## 1idnea2025003-print-pdf

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---

### EXECUTIVE SUMMARY — scope, coverage, and data
- FSAP team conducted top-down corporate and bank solvency, bank liquidity stress tests, and interconnectedness analysis using mid-2023 data.
- Stress test exercise covered a sample of "105 commercial banks".
- Analysis relied on supervisory data shared by OJK and BI and publicly available corporate information.

### Financial system structure and recent developments
- Total financial assets: "73 percent of GDP".
- Banks account for "76 percent of financial sector assets".
- Large banks operate as financial conglomerates; their assets increased by "about 8 percent since 2021 to June 2023", reaching "58 percent of total financial sector assets".
- NBFIs represent "up to a quarter" of financial sector assets.
- FinTech lending: "assets rising by 51 percent in 2022."
- Capital and liquidity buffers: CET1 ratios and LCRs remain "well above regulatory minima" (CET1 at "23 percent" cited elsewhere); LCR rose "from 151 to 210 percent since 2020".
- Banking sector sample: "105 commercial banks"; overall number of banks "around 1515"; conventional rural banks "around 1410"; sharia rural banks "171 banks as of June 2023".
- Top-10 banks hold "66 percent of total bank assets".
- Four state-owned banks constitute "42 percent of the banking sector" (included in top-10).

### Key risks and vulnerabilities
- Cyclical and external risks:
  - Cyclical risks moderate despite robust credit growth; higher credit risk in pandemic-hit industries and among highly leveraged corporations.
  - External downside risks include geopolitical fragmentation, slowdown in key trading markets, tightened global financial conditions, volatility in energy and commodity markets, and supply chain disruptions.
- Forbearance and legacy loans:
  - Loan forbearance measures may mask delays in credit loss recognition and impact NPLs; OJK’s forbearance program expired "March 2024".
  - Share of restructured loans: peaked at "20 percent of total loans in 2020"; "around 10 percent in June 2023"; "8 percent in Dec 2023"; pre-pandemic level "5 percent".
  - COVID-related restructured loans: "46 percent of total restructured loans or 3.7 percent of total loans", reaching "IDR 266 trillion"; NPL ratio for COVID-related restructured loans "13 percent".
- Sovereign–bank nexus and concentration:
  - Banks’ holdings of government bonds: "around 13 percent of total assets".
  - Banks shifted portion held in fair value to amortized cost: "43 percent of debt securities".
  - Banks’ exposure to SOE loans: "7 percent of total loans"; for state-owned banks: "8 percent".
  - Unrealized valuation changes: unrealized loss "5.7 trillion IDR as of June 2023"; unrealized gain "11.2 trillion IDR in December 2021".
- FX risks:
  - Offshore FX debt issuance by large publicly traded companies may be subject to refinancing risks.
  - Banks’ FX net open position: "around 1 .5 percent of total capital" (noting spacing in source).
- Climate and transition risks:
  - Energy transition and climate-related natural disasters expose financial sector to transition and disaster risks.
- Heterogeneity and tail risks:
  - Small banks: lower profitability, higher funding and operational costs; regional development banks show higher vulnerabilities due to interbank deposit reliance and funding from other banks/public entities.
  - Rural banks: asset share "around 1.7 percent of total banking sector assets" and NPL ratio "around 10 percent" relative to commercial banks at "around 2 percent".

### Corporate risk analysis — descriptive and stress-test outcomes
- Corporate vulnerabilities improved since the pandemic but remain susceptible under stress.
- Sample: "460 NFCs" (441 publicly listed, 19 private, including 21 SOEs); sample firms account for about "52.7 percent of total outstanding NFC debt" and "23.1 percent of GDP" in 2022.
- Median firm metrics (2020 → 2022):
  - Median debt-to-equity: "40" → "30".
  - Median return on assets: "2.5" → "3.5".
  - Median interest coverage ratio (ICR): "2" → "4".
- Debt-at-risk and stress-test results:
  - Baseline: share of debt for firms with ICR<1 is "17 percent".
  - Recession scenario: share increases to "27 percent".
  - Stagflation scenario: share increases to "31 percent".
  - Corporate stress test results summarized: debt-at-risk rises by "10 and 14 percentage points under the recession and stagflation downside scenarios, respectively" (executive summary wording).
- Concentration by firm size:
  - Bottom decile of firms by size: share of debt for firms with ICR<1 is "over 60 percent".
  - Top decile: share around "10 percent".

### Bank solvency stress test results — aggregate outcomes and heterogeneity
- Coverage and data:
  - Top-down, scenario-based solvency stress test of "105 commercial banks" covering almost "100 percent of banking sector assets"; supervisory data as of "2023:Q2".
- Baseline scenario:
  - Systemwide CET1 ratios remain "above 22 percent" over a five-year horizon with marginal capital depletion of "72 basis points".
- Adverse scenarios:
  - Stagflation scenario: systemwide CET1 ratio declines "by about five percentage points"; fully loaded CET1 ratio declines "by about 5 percentage points at the trough" and stabilizes around "19 percent by the 5th year".
  - Recession scenario: CET1 ratios experience initial shock of "2.6 percentage point" before rebounding to "22.4 percent".
- Heterogeneity of capital depletion at trough (stagflation, banking group level):
  - Small banking groups: "between 6.4 and 9.4 percentage points".
  - Large banking groups: "between 3.5 and 4.9 percentage points".
  - By business model at trough: regional banks "1.9 percentage points"; state-owned banks "5 percentage points"; private banks "5.3 percentage points".
- Weak banks:
  - Under stagflation: "9 banks" fall below CET1 hurdle rates; assets ranging "from 2.9 to 7.4 percent of total banking system assets"; capital shortfall "from 0.2 to 0.3 percent of GDP".
  - Under recession: "6 banks" fall below hurdle; capital shortfall "from 0.2 to 0.3 percent of GDP".
- Loss drivers:
  - Credit losses dominate: cumulative decline "9.4 percentage points up to year 2 and 19 percentage points over a five-year horizon".
  - Cumulative market risk impact at trough: "3.6 percentage points"; interest rate risk impact: "0.8 percentage points".
  - Market valuation: domestic sovereign securities experience highest value drop "around 12 percent".
  - NIM declines by "about 1 percentage point" on average at the trough (historical sector NIM around "5 percent" in one section; elsewhere overall NIM cited as "4.8 percent").
- Sensitivities:
  - Parallel 400 basis points additional yield shock → additional capital depletion "about 1.7 percentage points"; repricing impact by ownership: SOBs "1.9 percentage points", regional banks "1.8 percentage points", domestic private banks "1.4 percentage points".
  - Restructured loans deterioration:
    - 50 percent conversion of performing restructured loans to non-performing → further capital ratio decline "about 1.3 percentage points" vs stagflation; "3 to 4 additional small banks" fall below CET1 hurdle; capital shortfall "0.4 percent of GDP".
    - 100 percent conversion → further decline "about 2.6 percentage points"; "3 to 4 additional small banks" fail; capital shortfall "0.5 percent of GDP".
  - Simultaneous default of top 5 corporate borrowers (100 LGD) → CET1 depletion "5.5 percentage points"; ten banks—mostly small—would fall below hurdle rates.
  - Simultaneous default of SOEs within top 20 exposures → CET1 drop "about 3.6 percentage points" and failure of "4 small banks".

### Bank liquidity and FX liquidity — stress-test design, findings, and metrics
- Liquidity tests conducted:
  - Cash-flow based stress test for "105 commercial banks" with maturity profile up to a "3-month horizon", assessed separately in Rupiah and US dollar.
  - NSFR stress test for "51 commercial banks" (total currency).
- Key liquidity metrics and trends:
  - LCR increased "from 151 to 210 percent since 2020".
  - Aggregate NSFR as of mid-2023: "136 percent" (above minimum "100 percent"); no single bank below threshold at starting point.
- Maturity mismatch and funding structure:
  - Banks obtain most funding via retail and wholesale deposits, of which "100 and 90 percent were placed within the overnight bucket".
  - On the asset side, "over 85 percent of cash inflows" materialize beyond the first three months.
  - Large individual depositors (deposit >IDR 2 billion) account for "60 percent" of total deposit.
- Cashflow stress-test horizons and scenarios:
  - Horizons: "5-days, 4-week, and 3-months".
  - Scenarios: "mild market stress", "medium market stress" and "severe market stress".
- Aggregate cashflow stress-test findings:
  - Total currency assessment: system can withstand severe outflow shock up to 3 months with positive CBCs balance at "around 6 percent of total assets".
  - Asset share of banks experiencing liquidity shortfalls:
    - Up to 30 days: "0.2 percent of total assets".
    - Up to 3 months: "13.3 percent of total assets".
  - Size of liquidity shortfall relative to total assets: "around 0.3 percent of total assets".
  - IDR-based assessment: asset share of failing banks "15.7 percent of total assets"; liquidity shortfall "0.5 percent of total assets".
- FX (USD) cashflow analysis:
  - FX exposure on bank balance sheet: "around 17 percent of assets and 18 percent of total liabilities".
  - Under USD analysis, share of banks facing liquidity shortages reaches "47 percent of total asset by the 3-month horizon" (includes a few large banks).
  - Size of FX liquidity shortfalls: "0.7 percent of total assets".
- NSFR stress-test assumptions (migration rates):
  - "50 percent of funding within the six- to twelve-month bucket flows to the less than six-month bucket".
  - "35 percent of funding with more than one year maturity migrates to the six- to twelve-month bucket".
  - "15 percent flow rate from the over one-year bucket to the less than six-month bucket".
- NSFR results:
  - Most banks remain above 100 percent after shock; only "two small banks" marginally below threshold post-shock.
- Liquidity–solvency interaction:
  - Under severe scenario, about "35 banks" would need to use AC securities to meet severe cash outflows.
  - CET1 depletion from valuation losses on AC securities: KBMI1 "2.3 percentage points"; KBMI4 "1.6 percentage points".
  - Note: solvency impact smaller if banks repo AC securities to central bank instead of selling.
- Reverse stress-test results:
  - Most banks can sustain outflow rates of "about 35 percent of total deposit" and "45 percent of uninsured deposits" before fully running out of liquid assets.
  - Large variation across banks: some withstand only "15 percent", others "more than 100 percent".
- Run-off factors and haircuts (selected ranges preserved exactly as in source):
  - Retail deposits (households CASA, etc): "3-20%".
  - Insured deposits: "3-10%".
  - Uninsured deposits: "6-20%".
  - Corporate deposits (Large corporates and MSMEs): "6-40%".
  - Corporate uninsured: "12-40%".
  - Other deposits and unsecured borrowing (non-resident, banks, NBFIs, government): "20-100%".
  - Haircuts on CBC items (examples): Cash "100%"; Central Bank exposures "100%"; 0% RW securities "95-85%"; 20% RW securities "85-75%"; Corporate bonds "85-70%".

