## 1thaea2019003

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---

### Overall assessment
- The Thai banking system shows a substantial resilience to severe shocks.
- Solvency stress tests indicate the largest banks can withstand an adverse scenario broadly as severe as the Asian financial crisis.
- Three banks would deplete their capital conservation buffer (CCB) under the adverse scenario; recapitalization needs would be approximately THB 5 billion (about 0.03 percent of Thailand’s GDP).
- Complementary sensitivity tests:
  - No particular vulnerability from bond portfolio exposure to increases in government and corporate spreads.
  - No particular vulnerability from foreign exchange risk.
  - No particular vulnerability from loan concentration risk, except one entity with marked single-name concentration.
- The Bank of Thailand (BoT) solvency stress test produced very similar results despite methodological differences.

### Liquidity resilience of banks
- Funding and liquidity structure:
  - Funding maturity structure is front-loaded mostly to sight deposits in the near-term.
  - Under the current regulatory regime, banks have sufficient liquidity buffers to withstand a one-month risk horizon.
- LCR results (sample of 5 D-SIBs + 3 IRBs, end-June 2018):
  - Aggregate baseline LCR: 188 percent.
  - Severe scenario aggregate LCR: 104 percent (above FSAP hurdle of 100 percent).
  - Three banks fall below the 100 percent hurdle in the severe scenario; one bank falls below the 80 percent Basel III transitional threshold.
  - Aggregate liquidity shortfall in severe scenario: THB 24.8 billion; 0.7 percent of banks' assets in sample; 1.5 percent of GDP.
  - Retail shock aggregate LCR: 138 percent; liquidity shortfall THB 24.7 billion (0.2 percent of sample assets).
  - Wholesale shock aggregate LCR: 139 percent; liquidity shortfall THB 53.7 billion (0.4 percent of sample assets).
  - Investment funds shock: all banks remain above 100 percent; liquidity shortfall 0.0.
- Cash-flow (maturity-ladder) analysis:
  - Broadly consistent with the LCR test over a one-month horizon.
  - Some banks would face increased funding pressures under sizable withdrawals; two banks show negative cash balances in the “more than 180 days” horizon even after using required reserves (shortfalls of 6 percent and 7 percent of each bank’s total assets, respectively).
- HQLA and funding composition:
  - 93 percent of HQLA are level 1 unencumbered assets (mostly government securities).
  - Retail deposits account for 50 percent of funding for the eight-bank sample; demand and savings accounts represent 66 percent of total deposits.
  - Wholesale funding share: 57 percent for the 3 IRBs; 47 percent for the 5 D-SIBs.
  - Foreign exchange exposures represent less than 8 percent of total liabilities; net open FX position is less than 2 percent of capital.

### Investment funds (IFs) liquidity stress test
- Scope and coverage:
  - Covered open-ended daily fixed income funds (daily FI) and money market funds (MMF), accounting for about 33 percent of NAV (sample: 32 daily FI and 11 MMFs; market share 31 percent of total AUM; combined Daily FI + MMF total NAV reported as 880 and 149 in table sequence).
  - As of September 2018: 446 fixed-income funds totaling US$81 billion; Daily FI and MMF account for 72 percent of total fixed income funds.
- Calibration and redemption shocks:
  - Three calibration approaches: fund-homogeneity, fund-heterogeneity, fund-type.
  - Fund-homogeneity redemption rate: 22 percent for daily FI funds and MMFs.
  - Fund-heterogeneity average redemption rates: 14 percent for daily FI funds and 19 percent for MMFs; MMF outflow rates range between 7 and 60 percent; Daily FI funds range between 0.1 and 36 percent.
  - Fund-type first percentile redemption rates: daily FI 19.9 percent; MMFs 22.7 percent.
- Asset composition and maturity (September 2018):
  - Daily FI funds asset composition: Cash liquidity 25.4; Short-term bonds 31.8; Medium-term bonds 1.8; Long-term bonds 2.7; Corporates 38.2 (percent of total daily fixed income assets).
  - MMF asset composition: Cash liquidity 11.6; Short-term bonds 68.1; Corporates 20.3.
  - Maturity profile Daily FI: <3m 40.6; 3-6m 21.2; 6m-1y 17.8; 1-2y 7.7; >2y 12.7.
  - Maturity profile MMF: <3m 83.2; 3-6m 13.4; 6m-1y 3.4.
- Liquidation strategies and results:
  - Two strategies: Waterfall (sell most liquid assets first) and Pro rata (sell proportionally).
  - Waterfall approach (fund-heterogeneity): IFs sell THB 26.7 billion of government bonds; fund-type: THB 11 billion.
  - Pro rata approach requires much larger government bond sales: THB 214 billion (fund-homogeneity); THB 97 billion (fund-heterogeneity); THB 193 billion (fund-family).
  - Aggregate RCRs:
    - Fund-homogeneity and fund-type calibration: daily FI funds RCR 1.9 and 2.2, respectively.
    - MMFs RCR: 3.2.
  - Aggregate liquidity position of IFs, excluding corporate assets: THB 1 trillion.
  - Total redemption values under calibrations: THB 335 billion (fund-homogeneity), THB 149 billion (fund-heterogeneity), THB 304 billion (fund-family).
  - Under fund-heterogeneity, eight funds have RCR < 1 accounting for 9.7 percent of total NAV of daily FI funds and MMFs; including corporate bonds in buffers, all but one of these eight could meet redemptions.
  - Credit lines between banks and AMCs provide additional liquidity buffer; parental bank support assumed up to 10 percent under stress (5 percent in baseline).
- Market impact:
  - Price effects estimated using Amihud illiquidity measure:
    - Under waterfall, short-term government bond prices fall by 77 basis points (fund-heterogeneity); long-term by 19 basis points (fund-heterogeneity); short-term fall 35 basis points (fund-family).
  - Pro rata sales lie at the uppermost end relative to historical monthly turnovers and could materially stress the government bond market.

### Solvency stress testing: adverse scenario and results
- Scenario design and calibration:
  - Three-year horizon: June 2018–June 2021.
  - Adverse scenario includes a U-shaped GDP profile with a cumulative deviation of -15.6 ppt over the first two years; first-year GDP deviation of -5.6 percent.
  - Exchange rate depreciation limited to 12 percent in the first year; central bank lowers policy rate to the zero lower bound.
- Credit growth and NPLs under scenarios:
  - Baseline loan growth accelerates from +8.5 to +12.8 percent (2019–2021).
  - Adverse scenario credit growth: +3.9 percent (2019); -1.4 percent (2020); -5 percent (2021).
  - Widespread increase in NPLs across sectors driven by high unemployment and higher debt burdens.
- Main drivers of capital declines:
  - Credit losses are the principal driver.
  - Net interest margin compression: average NIM falls from 3.3 to less than 2.6 percent for the eight banks due to policy rate at zero lower bound.
  - Solvency–liquidity feedback increases funding spreads as capital weakens.
  - Equity investments: assumed SET drop of 55 percent in year 1, with recoveries of 20 percent and 10 percent in years 2 and 3.
  - IRB banks face increased RWAs from higher PDs and LGDs.
- Outcomes:
  - Most banks incur negative net income throughout the horizon.
  - Three banks deplete their CCB in 2021; recapitalization needs approx. THB 5 billion (0.03 percent of GDP).
  - No bank breaches regulatory capital requirements over the risk horizon; two banks see partial erosion of capital buffers.
- Sensitivity tests:
  - Concentration risk: single-name and sectoral surcharges affect primarily one bank; reverse stress-test indicates default of top five borrowers would cause two banks to breach Tier-1; top three borrowers' default would cause one bank breach.
  - IRRBB: EVE shocks less than Basel outlier threshold; historical VaR at 99th percentile indicates largest negative parallel shock of -13.8 percent (vs -12.6 percent Basel methodology). NII impacts limited.
  - Trading book VaR: government bond 99th percentile VaR around 1 percent of Tier 1 or less for all banks except one (where it exceeds 5 percent of Tier 1).

### Interconnectedness and contagion analysis
- Methods used:
  - Espinoza and Solé (2009) interbank network simulation; Diebold and Yilmaz (2012) market-data connectedness; Financial Stability Measures; balance-sheet flow-of-funds analysis.
- Findings:
  - Interconnectedness is at its lowest point in the last decade both within the banking system and across sectors.
  - Domestic interbank contagion limited: no single domestic bank failure would trigger a cascade; failure of certain large banks could erode aggregate capital buffer (e.g., failure of bank 2 would erode others by over 8 percent).
  - Cross-border exposures generally small except for one bank with parent-related links.
  - Market-data measures confirm low connectedness between banks and non-banks; banks show net outward spillovers.
  - Two insurers show relatively high outward spillovers to nonbank sector.
- Caveats:
  - Interconnectedness and contagion are difficult to measure and to operationalize in scenario-based exercises.
  - Structural (data-intensive) and market-based (faster) approaches are complementary; linking them would strengthen systemic risk analysis.

### Data, analytics, and supervisory recommendations
- Data improvements:
  - Improve time series quality of IRB banks’ PDs and LGDs (point-in-time estimates and dynamic characteristics).
  - Enhance liquidity risk data management: more granular data by product, frequency, currency, maturity; decompose deposits by type (sight, term), insured/uninsured, depositor type (retail/institution), and LCR by currency when significant.
  - Refine maturity buckets and cash-flow reporting templates (finer short-end buckets and beyond 90 days).
- Stress testing and model enhancements:
  - Revise modeling of banks’ net interest margin under stress to ensure conservative and plausible NII projections.
  - Invest in analytical tools for concentration risk estimation and systemic implications; calibrate capital surcharges for IRB banks and add-ons for other banks.
  - Continue to strengthen liquidity stress testing capacity by expanding staff and enhancing collaboration between Financial Stability Unit and banking supervision.
  - Expand SEC stress testing scope beyond daily FIs and MMFs to include equity and mixed funds.
  - Implement coordinated stress testing dialogue among SEC, BoT, AMCs, and banks on methodology and scenario design.
- Supervisory and policy priorities:
  - Continue calibration and integration of LCR run-off/roll-off and haircut assumptions with solvency testing.
  - Run LCR-based stress tests consolidated and adopt cash-flow-based analyses by maturity.
  - Monitor rapid growth and distribution channels of Daily FI funds given retail dominance (retail 83.3 percent of investor base) and bank distribution (73 percent of total AUM).
  - Address maturity mismatch between assets and liabilities, especially at the long end.

*Source: 2019 Thailand FSAP — EXECUTIVE SUMMARY and chapter excerpts (1thaea2019003).*

### EXECUTIVE SUMMARY __________________________________________________________________________ 7

### EXECUTIVE SUMMARY

### Overall assessment
- The Thai banking system shows a substantial resilience to severe shocks.
- The solvency stress tests indicate that the largest banks can withstand an adverse scenario broadly as severe as the Asian financial crisis.
- While three banks would deplete their capital conservation buffer (CCB) under the adverse scenario, recapitalization needs would be minimal.
- Complementary sensitivity stress tests confirmed the overall picture of a resilient banking system:
  - No particular vulnerability emerged from the analysis of the bond portfolio to an increase in government and corporate spreads.
  - No particular vulnerability emerged from exposure to foreign exchange risk.
  - No particular vulnerability emerged from concentration risk in the loan portfolio, with the possible exception of one entity with a particular concentration on single-name exposures.
- The Bank of Thailand (BoT) solvency stress test, conducted independently based on the same macro scenarios, showed very similar results despite some fundamental differences of approach.

