46 shows the results in terms of capital depletion across sectors49.

Figure 45.
Figure 45.

Korea: Financial System Network Structure

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: FSS supervisory exposure data and IMF staff calculations.Notes: The sample includes 21 banks, 25 life insurers, 29 non-life insurers, foreign bank branches as a block (38 underlying entities), Mutual Savings Banks as a block (79 entities), credit-specialized firms (102 entities), and investment firms (485 firms). The size of the bubbles is proportional to the size of the assets of an institution, or institution type. At the top: The width of a connecting line and arrow from A to B is proportional to the size of the exposure of A (asset side) to B (liability side). At the bottom: A line connects A to B if the default of A induces a positive capital depletion for B. The width of the connecting line and arrow is proportional to the capital impact of B expressed relative to the initial capital stock of B. The underlying multi-round simulation engine is adopted from Espinosa-Vega & Sole (2010), with an assumed LGD equal 100 percent.
Figure 46.
Figure 46.

Korea: Sector-Level Default Simulation: Capital Depletion

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: FSS supervisory exposure data and IMF staff calculations.Notes: The sample includes 21 banks, 25 life insurers, 29 non-life insurers, foreign bank branches as a block (38 underlying entities), Mutual Savings Banks as a block (79 entities), credit-specialized firms (102 entities), and investment firms (485 firms). The underlying multi-round simulation engine is adopted from Espinosa-Vega & Sole (2010), with an assumed LGD equal 100 percent.

119. The network and default simulations reveal that specialized banks are about as systemic as nationwide banks, which is confirmed as well by network centrality metrics (Figures 46 and 47). Nationwide banks are net lenders to specialized banks, and hence are more vulnerable to specialized banks than the other way around. Life and non-life insurance firms are not as systemic, but vulnerable to stress in specialized banks and nation-wide banks. Insurers are net lenders to all other subsegments of the Korean financial system.

Figure 47.
Figure 47.

Korea: Network Centrality (Eigenvector Centrality)

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: FSS supervisory exposure data and IMF staff calculations.Notes: The upper and lower ends of the boxes denote the 90th and 10th percentiles of the underlying distribution across banks. The bars extending to the up- and downside denote the maxima and minima. White lines denote the median. Eigenvector Centrality measures a node’s importance based on the number of links it possesses to other nodes in the network, while also taking account of extended indirect connections.

120. The banks and insurance firms that face the hardest hit on capital under the adverse scenario in the solvency stress test are not tightly linked with the rest of the financial system. The spillover potential from such firms appears sufficiently contained at the current juncture. The assessment should be treated cautiously, however, because confidence effects can imply spillover effects beyond the direct financial linkages between firms that the default simulation methodology captures.

B. Recommendations

121. The bilateral exposure data set compiled by the FSS for conducting the FSAP network and contagion analysis is highly valuable. The Korean authorities are recommended to keep maintaining this database, and further expanding the cross-section of financial sector firms where possible. Collecting the exposure data on a regular and frequent basis in the future, for example with a quarterly frequency, will be instrumental for supporting the network and contagion analysis for the Korean financial system. Dynamic network model methodologies can be developed to aid the analysis of such time varying exposure data in the future.

Demographic Developments in Korea

122. Demographic developments in Korea have shifted adversely over the past decades and are expected to keep rotating adversely, in terms of both stagnating population growth and rising dependency ratios. The trends point to a stagnating and soon shrinking population over the coming decades, coupled with the related shift in the age distribution from the young to the old. Figure 48 shows that the demographic rotation as materializing in Korea is one of the most adverse world-wide50. At the same time, old-age poverty in Korea is highest among OECD countries, affecting almost 44 percent of those aged 66 or older, while the OECD average is only 14 percent (Figure 49).

Figure 48.
Figure 48.

Korea: Demographic Developments Proportion of Population Ages 65+

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: UNDESA Population Division, World Population Prospects, 2019.
Figure 49.
Figure 49.

Korea: Old Age Poverty Across OECD Countries

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: OECD and IMF staff calculations.

123. Demographic shifts of the materiality observed in Korea will imply a longer-term structural drag on natural interest rates and inflation, reflecting in turn a continuous drag in demand. Demographic developments constitute one of a number of causes that have been related to the notion of “secular stagnation” which is a characteristic assigned to numerous countries around the world in the aftermath of the Great Recession of 2007–09.51 A comprehensive analysis as to the consequences of aging in Korea has been developed by the Bank of Korea52.

124. The extent to which demand would slow down as a result of adverse demographic trends is surrounded by sizable uncertainty. The investment component of output may slow down in selected industries, reflecting entrepreneurs’ acting on an expected fall in future consumption. However, some specific industries might benefit from demographic changes (e.g. health care, medical science, etc.). The elderly portion of the population can be expected to spend their savings, with consumptive expenditures implying a sustained velocity of money and contribution to GDP flows.

125. The demographic developments are expected to have a notable impact on housing market dynamics in Korea. The impact would stem from falling demand for housing, implying downward pressure on house and rent prices, which would be exacerbated by the leasehold rent system (Jeonse) which builds on the premise of rising house prices in the future (see Section 1.C. for a related discussion).

126. Next to demand side-oriented concerns rooted in demographics, supply side factors in Korea matter. Public and private investment in technology is instrumental to sustain productivity growth, amid a downward trend in potential output growth which may be due to a shrinking working age population which might not be avoided. Korea’s Ministry of Science appears to put forward meaningful and significant policies under its “Fourth Industrial Revolution” plan, aiming to boost productivity growth and innovation, foster female participation, devote larger budgets to childcare and elderly care, etc. Boosts in productivity may counteract an otherwise stronger upward pressure on real wages due to a shrinking working age population.

127. Public and private pension funds in Korea will experience the consequence of demographic shifts most directly and hence deserve special attention. The pension system will need to balance a shrinking contributing population with rising pension outflows. The contribution share of the working-age population will need to rise in absolute and in relative, per contributor terms, thereby reducing disposable income of the young population cohorts, and in turn implying an additional drag on demand and feedback to yet lower interest rates. In the long term, rising fund outflows from a pension fund perspective may imply a visible pullback of funds from corporate and sovereign bond markets which would let their prices fall (yields rise) and in turn imply some upward pressure on corporate and sovereign funding costs. The Korean sovereign may consider borrowing from banks instead (whose credit business to the private sector would meanwhile likely shrink; see below).