### Interconnectedness and contagion — domestic and cross-border
- Domestic interbank exposure: limited; sum of total bank gross exposures relative to total regulatory capital "18.9 percent" (down from "24 percent in 2017").
- Domestic network sample: "105 commercial banks" (4 SOBs, 27 regional development banks, 67 private banks, 7 foreign branches).
- Domestic contagion simulations:
  - Two spillover scenarios: credit only and credit + funding.
  - SOBs are the least vulnerable and most contagious; regional development banks show higher vulnerability.
  - Distributional results:
    - Private banks with vulnerability index exceeding average: "42 percent (simulation 2) and 40 percent (simulation 5)".
    - Regional development banks exceeding average vulnerability: "41 percent (simulation 2) and 44 percent (simulation 5)".
    - Foreign bank branches exceeding average vulnerability: "29 percent (both simulations)".
    - SOBs exceeding average vulnerability: "25 percent (both simulations)".
    - Contagion index above group average: "50 percent of SOBs, 33 percent of private banks, 29 percent of foreign bank branches, and 26 percent of regional development banks".
- Cross-border exposures:
  - Cross-border claims on United States, Singapore, China, and Japan collectively constitute "around 70 percent of total claims".
  - Liabilities to Singapore, Hongkong, the United States, and China comprise "72 percent of total liabilities".
  - KBMI4 banks hold most cross-border positions; SOBs account for "more than 50 percent of cross-border exposure".
- Cross-border simulations:
  - Two simulations: credit shock λ = "40 percent"; compounded credit and funding shock λ = "40 percent", ρ = "40 percent", δ = "50 percent".
  - Post-shock T1 ratios remain strong; moderate vulnerability to shocks from United States and United Kingdom.
  - Caveat: BIS reporting limitations and missing information on exposures with some Asian countries.

### Systemwide liquidity and NBFI analysis — structure, shocks, and outcomes
- NBFI size and composition:
  - NBFIs make up "24 percent of the overall financial sector".
  - Largest NBFI subsectors by assets: Insurance Companies "13 percent", Finance Companies "3.5 percent", Investment Funds "3.5 percent", Pension Funds "2.5 percent".
  - FinTech lending: lending rose by "63 percent" (assets and lending rose "51 percent and 63 percent, respectively").
- Static exposures:
  - Commercial banks hold "over 1400 trillion IDR in sovereign securities", tripling the amount held by insurance companies, investment funds, pension funds and finance companies together.
  - Share of sovereign securities on NBFI balance sheets: investment funds and pension funds "around 30 percent"; insurance companies "21 percent".
  - Percent of sovereign securities with maturities exceeding 10 years: insurance companies "68 percent", pension funds "48 percent", investment funds "38 percent".
- Systemwide liquidity stress-test design:
  - Monte Carlo simulations with compoundable layer shocks; correlation factor assumed at "0.9".
  - Shock layers (selected ranges preserved exactly):
    - Layer 1 Sovereign bond yield shock: "[0,4] with avg shock of 200bps".
    - Layer 1 Corporate bond yield shock: "[0,8] with avg shock of 400bps".
    - Layer 2 NFC FX funding shock: "[0,50]".
    - Layer 3 Wholesale deposit run-off: "[0,50]".
    - Layer 4 Resident retail deposit run-off: "[0,20]".
    - Layer 5 Share redemption rate: "[0,50]"; Insurance reserves outflow: "[0,50]"; Pension withdrawal rate: "[0,50]".
- Systemwide results:
  - The system remains resilient; commercial banks backstop liquidity needs of all sectors.
  - Under the most severe combined shocks, commercial banks show only marginal liquidity shortfalls; in "about 2 percent of simulations with all shocks, commercial banks have a small negative net liquidity position".
  - Largest driver of bank outflows: non-financial corporate sector.
- Caveats:
  - Analysis at sector level may mask entity-level vulnerabilities.
  - Analysis performed in total currency, not split into domestic vs foreign with currency frictions.
  - No policy support measures considered in scenarios.

### Granular NFC–bank FX liquidity stress test — scope and outcomes
- Coverage: "15 firms with the largest reported FX debt" from Capital IQ.
- Corporate external debt and domestic FX borrowing (end-2022):
  - Indonesian corporates external debt: "about 161 billion USD", of which "30 billion" short-term.
  - Additional "59.5 billion USD domestic borrowing in foreign currencies".
  - NFCs hold "about 52 billion USD in FX deposits in the domestic system".
  - Banking system’s FX CBC stood at "over 40 billion USD".
- Scenario design (macroeconomic and firm-level rollover rules):
  - Firms' predicted ICR used to calibrate rollover constraints:
    - Predicted ICR > "2": no rollover pressures.
    - Predicted ICR between "1 and 2": rating downgrade dries up external financing for rollover over "1 month".
    - Predicted ICR < "1": treated as technically in default and need to repay entire debt immediately.
  - Firm pecking order: FX revenue (1/12th of annual 2022 FX revenue), offshore FX deposits, domestic FX deposits, domestic IDR deposits, new domestic FX financing last.
- Key findings:
  - Under macro-based stagflation scenario, stressed firms left with large portion of FX refinancing need uncovered, but banking system retains enough FX liquidity to allow new domestic financing to fill corporates’ FX shortfall in many scenarios.
  - Proportional shock to ST FX debt: simulated total FX outflow "17 billion USD", over "50 percent of the total short-term debt for all firms".
  - Extreme proportional shocks to total FX debt (75–100 percent) can exhaust system CBC and cause several medium to small banks to run out of FX liquidity.
- Policy implications:
  - Increase data coverage and monitoring of FX exposures for banks and corporates; regularly conduct bank–corporate liquidity stress tests with broader coverage.
  - Monitor FX deposit concentration and systemwide/bank-by-bank FX HQLA concentration.
  - If FX stress absorption capacity declines, consider increasing FX reserve requirements and/or limiting FX net outflows in FX LCR.

### Macroeconomic scenarios and Growth-at-Risk (GaR) — scenario path and key macro projections
- Scenarios: one baseline and two adverse scenarios (stagflation and recession). Baseline draws on IMF October 2023 WEO projections.
- Scenario severity:
  - Stagflation implies "15 percent or 2.6 standard deviation shock to real GDP growth relative to the baseline, and 6 percent decline relative to the starting point over a two-year horizon".
  - Recession implies "14 percent or 2.4 standard deviation shock and 5 percent decline relative to the starting point over a two-year horizon".
- Selected macro projections (Real GDP growth; Baseline / Adverse - Stagflation / Adverse - Recession) by quarter-year points shown in Appendix III (values preserved exactly):
  - Real GDP growth (2023Q2, 2024Q2, 2025Q2, 2026Q2, 2027Q2, 2028Q2):
    - Baseline: "5.2 4.9 5.0 5.0 5.0 5.0"
    - Adverse - Stagflation: "5.2 -3.0 -3.3 0.2 4.6 8.0"
    - Adverse - Recession: "5.2 -3.9 -1.2 6.8 7.5 6.4"
  - Exchange rate (growth, percent):
    - Baseline: "2.3 -1.1 -0.7 -0.4 -0.6 -0.7"
    - Adverse - Stagflation: "2.3 21.6 15.7 5.1 -1.9 -5.4"
    - Adverse - Recession: "2.3 19.4 -3.4 -9.7 -9.4 -7.8"
- GaR result:
  - Based on financial conditions at "2023:Q2", the 5 percent left tail implies annualized GDP growth falling below "-3.1 percent over the two-year horizon".

### Key policy recommendations (excerpted, mandates and timing preserved)
- Implement LCR and NSFR across all banks. Monitor LCR for significant foreign currencies. Enhance data collection and stress testing for small banks. (¶69) — BI, OJK — ST
- Require banks to recognize NPLs in a timely and full basis; increase scrutiny of restructured loans and use multiple benchmarks to capture problem loans properly and promptly in top-down stress tests; fully implement IFRS9 consistent forward-looking provisioning and stress testing methodology. (¶45) — BI, OJK — MT
- Collect additional data on large corporates and MSMEs by sectors, regularly conduct top-down concentration analysis allowing distinction between SOEs vs non-SOEs, and perform regular corporate stress tests. (¶45) — BI, OJK — MT
- For liquidity monitoring, apply an array of unified liquidity stress scenarios with different severities and time horizons across all banks. (¶69) — BI, OJK — ST
- Consider implementing contagion analysis for interbank spillover risk assessment. (¶83) — BI, OJK — ST
- Collect more data on: 1) NBFI balance sheet, and solvency and liquidity conditions at institutional and sectoral level; 2) Bank-NBFI linkages at sectoral and institutional level. (¶109) — BI, OJK — MT
- Integrate FX liquidity analysis of non-financial firms and banks to strengthen FX liquidity risk analysis and inform relevant micro and macroprudential instruments. (¶116) — BI, OJK — ST
- Monitor largest FX deposit concentration to help calibrate FX outflow rates. (¶116) — BI, OJK — ST
- Monitor systemwide FX HQLA concentration, and if excessive exposures exist, consider removing assets potentially subject to ringfencing by foreign jurisdictions; if capacity is reducing, consider increasing FX RR and/or limiting FX net outflows in FX LCR. (¶116) — BI, OJK — MT
- Enhance data collection on financial conglomerates, including sub-entities and interconnections (ownership structure and bilateral balance sheet exposure). (¶116) — BI, OJK — ST

*Source: EXECUTIVE SUMMARY, INTRODUCTION, and selected chapters and appendices of 1idnea2025003-print-pdf*

### EXECUTIVE SUMMARY __________________________________________________________________________ 7

### EXECUTIVE SUMMARY

### Scope and data
- FSAP team conducted top-down corporate and bank solvency, bank liquidity stress tests, and interconnectedness analysis using mid-2023 data.
- Stress test exercise covered a sample of 105 commercial banks.
- Analysis relied on supervisory data shared by OJK and BI and publicly available corporate information.