### Liquidity resilience of banks
- Thai banks’ funding maturity structure is front-loaded mostly to sight deposits in the near-term.
- Under the current regulatory regime, banks have sufficient liquidity buffers to withstand a one-month risk horizon.
- The aggregate Liquidity Coverage Ratio (LCR) remains above the hurdle rate of 100 percent under the severe scenario.
  - Three banks fall below the hurdle rate with the aggregate liquidity shortfall of 0.7 percent of total assets (1.5 percent of GDP).
- Cash-flow-based analysis results were broadly consistent with the LCR test over a one-month horizon.
- Some banks would face increased funding pressures under sizable withdrawals of liquidity.

### Investment funds (IFs) liquidity stress test
- The exercise covered open-ended daily fixed income funds (daily FI) and money market funds (MMF), accounting for 33 percent of net asset value (NAV).
- Findings on IFs:
  - Cash positions were mostly sufficient to meet redemption demands under the waterfall strategy.
  - A majority of the IFs retains a good amount of liquid assets under the pro rata strategy despite more aggressive sale of government bonds required.
  - Of the eight individual funds that would see a significant depletion of their liquidity reserves, all except one would be able to withstand the shocks when the liquidation of corporate bonds is included.
  - Credit lines between banks and asset management companies (AMCs) would provide an additional layer of liquidity buffer.
  - The impact on the bond market could be substantial depending on the type of liquidation strategy.

### Interconnectedness and contagion analysis
- An analysis of interconnectedness and contagion in the banking sector and in the financial system at large did not find any particular vulnerabilities.
- Interconnectedness appears to be at its lowest point in the last decade, both within the banking system and across sectors.
- Caveats:
  - Interconnectedness and contagion are inherently difficult to measure and operationalize.
  - It is challenging to incorporate the potential channels of contagion identified by the analysis into scenario-based exercises to test the resilience of the system when shocks travel through those channels and get amplified.
- The BoT’s ongoing effort to explore an analysis aimed at capturing interconnection between main financial entities and economic sectors as well as across the border is welcome.

### BoT stress testing framework and model improvements
- The BoT continued to improve its stress testing framework since its first top-down solvency macro stress test in 2017.
  - The activity is based on the joint effort of different units within the BoT, under the coordination and with the active involvement of the Financial Stability Unit.
  - The decentralized, network-like approach supports cross-feeding between different and complementary skills across areas of the bank.
- The BoT has addressed many recommendations from the 2018 IMF technical assistance (TA):
  - Improved modeling of credit losses and feedback effects under adverse scenarios.
  - Introduction of macroprudential liquidity stress test.
- Area identified for further strengthening:
  - Modeling of Net Interest Income (NII) in times of stress could be strengthened further, as identified by the 2018 TA:
    - “[T]he effect of the stress scenario on NII and other income sources appears to be too moderate.”

### Data and analytics recommendations
- The BoT should invest in improving the quality and granularity of certain datasets.
  - While the BoT has a wide range of well-structured data, improvements are needed, in particular:
    - Time series of Internal Ratings-Based (IRB) banks’ Probability of Defaults (PDs) and Loss Given Default (LGD).
    - Data management for liquidity risk to ensure availability of more granular data including a finer breakdown by type.

### Macroeconomic context (selected figures)
- Thailand’s economy:
  - Growth was 4.1 percent in 2018.
  - Projected to slow down to around 3.0 percent in 2019 and 2020.
  - Average headline inflation reached 1.1 percent in 2018 and is projected to decline to just below the lower end of BoT’s target band of 1.0–4.0 percent in 2019.
- Foreign exchange exposures of the financial sector:
  - Limited at 5–6 percent of the commercial banks and Specialized Financial Institutions (SFIs)’ aggregate assets and liabilities.
- Household sector:
  - Household debt reached 80.8 percent of GDP in 2015 (up from 59.3 percent in 2010).
- Corporate sector:
  - Corporate debt stood at 70.5 percent of GDP in 2017.

### Key recommendations (summary of Table 1)
- Solvency Stress Testing
  - Ensure the data quality of IRB banks’ PD and LGD estimates and their respondence to Basel requirement in terms of dynamic characteristics (through-the-cycle vs point-in-time).
    - Responsible: BoT
    - Time: I (immediate)
    - Priority: M (Medium-Priority)
  - Revise the modeling of banks’ net interest margin under stress to ensure that it is adequately conservative and plausible.
    - Responsible: BoT
    - Time: NT (near-term)
    - Priority: M (Medium-Priority)
  - Invest in the development of analytical tool for the estimation of concentration risk in the loan portfolio and other forms of asset concentration.
    - Responsible: BoT
    - Time: NT (near-term)
    - Priority: H (High-Priority)
- Liquidity Stress Testing
  - Continue to improve and strengthen the liquidity stress testing capacity by expanding staff resources and increased collaboration with banking supervision unit.
    - Responsible: BoT
    - Time: MT (medium-term)
    - Priority: M (Medium-Priority)
  - Enhance the data management system for liquidity risk analysis to include more granular data by product, frequency, currency, and maturity and to conduct liquidity stress test by currency.
    - Responsible: BoT
    - Time: MT (medium-term)
    - Priority: H (High-Priority)
- Stress testing on Investment Funds
  - Expand the scope of stress testing beyond daily FIs and MMFs.
    - Responsible: SEC
    - Time: MT (medium-term)
    - Priority: M (Medium-Priority)
  - Implement a coordinated stress testing approach where all parties can have dialogue on the methodology of stress testing, scenario design, and share latest approaches and techniques to stress testing.
    - Responsible: SEC
    - Time: MT (medium-term)
    - Priority: M (Medium-Priority)
- Interconnectedness and contagion analysis
  - Explore the potential links between balance-sheet-based and market-based interconnectedness metrics as a way to strengthen the analysis of systemic risks.
    - Responsible: BoT
    - Time: NT (near-term)
    - Priority: M (Medium-Priority)

*Source: 2019 Thailand FSAP — EXECUTIVE SUMMARY*

### 3.      Risks to the macrofinancial outlook have shifted to the downside. Near-term risks have

### 3.      Risks to the macrofinancial outlook have shifted to the downside. Near-term risks have

### Near-term macrofinancial risks and transmission channels
- Near-term risks have shifted to the downside, reflecting external and domestic headwinds.
- External risks:
  - If trade tensions intensify, export growth could decline and spill over to domestic demand.
  - A sharp rise in risk premia could precipitate capital outflows, adding to FX volatility and higher borrowing costs.
- Domestic risks:
  - A difficult transition to a new government could lead to policy paralysis, derailing the Eastern Economic Corridor infrastructure push.
- Offsetting factors:
  - The country’s ample buffers and strong fundamentals should be sufficient to help smooth these shocks.
- Medium-term downside pressures:
  - High level of household debt.
  - Weaker-than-expected fiscal stimulus.
  - Anemic productivity growth.

### Main macrofinancial developments (selected facts and indicators)
- Growth and inflation:
  - Growth continues its moderate recovery amid low inflation.
  - Growth momentum appears to be faltering.
  - Inflation remains subdued.
- External position and reserves:
  - A large current account surplus led to a rapid accumulation of international reserves.
  - Portfolio flows turned positive in the third quarter of 2018, partly due to a flight to safety within emerging economies.
- Exchange rate and financial conditions:
  - The appreciation of the exchange rate in 2017 (reflecting a large current account surplus and portfolio inflows) was reversed in the first semester of 2018 due to outflows.
  - Financial conditions have been accommodative during the last decade.
- Real GDP growth and regional comparison:
  - Charts compare Thailand with Indonesia, Malaysia, and the Philippines (Real GDP Growth, annual percent change).
- Inflation and monetary stance:
  - Headline and Core Inflation (annual percent change); a headline target band and policy rate are shown in the figures.

### Financial vulnerabilities and sectoral balances
- Aggregate financial sector size:
  - Financial sector assets reached 266 percent of GDP at end-2018 (up from 183 percent in 2007).
- Banks and nonbank composition (end-2018):
  - Banks: 46% of total financial assets.
  - SFIs: 16% of total financial assets.
  - Other deposit-taking institutions: 7% of total financial assets.
  - Insurance: 9% of total financial assets.
  - Mutual funds: 11% of total financial assets.
  - Other NBFIs: 11% of total financial assets.
- Comparison to 2007:
  - Banks were 56% in 2007; SFIs 14%; other deposit-taking institutions 5%; insurance 6%; mutual funds 10%; other NBFIs 9%.
- Credit and house prices:
  - The credit gap turned negative in mid-2016, reflecting a slow-down in credit growth between 2015 and 2016.
  - Corporate debt-to-GDP has been broadly stable.
  - Total household indebtedness has decreased somewhat since its peak in 2015 but remains high compared to regional peers.
  - Growth in housing loans is picking up in recent months after a significant slowdown in previous years.
  - Condominium prices have been increasing steadily (Nominal House Prices, Index; 2009Q1=100).

### Household sector: composition, trends, and vulnerabilities
- Household debt dynamics:
  - While total household debt as a share of GDP has stabilized since 2015, household debt in nominal terms continued to grow driven by hire purchase, mostly associated with auto loans.
  - Growth in hire purchase (auto loans) accelerated in recent months; the contribution to the growth in personal loans is shifting towards non-bank institutions.
- Credit demand drivers:
  - The recent pick-up in credit demand is supported by favorable labor market developments (Labor force, Employment, Average real wage, Unemployment rate).
- Asset quality and borrower profile:
  - The NPL ratio of mortgages has been gradually edging up.
  - Mortgages are largely extended to high income individuals who earn more than THB50,000 per month.
- Institutions and loan composition:
  - Household Debt by Type (Mortgage, Hire purchase, Credit card, Personal loan) and by institution (Commercial banks, SFIs, Saving Cooperatives, Other Financial Corporations) are highlighted in the figures.

### Corporate sector: profitability, leverage, and risk concentration
- Profitability and leverage:
  - Corporate profitability has been on the decline since the fallout of the Global Financial Crisis in 2008–2009.
  - Leverage has increased substantially in sectors such as real estate, utilities, and some manufacturing (Leverage measured as Gross Debt to EBITDA).
- Debt-at-risk and coverage:
  - Debt held by Thai corporates with ICR less than 1 has increased somewhat and remains relatively high by regional standards.
  - The number of firms with ICR less than 1 for three consecutive years has increased.
  - Distribution of debt per ICR threshold shows percent of debt with ICR < 1 and percent with 1 < ICR < 2.
- Corporate bond market:
  - The corporate bond market has been growing rapidly, and securities are being issued more broadly across sectors.
- SME vulnerabilities:
  - NPLs are larger in SMEs than in large companies across sectors.
- Sample and sector notes:
  - Figures are based on a sample of 459 listed companies with asset size larger than US$25 million; “Others” include Agriculture, recreation and hotel, electronic and computer; and others.

### Financial system structure, soundness, and liquidity
- Banking sector structure:
  - The sector consists of 30 commercial banks, with five domestic systemically important banks (D-SIBs) accounting for 70 percent of assets.
  - Loans account for around two-thirds of banks’ assets.
  - 70 percent of banks’ liabilities are deposits, with a loan-to-deposit ratio slightly below 100 percent.
  - Corporates and households account for roughly one-third of banks’ portfolio each, with small exposure to the public sector.
  - Banks rely mostly on retail deposits for funding.
- Capital and asset quality:
  - The aggregate capital adequacy ratio (CAR) stood at 18.0 percent in the second quarter of 2018, well above the minimum of 10.375 percent in 2018 and 11 percent from 2019 (including the conservation buffer).
  - The ratio of NPLs to total loans is relatively low at 3.1 percent, but the quality of credit to SMEs has deteriorated.
  - Current weaknesses in loan management practices may be understating the level of NPLs, though this is being mitigated by high levels of provisioning and targeted in-depth supervision.
- Liquidity and profitability indicators:
  - The liquidity coverage ratio (LCR) was almost 170 percent in the third quarter of 2017, higher than in other regions (130 percent in Europe, 126 percent in the Americas, and 128 percent for the rest of the world; Basel III Monitoring Report, March 2018), yet liquidity metrics in FSIs indicate Thailand is below the median for peer countries.
  - The share of liquid asset to total asset is moderately below the median of peer countries.
  - Thai banks appear to rely more on short-term liabilities.
  - Profitability (Return on Asset) is somewhat below peer countries.
- Balance-sheet structure (end-2018):
  - SFIs: Households account for 60 percent of SFIs’ loan portfolio and close to three-quarters of SFIs’ funding.
  - Structure of Bank Assets and Liabilities depicted in the figures.