128. Korea’s National Pension Service (NPS), the first pension pillar, is still in an accumulation phase and expected to double its assets until 2040 when payouts will start exceeding inflows. Managing close to KRW 700 trillion, NPS is one of the largest pension funds globally. With 22.1 million members and 4.7 million beneficiaries, net inflows are still positive and contribute to steadily growing assets over the next 20 years. The second pension pillar (“retirement pensions”) is currently undergoing reforms aiming for a more extensive coverage—as of 2018, only half of the employees are covered by such a pension scheme, resulting in a sector size of around KRW 190 trillion. Necessary reforms, however, have been stalled in the parliamentary process for more than four year already.

129. The impact of demographic change specifically on banks goes through numerous channels whose net effect for the banking system would likely point to consolidation, business model restructuring in general, and a rotation to non-interest income sources specifically. The impact on banks would stem from lower interest rates, a flatter yield curve respectively, hence structural downward pressure on net interest income and in turn a lower potential to retain profits and hence capital accumulation. Demand for long duration loans (e.g. mortgages) would fall because of a shrinking young population cohort, keeping interest income low. In order for banks to maintain sizable enough capital buffers, they may need to consider i) shrinking in terms of branch structure to reduce staff costs, ii) shifting to non-interest income sources, to offset falling interest income with higher fee and commission income, iii) fostering innovation to remain competitive in view of the strongly rising non-bank sectors, one aspect of which is to conduct non-bank pure intermediary business for instance (e.g. peer-to-peer lending) next to loan granting (money creation). The dis-saving process of the elderly population will meanwhile mean more spending flows between households and firms, which may leave the liquidity position of banks in terms of a deposit base rather unchanged, however. Figure 50 summarizes the channels through which aging can affect the financial system and its components. It lists the potential mitigating strategies and effects at the bottom, including, inter alia, overseas expansion for banks and other financial firms to seek higher returns, cost cutting and consolidation domestically, and digitalization and Fintech developments.

Figure 50.
Figure 50.

Korea: Channels through which Demographic Developments May Impact the Financial System

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff.

130. Life insurance companies are venturing into protection products and health insurance to compensate the lower demand for traditional savings products. Health insurance includes coverage for dread diseases including cancer and dementia, and life insurers also aim to provide health management services.53 The shift in product design also implies targeting a different age cohort—savings products are typically bought by 30 to 40-year olds, while health insurance is mostly demanded after an age of 50. As of now, improvements in longevity have not materialized in the form of lower underwriting profits for life insurers and longevity risks are—as of now— contained. Many savings products are lapsed by policyholders and hence do not reach the contractual maturity date; the benefits are typically paid out as lump-sums or term annuities (usually 10 years). As financial literacy increases and policyholders increasingly understand the risks of old-age poverty, their behavior might likely change, and whole-life annuities will be more appreciated and sought after, exposing life companies to heightened longevity risks.

131. A quantitative assessment has been conducted for the purpose of deriving scenario-conditional forecasts of the nominal size of different components of the financial system. A long-run equilibrium vector model has been set up and estimated which relate the total asset dynamics for the nine components of the financial system as shown in Figure 51 to macroeconomic variables to thereby capture their dependence on cyclical factors as well as on population growth and dependency ratios54. Historically, the insurance sector and the pension fund sector have yet grown significantly in Korea (Figure 51).

Figure 51.
Figure 51.

Korea: Korean Financial System’s Total Asset Dynamics

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Bank of Korea, Flow of Funds Statistics. Only second-pillar pension funds (retirement pensions) are included in the pension fund category in this chart. The National Pension Service (NPS) is included in the residual category.

132. Long-term scenarios regarding population growth and dependency ratios were sourced from KOSTAT and form the basis for the analysis55. A scenario-conditional forecast for the financial system assets has then been derived until the year 2040, conditional on 27 scenarios in terms of population growth and dependency ratios (Figure 52). The 27 scenarios are the result of different assumptions for three drivers of population dynamics, that is, fertility, life expectancy, and migration.

Figure 52.
Figure 52.

Korea: Scenarios for Population Growth and Dependency Ratios in Korea until 2067

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: KOSTAT.

133. The demographics scenario-conditional forecasts of the nominal size of different components of the financial system more broadly suggest that the system may likely grow less intensely and shrink in some sub-segments. Figure 53 shows that the scenario-conditional paths for average per annum total asset growth from 2019–40 varies across segments: all financial sector components except pension funds are expected to shrink or move side-wards. For pension funds, the rising old age dependency ratio appears to dominate and let the sector grow56. Banks, including nation-wide, regional and specialized banks, are expected to move sideward in terms of nominal asset size, without much of a dependence on the demographic scenarios (reflected by the small range of the cross-scenario bounds).

Figure 53.
Figure 53.

Korea: Demographics Scenario-Conditional Total Asset Dynamics

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: KOSTAT, Bank of Korea, and IMF staff calculations. Percentages are natural log percent. The bars depict the median of the average per annum growth rates across the 27 scenarios; the lower and upper bounds mark their 10th and 90th percentiles.

134. The analysis suggests that non-life insurers and pension funds are most dependent on the demographic scenario assumptions. This is reflected by the wider bounds around the median estimates in Figure 53. The analysis also suggests that the pension fund asset growth trajectories depend most on the assumptions about fertility, while all other financial sector components depend more on the assumptions about migration57.

135. Monetary policy ought to be conducted with particular care at times of slow-moving, yet significant, demographic change. It is important that models for supporting monetary policy be designed in a way that take explicit account of population dynamics (per capita thinking is warranted), to thereby be able to delineate cyclical and structural (demographic in this case) factors.

136. Fiscal policies to counteract adverse demographic developments naturally come along with sizable implementation lags. Modifying fiscal policy measures nowadays, such as in relation to retirement age and policies to foster family creation, take a long time to reveal their effect. That is the reason why demographics-related analyses and policies should be conducted in a very forward-looking, pre-emptive manner.

137. The materiality of the concerns stemming from demographic change in Korea warrant great attention going forward. Meanwhile, the banking system does not appear to be at particular high risk of a sudden disruption due to a structurally slow-moving demographic change.

Appendix I. FSAP Risk Assessment Matrix (RAM)

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Appendix II. Solvency and Liquidity Stress Test Matrices for Banks and Insurers (STeMs)

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Appendix III. Liquidity Stress Test – Calibration Details

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Appendix IV. Solvency Stress Test – Mapping MR and CR Methodology

1. The mapping of the CR and MR methodologies to different line items in the reporting templates for the Korean banks was a function of their accounting treatment. Appendix IV Figure 1 shows the mapping.

Appendix IV. Figure 1.
Appendix IV. Figure 1.

Mapping of CR and MR Methodology to Exposures

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: IMF staff. See glossary for abbreviations.