### Financial system structure and recent developments
- Total financial assets equal 73 percent of GDP.
- Banks account for 76 percent of financial sector assets.
- Large banks operate as financial conglomerates; their assets increased by about 8 percent since 2021 to June 2023, reaching 58 percent of total financial sector assets.
- NBFIs represent up to a quarter of the overall financial sector assets.
- FinTech companies remain a small portion of the financial system but have experienced significant growth.
- Capital buffers and liquidity coverage ratios remain well above regulatory minima; banks are generally highly profitable with notable variation across banks.
- State-owned banks (SOBs) play an important role.

### Key risks and vulnerabilities
- Multiple structural and cyclical risks exist:
  - Cyclical risks are moderate despite robust credit growth; credit risk is higher in pandemic-hit industries and among highly leveraged corporations.
  - External downside risks: intensified geopolitical and fragmentation risks, slowdown in key trading markets, tightened global financial conditions, volatility in energy and commodity markets, and supply chain disruptions.
  - Loan forbearance measures could mask delays in credit loss recognition and impact NPLs.
  - Structural risks include increased banks’ holdings of government bonds and concentrated loans to State Owned Enterprises (SOEs), heightening the “sovereign-bank nexus”.
  - Offshore FX debt securities issuance by large publicly traded companies may be subject to refinancing risks.
  - Energy transition and climate-related natural disasters expose the financial sector to transition and disaster risks.
- Small banks: lower profitability, higher funding and operational costs; regional development banks show higher vulnerabilities due to interbank deposit reliance and funding from other banks/public entities.
- Corporate sector: post-COVID-19 recovery uneven; smaller firms have higher shares of debt at risk than larger ones.

### Corporate risk analysis
- Corporate vulnerabilities declined since the pandemic but remain susceptible under stress.
- Corporate stress test results: debt-at-risk rises by 10 and 14 percentage points under the recession and stagflation downside scenarios, respectively.
- Cash buffers for at-risk firms were already low in 2022, increasing the risk that shocks impair debt servicing capacity.

### Bank solvency stress test results
- Under baseline scenario: systemwide Core Equity Tier 1 (CET1) ratios remain above 22 percent over a five-year horizon.
- Under adverse stagflation scenario: systemwide CET1 ratio declines by about five percentage points.
- Capital losses mainly driven by credit risk, followed by market and interest rate risks.
- Banks with CET1 falling below the regulatory minimum in the stress test tend to have:
  - Lower initial capital ratios
  - Lower net interest margins
  - Higher cost-to-income ratios
  - Higher NPL ratios
- Authorities recommended addressing credit risks from legacy loans to COVID-sensitive sectors and MSMEs; improvements in loan quality data and timely/full recognition of NPLs would strengthen credit quality assessment.

### Bank liquidity and FX liquidity
- Overall liquidity position is sound; Liquidity Coverage Ratios (LCR) and other buffers are above minima.
- Identified liquidity risks:
  - A few small and medium sized banks with higher reliance on wholesale and uninsured funding face challenges meeting severe liquidity outflows over a 3-month horizon.
  - FX liquidity shortfalls are signaled at 0.7 percent of total assets.
  - Funding concentration risks in smaller regional banks driven by large interbank deposits and funding from other banks or public entities.
  - Liquidity–solvency interaction exercise: a non-negligible share of banks would need to sell or repo bonds to meet severe liquidity drawdowns; overall capital impact is small.
- Systemwide liquidity analysis:
  - Suggests limited liquidity impact stemming from the NBFI sector.
  - Banks have enough FX liquid assets to cover severe FX outflows from corporate FX deposits, with caveats due to data constraints.
  - Data constraints limit analysis to sector-level and total-currency exposure, potentially masking entity-level and currency-specific vulnerabilities.
  - A combined corporate and bank FX liquidity stress test on the 15 largest firms found adequate FX liquid assets to cover severe FX outflows from large corporate FX deposits.

### Interconnectedness and contagion
- Domestic interbank exposure is limited according to available data.
- Domestic interbank analysis findings:
  - SOBs are the least vulnerable.
  - Regional development banks face higher vulnerabilities.
  - Large banks (mostly SOBs) exhibit the highest contagion levels.
  - Small banks are typically less contagious yet highly vulnerable.
- Cross-border analysis (constrained by data availability) shows strong interconnection between Indonesian banking sector and global banking system, particularly with Asian countries: Singapore, China, Japan, Hongkong, and also the United States.

### Overall assessment
- Banking sector is resilient to multiple macroeconomic shocks; tail risks concentrated among small banks.
- Systemwide capital and liquidity buffers are relatively strong, but targeted supervisory and data improvements are necessary to monitor and mitigate credit, liquidity, FX, and interconnectedness risks.

### Key policy recommendations (excerpted from Table 1)
- Implement LCR and NSFR across all banks. Monitor LCR for significant foreign currencies. Enhance data collection and stress testing for small banks. (¶69) — BI, OJK — ST
- Require banks to recognize NPLs in a timely and full basis; increase scrutiny of restructured loans and use multiple benchmarks to capture problem loans properly and promptly in top-down stress tests; fully implement IFRS9 consistent forward-looking provisioning and stress testing methodology. (¶45) — BI, OJK — MT
- Collect additional data on large corporates and MSMEs by sectors, regularly conduct top-down concentration analysis allowing distinction between SOEs vs non-SOEs, and perform regular corporate stress tests. (¶45) — BI, OJK — MT
- For liquidity monitoring, apply an array of unified liquidity stress scenarios with different severities and time horizons across all banks. (¶69) — BI, OJK — ST
- Consider implementing contagion analysis for interbank spillover risk assessment. (¶83) — BI, OJK — ST
- Collect more data on: 1) NBFI balance sheet, and solvency and liquidity conditions at institutional and sectoral level; 2) Bank-NBFI linkages at sectoral and institutional level. (¶109) — BI, OJK — MT
- Integrate FX liquidity analysis of non-financial firms and banks to strengthen FX liquidity risk analysis and inform relevant micro and macroprudential instruments. (¶116) — BI, OJK — ST
- Monitor largest FX deposit concentration to help calibrate FX outflow rates. (¶116) — BI, OJK — ST
- Monitor systemwide FX HQLA concentration, and if excessive exposures exist, consider removing assets potentially subject to ringfencing by foreign jurisdictions; if capacity is reducing, consider increasing FX RR and/or limiting FX net outflows in FX LCR. (¶116) — BI, OJK — MT
- Enhance data collection on financial conglomerates, including sub-entities and interconnections (ownership structure and bilateral balance sheet exposure). (¶116) — BI, OJK — ST

*Source: EXECUTIVE SUMMARY, 1idnea2025003-print-pdf*

### INTRODUCTION

### INTRODUCTION

### Macro and financial backdrop
- Economic growth: "around 5 percent since the pandemic".
- Inflation: "contained within BI’s target band".
- Fiscal position: "ample fiscal space".
- Pandemic response: "targeted forbearance measures supporting COVID-sensitive and structurally weak sectors."
- Banking and financial system performance described as having "weathered the global pandemic on the back of strong fundamentals".

### Financial system structure and composition
- Financial system size: "72 percent of GDP".
- Banking share: "76 percent of total financial system assets".
- Number of banks: "around 1515".
- Conventional rural banks: "around 1410 banks".
- Sharia rural banks: "171 banks as of June 2023".
- Commercial banks: "105 commercial banks".
- Top-10 banks: "hold 66 percent of total bank assets".
- State-owned banks: "four state-owned banks that constitute 42 percent of the banking sector" (included in the top-10).
- Bank business model: "traditional banking, engaging in deposit taking and lending activities and interest income is the main source of profit."
- Non-bank financial institutions: "remain relatively small with total assets of   up to 24 percent of the overall financial sector."
- Largest non-bank subsectors by assets: "insurance companies, finance companies, investment funds, and pension funds."
- Insurance: "Life insurance companies dominate insurance sector with gross premium income making up 62 percent of that of the industry."
- Finance companies: "rely on loans from banks and issuance of long-term bond in capital market to finance relatively short-term credit, mainly in motor vehicles."
- Investment funds: "only account for 4 percent of system assets" and are "dominated by protected and fixed income mutual funds, equity mutual funds, and money market mutual funds."
- FinTech lending: "assets rising by 51 percent in 2022."
- Large non-financial companies: "tend to borrow internationally."
- Rural bank asset share note: rural banks "around 1.7 percent of total banking sector assets" and "carry a higher NPL ratio at around 10 percent relative to that of the commercial banks at around 2 percent."

### Financial conglomerates
- Definition change in 2020: introduced a criterion of assets "greater than or equal to 100 trillion IDR" and engagement "in more than one type of financial services".
- Number of financial conglomerates: "declined in the number of financial conglomerates from about 49 to 15 entities" (resulting number referenced as "15 entities in 2023").
- Asset dynamics: "the total asset of this segment has increased since 2021 by about 8 percent" and "remains high at 58 percent of the total financial sector assets as of June 2023."

### Banking sector resilience, indicators, and heterogeneity
- Capital buffers and regulation: "significantly strengthened and stringent regulatory and supervisory framework post-GFC" with "CET1 ratios at 23   percent,  well above regulatory minima."
- Liquidity: "LCR increasing from 151 to 210 percent since 2020" (driven by higher deposit inflows supported primarily by COVID-related policies, and subsequently declined as economic activity rebounded in 2022).
- Profitability: "Profitability remains robust" with "Overall ROA and NIM stands at 2.7 and 4.8 percent".
- Recent trend: "In early 2024, there has been a moderate deterioration of banks’ return on assets and net interest margin due to a higher pick-up of deposit rates than lending rates."
- Non-Performing Loans (NPLs): "headline Non-Performing Loan (NPL) ratios appear low at about 2 percent", with caveat that "there could be delays in NPL recognition due to forbearance measures."
- Risk-weighted assets density: "RWAs density has declined since 2017 and is now aligned with regional peers at around 62 percent."
- Credit penetration: "The credit to-GDP ratio reached 31.4 percent by the end of 2023."
- FX exposure: "FX net open position of the banks has been small at around 1 .5 percent of total capital."
- Bank heterogeneity and vulnerabilities:
  - Small banks: "Small banks are structurally less profitable due to high funding costs and operating expenses, especially banks classified under KBMI1 and KBMI2 as well as local rural banks, and therefore require higher capital buffers."
  - SOBs (state-owned banks): "Although SOBs are more profitable, they also carry a higher NPL ratio - at a   level similar to the p rivate banks—than other banking groups,  due to their large credit exposure to corporate lending, including to MSMEs."
  - Asset/liability profile: "The higher credit exposure also leads to a less liquid asset profile of the SOBs."
  - Domestic sovereign-bank nexus: "the reduced foreign ownership of sovereign bonds has increased the domestic sovereign-bank nexus."
- Definitions: "Small banks are defined as those classified under the KBMI1 and KBMI2 groups. Large banks are defined as those classified under the KBMI3 and KBMI4 groups."
- KBMI core capital bands (note from figure text):
  - "KBMI1 denotes banks with a core capital of up to IDR 6 trillion"
  - "KBMI2 denotes banks with a core capital of between IDR 6 trillion to IDR 14 trillion"
  - "KBMI3 denotes banks with a core capital of between IDR 14 trillion to IDR 70 trillion"
  - "KBMI4 denotes banks with a core capital of more than IDR 70 trillion"
- Ownership composition excerpt (figure note):
  - Bank classification by ownership into "state-owned banks, regional development banks, private banks, and foreign branches."
  - "Islamic banks ... asset size relative to the overall banking system is limited at about 7 percent."