### SFIs, cooperatives, and nonbank financial institutions
- SFIs and cooperatives:
  - The supervisory responsibility for the SFIs was shifted to the BoT in April 2015.
  - There are eight SFIs (four take retail deposits), 566 CUs, and over 1,400 TCCs.
  - SFIs' loans and deposits are equivalent to about 40 percent of those of commercial banks.
  - SFIs, CUs, and TCCs account for about 45 percent of loans to households.
  - SFIs' asset quality: average NPL ratio at 4.5 percent as of Sep 2017.
- Size of main NBFIs:
  - The assets of the main NBFIs reached 61 percent of GDP in 2018 (up from 33 percent in 2007).
- Insurance sector:
  - Insurance penetration ratio increased from 3.6 percent in 2008 to 5.6 percent in 2017.
  - Of the 23 life (re)insurers operating in Thailand, the top 5 represent 72 percent of total assets in the sector.
  - Of the 53 non-life (re)insurers operating in Thailand, the top 5 represent 42 percent of direct premium in the sector (all data end-2017).
  - The industry is well-capitalized and has a diversified asset allocation, but profitability has been weakening; asset allocation to equity is relatively high for non-life at around 30 percent.
- Pension system:
  - The pension system is fragmented and coverage is low.
  - The private pension incentive structure encourages overly conservative, low-growth investments and pay lump-sum payments upon retirement rather than lifetime pensions, increasing the risk of retirement poverty for Thailand’s fast-aging population.
- Mutual funds and asset management:
  - Mutual funds’ AUM are equivalent to close to 37 percent of bank deposits, with foreign investment funds (FIFs) representing about 20 percent of AUM.
  - The largest five AMCs, accounting for 70 percent of AUM, are all part of bank conglomerates.
  - Major Asset Management Company shares (in percent of total AUM, 2018): Kasikorn AM, 20.3; SCB AM, 18.7; BBL AM, 14.3; Krung Thai AM, 11.7; TMB AM, 8.1; Other, 27.
  - Roughly half of the AUM are in fixed income, but equity and infrastructure funds have been growing.
  - Retail clients dominate the investor base for mutual funds, which account for 83 percent of the total, potentially exacerbating liquidity risks.

### Key statistics and indicators (explicit numeric values from text)
- Financial sector assets: 266 percent of GDP at end-2018; 183 percent in 2007.
- Banks' share of total financial assets: 46% at end-2018; 56% in 2007.
- SFIs' share of total financial assets: 16% at end-2018; 14% in 2007.
- Aggregate capital adequacy ratio (CAR): 18.0 percent in Q2 2018.
- Regulatory minimum CAR: 10.375 percent in 2018 and 11 percent from 2019 (including the conservation buffer).
- NPL ratio (banks): 3.1 percent.
- LCR: almost 170 percent in Q3 2017.
- Comparator LCRs (Basel III Monitoring Report, March 2018): 130 percent in Europe, 126 percent in the Americas, 128 percent for the rest of the world (systemically important banks).
- SFIs' average NPL ratio: 4.5 percent as of Sep 2017.
- Assets of main NBFIs: 61 percent of GDP in 2018; 33 percent in 2007.
- Insurance penetration ratio: 3.6 percent in 2008; 5.6 percent in 2017.
- Top-5 life (re)insurers: 72 percent of total assets in the sector (end-2017).
- Top-5 non-life (re)insurers: 42 percent of direct premium in the sector (end-2017).
- SFIs' loans and deposits relative to commercial banks: about 40 percent.
- SFIs, CUs, and TCCs share of loans to households: about 45 percent.
- Number of institutions: eight SFIs (four take retail deposits), 566 CUs, and over 1,400 TCCs.
- Mutual funds’ AUM relative to bank deposits: close to 37 percent.
- Foreign investment funds (FIFs) share of AUM: about 20 percent.
- Major AMC market shares (2018): Kasikorn AM, 20.3; SCB AM, 18.7; BBL AM, 14.3; Krung Thai AM, 11.7; TMB AM, 8.1; Other, 27.
- Household mortgage borrowers income bucket highlighted: mortgages largely extended to those earning more than THB50,000 per month.

### Financial system soundness and supervisory developments
- Supervision and oversight:
  - The sector is supervised by the Bank of Thailand (BoT).
  - Supervisory responsibility for SFIs was shifted to the BoT in April 2015; a structured framework for prudential supervision is being developed.
  - Oversight of financial cooperatives (FCs, including TCCs and CUs) is under the Ministry of Agriculture Cooperatives.
- Observations on banks:
  - Commercial banks appear to be sound, though profitability is weak and below peer countries.
  - Liquidity indicators and profitability are somewhat below peer countries despite high LCR.
  - Commercial banks rely mostly on retail deposits and have been improving liquidity risk management.
- Insurance and mutual fund regulation:
  - Insurance sector supervised by the OIC and accountable to the MoF.
  - The SEC oversees capital markets and investment intermediaries.

### Key risk factors and stress-testing approach (introductory note)
- The document transitions to "KEY RISK FACTORS AND STRESS TESTING APPROACH" and begins Section A: Stress Testing under FSAP program.

*International Monetary Fund — Thailand: Macrofinancial Developments and Financial System Assessment (extracted content).*

### 8.      The FSAP, established in 1999, is a comprehensive, in-depth assessment of a country’s

### 1thaea2019003 - 8.      The FSAP, established in 1999, is a comprehensive, in-depth assessment of a country’s

### FSAP purpose and role of stress tests
- The FSAP, established in 1999, is a comprehensive, in-depth assessment of a country’s financial sector.
- The stability assessment under the FSAP is the main responsibility of the Fund in countries where FSAPs are done jointly with the World Bank (developing and emerging market countries).
- In FSAPs, a stress test is a financial stability tool to assess bank resilience to extreme but possible scenarios; the goal is to provide recommendations to help preserve financial stability, i.e. minimize the probability of financial disruptions and crisis.
- Stress tests under FSAPs assess the resilience of the banking sector at large from a macrofinancial perspective (as opposed to microprudential focus on individual institutions).

### Key risk factors for Thailand
- The Thai financial sector is exposed to macrofinancial risks from external and domestic factors (Risk Assessment Matrix (RAM), Table 4).
- External risks:
  - Rising protectionism, adverse changes in market sentiment and investment could lead to weak (even negative) growth in key advanced economies and in China, depressing Thailand’s exports.
  - Consequences include lower GDP growth, higher unemployment, increased corporate vulnerabilities, deterioration in households’ repayment capacity, and weakening of banks’ asset quality.
  - Sharp rise in risk premia could reverse capital flows and depreciate the baht, raising financial sector funding costs and weakening balance sheets of corporates with unhedged foreign currency exposures and currency mismatches.
- Domestic risks:
  - An increase in real interest rates and the real debt burden could pose private sector balance sheet risks.
  - General election outcomes may lead to political gridlock, disrupting public investment and raising sovereign and corporate risk premia.
  - In an unlikely crisis of confidence, possible outcomes include a collapse in equity prices, sharp exchange rate depreciation, and funding pressures from sudden withdrawal of retail and wholesale deposits.

### Adverse scenario design (aligned with RAM)
- Triggers:
  - (i) weaker-than-expected growth in the U.S. (due to waning confidence and weaker investment);
  - (ii) prolonged anemic growth and low inflation in the euro area (weak foreign demand, Brexit, concerns about high-debt countries, faltering confidence);
  - (iii) lower growth in China (weaker external demand, potential reversal of globalization, increasing role of the state).
- Modeled as a shock to demand in the United States, Euro Area, and China, leading to:
  - Emerging market sell-off affecting Thailand via weaker exports/imports and investor uncertainty.
  - Rise in corporate and household risk premia, strong decline in investment, consumption, and asset prices.
  - Portfolio outflows and exchange rate depreciation.
- Specific assumptions:
  - Exchange rate depreciation limited to 12 percent in the first year of the shock due to substantial reserve buffers and expectation of authorities’ support.
  - Central bank response: lower policy rate to the zero lower bound.
  - Households and corporates are debt constrained and assumed to sell assets to meet obligations, further depressing stock prices.

### Stress testing approach for the Thailand FSAP
- Battery of stress tests conducted:
  - Solvency stress test and sensitivity tests: estimated banks’ profitability and capitalization under baseline and one adverse scenario; sensitivity tests on interest rates, corporate spreads, concentration risk.
  - Liquidity stress tests: (i) Basel III LCR under a severe scenario combining retail, wholesale, and mutual fund deposit outflows and sharp exchange depreciation; (ii) implied cash-flow-based analysis by maturity bucket.
  - Investment funds’ redemption risk test: assessed capacity to withstand severe redemption shock, impact on banking sector and bond market.
  - Interconnectedness and contagion: four approaches used:
    - (i) Espinoza and Sole (2009) to simulate credit and funding shocks across domestic interbank network and cross-border spillovers;
    - (ii) Diebold and Yilmaz (2012), market-data based, to measure network interconnectedness between listed banks and nonbanks (possible extension to major Thai corporates);
    - (iii) Financial Stability Measures to quantify systemic risk amplification across banks, insurance, IFs, and other intermediaries;
    - (iv) balance sheet analysis based on flow of funds data.
- Further methodological details referenced in the Stress Testing Matrix (STeM) in Appendix I.

### Solvency stress test: scope and structure
- Objective: combine scenario-based assessment with sensitivity analyses on single risks.
- Scenarios span a three-year period from June 2018 to June 2021.
  - Baseline based on October 2018 World Economic Outlook (WEO) projections.
  - Adverse projections based on IMF’s Flexible System of Global Models (FSGM), previous crisis observations (e.g., Global Financial Crisis), and expert judgment (Table 5).
- Adverse scenario features:
  - U-shaped GDP profile with a prolonged decline similar to the Asian Financial Crisis.
  - Fundamental assumption: deviation of GDP from baseline of -15.6 ppt over the first two years (2019 and 2020).
    - This represents approximately 2.1 standard deviations of GDP growth (calculated over the 1980–2017 period).
  - Calibration notes:
    - Based on Growth-At-Risk methodology at low percentiles.
    - Assumed decline in growth rate of 5.6 percent in the first year has a likelihood of about 7 percent, between the 5 percent GaR threshold of -6.75 percent and the 10 percent GaR threshold of -3.3 percent.
    - Estimate for the 10 percent GaR for the second year is equal to -2.5 percent, close to the assumed decline in GDP growth of 2.4 percent in the second year.
- Sample: eight commercial banks representing 75 percent of banking sector assets, including 5 D-SIBs (all using standardized approach for credit risk) and 3 banks authorized to use IRB models.