2. Sovereign bonds under the AC approach were subject to the MR methodology. The reason for doing so was that for the sovereign bond portfolio, no historical no “year 0” transition matrices were available, hence not allowing the application of the transition matrix-based ECL model (see main text for details). The impact from marking-to-market these sovereign bond holdings is not significant, because their exposure share amounts to about 4–6 percent across nation-wide, regional and specialized banks.

3. Sovereign and financial corporate bond holdings under the FVOCI accounting category were subject to only the MR methodology. FVOCI exposures should actually be treated under both the CR and MR methodology; for credit risk-related impairment charges to flow through the P&L and the remainder to a full fair value change be reflected through the OCI account. For sovereign and financial corporate bonds, no historical transition matrices were available, hence not allowing the application of the CR methodology. Their treatment under only the MR methodology is not expected to imply any distortion, however, because net income of the banks under the adverse scenario would be negative anyhow. It would, therefore, not make a difference whether the capital impact is measured through the P&L or the OCI, as negative income implies zero tax expense by design of the stress test methodology.

4. Nonfinancial corporate bonds under the FVOCI accounting category were treated under both the MR and CR methodology. For nonfinancial corporate exposures, historical transition matrix data was available across banks, hence allowing the dual treatment under the MR and CR methodologies.

Appendix V. Solvency Stress Test – Credit and Interest Income and Expense Models

Credit Risk

1. A BMA methodology1 was employed to develop the models which link bank portfolio PDs to macro-financial variables. The left hand-side variables included historical default rates for large corporates, SME loans, household mortgages and consumer credit. The potential right hand-side predictor variables included real GDP growth and a term spread for all models, the unemployment rate for the mortgage and consumer credit portfolio, and the USD-KRW exchange rate in the large corporate loan portfolio model. The model structures are summarized in Appendix V Figure 1. It shows the LRMs which reflect the sum of the coefficients on a given predictor for its time contemporaneous and possibly lags, where present. Historical data and fit are not presented here because the underlying default rate data by the Korean banks is classified as confidential.

Appendix V. Figure 1.
Appendix V. Figure 1.

PD Model Structures

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations. FX1/4 = KRW-USD exchange rate, up means depreciation of KRW (1=QoQ, 4=YoY). RGDP4 = real GDP growth YoY. LTN = long-term interest rate. TS = term spread (long-term minus short-term interest rate). URX = unemployment rate. The LRM (green bar, right axis) is normalized by multiplying the raw LRM with the ratio of the historical standard deviation of the respective right and left-hand side variables. Where the posterior inclusion probability (blue bar, left axis) exceeds the prior inclusion probability (dashed line, left axis), a predictor variable is said to be significant from a Bayesian perspective. See Gross, M. and Población, J. (2017) for details.

2. The models contain lags of the predictor variables beyond their contemporaneous inclusion, and no autoregressive lags. No autoregressive lags of the LHS default rate variables were allowed to thereby maximize the content that could be extracted from the variation of the predictor variables on the RHS of the models, to make sure that the adverse-baseline gaps for the default rates are not unduly compressed. The large corporate loan portfolio, e.g., contained two lags of the exchange rate in addition to its contemporaneous inclusion. The SME default rate had an additional lag of real GDP growth; the consumer credit portfolio an additional lag of the unemployment rate.

Interest Income and Expenses

3. Definition of interest income and expense rates. The interest income rate (IIR) and interest expense rate (IER), both measured at bank-level, were defined as

I I R = 4 × I I S E C + l o a n s   g r o s s   o f   L L A × ( 1 − S B L   r a t i o ) a n d   I E R = 4 × I E T A   n e t   o f   L L A − E

where II denotes quarterly interest income flows in nominal terms, IE the quarterly total interest expense flows, SEC (securities) measured as banks’ total financial assets minus their loan stock, LLA the loan loss allowance stock (accounting), and SBL the substandard and below ratio. The historical data for all variables involved were sourced from the FSS’ statistical data warehouse, for a sample of 19 banks (all but ODIs from Table 5), covering the 2000Q4–2018Q4 period (73 time series obs.).

4. The IIR model structure is shown in Appendix V Table 1. The IIR panel model contains a simple distributed lag structure for real GDP growth on the right hand-side; in addition to the bank-specific IER variable which captures the pass-through of banks’ funding cost to their loan prices. Numerous alternative model structures were considered, involving the unemployment rate and other macro-financial variables, and considering a market interest rate instead and/or next to the bank-level funding cost variable. The model-implied interest income paths were found to be robust to such alternative model structures. It is interesting to note that the simple model structure as employed here results in sizable R-squares despite its simplicity. The lags of GDP growth and the autoregressive lag were included to reflect that interest income and hence the effective interest rate relate to outstanding business whose interest rate in part were set in past periods at fixed interest rate terms. The IER variable is endogenized using the IER model.

Appendix V. Table 1.

IIR Model

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Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations. Quarterly frequency, 2000Q4–2018Q4 (73 time series obs.). LHS: banks’ interest income rate (IIR) annualized on [0,100]. IER = bank-level interest expense rate on [0,100]. RGDP = real GDP.

5. The IER model structure is shown in Appendix V Table 2. The IER penal model contains the short- and long-term market interest rates along with a bank-specific level and squared capital ratio term, defined as an accounting ratio (shareholder equity E over total assets net of LLA). The inclusion of the short- and long-term interest rates is to capture the pass-through of monetary policy, the market price of risk, and sovereign risk (relevant through implicit/explicit guarantees), to banks’ cost of debt. The E/TA terms are lagged by one quarter to avoid the need for Gauss-Seidel-type solution methods; still it is effectively a two-way contemporaneous feedback at annual frequency that the model captures. Importantly, the E/TA terms are connected, i.e. endogenized, through the overall stress test machinery, reflecting all risk drivers that influence banks’ capital ratios moving forward in time. As for the IIR model, the IER model is characterized by sizable R-squares despite its simple structure.

Appendix V. Table 2.

IER Model

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Sources: Supervisory data from the FSS, publicly available data for banks, and IMF staff calculations. Quarterly frequency, 2000Q4–2018Q4 (73 time series obs.). LHS: Banks’ interest expense rate (IER) on [0,100]. STN = short-term interest rate. LTN = long-term interest rate. E = shareholder equity. TA = total assets net of accounting LLA.