*Source: 1idnea2025003-print-pdf - INTRODUCTION*

### 5.      Notwithstanding the generally positive developments, the banking sector and the

### 5.      Notwithstanding the generally positive developments, the banking sector and the

### Banking sector: structural and cyclical vulnerabilities
- Tail risks across banks vary with size and business models:
  - Notable variations of capital and profitability across banks depending on their size and business model; wider distribution with fatter tails in various capital, NPL and profitability indicators (Figure 4).
  - Large banks—especially state-owned banks—act as safe havens and have stronger profitability than small banks.
  - Wider distribution of open-maturity liquidity mismatches for small banks (small private banks, regional banks, and foreign branches) with larger tail risks.
- NPL ratio and provisioning:
  - NPL ratio for the banking system: around 2 percent.
  - NPL provision coverage ratio: almost 70 percent.
  - Estimated impairment based on NPL flow measure: 69 trillion IDR in 2023; actual impairment: 146 trillion IDR.
  - Gap underscores importance of timely and full recognition of NPLs and intensified scrutiny on restructured loans.
  - Expiration of OJK’s forbearance program: March 2024; need for a well-defined bank forbearance policy aligned with borrowers’ viability.
- Exposure to corporate credit risks (sectoral and borrower concentration):
  - Bank lending to corporates: about 60 percent of total loans (about 40 percent to large corporates; 20 percent to MSMEs).
  - NPL ratio: MSME loans 3.7 percent; large corporates 2.9 percent.
  - Share of restructured loans: MSME 25 percent; SOE loans 17 percent.
  - Higher NPL ratios in COVID-sensitive and structurally weak sectors: construction 4.9 percent, accommodation 3.6 percent, processing 3.8 percent, wholesale 3.7 percent.
- Loan forbearance measures and legacy restructured loans:
  - Share of restructured loans: peaked at 20 percent of total loans in 2020; around 10 percent in June 2023; 8 percent in Dec 2023; pre-pandemic level 5 percent; regional peers 2–3 percent.
  - COVID-related restructured loans: 46 percent of total restructured loans or 3.7 percent of total loans, reaching IDR 266 trillion; NPL ratio for COVID-related restructured loans reaching 13 percent.
  - Provisioning requirements for COVID-19 restructured loans: 21 percent of performing restructured loans; 70 percent of non-performing restructured loans.
- Sovereign-bank nexus:
  - Banks’ holdings of government bonds: around 13 percent of total assets.
  - Banks shifted portion held in fair value to amortized cost: 43 percent of debt securities.
  - Average duration: sovereign debt securities held by banks ~3.5 years; corporate securities ~2.4 years.
  - Banks’ exposure to SOE loans: 7 percent of total loans; for state-owned banks: 8 percent.
  - 40 percent of the top 20 corporate loans of the state-owned banks are granted to SOEs.
  - Unrealized valuation changes: unrealized loss of 5.7 trillion IDR as of June 2023; unrealized gain of 11.2 trillion IDR in December 2021.
- Bank exposure to commodity price volatility:
  - Periodic assessment needed given commodity-related credit growth (e.g., mining).
  - Supply-chain disruptions could increase commodity prices, raise inflation, and tighten financial conditions, impeding recovery.

### Corporate sector: recovery, external debt, and vulnerabilities
- Rebound and sectoral imbalances:
  - Median ICR and return on assets have risen; leverage has fallen; cash buffers recovered for many sectors.
  - Recovery uneven; scars in sectors hardest hit by the pandemic (e.g., tourism).
  - SOE construction firms: increased leverage and sharp decline in ICR over the past decade; a few in default.
- External debt profile and FX risks:
  - Corporate external borrowing: declined from about 15 percent of GDP to about 9 percent of GDP over the past decade.
  - About 20 percent of external debt is short-term.
  - FX debt-at-risk (debt held by firms with ICR less than 1): between 10–20 percent in the late 2010s; declined sharply in 2022 following pandemic spike.
  - Corporates’ external and FX debt as proportions of total debt in 2022: external debt 37 percent; FX debt 51 percent.
  - Natural hedging: sectors with significant export earnings more likely to have FX debt, but some signs of currency mismatch.
  - Higher external borrowing reflects a relatively shallow domestic financial sector.

### Household sector: leverage and mortgage LTV dynamics
- Household debt and LTV:
  - Household debt: about 16 percent of GDP in 2023Q3; end-2019 level was 17 percent of GDP.
  - Possible underreporting due to household borrowing via cooperatives not fully reflected.
  - LTV regulation (March 2021): banks can offer mortgage loans with LTV ratio up to 100 percent, subject to bank’s gross NPL ratio below 5 percent.
  - Average LTV ratio increased by about 13 percentage point since 2021: from 69 to 83 percent across all segments.

### NBFI sector: insurance and nonbank linkages
- Insurance sector risks:
  - Rising risks from inadequate premium management due to contraction in unit-linked business and disputes leading to market confidence deterioration.
  - High ratio of claims to premiums (RKP) for life insurance companies: 92 percent.
  - Spillover risk to banks appears contained due to low share of loans covered by credit insurance: around 1 percent (coverage may be understated).
- Nonbank financial interlinkages and exposures:
  - NBFI investment in banks (deposits and stocks): around 14 percent of total NBFI investment.
  - Loans from banks to NBFIs: 5.2 percent of total bank loans.
  - Deposits to banks from NBFIs: 6.8 percent of total bank deposits.
  - Finance companies generally rely on funding from banks.
- Common exposure to capital markets:
  - Sovereign bonds on NBFI balance sheets: around 26 percent of total assets, with longer duration than banks.
  - Large fluctuations in sovereign bond yields could imperil NBFIs’ solvency and liquidity.

### Direct and indirect external shock exposures
- Direct exposure to Russia-Ukraine and Israel-Gaza conflicts:
  - Deposit funds from both countries: IDR 1.4 trillion or less than 0.02 percent of total banks deposit in Indonesia.
  - Indirect exposures via supply disruptions could lead to large volatility in energy and commodity prices, higher inflation, and impeded recovery.

### Stress tests and systemic risk analysis workstreams
- Overall FSAP coverage and approach:
  - Comprehensive set of stress tests and risk analyses to assess resilience and vulnerabilities of banking, corporate and NBFI sectors (Figure 7).
- Solvency stress test:
  - Top-down, scenario-based stress test of 105 commercial banks covering almost 100 percent of banking sector assets, including cross-border subsidiaries and branches.
  - Uses supervisory data as of 2023:Q2 at bank level.
  - Complemented by sensitivity tests: restructured loans, lending concentration, revaluation of bond portfolios, further tightening of financial conditions.
  - Two adverse macro scenarios used: Stagflation and recession.
- Liquidity stress test:
  - Cash-flow based approach using maturity ladder data to assess resilience to large withdrawals across multiple time horizons.
  - Regulatory approaches include NSFR (longer-term structural liquidity).
  - Tests concentration risk of funding; reverse stress testing of selected funding items; explores liquidity-solvency interactions by selling AC securities at market price.
- Contagion analysis:
  - Covers domestic interbank exposures and cross-border interlinkages using BIS and supervisory templates.
- Systemwide liquidity analysis:
  - Assesses adequacy of liquidity buffers across banks, insurers, pension funds, investment funds, financing companies and other NBFIs and their interconnections.
  - Granular FX liquidity risk assessment of top nonfinancial firms and potential spillovers to banking sector.
- Corporate solvency analysis:
  - Uses firm-level balance sheet data to assess corporate resilience under the same adverse scenarios as bank solvency stress test and simulates evolution of key corporate risk indicators under stress.

*Source: IMF staff.*

### 9.      The stress test adopts a tailored approach in assessing specific vulnerabilities facing

### 9.      The stress test adopts a tailored approach in assessing specific vulnerabilities facing the Indonesian banking sector.

### Tailored stress-testing approach and scope
- Complete coverage of banks could identify potential tail risks, especially small banks.
- The stress test gauges potential credit losses associated with the unwinding of supervisory forbearance measures, given a non-negligible amount of restructured performing and non-performing loans targeted at COVID-sensitive sectors.
- Market losses from banks’ holdings of sovereign securities (currently amounting to 13 percent of total assets) are assessed because of the banking sector’s significant role in the sovereign bond market.
- Underlying risks from securities booked under the amortized cost (AC) category are evaluated amid migration of debt securities from fair value to AC to prevent valuation losses as financial conditions tighten.
- Concentration analysis on bank lending portfolios identifies large exposures to specific segments (SOEs, private corporates, other banks).
- Interest rate risk analysis differentiates bank groups and vulnerabilities to funding risk; small banks tend to be funded by less stable deposits and bear higher funding cost.