### Balance sheet and income projection methodology
- Quasi-static allocation balance sheet assumption:
  - (i) Interest earning assets and exposures at default grow at a rate consistent with macro scenario (based on relationship between total bank credit and domestic demand and unemployment, with a judgmental floor), adjusted for previous period losses and exchange rate changes for foreign currency assets.
  - (ii) Non-interest earning assets grow at a rate aligned with historical experience.
  - (iii) Bank equity evolution depends on stress test results—profits realized net of losses.
  - (iv) Interest earning liabilities grow to equate assets to total liabilities.
  - Asset allocation and funding composition remain the same throughout the risk horizon.
- Interest income estimation:
  - Effective interest rate on deposits projected from a panel data model with an autoregressive component and short-term rate as exogenous explanatory variable.
  - Effective interest rates on loans estimated bank by bank using a system of seemingly unrelated regressions (SUR).
  - Impact of idiosyncratic funding cost increases estimated via a nonlinear feedback mechanism linking solvency (total capital ratio) and liquidity (spread on wholesale borrowings).
- Fees, commissions, operating expenses, taxes, dividends:
  - Fees and commissions assumed to evolve with asset growth, adjusted for competitive pressures (e.g., e-banking fee reductions, FinTech competition).
  - Operating and other non-interest expenses grow in line with growth of interest-bearing assets.
  - Taxes set at marginal tax rate (30 percent) if positive net income, zero otherwise.
  - Dividends paid only with positive income, at a flat 30 percent payout ratio, consistent with historical experience and subject to restrictions if CCB erodes.
  - Note: effective tax ratio for banks in scope ranged approximately between 20 and 25 percent in 2018; 2018 dividend payout ratios of the eight banks ranged from 0 to 40 percent.

### Credit risk, RWAs, and provisioning
- RWAs calculation:
  - For standardized approach banks: RWAs under stress adjusted for asset growth in the current year, impairments accrued in the past year, and exchange rate changes for foreign currency exposures.
  - For IRB banks: RWAs recalculated from projections of PDs, LGDs, and EADs in the adverse scenario; satellite models used to estimate bank-by-bank and portfolio-by-portfolio link between PDs/LGDs and macro variables.
- Credit loss estimation:
  - For standardized exposures (and standardized-treated IRB exposures), credit loss estimates based on a satellite model linking NPLs to macro variables.
  - NPL inflows modeled quarter by quarter via SUR for each of 11 sectors with public data: agriculture forestry and fishing; mining and quarrying; manufacturing; wholesale and retail trade; financial and insurance activities; construction; real estate activities; public utilities and transportation; services; households; other (residual).
  - Main explanatory variables in the SUR system: unemployment rate and long-term interest rate (GDP growth and exchange rates were not significant).
  - NPL outflows calibrated bank by bank from recent experience, with reduced outflows under stress.
  - New NPLs net of old NPL outflows determine additional provisions to be expensed; net flow of NPLs (standardized) and expected losses (IRB) assumed to be fully provisioned—i.e., full amount of new NPLs and expected losses enter the income statement and losses cannot be distributed over time.
  - Existing ‘excess’ provisions are not allowed to be used to absorb emerging losses (assumed to cover existing losses and not available for new ones).
- PDs and LGDs were estimated point-in-time.

### Market risk and FX exposures
- Market risk impacts captured via duration gap analysis for interest rate and credit spread shocks in the trading book and FX risk for the whole balance sheet.
- Shocks to major foreign currencies (USD, CNY, JPY, EUR) affect banks’ net open positions.
- Applied shock to the stock exchange index to all equity holdings.

### Measurement of outcomes and hurdle rates
- Outcomes measured by capital ratios against current and future requirements and buffers.
- Three distinct hurdle rates used: Common Equity Tier 1 (CET1) ratio, Tier 1 (T1) ratio, and Total Capital ratio (CAR), each considered with and without buffers.
- BoT Regulation on Supervision of Capital for Commercial Banks introduced a CCB and the possibility of a CCyB:
  - CCyB currently set at 0 percent.
  - CCB was phased in and reached final level of 2.5 percent of RWAs.
  - Reduction of the CCB below 2.5 percent triggers specific limitations to earnings distribution.
- D-SIB capital surcharge:
  - 0.5 percent of RWAs in 2019;
  - 1 percent of RWAs from 2020 onwards.

*Source: Thailand FSAP chapter text provided.*

### 25.      Under the adverse scenario, credit growth would slow down and then turn negative

### 1thaea2019003 - 25.      Under the adverse scenario, credit growth would slow down and then turn negative

### Adverse scenario: credit growth, NPLs, and losses
- Under the baseline banks’ loans to customers would grow and accelerate (from +8.5 to +12.8 percent between 2019 and 2021).
- Under the adverse scenario credit growth would:
  - slow down in the first year (+3.9 percent), and
  - decrease in the following two years (-1.4 and -5 percent in 2020 and 2021, respectively).
- There would be a widespread increase of NPLs across the financial system driven by:
  - very high unemployment rate,
  - consequent impact on domestic demand, and
  - increase in the weight of debt (via higher interest rates) against dwindling incomes in the corporate and household segments.
- NPL inflow rates would increase substantially in most business segments (agricultural, manufacturing, and household).
- NPL ratios would spike, leading to losses far larger than under the baseline scenario.
- Losses would likely be larger if FX depreciation were included; however, the portion of FX loans is small and the negative effect is already captured to a large extent by the decline in GDP.
- Losses would be larger also if the policy rate were to increase instead of decrease; the exercise assumes the central bank would cut the policy rate to restore growth.

### Main solvency stress-test results and recapitalization needs
- Most banks would incur negative net income throughout the horizon of the exercise.
- Three banks would experience a depletion of their CCB, with the shortfall occurring in the last year (2021).
- The resources needed by the three banks to restore their capital buffers would be approximately THB 5 billion, equivalent to about 0.03 percent of Thailand’s GDP.
- The THB 5 billion would be easily covered by one quarter of ‘normal’ profits for the three banks (measured with respect to their average profits earned in the previous 5 years).
- No bank would experience, over the risk horizon and under the adverse scenario, breaches of their capital requirements; two banks would see their capital buffers partially eroded (marginally in one case, slightly more substantially in the other one).

### Drivers of capital ratio declines
- Credit losses are the main factor behind the decline in capital ratios.
- Additional impacts include:
  - Compression of the net interest margin: the adverse scenario assumes the policy rate dropping to zero and remaining at that level (no negative rates). As deposit rates approach the lower bound while lending rates continue to decline, the net interest margin would shrink from 3.3 to less than 2.6 percent, on average, for the eight banks.
  - A solvency-liquidity feedback: spreads paid by banks on market funding would increase over the three-year horizon as perceived weakness (proxied by falling capital ratios) leads to higher funding costs; this feedback includes a nonlinear component that amplifies the effect as capitalization declines.
  - Equity investments: significant penalty from assumed Thai stock exchange index (SET) movements—55 percent drop in the first year, and partial recoveries of 20 and 10 percent in 2020 and 2021, respectively.
  - Increase in RWAs for IRB banks: deterioration in borrowers’ financial conditions and recovery rates raises PDs and LGDs, translating into larger RWAs.

### Bank of Thailand (BoT) stress-test comparison
- The BoT conducted a top-down macro solvency stress test in parallel using the same scenarios and cut-off date.
- At a broad level the approaches are very similar; differences arise from granular methodological decisions and assumptions.
- In the BoT approach, banks, on average, would not experience a compression of their net interest margin—differences attributable to how effective rates on loans and deposits are estimated.
- Notwithstanding methodological differences, the IMF and the BoT results are very similar in outcomes.

### Sensitivity tests — concentration risk
- Capital surcharges for single-name and sectoral concentration affect only one bank, which has sufficient capital buffers to absorb the shock.
- Surcharges estimated by calculating the Herfindahl–Hirschman Index (HHI) on the top 20 exposures (single name) and total exposures by sector (sectoral).
- HHIs translated into capital surcharges using multipliers developed by the U.K. Prudential Regulation Authority.
- Reverse stress test on the top 20 exposures: default of the five largest borrowers would cause two banks to breach their Tier-1 capital requirements; default of the top three borrowers would cause one bank to breach the required threshold.
- Recommendation: BoT could further develop analytical tools for concentration risk assessment (including systemic implications) and calibrate capital surcharges for IRB banks and add-ons for other banks.

### Sensitivity tests — Interest Rate Risk in the Banking Book (IRRBB)
- IRRBB analyzed from two perspectives:
  - Economic Value of Equity (EVE): change in net present value of a bank’s assets and liabilities under stressed interest rate scenarios (‘stock’ perspective).
  - Net Interest Income (NII): difference between total interest income and total interest expense within a one-year horizon (‘flow’ perspective).
- Six interest rate shock scenarios per the Basel standard: (i) parallel shock up; (ii) parallel shock down; (iii) steepener (short rates down and long rates up); (iv) flattener (short rates up and long rates down); (v) short rates shock up; and (vi) short rates shock down.
- Calibration based on daily zero-coupon sovereign rates and money market rates over a 16 year-time span, yielding revised interest rate shocks of 199, 282, and 133 basis points for the ‘Parallel’, ‘Short,’ and ‘Long,’ respectively.
- Shocks applied to aggregate assets and liabilities as of end-June 2018, broken down by maturity band; impact approximated via modified duration and convexity for the median tenor in each time band.
- Results:
  - Exposure to IRRBB is relatively contained.
  - Parallel shocks give larger impacts on EVE than non-parallel shocks; all banks are exposed to upward shocks to interest rates.
  - All banks would experience a drop in EVE, as a percent of Tier 1 capital, lower than the -15 percent level identified by Basel as the threshold for “outlier banks.”
- Historical simulation Value-at-Risk (VaR) over 2002–2018 broadly confirms results, but at the 99th percentile shows a largest negative parallel shock impact of -13.8 percent versus -12.6 percent in the Basel methodology.
  - The Basel methodology results correspond to percentiles of the historical simulation lower than the 99th and as low as the 96th, underlining the value of using a wide range of tools for IRRBB monitoring.
- NII analysis points to limited impact across banks; negative impacts are overall quite small and would not, per se, compromise capitalization.

### Sensitivity tests — Trading book
- Fixed income instruments categorized as Held For Trading (HFT) and Available For Sale (AFS) determine immediate capital impact (HFT via profit and loss; AFS via other comprehensive income).
- Focused on two asset classes: own sovereign and corporate bonds.
- VaR historical simulation at the 99th confidence level with differing liquidity horizons:
  - Government bonds: 16 years of daily zero-coupon sovereign rates and money market rates; liquidity horizon is 20 business days.
  - Corporate bonds: 10 years of monthly yields on THB-denominated BBB-rated corporate bonds; liquidity horizon is 60 business days.

*Source: IMF staff and Bank of Thailand estimates, as presented in the chapter content.*

### 38.      The results indicate a small impact both for government bonds and corporate bonds,

### 1thaea2019003 - 38.      The results indicate a small impact both for government bonds and corporate bonds,

### Sovereign and Corporate Bond Market Risk (VaR)
- Government bonds:
  - The 99th percentile VaR represents around 1 percent of Tier 1 capital or less for all banks except for one bank.
  - For one bank the 99th percentile VaR represents more than 5 percent of Tier 1 capital and a potential reduction of its Tier 1 capital ratio by up to two percentage points, suggesting a non-negligible exposure to sovereign risk.
  - Footnote: This is mitigated by abundant capital in excess of the minimum + buffer; further investigation is warranted to estimate government bond portfolio VaR and verify possible hedging instruments.
- Corporate bonds:
  - The 99th percentile VaR represents less than 0.4 percent of Tier 1 capital for all banks.

### Foreign Exchange Risk
- Regulatory limits:
  - Net open position rule limits exposure to no more than 15 percent of total capital (or US$5 million, if greater) per single currency.
  - Aggregate exposure limit to all currencies is 20 percent (or US$10 million, if greater).
- Historical-simulation result:
  - A 10-year daily-change simulation over a 2-week horizon shows losses that represent less than 0.3 percent of Tier 1 capital at a 99 percent confidence level.
- Overall assessment:
  - Exposure of Thai banks to foreign exchange risk is described as moderate.