6. The inclusion of the quadratic capital ratio term serves to capture a suspected nonlinearity in the relation between banks’ capital ratios and their cost of funding. Such nonlinearity is of the kind implied by the trade-off theory of capital structure; suggesting that the marginal benefit of more debt (due to tax benefits) would vanish with higher levels of debt (more risk)2. The effect of higher risk will dominate at some point and let the initially negative relation between risk and cost of debt become positive. This rationale is depicted in the schematic in Appendix V Figure 2. It has found direct empirical support in the estimates for Korea as presented in Appendix V Table 2 and visualized in Figure 23 (main text). Figure 30 (main text) revealed the importance of allowing for the feedback from banks’ solvency to their funding cost. It is found to matter in quantitative terms despite the Korean banks’ generally and comparably high deposit-in-total liability shares, in turn reflecting that wholesale funding cost pressures would likely be significant under a severe downturn scenario.

Appendix V. Figure 2.
Appendix V. Figure 2.

Rationale Underlying Nonlinear IER Model Structure

Citation: IMF Staff Country Reports 2020, 279; 10.5089/9781513557052.002.A001

Sources: Gross et al. (2020, see footnote above for reference). The schematic illustrates the rationale underlying the trade-off theory of capital, including the hypothesis about how banks’ funding costs would behave in relation to changes in capital ratios as a function of initial capital ratios. E denotes equity capital; TA denotes total assets.

7. A steady state adjustment was considered for all IIR and IER models. The steady state adjustment means that the intercepts (fixed effects) in the IIE and IER panel models were adjusted, that is, set to a value for the models to imply a fit that precisely matches the average left hand-side interest rates as observed through the 2018Q1–2018Q4 period; conditional on realized values of all right hand-side variables.
1

This author of this note is Marco Gross (overall workstream lead; scenario design, bank solvency, network analysis, demographics analysis), member of the FSAP 2019 team led by Udaibir Das, as well as, Farid Jimmy Boumediene (housing market analysis and bank liquidity stress testing), Timo Broszeit (insurance sector stress testing), and Elizabeth Mahoney (research assistance). Selected inputs were provided by Sohrab Rafiq. The analysis has benefitted from discussions with the staff of the BOK, FSC, FSS, the Korea FSAP team, and reviewers at the IMF. The FSAP team is grateful for the excellent cooperation of the Korean authorities and for sharing all data for conducting the systemic risk analysis.

2

A conforming loan is a long-term, fixed-rate mortgage loan provided by the KHFC.

3

Loans, securities and guarantees issued and provided in 2017 amounted to about 7 percent of GDP.

4

There are several Asset Management Corporations and an active NPL market around them in Korea. Their presence is deemed to foster cost efficiency since banks outsource the NPL management process to specialized firms.

5

Nation-wide banks’ largest exposure is to China, amounting to 1.7 percent of total assets in 2018 (1.1 percent in 2013).

6

Most notably, the ultimate forward rate used in the interest rate term structure modeling for deriving the value of insurance liabilities was increased to 5.2 percent, which is significantly above the 3.9 percent used in the EU’s Solvency II regime for KRW-denominated liabilities.

7

Assessment as of March 2020.

8

For an additional assessment in terms of house price dynamics see the Korea 2019/20 Technical Note titled “Macroprudential Policy Framework and Tools”.

9

The Jeonse deposit price is determined by demand and supply conditions in the Jeonse market. Historically, the leasehold deposit to house price ratio has been relatively stable over time.

10

The Bank of Korea, Financial Stability Report (June 2018), assumed a 20 percent fall in the Jeonse deposit price and a 13 percent house price fall. Financial assets were limited to accumulations and deposit-type savings with high liquidity, and mortgage loans to those on housing currently being resided in.

11

These loans, known as Didimdol and Bogeumjari, are available to households meeting certain criteria regarding income, price of property, size of property, mortgage loan size and LTV.

12

There are two additional housing market insurers, one private (Seoul Guarantee Insurance) and one public (Housing and Urban Guarantee Corporation).

13

Definition of risks and vulnerabilities: By vulnerabilities we denote features of the macro-financial system that can be of cyclical or structural nature, which imply the potential for adverse macro-financial dynamics to unfold as a result of shocks. The notion of risk is equated to triggers, which set an adverse development in motion. The product of a probability of a risk to materialize (small) and “loss given materialization” of a risk (shall be large) in exports, its limited export diversification, its strongly intertwined network of interdependencies across firm conglomerates, rising competition from new financial firm types (Fintech), and adverse demographic developments. Fintech developments such as the Open Banking initiative that was launched in December 2019 as well as the growing popularity of e-money providers imply changing liquidity dynamics, with implications for solvency (through competition) and system-wide operational risks.

14

China, the US and Japan constitute about 35, 15 and 7 percent of Korea’s total export flows in 2018. Korea received about 25, 14, and 13 percent of its total imports from these three countries in 2018. These shares suggest a notable exposure to precisely those countries that are at the center of the trade dispute (US and China), and the more bilateral tensions with Japan in addition.

15

Such capital outflows would distress banks’ liquidity, particularly in FX, directly through funding and liquid asset valuation shocks and indirectly through NFCs export revenues given the importance of trade in the Korean economy. The channels through which Korean banks and NFCs access FX, including the importance of the interbank swap market and related high interconnectedness of banks, will also determine the extent of FX liquidity risk in such a scenario.

16

For example, Korea and China are very much intertwined in terms of supply chains for semiconductors that China imports from Korea to produce smartphones and other electronics, which then are exported back to Korea.

17

A regime-switching structural vector autoregressive model framework has been employed for the purpose of quantifying the external demand shock scenario deviations for Korea. The base variant of the model contains real GDP growth, GDP deflator inflation, house prices, interest rates, and the USD-KRW exchange rates. Numerous variants of the model were considered, considering the inclusion and exclusion of numerous variables, exploring different lag structures, etc., in order to assess the robustness of the resulting scenario trajectories.

18

The solvency position of the financial institutions that are in-scope of the quantitative risk analysis will be assessed along the scenario horizon (i.e. not only at the end of the scenario horizon). Emphasis will be placed on the “low-point” (minimum capital ratios) for each bank along the scenario horizon.

19

For additional details on the Open Banking Initiative as well as e-money developments in Korea—in particular from a legal perspective—see the Technical Note titled “FinTech, Legal Frameworks, and Implications for Financial Stability”.

20

E-money service providers are currently not allowed to pay interest in the form of e-money on their customers’ e-money holdings. They may, however, consider other remuneration or reward schemes, that is, purchases in e-money form may be offered with a discount, which would effectively be equivalent to interest payments from an economic perspective.

21

Flow constraints are currently in place, for the Open Banking System to not allow more than KRW 10 million to be transferred, and no more than KRW 3 million through e-money service providers. Such constraints may not prevent a gradual outflow over a sequence of days to happen.