### Banking Sector Characteristics and Vulnerabilities — Credit Risks
- Credit risk is the largest risk factor: RWAs of credit risk account for 90 percent of total RWAs in the sample banks.
- All banks use STA approach for computation of credit RWAs.
- In 2020, the COVID-19 shock and accompanying public support contributed to a decline in RWA densities, driven by strong growth of high-quality liquid assets (fueled by increasing customer deposits) and reduced customer lending.
- RWAs declined further in 2023 compared to 2022 due to a downward adjustment of the Operational Risk RWA under Basel III implementation; total RWA declined while banks’ capital ratios increased.
- Credit risk RWAs are allocated mainly to large corporates and MSMEs.
- KBMI bank group definitions preserved:
  - KBMI1: core capital up to IDR 6 trillion
  - KBMI2: core capital between IDR 6 trillion to IDR 14 trillion
  - KBMI3: core capital between IDR 14 trillion to IDR 70 trillion
  - KBMI4: core capital more than IDR 70 trillion (out of four banks in KBMI4, three are state-owned banks).
- Asset composition:
  - Loans represent 60 percent of total assets across all banks.
  - Securities account for about 17 percent.
  - Small banks: loan share around 54 percent; large banks: loan share around 64 percent.
  - By sector: loans concentrated in corporates at around 60 percent (40 percent to large corporates; 20 percent to MSMEs), households around 27 percent (17 percent household retail; 10 percent mortgages).
  - KBMI4 (three SOBs and one large private bank) hold about 66 percent of total loans in corporate lending.
  - Regional banks focus on credit to civil servant and government employees; retail loans make up 60 percent of total loans for regional banks.
- Asset quality:
  - Corporate loans are generally riskier than household loans.
  - Within corporate loans, MSMEs carry higher NPL ratios than large corporates.
  - COVID-sensitive or structurally weak sectors (tourism, construction, processing and wholesale) carry higher NPL ratios.
  - Winding down of forbearance measures—focused on weak sectors—requires more timely and full recognition of NPLs.

### Banking Sector Characteristics and Vulnerabilities — Market Risks
- Sovereign securities holdings:
  - Sovereign securities amount to 13 percent of total banking assets (around 80 percent of total debt securities).
  - Foreign sovereign bonds account for a small share at 3 percent of total securities outstanding.
  - A significant portion of sovereign securities placed in AC category rather than fair value; recent shift from fair value to AC.
  - Average duration of sovereign securities is 3.5 years.
  - Average duration of non-sovereign securities is around 2.4 years.
- Risks:
  - Migration to AC could intensify liquidity-solvency linkages if banks are prompted to sell AC securities at market price to meet large liquidity drawdowns.
  - Sovereign holdings introduce capital and liquidity channel linkages given banking sector’s role in sovereign bond market.

### Banking Sector Characteristics and Vulnerabilities — Interest Rate Risks
- Loan and deposit repricing:
  - As of June-2023, more than 70 percent of newly issued loans were denominated in variable rates.
  - Household mortgage variable-rate share around 60 percent.
  - Majority of deposits are repriced within one-month and generally faster than repricing of loans.
- Funding structure and vulnerability:
  - Small banks (regional and private banks) are more dependent on funding via time deposits than large banks, exposing them to funding cost shocks and higher share of unstable funding.
  - Large share of demand and saving deposits subject to short-term repricing, slower repricing on asset side, and slower passthrough from funding to lending rates could compress bank net interest margin in a context of rising interest rate.

### Macroeconomic Scenarios and Growth at Risk
- Scenario framework:
  - One baseline scenario and two adverse scenarios (stagflation and recessionary), baseline draws on IMF October 2023 WEO projections.
  - Adverse scenarios simulated using the Global Macro-financial Model disaggregated into forty national economies.
- Stagflation scenario features:
  - Resurgence of COVID-like lockdown in the first year compounded by escalation of global/regional geopolitical tensions, global trade disruptions (energy, food, tourism, critical supply chains), declines in remittances, FDI and financial flows.
  - Supply-chain disruptions cause synchronized slowdown in advanced economies and emerging markets, de-anchoring of inflation expectations, higher energy prices, and lower commodity prices for key commodities (e.g., metals such as nickel).
  - Knock-on effects: reduced trade and capital inflows, currency depreciation, reduced consumer confidence, policymakers tighten monetary policy to tame inflation, tighter financial conditions reduce credit growth, domestic consumption, real estate prices, and increase firms’ cost of funding and unemployment.
  - Higher term premium on long-term bonds, asset price declines (equity and house prices), and rising markups.
- Recessionary scenario features:
  - Global, synchronized recession triggered by global and idiosyncratic factors and systemic financial instability.
  - Sharp swings in real interest rates and risk premia, asset repricing amid economic slowdown, triggering insolvencies in countries with weak banks or non-bank financial institutions, market dislocations and cross-border spillovers.
  - Sharp fall in trade and FDI inflows, subdued domestic demand, rising unemployment, and further declines in housing, equity, and commodity prices.
- Scenario severity and duration:
  - Differences: stagflation entails structural change and potentially prolonged duration/recovery; recessionary scenario is a V-shape growth shock with transitory impact and quicker rebound.
  - Severity aligned with a 5 percent Growth-at-Risk estimate (Appendix III): stagflation implies 15 percent or 2.6 standard deviation shock to real GDP growth relative to the baseline, and 6 percent decline relative to the starting point over a two-year horizon; recessionary implies 14 percent or 2.4 standard deviation shock and 5 percent decline relative to the starting point over a two-year horizon.
- Comparison with past shocks:
  - Over two years, the adverse scenarios feature a real GDP shock more severe than the COVID shock but milder than the Asian Financial Crisis.
  - In the stagflation scenario, short-term interest rate tightens till 2024 and declines thereafter; in the recessionary scenario, short-term interest rate immediately declines below the baseline.

### Scenario-Based Solvency Stress Test — Methodology and Implementation
- Capital metrics and requirements:
  - Performance assessed using CAR, Tier 1 (T1), Common Equity Tier 1 (CET1) capital, and leverage ratios.
  - Capital requirements include minimum capital ratios, bank specific D-SIB buffer, and Pillar-II requirements where applicable.
  - Banks allowed to deplete their capital conservation buffers (CCBs) and countercyclical capital buffer under the adverse scenario.
  - As of June 2023: Capital Conservation Buffer is bank specific and at around 2.5 percent of RWA applied to KBMI group 2–4; Countercyclical Buffer at 0 percent.
- Solvency framework covers:
  - Credit risk associated with all exposures, market risks, sovereign risk, and interest rate risk in the banking book.
  - Derivatives book is not considered due to small exposure (as of June 2023 derivatives exposure accounts for less than 1 percent of total and credit RWA).
- Translation of macro scenarios:
  - Macro scenarios translated into evolution of PDs, LGDs and interest rates using satellite models.
  - Indirect effects on growth of balance sheet items, pre-provision net income, and other components.
  - Shocked risk parameters drive RWAs and provisions (via incurred loss approach), market valuation and net interest income changes.
  - Final aggregation of P&L items and balance sheet/RWA evolution yields CET1, Tier 1 and total capital and leverage ratios over the stress horizon.
- Balance sheet growth assumption:
  - Quasi-static approach: balance sheet growth assumed to follow Indonesia’s nominal GDP growth given limited cross-border lending exposure.
  - A floor of zero percent is set on rate of change of balance sheets to prevent deleveraging; this constraint could be binding in adverse scenarios.
  - Balance sheet growth can be driven by FX movements based on bank exposure to FX-denominated assets/liabilities and conversion of some off-balance-sheet items to on-balance-sheet exposures.
  - Balance sheet expansion could lead to higher RWAs and subsequently lower capital ratios.

*Source: IMF staff.*

### 22.      The evolution of default under stress in Indonesia is estimated separately for

### 22. The evolution of default under stress in Indonesia is estimated separately for

### Default probabilities (PDs) — methodology and projections
- Historical default rates (PDs) at the aggregate level are provided by the OJK and estimated separately for household and corporate loans.
- Point-in-time (PiT) PDs are projected using a model selection technique with macro variables (GDP growth, unemployment rate, exchange rate, asset and commodity prices and interest rates) as independent variables.
- A logit transformation is applied before model estimation to address the truncated nature of default rate distribution and to ensure projected rates remain within the 0-1 bound.
- Conditional PD forecasts indicate a larger impact on corporate than household portfolios.
  - Under the baseline (stable macroeconomic outlook), PDs in both segments are projected to remain flat.
  - Under the adverse scenario, PDs notably increase, with the impact on corporate portfolios more sizable than on household mortgages.
  - The magnitudes of projected PD shocks under the adverse scenario are in general milder than those observed during the Asian Financial Crisis in accordance with the calibration of scenario severity.
- GDP growth, unemployment rate, interest rates, and asset prices are relevant drivers:
  - GDP growth and short-term interest rate: significant for PDs of household mortgages and large corporates.
  - Unemployment rate: significant for PDs of consumer loans.
  - Asset prices (housing and stock prices): significant drivers of coverage ratio and PDs of large corporates, respectively.
- Risk-neutral PDs for sovereigns, banks, and NBFIs are generated using a Merton approach that translates credit spreads under stress to PDs, using the scenario-linked credit spreads, time to maturity (T-t), and assuming an LGD of 50 percent.

### Loss given default (LGD) — projection approach
- Point-in-time LGD paths under baseline and adverse scenarios rely on an econometric approach using historical time series on coverage ratio for the total loan portfolio provided by the authorities.
- The stressed coverage ratio under the adverse scenario is applied to household and corporate portfolios separately, accounting for differences in collateralization at the starting point.
- Forward paths under both scenarios are attached to bank-specific PiT LGDs, with a floor imposed on the adverse scenario at the starting point LGDs.

### Credit risk effects on capital and provisions
- Credit risk affects banks’ capital ratios through loss provisions (numerator) and risk weights (denominator).
- The credit risk analysis uses an incurred loss approach: loan loss provisions are calculated using PiT PDs and PiT LGDs under stress.
- For Indonesia, all banks adopt the standardized approach; RWA densities at the starting point of the stress test are assumed to remain the same over the scenario horizon, with differentiation between performing and non-performing loans.