### Residential Property Market Risks
- Data and prices:
  - Risks are difficult to assess due to lack of granular data and updated LTVs.
  - House prices have risen almost continuously over the past 10 years, with limited price corrections.
  - Largest recent correction: single-detached house prices mid-2016 to mid-2017 (-3.1 percent).
  - Historical Asian crisis: average -8.5 percent YoY with a spike of -26.9 percent drop in 5 quarters between 1998Q1 and 1999Q2.
- Borrower metrics:
  - Share of new mortgage loans with a Loan-To-Value (LTV) ratio above 90 percent increased from 33 to 46 percent since end-2012.
  - Staff estimates based on flows of mortgage loans by income bracket point to a likely steady increase in debt-service-to-income (DSTI) ratios in the past 5 years across all income brackets.
  - The lowest income bracket likely recorded, on average, a DSTI in excess of the conventional wisdom threshold of one-third (and this excludes other possible household debt).
- Modeling note:
  - In the BoT ST framework, these risks are indirectly captured in the PD satellite models for banks with significant housing loan proportions by including the change in house prices among regressors.

### Banks' Current Liquidity Conditions and Funding Profiles
- Funding composition (eight banks sample):
  - Retail deposits account for 50 percent of banks' funding.
  - Within total deposits for the eight banks: demand and savings accounts represent 66 percent; term deposits account for 34 percent.
  - Stable deposits (fully insured) are about a quarter of total retail deposits.
  - Retail deposits rose by 9 percent in 1998 during the Asian Financial Crisis (indicative of depositor stability).
- HQLA composition:
  - 93 percent of HQLA are level 1 unencumbered assets (mostly government securities) for the eight banks.
  - The 5 D-SIBs include both level 1 and 2 assets; the 3 IRBs hold mainly level 1 assets.
  - Holdings of government and central bank securities are largely domestic; foreign-currency-issued securities represent less than one percent of total HQLA.
- Funding differences by bank type:
  - Wholesale funding accounts for 57 percent of total funding for the 3 IRBs and 47 percent for the 5 D-SIBs.
  - Nonfinancial corporations (NFCs) are the largest source of wholesale funds for IRBs.
- Foreign funding reliance:
  - Foreign exchange exposures represent less than 8 percent of total liabilities of the banking system (mainly loans and repos).
  - Net open FX position is less than 2 percent of capital.
  - BoT can supply USD liquidity via its FX swap window as part of regular open market operations in a market-wide USD liquidity stress.

### Liquidity Metrics and Comparisons
- LCR and liquidity ratios:
  - Thailand’s LCR ratio: 170 percent (country level) — well above the regulatory threshold of 80 percent.
  - Comparative LCRs: Europe 130 percent; Americas 126 percent; rest of the world 128 percent.
  - Aggregate LCR for the eight banks as of end-June 2018: 188 percent.
  - Liquid assets to total assets for all Thai banks around 19.5 percent in September 2018.
  - Liquid assets to short-term liabilities: 32 percent.
  - Note: liquid assets to total assets is moderately below the peer-country median.

### LCR-Based Stress Tests: Scenarios, Calibration, and Data Constraints
- Tests conducted:
  - LCR-based 30-day test using HQLA stock.
  - Cash-flow-based analysis by maturity buckets (one week to more than one year).
  - AMCs’ resilience to redemption shocks and impacts on banks and government bonds were assessed.
- LCR scenarios and shocks:
  - Baseline LCR scenario: parameters as required by authorities under LCR implementation (aggregate currency level).
  - Severe scenario: one-month severe scenario calibrated to an unlikely crisis of confidence with collapse in equity prices and sharp exchange depreciation; includes sensitivity analysis on retail deposits, wholesale, and mutual funds; incorporates higher yields and altered haircut rates.
  - Retail shock: run-off rates increased to insured demand 10 percent, uninsured demand 20 percent; savings insured 15 percent, savings uninsured 30 percent; other rates per baseline.
  - Wholesale shock: higher run-off rates for operational/non-operational deposits of NFCs, government, banks, OFIs; insured non-operational deposits 30 percent, uninsured 50 percent.
  - Investment funds shock: run-off rates up to 15 percent (insured operational deposits of other financial institutions) and 50 percent (uninsured); parent bank liquidity assistance assumed up to 10 percent (5 percent in baseline).
- Calibration approach:
  - BoT used 11 years of historical data, selecting the 90th to 92nd percentile of outflow rates, yielding the 27 percent outflow rate experienced by finance companies in 1997, supplemented by expert judgement.
  - Loan maturing inflow rates pinned by stressed NPLs from solvency stress tests plus expert judgement.
- Data constraints:
  - Lack of granular, long time-series, and high-frequency data limits robustness of run-off rate calibration.
  - BoT provided bank-by-bank deposit data segmented by guaranteed and non-guaranteed accounts quarterly from 2009 and monthly from 2017.
  - Inadequate current data reporting to assess unexpected exchange rate shocks: banks are not required to report LCR by currency unless they have a significant outstanding position; only one of eight banks reports LCR in significant currency in detail.

### LCR Stress Test Results (June 2018)
- Aggregate outcomes:
  - Baseline aggregate 30-day weighted average LCR ratio: 188 percent.
  - Severe scenario aggregate LCR ratio: 104 percent (remains above the FSAP hurdle of 100 percent).
  - In the severe scenario: three banks fall below the 100 percent hurdle rate; one bank falls below the Basel III transitional threshold of 80 percent.
  - Aggregate liquidity shortfall of the three banks: 0.7 percent of total assets (1.5 percent of GDP).
- Retail shock:
  - Aggregate LCR falls to 138 percent.
  - Two banks (one D-SIB and one IRB) fall below the 100 percent hurdle rate but remain above the 80 percent regulatory threshold.
  - Liquidity shortfall associated with these two banks: THB 25 billion or 0.2 percent of total assets of the eight banks.
- Wholesale shock:
  - Aggregate LCR falls to 139 percent (report also notes 140 percent in context).
  - One D-SIB and one IRB fall below the 100 percent hurdle.
  - Total liquidity shortfall: THB 54 billion or 0.4 percent of total banks’ assets.
- Investment funds shock:
  - All banks remain above the 100 percent mark.
  - Analysis notes usefulness of studying bank–AMC linkages given bank-AMC nexus.
- Detailed reported figures (from stress-test summary chart):
  - 5 D-SIBs + 3 IRBs LCR ratios (percent): Standard 188; Severe 104; Retail 138; Wholesale 139; Investment Funds 174.
  - Liquidity shortfall (THB billion): Severe 24.8; Retail 24.7; Wholesale 53.7; Investment Funds 0.0.
  - Liquidity shortfall (Percent of GDP): Severe 1.5; Retail 0.2; Wholesale 0.3; Investment Funds 0.0.
  - Liquidity shortfall (Percent of banks' assets in sample): Severe 0.7; Retail 0.2; Wholesale 0.4; Investment Funds 0.0.
  - Number of banks failing to meet the regulatory rate (80 percent): Severe 1 out of 8; Retail 0 out of 8; Wholesale 0 out of 8; Investment Funds 0 out of 8.
  - Number of banks failing to meet the hurdle rate (100 percent): Severe 3 out of 8; Retail 2 out of 8; Wholesale 2 out of 8; Investment Funds 0 out of 8.

### Cash-Flow (Maturity Ladder) Analysis and Emergency Liquidity
- Cash-flow methodology:
  - Maturity buckets: one day to one week; one week to one month; 1–3 months; 3–6 months; over six months.
  - Analysis conducted for all currencies due to data constraints.
  - Projected contractual cash flows by liability type distributed across maturity buckets.
  - Severe scenario applies higher run-off rates, lower roll-off rates, asset liquidation with a 3 percent haircut (counterbalancing capacity includes only HQLA).
- Net cash balance metric:
  - Net cash balance = existing cash position + counterbalancing capacity + net funding inflows.
  - Liquidity shortfall occurs when net cash balance is negative after fully using counterbalancing capacity.
- Counterbalancing capacity use:
  - Observed usage in the “1–7 day” window and “more than 180 days” window due to large proportion of sight deposits and other liabilities.
- Emergency Liquidity Assistance (ELA):
  - BoT can provide ELA under stringent conditions:
    - Bank must be adequately capitalized (comply with BoT capital requirements or be on path back to compliance).
    - Bank must have sufficient collateral to cover borrowings.
  - If standard collateral already used, BoT can consider alternative collateral (e.g., parts of a bank's loan portfolio).
  - Under the FSAP liquidity stress test, alternative collateral such as loan portfolios were not considered as counterbalancing capacity since they are not part of HQLA under the LCR requirement.

*Source: IMF staff summary based on the provided chapter content.*

### 55.      The robustness of the analysis is somewhat affected by the lack of granular data, and

### Liquidity Stress Tests on Investment Funds and Bank Cash Flow Analysis

### Data limitations and caveats
- The robustness of the analysis is somewhat affected by the lack of granular data, and the results should be interpreted carefully.
- Deposits cannot be differentiated by type of depositor (retail and wholesale) and maturity; the analysis uses each bank’s share of retail and wholesale deposits and applies a constant ratio across five maturity buckets.
- Data is unavailable for deposits by type and maturity (sight, term, stable, unstable) (Table 8).
- Assumption: the proportion of retail deposits stable vs unstable is a weighted average of 60 percent and 40 percent.
- This is the first time the authorities are conducting the test; there is room for further refinement and sourcing of datasets.

### Bank cash flow analysis and LCR comparison
- The results of cash flow analysis were consistent with the LCR test over a one-month horizon.
- All banks, except two, have a positive funding over all the time horizons (“1–7 days” and up to “more than 180 days”) (Figure 17).
- The counterbalancing capacity is mostly utilized in the “1–7 days” window and “180 days and beyond” window as most banks experience shortfalls based on their cash inflows and outflows.
- Two banks would have a negative cash balance in the “180 days and beyond” horizon even after utilizing their existing required reserves.
- The nominal amount of the cash shortfall for each of those two banks is small, 6 percent and 7 percent respectively, of each bank’s total assets during the “180 days and beyond” window.

### Maturity structure and mismatch
- Banks should address the maturity mismatch between assets and liabilities, particularly at the long-end.
- The maturity structure of funding is more front loaded compared with cash inflows.
- Sight deposits are treated as having instantaneous maturity in the analysis.
- Based on available data (which lacked a detailed decomposition of assets and liabilities by maturity), the maturity structure of the cash flow of banks seems to point to a possible significant maturity mismatch.
- Sight deposits average around 60 percent for D-SIBs and 40 percent for the 3 IRBs; other deposits average around 60 percent for the 3 IRBs and 40 percent for the 5 D-SIBs (Figure 17).

### Investment Fund (IF) industry overview
- The investment fund industry has grown during the last 10 years: total net AUM increased from 18 percent of GDP in 2007 to slightly over a third of GDP in 2018, with growth averaging 11 percent per annum.
- As of September 2018, there were 1,411 funds covering bonds, equity, property, infrastructure, money market, and retirement fund.
- Fund composition and distribution:
  - Fixed income funds: 51 percent of total AUM.
  - Foreign investment funds account for one-fifth of total funds.
  - Most investment funds are open-ended funds accounting for 90 percent of the total industry.
  - Investor profile, June 2018: Institutional 5.4; Corporate 11.3; Retail 83.3 (In percent).
  - Distribution of mutual funds: Banks are the main distribution channel; bank distribution accounted for 73 percent of total AUMs.
- Retail dominance and vulnerability:
  - Retail investors account for 83 percent of total AUM; empirical research suggests retail investors are more inclined towards momentum investing and reactive to global shocks.
  - The investor base is largely domestic with a 98 percent share of total value of investment funds.
  - The close nexus between banks and investment funds (top six AMCs, part of banking conglomerates, accounted for 80 percent of AUM as of September 2018) exacerbates transmission risks and reputational risks.