22

For a detailed treatment of various elements of the credit risk model methodology see Gross, Laliotis, Leika, Lukyantsau (2020), “Expected Credit Loss Modeling from a Macro-Financial System and Stress Testing Perspective,” IMF Working Paper No. 20/111.

23

See Gross, M. and Población, J. (2017), “Implications of model uncertainty for bank stress testing,” Journal of Financial Services Research, Vol. 55, pp. 31–58.

24

The default rate models and scenario-conditional forecasts are not presented here because this information is classified as confidential.

25

The banking system-wide portfolio-specific default rate scenario paths are attached to the banks’ portfolio-specific starting points using a distance-to-default transformation scheme.

26

TR1–3 and TR2–3, a weighted average of which can also be computed to obtain a performing loan stock-related PD.

27

A sensitivity analysis was conducted with respect to the length of this mean reversion period. The solvency stress test results are not very sensitive to varying this assumption between a 5–12-year range, which is partially due to the fact that longer-duration mortgage portfolios are not overly sizable and, moreover, subject to mortgage insurance in non-negligible shares, hence rendering their risk parameters (LGDs in particular) low.

28

Applied for residential real estate portfolios including Jeonse leasehold loans for the Korean banks. Data for commercial real estate portfolios were not separately available and part of the nonfinancial corporate portfolios (either SME or large corporates).

29

For details see Gross et al. (2020, forthcoming), “Expected Credit Loss Modeling from a Macro-Financial System and Stress Testing Perspective”.

30

This conservative overlay was used because the choice of a simple scaling factor for LGDs of uncollateralized portfolios is rather ad hoc; and the various caveats apply to the structural model for real estate-collateralized portfolios as well (e.g., it could not be back-tested on Korean bank data because historical data for LGDs was not available).

31

Capital floors for banks with IRB portfolios (see Detailed Regulations on Supervision of Banking Business, Appendix 3), as relevant for four banks in the Korean bank stress test sample, were not accounted for.

32

For a discussion as to the rationale and theoretical background to such nonlinearity see Gross, Hansen, and Kok (2020, forthcoming), “Break-Even Capital Ratios”.

33

Even a rather linear relationship would already imply a nonlinear feedback effect; which is even more nonlinear if the relationship at this level is found to be nonlinear.

34

Sovereign and financial bonds under the AC accounting category were also subject to the MR Module. Such AC exposures should actually be subject to the CR Module, but due to their missing information as to the Year 0 transition matrices, they were treated under the MR Module instead. Korean banks’ foreign bond exposures were excluded from the scope of the MR Module because they were deemed sufficiently insignificant in terms of exposure size.

35

For ODIs, capital thresholds were employed only in an indicative manner, when examining the bank-level results jointly with the Korean authorities during the second FSAP mission in December 2019. The reason for not referring to capital shortfalls for ODIs, should there be any, was that the data quality for ODIs was deemed to be insufficient to an extent. The analyses related to ODIs as presented in the result subsection of this note was deemed sufficiently robust, however.

36

For nation-wide, regional, and specialized banks, ‘capital’ refers to CET1 in the following, unless otherwise stated. For ODIs, capital is expressed in accounting terms, net of loan loss provision stocks.

37

Currently, there are no Pillar 2-related capital requirement add-ons in place for the Korean banks.

38

The insured portion of the mortgage portfolios amounts to 26 percent, 45 percent, and 27 percent for nation-wide, regional, and specialized banks respectively (exposure-weighted averages) at end-2018. The shares were assumed to stay constant along the horizon under both the baseline and the adverse scenario.

39

Data for commercial real estate portfolios were not separately available, i.e. they were part of the nonfinancial corporate portfolio aggregate.

40

They were held constant for both the derivation of the “headline” solvency stress test results as well as the sensitivity analysis presented here.

41

Upon its estimation, the equation has been used to set the insured mortgage share to zero percent to imply a fitted RWD on the assumption that the whole portfolio was uninsured, along with the fitted risk weight densities at 2018Q4. The difference in the resulting two fitted RWDs for each bank were then multiplied with the initial risk-unweighted mortgage portfolios to obtain the change in the risk-weighted mortgage exposures. The R-square (89 percent) was deemed sufficiently high to not consider a nonlinear equation structure which in principle might be warranted due to the nonlinear nature of the Basel risk weight formulas.

42

ODIs have been excluded from this counterfactual analysis because mortgage insurance shares were not available for them; the average across the regional banks was employed for them.

43

A differential, if one would arise, would concern largely Stage 1 and Stage 2 stocks, because Stage 3 exposures are provisioned, from a regulatory perspective, based on net-of-expected-recovery exposures, hence rendering them effectively similar to accounting provisions.

44

The BOK has developed its Systemic Risk Assessment Model (SAMP) since 2012; and its Non-bank Financial Institution Stress Test Model since 2019, which covers insurance companies, credit cooperatives, savings banks, securities firms, and credit-specialized finance firms (BOK Financial Stability Report, June 2018, p. 85–97). The BOK’s Extensive Stress Test Model (2019) integrates banks and non-banks (BOK Financial Stability Report, December 2018, p. 95–102). The One distinguishing feature of the BOK model suite is its integrated contagion module. The FSS developed its Stress Test for Assessing Resilience and Stability of the Financial System (STARS) framework since 2017, to support its macroprudential supervision tasks. STARS include a suite of models that capture contagion and macro-feedback effects that were developed jointly with the BOK. Results from the BOK stress tests are published in the FSR.

45

Based on 2018Q4 data.

46

For example, commercial banks faced a minimum FC LCR requirement of 70 percent in 2018 and 80 percent in 2019.

47

For example, the LCR assumes 40 percent of runoff rate on unsecured non-operating deposits, whereas the LST assumes 50 percent. These assumptions are both more severe than what occurred during the Asian Financial Crisis or the Global Financial Crisis.

48

The default simulation follows closely the methodology developed by Espinosa-Vega and Solé (2010).

49

Firm-level results in terms of impact and vulnerability across the system have been developed and discussed in detail with the Korean authorities. They are not presented here because the information is deemed confidential.

50

A useful descriptive analysis involving data from the United Nations can be found in Moody’s (2018), “Governments of Japan and Korea: Demographics will weigh on long-term economic and fiscal strength, despite offsets from technology and labor participation”. Investor Report.