### Interest rate risk — decomposition, projection, and impacts
- Interest rate risk assessment decomposed into:
  - Base effect: change of interest income or expense due to changes in outstanding amounts of interest earning assets or liabilities in the absence of rate shocks; computed as product of effective interest rate and outstanding amount under stress horizons.
  - Gains or losses under stress: add-on component estimated with a gap-analysis assessing cash-flow effects from general increases in interest rates across repricing buckets (less-than-1-year to 5-year).
- Interest payments are assumed to accrue only on performing exposures under both baseline and adverse scenarios; stress test conservatively disallows accruing interest on non-performing exposures.
- Projections use historical aggregated interest rates and interest-rate-sensitive asset and liability series provided by authorities. New-business (front-book) rates proxied by lending and funding accounts starting within one quarter are mapped into:
  - Asset categories: loans and securities purchased.
  - Liability categories: demand deposits, saving deposits, time deposits and securities issued.
- Debt securities liability rates follow sovereign bond yield dynamics plus a spread benchmarked to historical average.
- Satellite models show:
  - Lending rates highly correlated with short-term interest rates (dominant share of floating rate loans).
  - Cost of saving and current deposits largely determined by short-term rate; time deposits driven by long-term rates.
  - Unemployment rate strongly determines lending rates, saving, and current deposits.
  - Pass-through of short-term rate on lending rates approximately 0.36; for saving and current deposits around 0.1.
- Projected impacts:
  - Time deposits more severely affected than current and saving deposits.
  - Lending rate increase moderate due to constraints on raising lending rates under stress.
  - Relative to baseline, net interest margin declines in both adverse scenarios by about one percentage point on average at the trough for the sample banks, relative to a historical net interest margin of the total banking sector at around 5 percent.

### Market risk and valuation of securities
- Valuation of fixed income securities uses a discounted cashflow-based approach to capture non-linear impacts (convexity) from changes in yield-to-maturity:
  - P_t = C * [1 − (1 + r_t)^(−n)] / r_t + P_Principal / (1 + r_t)^n
  - Where P_t = bond price at time t; C = annualized coupon payments; r_t = yield-to-maturity at time t; n = number of years until maturity (or proxied by duration).
  - As r_t changes with yield shocks, updated bond price is computed and compared to initial price; yield shocks calibrated by linearly interpolating the yield curve specified by short- and long-term yields over the 5-year horizon.
- Analysis covers debt securities in FVTPL and FVOCI, separated into sovereign, banks, NBFIs and corporate bonds. Rebalancing of the portfolio is not allowed throughout the horizon.
- Securities held at amortized cost (AC) are revalued for conservative scenario analysis:
  - AC securities are revalued at the starting point (to account for unrealized gains/losses up to the starting point) and under the stress horizon using the same cashflow-based approach, and compared against book value.
  - Unrealized gains or losses at the starting point were adjusted to reflect market valuation changes since the onset of interest rate hikes after the pandemic up to the starting point, to account for migration of securities from fair value category into the amortized cost category.
- Valuation changes from FX and equity exposures:
  - FX losses/gains = starting net open FX position × shocks to nominal exchange rate under each scenario.
  - Equity valuation gains/losses = net equity position × shocks to stock prices under each scenario.

### Net income, profit distribution, and tax treatment
- Net income projection uses all risk factors; net profits mainly driven by gains and losses from credit risks, market risks and interest rate risks.
- Remaining income items:
  - Net fee and commission income and other non-interest income projected to grow in line with nominal GDP growth under both scenarios.
  - Operational and administrative expenses assumed constant over the risk horizon.
  - Extraordinary income and loss assumed not to recur.
- Corporate income tax set at banks’ effective tax rate of 20 percent.
- Dividend policy in stress test:
  - Dividends assumed paid at a rate of 50 percent of current period profits after taxes by banks that are making profits (only if profits are positive) and in compliance with supervisory capital requirements.
  - Banks are not allowed to issue new shares or make repurchases during the stress test horizon.

### Sensitivity analyses
- In addition to scenario-based stress tests, sensitivity analyses include:
  - Parallel shift in yield curve: parallel increases in both short- and long-term rates to assess sensitivity of bank solvency, focusing on market risks and valuation losses in FVTPL, FVOCI, and potential losses from AC securities sold for liquidity.
  - Credit deterioration of restructured loans and loans under COVID forbearance: assumes range of percentage migrations from performing to non-performing, inducing higher provisions; alternatively, bank reported capital ratio in absence of forbearance can be used to compare capital impact pre- and post-forbearance.
  - Bank concentration risks on top lending exposures: assumes simultaneous hypothetical default of five largest corporate borrowers; impacts on capital reported separately for each experiment. Also assumes failure of SOEs within the top 20 corporate exposures to distinguish SOE vs non-SOE large-exposure impacts and assess sovereign–bank nexus and idiosyncratic vulnerabilities.

*Source: IMF staff.*

### 39.      The result of the scenario-based bank solvency stress test confirms the sector’s

### The result of the scenario-based bank solvency stress test confirms the sector’s

### Solvency stress test: aggregate outcomes and heterogeneity
- Baseline scenario:
  - Systemwide CET1 ratios remain at a high level above 22 percent over the five-year horizon with marginal capital depletion of 72 basis points.
  - Large state-owned banks outperform small banks with strong capital accumulation supported by high profitability.
  - Small banks are affected more due to lower NIM and higher operating cost, as well as initial effects from credit and market losses and increase in RWAs due to balance sheet growth.
- Adverse scenarios (stagflation and recession):
  - Stagflation scenario (aggregate):
    - Fully loaded CET1 ratio declines by about 5 percentage points at the trough relative to the baseline, before stabilizing and slightly rebounding to 19 percent by the 5th year.
    - Aggregating at banking group level, capital depletion at trough relative to starting point:
      - Small banking groups: between 6.4 and 9.4 percentage points.
      - Large banking groups: between 3.5 and 4.9 percentage points.
    - By business model at trough:
      - Regional banks: 1.9 percentage points decline.
      - State-owned banks: 5 percentage points decline.
      - Private banks: 5.3 percentage points decline.
  - Recession scenario (aggregate):
    - CET1 ratios on aggregate experience an initial shock of 2.6 percentage point before swiftly rebounding to a level similar to the baseline, at 22.4 percent.
    - Variation among banking groups observed, with small banks experiencing larger shocks.
  - Banks falling below CET1 hurdle rates:
    - Stagflation: 9 banks; assets ranging from 2.9 to 7.4 percent of total banking system assets; capital shortfall from 0.2 to 0.3 percent of GDP.
    - Recession: 6 banks; capital shortfall from 0.2 to 0.3 percent of GDP.
  - Small banks display wider variation of capital depletion under stagflation, ranging from 0 to 35 percentage points drop, confirming large heterogeneity and tail risks.
- Leverage:
  - Starting leverage ratios at 14 percent are not binding; marginal decline of two and one percentage point at the trough relative to the starting point under stagflation and recession scenarios, respectively.

### Role of securities valuation and loss drivers
- Repricing of Amortized Cost (AC) securities:
  - Scenario-based stress test considers a full repricing of securities booked under the Amortized Cost category.
  - Without AC repricing effect, aggregated CET1 ratio under the adverse scenario would improve by about 120 and 30 basis points in the first year and over the five-year horizon, respectively, without significant changes in the number of failing banks.
- Primary drivers of capital declines under adverse scenarios (stagflation):
  - Credit losses dominate:
    - Cumulative decline of 9.4 percentage points up to year 2 and 19 percentage points over a five-year horizon.
    - By borrower type (cumulative over five years, stagflation):
      - MSMEs: up to 30 percent of total MSME loans.
      - Large corporates, banks and NBFIs: ranging from 16 to 18 percent.
      - Household mortgages and retail loans: about 11 percent.
  - Market and interest rate risks:
    - Cumulative impact on CET1 ratio at trough: market risk 3.6 percentage points; interest rate risk 0.8 percentage points.
    - Within market risks, domestic sovereign securities experience highest value drop at around 12 percent.
  - Net interest margin (NIM):
    - Net interest margin declines by about 1 percentage point across banking groups, with slightly higher drop for KBMI1 and KBMI2 groups.
    - Interest rate risk effects on NIM: initial negative impact in first two years; interest rate effects recover after the 3rd year; effects on NPLs appear persistent over 5 years.

### Weak bank assessment and characteristics
- Definition and scope:
  - Weak banks defined as those with total capital ratio falling below CET1 requirement under the stagflation scenario (including pillar II and D-SIB surcharge and excluding capital conservation buffer).
  - Weak banks account for about 9 banks and 7.4 percent of total banking sector assets and mostly are small banks.
- Common characteristics of weak banks:
  - Lower initial capital ratio.
  - Lower NIM.
  - Higher cost to income ratio.
  - Higher NPL ratio.
  - These factors explain higher capital drops and amplify likelihood to fall below hurdle rates.

### Sensitivity analyses
- Additional parallel 400 basis points yield shock (on top of initial stagflation shock):
  - Simulated 400 percent points parallel upward shift on both short- and long-term yield leads to additional capital depletion of about 1.7 percentage points.
  - Repricing impact by ownership:
    - SOBs: 1.9 percentage points.
    - Regional banks: 1.8 percentage points.
    - Domestic private banks: 1.4 percentage points.
  - Only one additional small bank falls below the hurdle rate under this shock.
- Restructured loans deterioration scenarios:
  - As of June 2023, restructured loans account for about 10 percent of total loans—of which 8 percent are targeted at performing loans—and subsequently decline to 8 percent in December 2023.
  - MSMEs, large corporate and mortgage loans have higher share at around 10–12 percent within their respective loan category.
  - Within the corporate sector, shares of restructured loans by sector (as of June 2023):
    - Accommodation: 75 percent of loans in category.
    - Construction: 37 percent of loans in category.
    - Energy: 26 percent of loans in category.
  - Coverage ratios (as of June 2023):
    - Non-performing restructured loans: 73 percent.
    - Performing restructured loans: 23 percent.
  - Sensitivity converting performing restructured loans to non-performing:
    - 50 percent conversion → further capital ratio decline by about 1.3 percentage points compared to stagflation scenario; 3 to 4 additional small banks fall below CET1 hurdle rates; capital shortfall 0.4 percent of GDP.
    - 100 percent conversion → further capital ratio decline by about 2.6 percentage points compared to stagflation scenario; 3 to 4 additional small banks fail; capital shortfall 0.5 percent of GDP.