### Daily Fixed Income (Daily FI) funds and Money Market Funds (MMF)
- Size and growth:
  - There are 446 funds, totaling US$81 billion as of September 2018.
  - Daily FI and MMF account for 72 percent of the total fixed income funds.
  - Daily FI funds increased from US$12 billion in 2012 to US$51 billion in September 2018.
- Asset composition and maturities (September 2018):
  - Daily FI funds asset composition (Percent of total daily fixed income assets): Cash liquidity 25.4; Short-term bonds 31.8; Medium-term bonds 1.8; Long-term bonds 2.7; Corporates 38.2.
  - MMF asset composition (Percent of total money market funds' assets): Cash liquidity 11.6; Short-term bonds 68.1; Corporates 20.3.
  - Maturity profile of Daily FI (Percent of total daily fixed income assets): <3m 40.6; 3-6m 21.2; 6m-1y 17.8; 1-2y 7.7; >2y 12.7.
  - Maturity profile of MMF (Percent of total money market funds' assets): <3m 83.2; 3-6m 13.4; 6m-1y 3.4.
  - For the 6 largest AMCs, asset maturities of Daily FI funds: <3m share substantial; MMFs’ maturities are mostly less than 3 months (for the 6 largest AMCs, assets with maturities of less than 6 months account for 60 percent of Daily FI and 96 percent of MMFs).

### Liquidity stress test on IFs: scope and assumptions
- Scope: Limited to open-ended Daily FI and MMF funds using monthly data from 2015–2018.
- Assumptions:
  - (i) Redemption shock transmits through liquidation of assets at fire sale prices to meet redemption demands.
  - (ii) Fire sale of assets by a captive fund could impact banks through step-in support and government bonds through higher yields.
  - (iii) Static balance sheet assumed (no new inflows are considered).

### Methodology: calibration of redemption shocks
- Redemption shock defined as net outflows in percent of total net asset value of a fund.
- Calibration approaches:
  - Fund-homogeneity: a common size redemption shock applied to each fund (first percentile of distribution of flow rates of all fund monthly observations).
  - Fund-heterogeneity: each fund experiences an outflow equivalent to the first percentile of its own historical flow rate (size of each redemption shock differs by fund).
  - Fund-type: based on the first percentile distribution of combined outflows by type of fund (daily FI and MMF).
- Regression findings on sensitivity of redemptions:
  - Smaller funds and funds that have higher returns in the previous month experience lower outflows.
  - Nonlinearities with respect to size; interactions between size and returns; momentum (lagged flow) significant.
  - Results support calibration approaches that take into account fund heterogeneity.

### Calibrated redemption rates (results depend on calibration)
- Fund-homogeneity calibration:
  - Redemption rate for daily FI funds and MMFs: 22 percent (overall rate for the first percentile was 21.9 percent for all funds).
  - Historical distribution: net flows follow a normal distribution with a large number of net flows of zero percent (1,791 observations).
- Fund-heterogeneity calibration:
  - Average redemption rates: 14 percent for daily FI funds and 19 percent for MMFs.
  - Large variation across funds: standard deviations of individual redemption rates amounting to 8 for Daily FI funds and 15 for MMFs.
  - MMF outflow rates range between 7 and 60 percent; Daily FI funds range between 0.1 and 36 percent.
- Fund-type calibration:
  - First percentile redemption rate: daily FI fund 19.9 percent; MMFs 22.7 percent.

### Methodology: asset sales to meet redemptions
- Two asset sale strategies analyzed (fire-sale haircuts applied):
  - Strategy 1 — Waterfall approach: assets sold in order: (i) cash; (ii) reverse repo; (iii) bank deposits; (iv) short-term government bonds; (v) medium-term government bonds; (vi) long-term government bonds; and (vii) corporate debt.
  - Strategy 2 — Pro rata approach: assets sold proportionally across all asset types to preserve asset structure.
- Waterfall strategy results:
  - Under fund-heterogeneity calibration: IFs have to sell THB 26.7 billion of government bonds.
  - Under fund-type calibration: IFs have to sell THB 11 billion of government bonds.
  - Under fund-homogeneity approach: cash alone is sufficient to meet the redemption value.
  - Cash liquidity position of funds would be sufficient to meet:
    - 100 percent of total value of redemptions under fund-homogeneity,
    - 85 percent under fund-heterogeneity,
    - 96 percent under fund-family (fund-type) calibration.

### Key systemic risk observations
- Daily FI funds and MMFs are identified as potential sources of systemic risk due to:
  - Rapid growth of daily FI funds and substantial holdings distributed as bank-channel substitutes for deposits.
  - Retail investor misperception of these funds as risk-free and liquid, increasing susceptibility to panic redemptions.
- Banking sector exposure:
  - Top six AMCs are part of banking conglomerates and account for 80 percent of AUM (as of September 2018), elevating reputational and step-in support risks.
  - Banks distribute 73 percent of total AUMs, intensifying bank–fund linkages.

### Policy and supervisory implications (derived from analysis)
- Improve data granularity: obtain deposit breakdowns by depositor type and maturity and detailed decomposition of assets and liabilities by maturity.
- Banks should address maturity mismatch between assets and liabilities, especially at the long end.
- Refine stress-testing frameworks and datasets to capture fund heterogeneity, investor behavior (retail momentum), and potential bank–fund contagion channels.
- Monitor rapid growth and distribution channels of Daily FI funds as substitutes for deposits given retail investor perceptions.

*Source: IMF staff analysis as presented in the provided chapter on Thailand liquidity stress testing of banks and investment funds.*

### 73.      The pro rata sales strategy requires a larger sale of government bonds to meet

### 1thaea2019003 - 73.      The pro rata sales strategy requires a larger sale of government bonds to meet

### Pro rata versus waterfall liquidation strategies
- The pro rata sales strategy results in forced selling of all assets and does not rely on the most liquid asset first.
- The waterfall approach liquidates the most liquid assets first; it would expedite the time to liquidation but can materially change portfolio composition and may undermine existing liquidity management tools.
- IFs can only liquidate cash holdings of 37 percent, 36 percent, and 38 percent, respectively, to meet total redemptions under fund-homogeneity, fund-heterogeneity, and fund-family calibration approaches.
- Under the pro rata approach IFs will have to sell THB 214 billion, THB 97 billion, and THB 193 billion of government bonds under the fund-homogeneity, fund-heterogeneity and fund-family calibration approaches respectively.
- Footnotes/assumptions in the analysis:
  - For government bonds, the authorities provided haircuts of 2–3 percent; for corporate bonds the team estimated haircut rates of 3–22 percent.
  - The liquidation approach uses historical rather than forward-looking data; market liquidity in stress could differ despite applied haircuts.

### Redemption Coverage and resilience of Investment Funds (IFs)
- Redemption Coverage Ratio (RCR) defined as:
  - RCR = Liquid Assets / Net outflows
- Interpretation:
  - If RCR > 1, the fund has sufficient liquidity assets to cope with the shock.
  - If RCR < 1, the fund will be pressured to sell less liquid assets at a discount; if the market does not absorb sales, investors bear losses and persistent selling pressures under tight liquidity can trigger contagion.
- Key calibrated results:
  - Even in a first percentile tail risk event, aggregate IFs are able to meet total redemptions.
  - RCRs reported:
    - Fund homogeneity and fund type calibration: 1.9 and 2.2 (daily FI funds)
    - MMFs: 3.2
  - Aggregate liquidity position of IFs, excluding corporate assets: THB 1 trillion.
  - Total redemption values under calibrations: THB 335 billion (fund-homogeneity), THB 149 billion (fund-heterogeneity), THB 304 billion (fund-family).
  - Under the fund-heterogeneity approach there are eight funds with RCR below 1; these account for 9.7 percent of total NAV of daily FI funds and MMFs.
  - Including corporate bonds in the liquidity buffer of those 8 IFs, except for one fund, the rest were able to meet redemption demands.
  - Credit lines of the 6 parent banks would serve as an additional protection layer if reputation risk required parental support.

### Impact on the government bond market
- Asset sales under the pro rata approach are much higher relative to historical turnover of government bonds in Thailand and lie at the uppermost end of the distribution; they do not fall within historical monthly turnovers.
- Asset sales under the waterfall strategy are at the lowest end of the distribution.
- The choice of liquidation strategy materially affects whether the government bond market can cope with rapid liquidation/fire sales.

### Price impact estimates using the Amihud illiquidity measure
- The Amihud measure estimates price impact using historical turnover and price changes: equally weighted average of the weekly ratio of absolute return to monthly market turnover over a year for each government security type and maturity.
- Reported price effects (basis points):
  - Under the waterfall strategy, the Amihud measure indicates:
    - Short-term government bond prices would fall by 77 basis points under the fund-heterogeneity calibration.
    - Long-term government bond prices would fall by 19 basis points under the fund-heterogeneity calibration.
    - Short-term government bonds would fall 35 basis points under the fund-family calibration.
- Summary implication: price effects on government bond prices are much lower in the waterfall strategy than in the pro rata strategy.

### Interconnectedness analysis and contagion risks
- Four analytical approaches used to explore systemic and contagion risks:
  - Espinoza and Solé (2009) simulation of credit and funding shocks across domestic interbank network and cross-border spillovers.
  - Diebold and Yilmaz (2012) network interconnectedness based on market data between listed banks and nonbanks.
  - Financial Stability Measures to quantify systemic risk amplification across banks, insurance companies, IFs, and other intermediaries.
  - Balance sheet analysis based on flow of funds data.
- Main findings:
  - Contagion risks from domestic interbank exposures are limited: no single domestic bank failure would trigger another bank failure (no cascade effect).
  - The largest four of the 19 banks have relatively large impact on others (failure of bank 2 would erode aggregate capital buffer of other banks by over 8 percent), but these four are themselves resilient to shocks.
  - Cross-border exposures are generally small, except for one bank (Bank 11) due to its relationship with a parent bank.
  - Inter-sectoral exposures point to limited cross-border exposures except for the corporate sector (primarily through FDIs).
  - Market-data measures confirm a low degree of interconnectedness between banks and non-banks; banks generally have net outward spillover effect.
  - Two insurers show relatively high outward spillovers to the rest of the nonbank sector.
  - Financial stability measures show contagion among the five largest banks peaked at the height of the GFC and has subsequently decreased to the lowest levels in the past 11 years.
- Cautionary note:
  - Limited measured interconnectedness should not lead to complacency: measuring interconnectedness and contagion is difficult and translating measures into operational stress scenarios is challenging.
  - Structural/analytical, granular approaches (data-hungry and slower) and market-based metrics (quicker, less clear signal) have complementary strengths; linking the two could reinforce systemic risk analysis and policymaking.