51

See, for example, Summers, L. (2014): “U.S. economic prospects: Secular stagnation, hysteresis, and the zero-lower bound”. Business Economics; Eggertsson, G. and N. Mehrotra (2014): “A model of secular stagnation”, NBER Working Paper; and Ferrero, G., Gross, M. and S. Neri (2017): “On secular stagnation and low interest rates: demography matters”, International Finance.

52

Bank of Korea (September 2017), “Population Aging: Impacts and Policy Imperatives”.

53

Medical services are, however, strictly regulated in Korea, and can only be offered by medical doctors.

54

The model variables include GDP per capita, equity price levels, short- and long-term interest rates, GDP deflator levels, the USD-KRW exchange rate, as well as the population and dependency ratio variables. The latter two are exogenous, all others endogenous.

55

See “Population Projections for Korea (2017–2067)”, KOSTAT, 2019.

56

The estimated elasticities relating total assets to the population and the level dependency ratio suggest that all financial system components depend positively on population growth. The pension fund sector would benefit from a rising dependency ratio. Hence, the pension fund sector would face two opposing effects (slowing population growth, rising dependency ratios), making the sign of the net effect on its asset growth not pre-determined a priori.

57

It should be kept in mind that the projections are conditional on the assumption of ‘no structural change’, as they are based on a model that captures the financial system dynamics over the past. Financial system structural change due to FinTech developments, for example, may exert an impact that would let future asset size dynamics vary from the ones implied by the model. The analysis is overall meant to be indicative, that is, no too strong emphasis shall be placed on the total asset projections growth in absolute terms, but rather be seen in a comparative manner.

1

The coverage as described here would amount to 89 percent of total banking system assets when foreign bank branches were part of the aggregate banking system.

1

See Gross, M. and Población, J. (2017). “Implications of model uncertainty for bank stress testing”. J of Fin Services Research.

2

See Kraus, A. and Litzenberger, R. H. (1973), „A state-preference model of optimal financial leverage”, Journal of Finance, 28(4), pp. 911–922. See also Gross, M., Hansen, I. and Kok, C. (2020), “Break-Even Capital Ratios”, forthcoming IMF Working Paper.

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Cover IMF Staff Country Reports
Republic of Korea: Financial Sector Assessment Program-Technical Note-Systemic Risk Analysis, Financial Sector Stress Testing, and an Assessment of Demographic Shift in Korea
Author:
International Monetary Fund. Monetary and Capital Markets Department
Volume/Issue:
Volume 2020: Issue 279
Publisher:
International Monetary Fund
ISBN:
9781513557052
ISSN:
1934-7685
Pages:
100
DOI:
https://doi.org/10.5089/9781513557052.002
  • <sup><xref ref-type="fig" rid="RA01fig01">Figure 1.</xref></sup>
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    Figure 1.

    Korea: Financial System Structure

  • View in gallery

    Financial Institutions’ Assets

    (in percent of total)

  • <sup><xref ref-type="fig" rid="RA01fig02">Figure 2.</xref></sup>
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    Figure 2.

    Korea: Depository Institutions’ Business Models

  • <sup><xref ref-type="fig" rid="RA01fig03">Figure 3.</xref></sup>
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    Figure 3.

    Korea: Structure of the Insurance Sector

  • <sup><xref ref-type="fig" rid="RA01fig04">Figure 4.</xref></sup>
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    Figure 4.

    Korea: Financial System Performance

  • <sup><xref ref-type="fig" rid="RA01fig05">Figure 5.</xref></sup>
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    Figure 5.

    Korea: Bank Profitability (Significant Institutions):1 Key Trends

  • <sup><xref ref-type="fig" rid="RA01fig06">Figure 6.</xref></sup>
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    Figure 6.

    Korean Banks’ Asset Portfolio Structure, Liability Structure, and P&L Components

  • <sup><xref ref-type="fig" rid="RA01fig07">Figure 7.</xref></sup>
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    Figure 7.

    Korea: Solvency and Profitability of the Insurance Sector

  • <sup><xref ref-type="fig" rid="RA01fig08">Figure 8.</xref></sup>
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    Figure 8.

    Korea: Macrofinancial Environment

  • <sup><xref ref-type="fig" rid="RA01fig09">Figure 9.</xref></sup>
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    Figure 9.

    Korea: Housing Market

  • <sup><xref ref-type="fig" rid="RA01fig10">Figure 10.</xref></sup>
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    Figure 10.

    Korea: Demographics Shift and Household Debt

  • <sup><xref ref-type="fig" rid="RA01fig11">Figure 11.</xref></sup>
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    Figure 11.

    Korea: Baseline and Adverse Macro-Financial Scenario

  • <sup><xref ref-type="fig" rid="RA01fig12">Figure 12.</xref></sup>
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    Figure 12.

    Korea: Baseline and Adverse Scenario Paths for Real GDP in Historical Perspective

  • <sup><xref ref-type="fig" rid="RA01fig13">Figure 13.</xref></sup>
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    Figure 13.

    Korea: Cumulative Real GDP Deviations (Adverse Relative to Baseline)

  • <sup><xref ref-type="fig" rid="RA01fig14">Figure 14.</xref></sup>
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    Figure 14.

    Korea: Implications of Open Banking and E-Money Developments in Korea

  • <sup><xref ref-type="fig" rid="RA01fig15">Figure 15.</xref></sup>
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    Figure 15.

    Korea: Dependencies Captured in Top-Down Solvency Model Suite for Korean Banks

  • <sup><xref ref-type="fig" rid="RA01fig16">Figure 16.</xref></sup>
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    Figure 16.

    Korea: P&L Structure and Model Approaches Adopted for the Korea Solvency Analysis

  • <sup><xref ref-type="fig" rid="RA01fig17">Figure 17.</xref></sup>
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    Figure 17.

    Korea: Credit Risk Module Structure

  • <sup><xref ref-type="fig" rid="RA01fig18">Figure 18.</xref></sup>
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    Figure 18.

    Korea: Baseline and Adverse Scenario-Conditional PDs

  • <sup><xref ref-type="fig" rid="RA01bx01fig01">Figure B1.1:</xref></sup>
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    Figure B1.1:

    Probability Density for BBB Rating (Left) vs. S2 Exposure Under IFRS 9 (Right)

  • <sup><xref ref-type="fig" rid="RA01bx01fig02">Figure B1.2:</xref></sup>
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    Figure B1.2:

    Estimated ρ for Korean Banks’ Loan Portfolios

  • <sup><xref ref-type="fig" rid="RA01bx02fig01">Figure B2.1:</xref></sup>
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    Figure B2.1:

    From Transition Matrices to the Z-Score and Vice-versa

  • <sup><xref ref-type="fig" rid="RA01fig19">Figure 19.</xref></sup>
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    Figure 19.