### Concentration and credit-insurance exposures
- Credit insurance:
  - Share of loans contracted with credit insurance stands at around 1 percent of total loans and mostly cover performing loans.
  - Coverage by bank type:
    - KBMI1: around 1.2 percent of total loans.
    - Regional banks: around 1.7 percent of total loans.
  - By sector:
    - MSME loans: around 3.5 percent of total MSME loans.
    - Followed by retail and interbank loans.
  - Note: Coverage could be understated as it only covers exposure which treats the credit insurance as its first collateral embedded in the calculation of provisions.
- Simultaneous default of top corporate exposures:
  - Simultaneous default of top 5 corporate loans (assuming 100 LGD) leads to CET1 ratio depletion of 5.5 percentage points; higher impact for small banks than large banks; ten banks—mostly small—would fall below CET1 hurdle rates.
  - Simulated default of SOEs within top 20 corporate exposures:
    - State-owned and regional banks have higher exposure to large SOEs than private banks, at 42 and 35 percent, respectively (share of SOEs among top 20 corporate borrowers).
    - Simultaneous default of these SOEs would cause about 3.6 percentage points drop of CET1 ratios and failure of 4 small banks due to the limited size of individual SOEs within the top 20 firms.

### Key recommendations from the solvency risk analysis
- Enhance monitoring of small banks and strengthen targeted stress testing:
  - Increase intensity in scrutiny of restructured loans and enhance risk monitoring and stress testing (both top-down and bottom-up) for small and weak banks, as identified in regular stress tests.
- Timely and full recognition of NPLs:
  - Require banks, to the extent possible, to recognize NPLs in a timely and full manner.
  - Use multiple benchmarks—such as loan-at-risk or loan impairment with a split between performing and non-performing loan impairment—to capture problem loans properly and promptly in top-down stress tests.
- IFRS9 implementation and reporting:
  - Ask banks to compile and report transition matrices between asset stages to allow for IFRS9-consistent forward-looking credit monitoring and provisioning for all asset stages based on the expected credit loss approach.
- Data enhancements:
  - Improve authorities’ data collection and analysis on the corporate sector, especially MSMEs and the informal segment, and allow disaggregation by sectors and ownerships (SOEs vs non-SOEs) to facilitate targeted credit risk mitigation in vulnerable and concentrated segments.
- Alignment with regulatory changes:
  - Recommendations reference expiration of OJK’s loan restructuring policy relaxation in March 2024 and encourage applying prudent loan restructuring principles as stipulated in OJK regulation No.40/POJK.03/2019 regarding Commercial Bank Asset Quality Assessment.

### Liquidity risk analysis: introduction and approach
- Two liquidity stress tests conducted:
  - Cash-flow based stress test for 105 commercial banks (maturity profile up to a 3-month horizon; assessed separately in Rupiah and US dollar).
  - NSFR stress test for 51 commercial banks (assessed in total currency due to data limitation by currencies).
- Data and reporting coverage:
  - All banks report maturity ladder information; 51 banks (mainly KBMI2 to KBMI4 banks) report their LCR and NSFR position.
  - Banks report their foreign currency position through the maturity ladder but not via LCR or NSFR reporting.
  - No LCR requirement on foreign currency positions currently.
- Stress test features:
  - Cash-flow based stress test integrates holdings of fair-value and amortized cost securities to allow liquidity–solvency interaction when banks tap into AC securities, incurring additional capital losses.
  - Split between encumbered and unencumbered liquid assets requested to gauge liquidity capacity.
  - Thresholds:
    - Cash-flow based stress test: amount of counterbalancing capacity; negative amounts indicate bank failure.
    - NSFR stress test: 100 percent threshold (minimum regulatory requirement).
- Objectives of liquidity analyses:
  - Identify vulnerabilities from maturity mismatches.
  - Assess adequacy of liquidity buffers given recent reversal of liquid-asset expansion.
  - Assess concentration risks from large single-named funding sources.
  - Assess funding stability across funding categories and potential stabilizing effects from deposit insurance.
  - Implement reverse stress testing to address scenario uncertainty of outflow rates.
  - Inform haircuts on collateral based on solvency stress test calibration and existing BI collateral framework.

*Source: IMF staff, selected sections from the IMF FSAP analysis provided in the source content.*

### 50.      Banks liquidity position has seen improvement since the pandemic, followed by

### 1idnea2025003-print-pdf - 50.      Banks liquidity position has seen improvement since the pandemic, followed by

### Liquidity trends since the pandemic
- Liquidity metrics, such as the LCR, NSFR and liquid asset to total asset ratio, have improved since 2020 driven by a series of government stimulus programs accompanied by an influx of deposit funding.
- Banks increased holdings of sovereign securities as high-quality liquid assets amid weak loan demand during the pandemic, increasing counter‑balancing capacity which peaked in 2021 before a moderate decline driven by recovery of economic activities and domestic consumption reducing deposit funding in the banking system.
- Figures referenced: Figure 21 (LCR Ratio; Liquid Assets to Total Assets Ratio; composition supported by central bank reserves, sovereign securities, and foreign currencies).

### Maturity mismatches and contractual obligations
- Aggregate funding composition and timing:
  - Banks obtain most funding via retail and wholesale deposits, of which 100 and 90 percent were placed within the overnight bucket.
  - On the asset side, over 85 percent of cash inflows, mostly comprised of maturing loans, would materialize beyond the first three months.
- Resulting structure:
  - A maturity mismatch characterized by frontloaded cash outflows and backloaded cash inflows, potentially leaving banks vulnerable to liquidity gaps under sustained liquidity stress.
  - Mismatch observable in both domestic and foreign currencies, although FX constitute a moderate portion of assets and liabilities on bank balance sheets.
- Data presented in Figure 22: Contractual obligations as share of assets, with detailed maturity-ladder buckets (Overnight up to Greater than 10 years) and line-item breakdowns (Counter-balancing capacity, Cash in vault, Placement at Bank Indonesia, Unencumbered securities, Existing credit/liquidity lines, Cash inflows, Cash outflows, and subcomponents).

### Cashflow-based liquidity analysis methodology
- Net liquidity positions defined as differences between:
  - Cumulated net funding gap (sum of inflows minus outflows across maturity buckets) and
  - Cumulated counterbalancing capacity (sum of liquid assets across maturity buckets).
- Liquidity shortfall recognition rule:
  - If the net liquidity position becomes negative after utilizing counterbalancing capacity, a liquidity shortfall is recognized and banks would not be able to meet further funding withdrawals.
- Data source and vintage:
  - Analysis builds on maturity ladder data in the regulatory report as of 2023:Q2.

### Calibration and assumptions for inflow/outflow shocks
- Key calibration principles:
  - Higher run-off rates for wholesale funding than retail funding to reflect first mover advantage of sophisticated depositors.
  - Run-off rates on secured funding sources (e.g., those covered under deposit insurance) are lower than unsecured funding sources.
  - Inflow parameters allow a maximum 100 percent of contractual inflows in principle, except assumption of no inflows from loans to retail and corporate customers (reflecting debt moratoria/postponement in repayment policy responses).
  - Outflow rates tailored to Indonesia-specific funding composition: higher for current and time deposits than saving deposits.
- Explicit data points and shares preserved from source:
  - "100 and 90 percent" placed within the overnight bucket (funding placement).
  - "over 85 percent" of cash inflows materialize beyond the first three months.

### Stress testing scenarios, counterbalancing capacity (CBC) assumptions, and horizons
- Stress horizons and scenarios:
  - 5-days, 4-week, and 3-month horizons.
  - Three stress scenarios: mild market stress, medium market stress, and severe market stress.
- CBC assumptions by scenario:
  - Full CBC (under mild market stress): fully endogenous liquidity supply by the BI as long as banks have unencumbered eligible collateral to pledge at BI haircut rate; no additional market haircut assumed.
  - CBC with market haircuts (under severe market stress): full CBC is assumed but subject to market-specific haircuts when pledging or liquidating assets to meet funding run-offs. Liquid assets haircuts could draw on market value declines from the solvency stress test or past FSAPs, and are informed by additional haircut when banks repo liquid assets to the BI.
- Scenario calibration:
  - Draws on historical time series, previous FSAP stress parameters, and recent liquidity episodes in several advanced economies.
  - Severe scenario shocks considered more conservative than those used in authorities’ stress tests.
- FX-specific analysis:
  - Cashflow analysis also assessed bank liquidity capacity for foreign currencies separately, using the same assumptions as the total and IDR-based stress test, to gauge potential liquidity pressure stemming from banks’ US dollar fundings.

### Additional sensitivity analyses and reverse stress testing
- Concentration risk of funding:
  - Given large individual depositors (deposit >IDR 2 billion) account for 60 percent of total deposit, a standalone exercise assumes loss of top 5 largest deposits from government, corporates, financial institutions, and individuals, then computes remaining CBCs to assess concentration risk of single-named funding sources.
- Reverse stress test:
  - Attempts to identify bank-specific outflow rates over which certain types of funding (e.g., uninsured deposits or total deposits) would fully deplete CBCs. The resulting bank-specific outflow thresholds form a distributional view across banks and identify outliers.
- Liquidity-solvency interaction:
  - Explores effects of selling available-for-sale and AC securities at market price to meet large liquidity drawdowns and assesses potential capital impact due to market repricing of AC securities sold subsequent to fair-value securities.

### Key numeric findings and data points (preserved exactly as in source)
- "100 and 90 percent" of deposits placed within the overnight bucket.
- "over 85 percent" of cash inflows materialize beyond the first three months.
- Large individual depositors (deposit >IDR 2 billion) account for "60 percent" of total deposit.
- Stress test horizons: "5-days, 4-week, and 3-months".
- Scenario labels preserved: "mild market stress", "medium market stress" and "severe market stress".
- CBC examples and numerous maturity-bucket numeric entries shown in Figures 21–22 and accompanying tables for Total, IDR, and USD analyses (regulatory maturity ladder as of "2023:Q2").

*Source: BI and IMF staff (figures and tables as presented in the chapter).*

### 56.      The total currency and IDR-based cashflow analysis confirms overall resilience of the

### The total currency and IDR-based cashflow analysis confirms overall resilience of the banking system to withstand severe outflow shocks over the short-term, while suggesting moderate liquidity gaps when extending the analysis beyond 30-days

### Liquidity cashflow stress-test: main findings
- On aggregate, for the total currency assessment, the banking system can withstand severe outflow shock up to 3 months, with positive CBCs balance at around 6 percent of total assets.
- At individual bank level:
  - Asset share of banks experiencing liquidity shortfalls up to 30 days: 0.2 percent of total assets.
  - Asset share of banks experiencing liquidity shortfalls up to 3 months: 13.3 percent of total assets.
  - Size of liquidity shortfall relative to total assets: around 0.3 percent of total assets.
  - Shortfalls are mostly in small and medium sized banks; no liquidity shortfall observed in the KBMI4 group.
- IDR-based assessment:
  - Asset share of failing banks: 15.7 percent of total assets.
  - Liquidity shortfall: 0.5 percent of total assets.
  - These figures are only slightly above those observed under the total currency analysis.
- Large variations in post-shock liquidity positions, especially in small banking cohorts, are consistent with bank solvency stress-test results.