### Conclusions and policy recommendations
- Banking system resilience:
  - Stress tests on a representative sample of Thai commercial banks suggest substantial resilience to severe shocks; solvency and liquidity of the largest banks can withstand an adverse scenario broadly as severe as the Asian financial crisis.
  - Three banks would deplete their CCB, but recapitalization needs would be minimal.
  - Banks would be resilient to sizable liquidity withdrawals, though some would face increased funding pressures.
- Validation:
  - The Bank of Thailand (BoT) solvency stress test run independently produced very similar results to the FSAP team’s exercise.
- Recommended priorities and ongoing work:
  - Continue strengthening capacity to monitor liquidity risk and stress testing. Recent IMF technical assistance in 2018 supported several implemented recommendations, including:
    - Calibration and integration of LCR run-off and roll-off and haircut assumptions with solvency stress testing framework.
    - Running LCR-based stress tests on a consolidated basis.
    - Adopting a cash flow based analysis by maturity.
    - Expanding staff resources in the financial stability unit.
    - Implementing feedback loops from mutual funds.
  - Further analytical and data improvements:
    - Improve time series quality of IRB banks’ PDs and LGDs where series are short and/or erratic.
    - Enhance liquidity risk data management to provide more granular data beyond a one-month horizon by differentiating deposits by type (sight and time), by insured and uninsured, by depositor (retail and institution), and by foreign currency.
    - Refine the time structure of maturities and projected cash flows in banks’ reporting templates (e.g., finer time buckets at the short end and after 90 days).
    - Adopt more granular differentiation of funding source types in templates and classify items generating inflows, such as loans, by borrower type (households, corporations, other financial institutions) to apply relevant roll-off rates in cash flow-based analysis.
- Analytical development:
  - The BoT’s Financial Stability Unit research program aimed at granular, structural capture of interconnections between main financial entities and economic sectors is promising; linking structural metrics with market-based metrics could substantially strengthen systemic risk analysis and operationalization.

*Source: 1thaea2019003 - 73. The pro rata sales strategy requires a larger sale of government bonds to meet (IMF staff estimates and Thailand FSAP text).*

### 91.      The stress testing methodology of the SEC encompasses credit risk, market risk, and

### 1thaea2019003 - 91.      The stress testing methodology of the SEC encompasses credit risk, market risk, and

### SEC stress testing methodology and liquidity management tools
- The SEC stress testing methodology encompasses credit risk, market risk, and liquidity risk.
- In its stress testing exercise the SEC identifies main risk transmission channels such as concentration risk, credit risk, market risk and spillover effects.
- The SEC participates with the BoT in stress testing of banks by providing estimates on portfolio losses due to the fire sale of bonds resulting from the redemption of mutual funds.
- The SEC conducts regular offsite monitoring:
  - microprudential surveillance on a daily basis
  - macroprudential surveillance on a monthly basis
- SEC liquidity management tools focus on pre-emptive and post-event measures and include:
  - investment restrictions
  - investor concentration limits
  - internal risk management units within AMCs
  - redemption in kind options
  - suspension of dealings
  - liquidity sources (cross trade, proprietary trade and temporary borrowing)
  - deferred payment of redemption

### Recommendations on coordination and routine stress testing with AMCs
- Current practice:
  - AMCs conduct their own stress testing and results are only shown to SEC during onsite supervision.
  - Frequency of onsite supervisor visits depends on AMC risk profile; lower-risk AMCs may be visited over a period of two to three years.
  - SEC sends an annual self-assessment questionnaire to AMCs covering topics including risk management.
- Recommendation:
  - Implement a coordinated stress testing approach where SEC and AMCs routinely dialogue on:
    - methodology of stress testing
    - scenario design
    - latest approaches and techniques to stress testing
- Rationale:
  - Benefits of stress testing are highlighted in the IOSCO Liquidity Risk Management Principles, calling for a holistic approach across the fund life cycle (product design, distribution arrangements, asset composition, performing investment and liquidity risk management tools on an ongoing basis).

### Scope expansion for stress testing of investment funds (IFs)
- Current scope:
  - SEC only conducts stress testing on daily FIs and MMFs.
- Recommendation:
  - Expand stress testing scope beyond daily FIs and MMFs to include equity funds and mixed funds.
- Rationale:
  - In a deteriorating equity market with persistent selling pressures, liquidating funds to meet panic redemptions will be a challenge even with measures such as suspension on dealings.

### Key financial system figures (selected excerpts from Table 2)
- Financial Sector Assets: 16,608 (2007) and 43,389 (2018)
- Financial Sector Assets as percent of GDP: 183 (2007) and 266 (2018)
- Deposit-taking financial institutions: 12,499 (2007) and 29,758 (2018)
- Banks assets: 9,356 (2007) and 19,997 (2018)
  - Private Banks: 6,857 (2007) and 15,274 (2018)
    - 3 largest private banks: 3,755 (2007) and 8,949 (2018)
  - State-owned: 1,261 (2007) and 2,852 (2018)
  - Foreign-majority owned: 1,238 (2007) and 1,871 (2018)
- Mutual Funds (incl. MMF): 1,611 (2007) and 4,914 (2018)
- AMCs: 709 (2007) and 316 (2018)

### Financial Soundness Indicators (selected values from Table 3, 2013–2018)
- Regulatory capital to risk-weighted assets: 15.5 (2013), 17.9 (2018)
- Regulatory tier 1 capital to risk-weighted assets: 11.9 (2013), 15.0 (2018)
- NPLs net of provisions to capital: 7.7 (2013), 9.1 (2018)
- NPLs to total gross loans: 2.3 (2013), 3.1 (2018)
- Return on assets (ROA): 1.8 (2013), 1.3 (2018)
- Return on equity (ROE): 15.9 (2013), 9.4 (2018)
- Liquid assets to total assets (Liquid asset ratio): 19.2 (2013), 18.9 (2018)

### Risk Assessment Matrix — selected risk drivers and channels (from Table 4)
- Global Risk — Background 1: Weaker-than expected global growth (Relative Likelihood: High/medium; Impact: Medium)
  - Channels: weaker exports and tourism income → lower growth; corporate vulnerabilities; higher NPLs and provisioning needs for banks.
- Global Risk — Background 2: Sharp rise in risk premia (Relative Likelihood: High; Impact: High)
  - Channels: higher global interest rates → reversal of capital inflows and baht depreciation; higher funding costs; stress on borrowers; increased stress on unhedged FX borrowers.
- Domestic Risk — Background 3: Entrenched low inflation (Relative Likelihood: Medium; Impact: High)
  - Channels: higher real interest rates and real debt burden; risks to corporates, households, and financial sector; search for yield and excess risk-taking.
- Domestic Risk — Background 4: Debt overhang (Relative Likelihood: Medium; Impact: Medium)
  - Channels: highly leveraged households may cut spending or face tighter credit; constrained debt-service capacity could lead to bank losses and credit contraction.

### Macroeconomic scenario projections (selected values from Table 5)
- Real GDP growth:
  - 2018: 4.6
  - 2019 Baseline: 3.9; Adverse deviations (2019): -5.6
  - 2020 Baseline: 3.7; Adverse deviations (2020): -2.4
  - 2021 Baseline: 3.5; Adverse deviations (2021): -6.1
- Real private consumption (growth):
  - 2018: 3.7; 2019: 4.6; 2020: 5.0; 2021: 5.5
  - Adverse deviations shown: -2.0; -1.3; 3.5; -6.6; -6.3; -2.0 (as presented)
- Headline CPI Inflation (percent):
  - 2018: 0.9; 2019: 0.9; 2020: 1.1; 2021: 1.4
  - Adverse deviations example entries: -0.5; -0.2; 0.9; -1.4; -1.3; -0.5 (as presented)
- One-year nominal corporate interest rate (percent):
  - 2018: 2.6; 2019: 2.9; 2020: 3.2; 2021: 3.4
  - Adverse deviations example entries: 4.3; 4.0; 3.5; 1.4; 0.9; 0.1 (as presented)
- Asset Prices (SET index, growth):
  - 2018: -10.8; 2019: 7.6; 2020: 9.3; 2021: 5.3
  - Adverse deviations example entries: -55.0; 20.0; 10.0; -62.6; 10.7; 4.7 (as presented)

### Liquidity Coverage Ratio (LCR) stress test assumptions (selected items from Table 6 and related tables)
- Run-off rates and shocks presented for Baseline and Severe Scenario across Retail, Wholesale, Investment fund shocks.
- Examples of specific run-off rates (in percent) under Baseline / Severe for selected categories:
  - Retail demand deposits — Insured: 5 10 10 55 (presented as sequence under table headings)
  - Retail demand deposits — Uninsured: 10 20 20 10 10
  - Term deposits with right of premature withdrawal but subject to significant penalty that affects interest receivable: 5 10 10 55
  - Non operational deposits — Uninsured (Wholesale): 40 50 40 50 40
  - Operational deposits — Banks — Uninsured: 25 35 25 35 25
  - Non operational deposits — Banks — Insured/Uninsured: 100 100 100 100 100 (for several categories)
- Secured funding involving HQLA Level 1: 03000 (presented in table formatting)
- Contractual obligations — Derivatives cash outflow: 100 100 100 100 100
- Drawdown on committed credit/liquidity facilities — Non-financial corporates and MDBs: 30 40 30 30 30
- Off-balance sheet items — Uncommitted obligations where banks are not the dealer or market maker: 55 55 55 55 55

### Cash flow and haircuts assumptions (selected entries from Tables 7–8)
- Roll-off rates on cash inflows (In percent, by maturity buckets 1 to 7 days / 8 to 30 days / 31 to 90 days / 91 to 180 days / More than 180 days):
  - Retail funding: sight deposits — Stable: 84 4 20 (presented across buckets)
  - Unstable: 12 66 40
  - Other deposits: 40 40 30 30 25
- Haircuts on liquid assets (in percent):
  - Cash items: 0
  - Securities (government bond): 3
  - Securities (other types of bond): 20
- Cap on cash inflows: 75 75 75 75 75
- Haircuts on liquidity buffers:
  - HQLA level 1 — Cash and deposit at central bank: 100 100 100 100 100
  - HQLA level 1 (other): 100 97 100 100 100
  - HQLA level 2A: 85 80 85 85 85
  - HQLA level 2B: 50 50 50 50 50

### Asset composition and redemption shock implications (selected figures from Table 9)
- Total NAV (Daily FI + MMF + Total): 880 149 1029 (presented as a sequence)
- Total (All funds) Cash Liquidity: 43 255
- Short-term bonds (All funds): 569 380
- Medium-term bonds: 24 16
- Long-term bonds: 44 29
- Waterfall Sales vs Prorata Sales totals presented: 598 111 710 (example aggregated row entries)

### Banking sector solvency stress testing approach (selected assumptions)
- Institutional perimeter:
  - 8 banks (5 D-SIBs and 3 IRB banks) [and 3 specialized financial institutions] included in top-down approaches.
  - Market share: banks representing 75 percent of banking sector assets; combined bank+SFI sector accounting for 80 percent (top-down by FSAP team).
  - Data and baseline date: Supervisory reports at June 2018; data on a ‘solo consolidated’ (banking group level); PD/LGD/EAD data for IRB banks.
- Channels of risk propagation and methodology:
  - Authorities: In-home macro-ST framework (balance-sheet model). Satellite models for macrofinancial linkages including VAR, OLS, dynamic panel regressions for Group 1 variables, OLS for Group 2, and expert judgment calibration for Group 3.
  - IMF FSAP team: IMF Solvency Stress Test Workbox (balance-sheet model); seemingly unrelated regression of NPL inflow rates by sector on macro variables; system-wide regression of credit growth as a function of domestic demand and unemployment with a judgmental floor; pre-impairment income estimated piecewise using panel estimations and historical evidence with judgmental adjustments.