    Korea: Perfect Foresight and Risk Parameter Behavior after Initial 5-Year Horizon

  • <sup><xref ref-type="fig" rid="RA01fig20">Figure 20.</xref></sup>
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    Figure 20.

    Korea: Baseline and Adverse Scenario-Conditional LGDs

  • <sup><xref ref-type="fig" rid="RA01fig21">Figure 21.</xref></sup>
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    Figure 21.

    Korea: Effective Interest Rates for ECL Discounting Purposes

  • <sup><xref ref-type="fig" rid="RA01fig22">Figure 22.</xref></sup>
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    Figure 22.

    Korea: Banks’ Historical Effective Interest Rate Evolution for Korean Banks

  • <sup><xref ref-type="fig" rid="RA01fig23">Figure 23.</xref></sup>
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    Figure 23.

    Korea: Nonlinear Solvency-to-Funding Cost Feedback for Korean Banks

  • <sup><xref ref-type="fig" rid="RA01fig24">Figure 24.</xref></sup>
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    Figure 24.

    Korea: Solvency Stress Test Results: CET1 Ratios, Sub-Sector Aggregates

  • <sup><xref ref-type="fig" rid="RA01fig25">Figure 25.</xref></sup>
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    Figure 25.

    Korea: Changes in CET1 Capital Ratios from Starting Point to Low Point Under the Adverse Scenario

  • <sup><xref ref-type="fig" rid="RA01fig26">Figure 26.</xref></sup>
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    Figure 26.

    Korea: Capital Depletion Under the Adverse Scenario

  • <sup><xref ref-type="fig" rid="RA01fig27">Figure 27.</xref></sup> <sup><xref ref-type="fig" rid="RA01fig27">Figure 27.</xref></sup>
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    Figure 27.

    Korea: Asset Exposure Shares vs. Credit and Market Loss Shares Under the Adverse Scenario

  • <sup><xref ref-type="fig" rid="RA01fig28">Figure 28.</xref></sup>
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    Figure 28.

    Korea: Fintech Overlay—Impact on Banks’ Capitalization Under Conservative Assumptions

  • <sup><xref ref-type="fig" rid="RA01fig29">Figure 29.</xref></sup>
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    Figure 29.

    Korea: Fintech Overlay—Impact on NIMs

  • <sup><xref ref-type="fig" rid="RA01fig30">Figure 30.</xref></sup>
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    Figure 30.

    Korea: Solvency-Liquidity Cost Feedback

  • <sup><xref ref-type="fig" rid="RA01fig31">Figure 31.</xref></sup>
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    Figure 31.

    Korea: Capital Ratios’ Sensitivity to Stronger House Price Shock for Residential Mortgage Portfolios

  • <sup><xref ref-type="fig" rid="RA01fig32">Figure 32.</xref></sup>
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    Figure 32.

    Korea: Mortgage Insurance Shares Across Korean Banks

  • <sup><xref ref-type="fig" rid="RA01fig33">Figure 33.</xref></sup>
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    Figure 33.

    Korea: Impact on Accounting Provision Stocks, Flows, and Risk Weights when Assuming that Mortgage Insurance was Absent

  • <sup><xref ref-type="fig" rid="RA01fig34">Figure 34.</xref></sup>
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    Figure 34.

    Korea: Capital Ratio Differentials under Different Static and Dynamic Balance Sheet Variants (Adverse Scenario)

  • <sup><xref ref-type="fig" rid="RA01fig35">Figure 35.</xref></sup>
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    Figure 35.

    Korea: Loan Loss Coverage as of End-2018, Regulatory Provision-based vs. Top-down Model-based (Adverse Scenario)

  • <sup><xref ref-type="fig" rid="RA01fig36">Figure 36.</xref></sup>
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    Figure 36.

    Korea: Impact of Regulatory Loan Loss Coverage on Capital Ratios and Depletion

  • <sup><xref ref-type="fig" rid="RA01fig37">Figure 37.</xref></sup>
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    Figure 37.

    Korea: Sensitivity of Capital Ratios to RWA Pass-Through Strength under the Adverse Scenario

  • <sup><xref ref-type="fig" rid="RA01fig38">Figure 38.</xref></sup>
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    Figure 38.

    Korea: Liquidity and Stable Funding

  • <sup><xref ref-type="fig" rid="RA01fig39">Figure 39.</xref></sup> <sup><xref ref-type="fig" rid="RA01fig39">Figure 39.</xref></sup> <sup><xref ref-type="fig" rid="RA01fig39">Figure 39.</xref></sup>
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    Figure 39.

    Korea: Liquidity Stress Test

  • <sup><xref ref-type="fig" rid="RA01fig40">Figure 40.</xref></sup>
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    Figure 40.

    Korea: Insurance Stress Test Sample—Fixed-Income Portfolio

  • <sup><xref ref-type="fig" rid="RA01fig41">Figure 41.</xref></sup>
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    Figure 41.

    Korea: Insurance Stress Test—RBC Coverage

  • <sup><xref ref-type="fig" rid="RA01fig42">Figure 42.</xref></sup>
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    Figure 42.

    Korea: Insurance Stress Test—Contribution of Individual Shocks

  • <sup><xref ref-type="fig" rid="RA01fig43">Figure 43.</xref></sup>
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    Figure 43.

    Korea: Insurance Profitability Projections After Stress

  • <sup><xref ref-type="fig" rid="RA01fig44">Figure 44.</xref></sup>
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    Figure 44.

    Korea: The National Pension Fund

  • <sup><xref ref-type="fig" rid="RA01fig45">Figure 45.</xref></sup>
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    Figure 45.

    Korea: Financial System Network Structure

  • <sup><xref ref-type="fig" rid="RA01fig46">Figure 46.</xref></sup>
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    Figure 46.

    Korea: Sector-Level Default Simulation: Capital Depletion

  • <sup><xref ref-type="fig" rid="RA01fig47">Figure 47.</xref></sup>
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    Figure 47.

    Korea: Network Centrality (Eigenvector Centrality)

  • <sup><xref ref-type="fig" rid="RA01fig48">Figure 48.</xref></sup>
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    Figure 48.

    Korea: Demographic Developments Proportion of Population Ages 65+

  • <sup><xref ref-type="fig" rid="RA01fig49">Figure 49.</xref></sup>
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    Figure 49.

    Korea: Old Age Poverty Across OECD Countries

  • <sup><xref ref-type="fig" rid="RA01fig50">Figure 50.</xref></sup>
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    Figure 50.

    Korea: Channels through which Demographic Developments May Impact the Financial System

  • <sup><xref ref-type="fig" rid="RA01fig51">Figure 51.</xref></sup>
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    Figure 51.

    Korea: Korean Financial System’s Total Asset Dynamics

  • <sup><xref ref-type="fig" rid="RA01fig52">Figure 52.</xref></sup>
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    Figure 52.

    Korea: Scenarios for Population Growth and Dependency Ratios in Korea until 2067

  • <sup><xref ref-type="fig" rid="RA01fig53">Figure 53.</xref></sup>
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    Figure 53.

    Korea: Demographics Scenario-Conditional Total Asset Dynamics

  • <sup><xref ref-type="fig" rid="RA01fig54">Appendix IV. Figure 1.</xref></sup>
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    Appendix IV. Figure 1.

    Mapping of CR and MR Methodology to Exposures

  • <sup><xref ref-type="fig" rid="RA01fig55">Appendix V. Figure 1.</xref></sup>
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    Appendix V. Figure 1.

    PD Model Structures

  • <sup><xref ref-type="fig" rid="RA01fig56">Appendix V. Figure 2.</xref></sup>
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    Appendix V. Figure 2.

    Rationale Underlying Nonlinear IER Model Structure

Figure 1.

Korea: Financial System Structure

Financial Institutions’ Assets

(in percent of total)
Figure 2.

Korea: Depository Institutions’ Business Models
Figure 3.

Korea: Structure of the Insurance Sector
Figure 4.

Korea: Financial System Performance
Figure 5.

Korea: Bank Profitability (Significant Institutions):1 Key Trends
Figure 6.

Korean Banks’ Asset Portfolio Structure, Liability Structure, and P&L Components
Figure 7.

Korea: Solvency and Profitability of the Insurance Sector
Figure 8.

Korea: Macrofinancial Environment
Figure 9.

Korea: Housing Market
Figure 10.

Korea: Demographics Shift and Household Debt
Figure 11.

Korea: Baseline and Adverse Macro-Financial Scenario
Figure 12.

Korea: Baseline and Adverse Scenario Paths for Real GDP in Historical Perspective
Figure 13.

Korea: Cumulative Real GDP Deviations (Adverse Relative to Baseline)
Figure 14.

Korea: Implications of Open Banking and E-Money Developments in Korea
Figure 15.

Korea: Dependencies Captured in Top-Down Solvency Model Suite for Korean Banks
Figure 16.

Korea: P&L Structure and Model Approaches Adopted for the Korea Solvency Analysis
Figure 17.

Korea: Credit Risk Module Structure
Figure 18.

Korea: Baseline and Adverse Scenario-Conditional PDs
Figure B1.1:

Probability Density for BBB Rating (Left) vs. S2 Exposure Under IFRS 9 (Right)
Figure B1.2:

Estimated ρ for Korean Banks’ Loan Portfolios
Figure B2.1:

From Transition Matrices to the Z-Score and Vice-versa
Figure 19.

Korea: Perfect Foresight and Risk Parameter Behavior after Initial 5-Year Horizon
Figure 20.

Korea: Baseline and Adverse Scenario-Conditional LGDs
Figure 21.

Korea: Effective Interest Rates for ECL Discounting Purposes
Figure 22.

Korea: Banks’ Historical Effective Interest Rate Evolution for Korean Banks
Figure 23.

Korea: Nonlinear Solvency-to-Funding Cost Feedback for Korean Banks
Figure 24.

Korea: Solvency Stress Test Results: CET1 Ratios, Sub-Sector Aggregates
Figure 25.

Korea: Changes in CET1 Capital Ratios from Starting Point to Low Point Under the Adverse Scenario
Figure 26.

Korea: Capital Depletion Under the Adverse Scenario
Figure 27.

Korea: Asset Exposure Shares vs. Credit and Market Loss Shares Under the Adverse Scenario
Figure 27.

Korea: Asset Exposure Shares vs. Credit and Market Loss Shares Under the Adverse Scenario
Figure 28.

Korea: Fintech Overlay—Impact on Banks’ Capitalization Under Conservative Assumptions
Figure 29.

Korea: Fintech Overlay—Impact on NIMs
Figure 30.

Korea: Solvency-Liquidity Cost Feedback
Figure 31.

Korea: Capital Ratios’ Sensitivity to Stronger House Price Shock for Residential Mortgage Portfolios
Figure 32.

Korea: Mortgage Insurance Shares Across Korean Banks
Figure 33.

Korea: Impact on Accounting Provision Stocks, Flows, and Risk Weights when Assuming that Mortgage Insurance was Absent
Figure 34.

Korea: Capital Ratio Differentials under Different Static and Dynamic Balance Sheet Variants (Adverse Scenario)
Figure 35.

Korea: Loan Loss Coverage as of End-2018, Regulatory Provision-based vs. Top-down Model-based (Adverse Scenario)
Figure 36.

Korea: Impact of Regulatory Loan Loss Coverage on Capital Ratios and Depletion
Figure 37.

Korea: Sensitivity of Capital Ratios to RWA Pass-Through Strength under the Adverse Scenario
Figure 38.

Korea: Liquidity and Stable Funding
Figure 39.

Korea: Liquidity Stress Test
Figure 39.

Korea: Liquidity Stress Test
Figure 39.

Korea: Liquidity Stress Test
Figure 40.

Korea: Insurance Stress Test Sample—Fixed-Income Portfolio
Figure 41.

Korea: Insurance Stress Test—RBC Coverage
Figure 42.

Korea: Insurance Stress Test—Contribution of Individual Shocks
Figure 43.

Korea: Insurance Profitability Projections After Stress
Figure 44.

Korea: The National Pension Fund
Figure 45.

Korea: Financial System Network Structure
Figure 46.

Korea: Sector-Level Default Simulation: Capital Depletion
Figure 47.

Korea: Network Centrality (Eigenvector Centrality)
Figure 48.

Korea: Demographic Developments Proportion of Population Ages 65+
Figure 49.

Korea: Old Age Poverty Across OECD Countries
Figure 50.

Korea: Channels through which Demographic Developments May Impact the Financial System
Figure 51.

Korea: Korean Financial System’s Total Asset Dynamics
Figure 52.

Korea: Scenarios for Population Growth and Dependency Ratios in Korea until 2067
Figure 53.

Korea: Demographics Scenario-Conditional Total Asset Dynamics
Appendix IV. Figure 1.

Mapping of CR and MR Methodology to Exposures
Appendix V. Figure 1.

PD Model Structures
Appendix V. Figure 2.

Rationale Underlying Nonlinear IER Model Structure