*Italic: Source — 1thaea2019003 (excerpt), Thailand, INTERNATIONAL MONETARY FUND.*

### Appendix I. Thailand:

### Appendix I. Thailand: Stress Test Matrix (STeM) for the Banking Sector: Solvency, Liquidity, and Contagion Risks

### Overview: Test design and scenarios
- Stress test horizon: 3 years (2019–2021).
- Approach:
  - Top-Down by Authorities and Top-down by FSAP Team both conduct 3-year horizon tests (2019–2021).
  - Scenario-based tests on the entire portfolio.
  - One baseline and two adverse scenarios (one of which coincides with the adverse scenario undertaken by the FSAP team).
  - Variables in scenarios include global variables (U.S., China, Japan, and Euro area GDP, USD, and JPY interest rates, and oil prices) and domestic macrofinancial variables (e.g., GDP, inflation, exchange rate, interest rates, unemployment rate, equity prices).
  - Baseline scenario based on the June 2018 WEO projections.
  - One Adverse Scenario simulated using IMF’s Flexible System of Global Models for the external context and calibrated judgmentally with the country team for the domestic impact.
- Major drivers of the Adverse Scenario:
  - External shocks: weaker-than-expected growth in China and in advanced economies, coupled with sharp rise in risk premia leading to a reversal of capital flows and a depreciation of the Baht.
  - Domestic amplifiers: excessive risk taking by investors and highly indebted households.

### Banking Sector: Solvency Risk — Domain assumptions and shocks
- Adverse Scenario GDP path (Authorities):
  - U-shaped growth path with annual GDP growth shocks of -5.6 percent, -2.4 percent, and +4.9 percent.
  - Represents a cumulative two-year deviation of 15.6 percentage points with respect to the baseline scenario, equivalent to a 2.1 standard deviation shock.
  - Compared with a GaR calibration, the GDP decline in the first year is close to the fifth percentile of GaR (-5.9 percent) for the first year; the growth rate over the second year is close to the two years ahead GaR threshold at the tenth percentile (-2.45 percent).
- Labor, equity, and FX impacts (Authorities):
  - Unemployment rising to 3.0 percent, 3.5 percent, and 2.8 percent over the 3-year horizon.
  - Cumulative decline of the stock price index is 40 percent over the three years, with a negative peak of -55 percent in the first year.
  - The Baht will depreciate by 12 percent in the first year and will still be 10 percent below the June 2018 level at the end of the horizon.
- Sensitivity analyses (Authorities):
  - Sensitivity of listed companies’ debt at risk to changes in sales (-10 to -50 percent).
  - Households’ resilience to a drop-in income (-20 percent).
- Credit loss determination:
  - Authorities: Credit losses determined by the increase in NPLs, estimated via panel data regression with a range of macro factors as exogenous variables.
  - FSAP Team: Credit losses determined by the increase in NPLs for non-IRB exposures and changes in PD/LGD for IRB exposures.
- Market losses and income projections:
  - Market losses determined by changes in interest rates (including spreads) and exchange rate.
  - Interest income evolution based on projected assets and liabilities’ growth and effective lending and borrowing rates.
  - Non-interest income forecast based on growth of net fee and commission and growth of other non-interest income; growth of non-interest expenses based on model (fees and commissions) and expert judgment (other expenses).
  - FSAP Team: Funding costs and interest on loans and bonds estimated as a function of short-term interest rates; interest on loans and bonds also incorporate a spread which reflects the increased credit risk in the economy.
  - Income forecast based on evolution of prices (interest rates), quantities (growth of assets and liabilities), and impairments (for credit risk).
  - Market risk: impact of financial variables’ evolution on fixed income holdings of sovereign/corporate bonds, FX and equity positions.
- Behavioral adjustments:
  - Authorities:
    - Growth rate of loans and interest-bearing liabilities (deposits and other borrowings) estimated via VAR with macro factors.
    - Growth of equity holdings assumed to be either zero (for banks showing no significant variation in the size of holdings across time) or via OLS with the return on stock index as explanatory variable (other banks).
    - Share of bond holdings (over total assets) estimated as an inverse relationship with the loans/assets share.
    - Non-interest-bearing liabilities modeled as a function of total liabilities.
    - Net open position in FX (NOP) projected as historical long-term average of year-on-year NOP.
    - Dividend payout based on historical experience.
  - FSAP Team:
    - Credit growth for the whole banking system estimated as a function of domestic demand and unemployment; portfolio allocation constant over the horizon.
    - Dividend payout judgmental, based on historical experience, with limits on distribution in case of breach of capital buffers.
- Calibration of risk parameters:
  - Authorities: PDs and LGDs: point in time for credit losses. RWA estimates via regression models.
  - FSAP Team: PDs and LGDs: point in time for both credit losses and stressed RWA calculations.
- Regulatory/accounting standards and RWAs:
  - Authorities:
    - RWAs for credit risk modeled at aggregate level, separately for performing and non-performing loans via a regression of credit risk weights on macro factors; via a regression of credit risk weights over specific provision over NPL and the share of retail NPL over total NPLs, respectively.
    - RWAs evolving according to assumed credit growth, net of increase in provisions; provisions modeled via changes in PD/LGD for IRB exposures and the increase in NPLs for non-IRB exposures.
  - FSAP Team:
    - Hurdle rates: capital (CET1, T1, CAR) requirements (inclusive of CCB) and leverage ratio requirements as per local regulation (largely implementing Basel III); D-SIB capital surcharge included for domestic systemically important banks.
- Reporting format for results:
  - Macroeconomic scenarios for the macro ST.
  - Results of the sensitivity tests on listed corporates and households.
  - Capital ratios pre and post-shock and capital shortfall, by bank (anonymized) and system wide.
  - Distribution of capital ratios: minimum, average, maximum.

### Banking Sector: Liquidity Risk — Domain assumptions and tests
- Institutional perimeter:
  - Institutions included: 5 D-SIBs and 3 IRB banks for the LCR and cash-flow analysis.
  - Simplified liquidity stress test for 3 largest SFIs (FSAP Team).
  - Market share: 75 percent banking sector assets (Authorities); 75 percent of banking sector assets and 95 percent of SFI sector assets. Combined accounting for 80 percent of bank+SFI sector (FSAP Team).
  - Data and baseline date: June 2018 LCR analysis and liquidity gap analysis; supervisory data.
- Methodology and channels:
  - Authorities: Basel III-LCR.
  - FSAP Team: Basel III-LCR and NFSR; LCR scenario with variants (baseline and severe) based on the RAM.
  - LCR and cash-flow test scenario with variants (severe, retail, wholesale funding, and mutual funds).
  - Cash-flow based liquidity stress testing using maturity buckets by banks.
- Risks and buffers:
  - Risks: Funding liquidity shock (short-term liquidity outflows) and Market liquidity shock (asset price shocks and fire-sales).
  - Buffers: Counterbalancing capacity; Central bank facilities; HQLA-equivalent assets (for cash flow analysis only).
- Tail shocks and run-off rates:
  - Run-off rates calculated following historical data, BoT expert judgement, as well as internal forecasts derived from RAM.
  - Bank run and dry up of wholesale funding markets, taking into account haircuts to liquid assets.
  - FSAP Team: Run-off rates calculated following historical events, IMF expert judgement, Thai authorities and LCR rates.
- Regulatory standards and parameters:
  - Regulatory haircuts and run-off rates based on regulatory parameters. For LCR, see BCBS (2013), The Liquidity Coverage ratio and Liquidity Risk Monitoring Tools Basel, January 2013.
  - Stressed: RAM severe scenario (one-month horizon for LCR Severe scenario). Inflow rates derived from projected NPL of solvency stress test.
  - Haircuts based on historical data of bond price movement as well as haircuts applied by BoT under the ELA framework.
  - Run off rates calibrated based on the percentile of the distribution of monthly changes in deposits where the percentile chosen mimics the weighted average of outflows during the 1997 Asian Crisis (Authorities).
  - FSAP Team: Stressed: more severe haircuts under a political turmoil scenario and larger run-off rates to reflect more severe episodes of market and funding based on historical events.
- Regulatory hurdles:
  - Authorities: For the LCR phase in, the hurdle is set to 80 percent.
  - FSAP Team: For the LCR, the hurdle is set to 100 percent.
  - For the cash-flow analysis, the hurdle rate is to have a non-negative cash balance.
- Reporting format for results:
  - Number of banks that fail to meet the hurdle and their assets share in the banking sector.
  - Bank-level survival period in days, number of banks that still can meet their obligations.

### Banking Sector: Contagion Risk — Domain assumptions and methods
- Institutional perimeter and market coverage:
  - Authorities:
    - All commercial banks (for analyses based on balance-sheet data) or listed banks, listed insurance companies, and listed finance and securities companies (for analyses based on market data).
    - 36 commercial banks included.
    - Ninety one percent of total banking assets covered.
    - Data and baseline date: June 2018; supervisory and market data.
  - FSAP Team:
    - Banks, Insurance companies, 6 AMCs (Percentage of total sector assets).
    - (i) 27 sectors listed in SET, and (ii) 43 companies listed in SET, including 10 banks (98.7 percent of sector market capitalization), 8 insurance companies (83.7 percent) and 25 finance and securities companies (55.5 percent).
    - Sixty five percent of banking and insurance assets covered.
    - Data and baseline date: June 2018; supervisory and market data.
- Methodologies for channels of propagation:
  - Authorities rely on five models and indicators:
    - (i) a bank network analysis model (Espinosa-Vega and Solé, 2010);
    - (ii) an interbank market network model (based on Bonacich’s Eigenvector Centrality measure);
    - (iii) payment system network model (also based on Bonacich’s Eigenvector Centrality measure);
    - (iv) CoVaR measures; and
    - (v) Variance Decomposition results from Diebold-Yilmaz methodology.
    - A new methodology to be introduced based on Civilize et al., 2018 (forthcoming) to profile and stress test the financial system via the Disaggregated Balance Sheet Network.
  - FSAP Team:
    - Interbank and cross border network model by Espinosa-Vega and Solé (2010).
    - Diebold-Yilmaz variance decomposition connectedness methodology.
    - A Comprehensive Multi-sector Tool for Analysis of Systemic Risk and Interconnectedness (SyRIN approach).
- Tail shocks and shock sizing:
  - Balance-sheet data: analysis of the impact of the default of single institutions or group of institutions on the whole network; ranking of institutions according to their degree of contagion (outward spillover) or vulnerability (inward spillover).
  - Market-based data: conditional probability of distress for single institutions or the whole network in case of one or more institutions defaulting; ranking of institutions according to their degree of “from” connectedness (inward spillover), “to” connectedness (outward spillover), and “net” connectedness (difference between “to” and “from” connectedness measures).
  - SyRIN: Various metrics, including tail risk, cross-entity interconnectedness and the contribution to systemic risk by different entities and sectors.
- Reporting format for results:
  - Number of undercapitalized, failed or illiquid institutions, and their shares of assets in the system.
  - Evolution and direction of spillovers within the network.

### Investment Funds: Liquidity Risk — FSAP Team
- Institutional perimeter:
  - Institutions included: 32 daily FI and 11 MMFs.
  - Market share: 31 percent of total AUM.
  - Date and baseline date: September 2018.
- Methodology and channels:
  - Liquidity measures by (i) cash and short-term debt securities < 1year; and (ii) cash and high-quality liquid assets.
- Risks and buffers:
  - Risks: Liquidity outflows and inability to liquidate assets to cope with redemptions.
  - Buffers: Liquidity buffers.
- Tail shocks:
  - Size of the shocks: Monthly redemption shock equal to 1th percentile of historical net flows.
- Regulatory and market standards:
  - Regulatory Standards: None.
- Reporting format for results:
  - Redemption coverage ratio by investment fund and liquidity shortfall.
  - Number of funds and share of funds that cannot meet their obligations.

*Appendix I. Thailand: Stress Test Matrix (STeM) for the Banking Sector: Solvency, Liquidity, and Contagion Risks — June 2018 baseline and FSAP/authorities inputs as presented in the source document.*

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_Source: https://www.imf.org/-/media/files/publications/cr/2019/1thaea2019003.pdf_