### Drivers of outflows (cashflow decomposition)
- Main drivers are wholesale deposits from corporate, banks, and NBFIs entities, especially in uninsured segments.
- For total currency up to 3-month horizon (contributions to change in CBCs as a share of total assets):
  - Deposits from corporate entities: about 6 percentage point.
  - Deposits from NBFIs: about 4 percentage point.
  - Undrawn credit lines: about 3.5 percentage point.
  - Retail deposits: about 2.2 percent.
  - Total uninsured deposits: about 9 percentage point contribution.
  - Insured deposits: about 1.3 percentage point contribution.

### Foreign-currency (USD) cashflow analysis
- Dollar-denominated analysis is more adverse beyond 30 days than domestic currency, although system-level shortfalls are broadly contained due to limited FX exposure.
- System-level CBCs post-shock remain positive up to 3-month horizon; small liquidity gaps for KBMI3 (mostly private banks).
- At individual bank level under USD analysis:
  - Share of banks facing liquidity shortages reaches to 47 percent of total asset by the 3-month horizon (includes a few large banks).
  - Size of liquidity shortfalls remains manageable at 0.7 percent of total assets.
  - FX exposure on bank balance sheet: around 17 percent of assets and 18 percent of total liabilities.
- Highlight: higher share of banks experiencing FX liquidity strains underscores the importance of continued monitoring of foreign currency liquidity conditions.

### NSFR-based liquidity stress test: setup and results
- Aggregate NSFR of sample banks as of mid-2023: 136 percent.
  - Above minimum requirement of 100 percent; no single bank below the threshold.
- Aggregate NSFR has been increasing since 2019, supported by increasing share of stable retail deposits.
- Composition differences:
  - Large banks (KBMI3 and KBMI4): retail deposits account for a larger share of stable funding.
  - Small banks (including regional banks): long-term wholesale deposits and capital dominate stable funding composition.
- Stress-test methodology (volume-based migration):
  - Assumes migration from long-term to short-term funding buckets.
  - Assumed migration rates:
    - 50 percent of funding within the six- to twelve-month bucket flows to the less than six-month bucket.
    - 35 percent of funding with more than one year maturity migrates to the six- to twelve-month bucket.
    - 15 percent flow rate from the over one-year bucket to the less than six-month bucket.
  - Applicable RSF and ASF factors are maintained under the stress scenario.
- Results:
  - Most banks remain able to maintain a stable funding profile under the adverse scenario.
  - Only two small banks have NSFR marginally below the 100 percent threshold post-shock.
  - Larger decline of NSFR observed for small banks; large banks retain high stable funding before and after the shock.

### Sensitivity analysis — funding concentration
- Withdrawal simulation of top five individual funding sources by sector (nonfinancial corporates, financial corporations, government, individuals) assesses concentration risk and impact on liquidity.
- Regional banks are more exposed to large funding withdrawals from financial corporations and public entities:
  - Top funding sources for regional banks are wholesale deposits from public entities, financial corporations, and nonfinancial corporations.
  - Funding from public entities can reach up to 55 percent of their liquid assets.
  - Funding from financial corporations up to 45 percent of liquid assets.
  - Funding from nonfinancial corporates up to 20 percent of liquid assets.
- State-owned banks have lower dependency on public entities than regional banks but higher reliance on large fundings from nonfinancial corporates.
- Private banks show moderate funding concentration from nonfinancial corporates and individuals.
- Small banks generally more exposed to concentrated funding than large banks.
- About 15 banks in total would have difficulties meeting top five funding withdrawals from all sectors; these are mostly small banks.

### Liquidity–solvency interactions
- Simulated interaction: banks forced to utilize amortized cost (AC) securities at market prices to meet large liquidity outflows can incur capital losses (valuation losses).
- Method: use maturity ladder and balance sheet data to proxy CBC share booked under amortized cost and estimate market losses as banks liquidate AC securities within CBC limit.
- Results:
  - Under the severe scenario and assuming prioritization of cash and fair-value securities over AC securities for up to 3 months, about 35 banks would need to resort to AC securities to meet severe cash outflows.
  - Capital impact of valuation losses is broadly contained.
  - CET1 ratio depletion for groups with higher AC sovereign holdings and longer duration:
    - KBMI1: 2.3 percentage points.
    - KBMI4: 1.6 percentage points.
  - Note: solvency impact would be smaller if banks use AC securities as collateral to seek central bank funding instead of selling at market price.

### Reverse stress test
- Two reverse stress-test types (up to 3-month horizon):
  - Type 1: Percentage of unsecured (or uninsured) deposits withdrawal that depletes liquidity assets (with BI haircut).
  - Type 2: Percentage of total deposits withdrawal that depletes liquidity assets (with BI haircut).
- Key result: most banks can sustain outflow rates of about 35 percent of total deposit and 45 percent of uninsured deposits before fully running out of liquid assets.
- Distributional features:
  - Large variation across banks: some withstand only 15 percent of outflows, others can withstand more than 100 percent (when deposits are a small share of liabilities).
  - Large banks (including SOBs) appear resilient due to higher share of stable deposits, but their large deposit base and large loan portfolios may render them vulnerable to very large deposit outflows.
  - Variation and higher tail risk concentrated among small banks and private banks.

### Stress-test parameters (selected ranges and haircuts)
- Run-off factors (In Percent) across Mild, Medium and Severe Scenarios (selected items):
  - Own securities issuance due: 40-100%
  - Regulated covered bonds: 25-70%
  - Securitisations and others: 100%
  - Repos across all asset classes: 100%
  - Retail deposits (households CASA, etc): 3-20%
  - Insured deposits: 3-10%
  - Uninsured deposits: 6-20%
  - Corporate deposits (Large corporates and MSMEs): 6-40%
  - Corporate insured: 6-20%
  - Corporate uninsured: 12-40%
  - Other deposits and unsecured borrowing (non-resident, banks, NBFIs, government): 20-100%
  - Derivatives: 100%
  - Committed facilities: 10-100%
  - Other contractual outflows: 0-100%
  - Reverse repos across all asset classes: 100%
  - Loan inflows from retail and corporates (performing and non-performing): 0-30%
  - Loan inflows from central banks: 100%
  - Loan inflows from banks and NBFIs: 0-100%
  - Derivatives (in inflows list): 100%
- Haircut based on market price (Counterbalancing Capacity items):
  - Cash: 100%
  - Central Bank exposures: 100%
  - 0% RW securities: 95-85%
  - 20% RW securities: 85-75%
  - Covered bonds: 85-75%
  - Corporate bonds: 85-70%
  - RMBS: 80-70%
  - Other CB eligible assets (including credit assets under ELA): 50-40%
  - Non-CB eligible equities: 0%
  - Other non-CB elig. assets: 0%
  - Undrawn committed credit lines: 100-0%

### Monitoring implications and policy priorities (implied by analysis)
- Continue monitoring foreign currency liquidity conditions given higher share of banks experiencing FX liquidity strains despite limited FX exposure.
- Focus supervisory attention on small and medium banks where post-shock liquidity variation is largest and where concentration and tail risks are higher.
- Monitor funding concentration risks for regional banks due to high exposure to wholesale deposits from public entities and financial corporations.
- Monitor interactions between liquidity stress and solvency, particularly for banks with large holdings of long-duration sovereign AC securities.

*Source: IMF staff (excerpt).*

### 69.      Key recommendations drawing from the bank liquidity risk analysis focus on

### 1idnea2025003-print-pdf - 69.      Key recommendations drawing from the bank liquidity risk analysis focus on

### Key recommendations (bank liquidity risk analysis)
- Expand the scope of LCR and NSFR reporting and enhance stress testing approaches.
- Implement LCR and NSFR across all banks and monitor LCR for significant foreign currencies.
- Regularly conduct cashflow stress tests for significant foreign currencies to promptly identify banks with weak FX position.
- Complement existing stress test methodology (which focuses on bank-specific outflow parameters) by applying an array of unified liquidity stress scenarios with different levels of severity and time horizons across all banks to allow the identification of weak banks in a more comparable way.

### Interconnectedness analysis — Introduction and context
- The Indonesian financial sector comprises approximately 73 percent of GDP and is primarily dominated by the banking sector.
- Interbank exposure appears low at only 3 percent of total assets.
- Banks have substantial exposures to non-financial corporates (NFCs) and households, making up approximately 60 percent of total banking sector assets and liabilities.
- Bank holdings of government debt obligations account for about 13 percent of total banking sector assets.
- NFCs have strong cross-border links via foreign direct and portfolio investment and holdings of Indonesian sovereign bonds.

### Methodology (network and contagion model)
- Interbank network and contagion analysis uses model developed by Espinosa-Vega and Sole (2010).
- Domestic contagion analysis uses confidential interbank exposure data as of 2023Q2.
- Cross-border analysis uses BIS locational banking statistics (2023:Q1) complemented by OJK regulatory data on bank-specific cross-border exposure.
- Two types of shocks:
  - Credit shock: modeled via loss given default parameter λ.
  - Funding shock: modeled via funding loss fraction ρ and fire-sale discount δ; debtor replaces (1-ρ) fraction of funding and suffers additional asset loss δ*ρ absorbed by capital.
- Default criterion: failure to meet minimum capital requirements (8 percent capital adequacy ratio in the domestic network exercise).
- Two main indices:
  - Index of contagion: measures average loss of other banks due to failure of bank i (formula preserved in source).
  - Index of vulnerability: measures average loss of bank i due to failure of all other banks (formula preserved in source).

### Domestic interbank contagion — Data and simulations
- Sample includes bilateral exposures among 4 state-owned banks (SOE), 27 regional development banks, 67 private banks, and 7 branches of foreign banks, totaling 105 commercial banks.
- Two spillover scenarios tested: i) simple credit shock, and ii) credit and funding shock.
- Results presented from simulation 2 (single credit shock) and simulation 5 (credit plus funding shock). All simulations and parameters included in Table 3 (as in source).
- Parameter excerpts from Table 3 (as presented in source):
  - simulation 1: credit, lambda1(λ1) 0.5, lambda2(λ2) 0.2
  - simulation 2: credit, lambda1(λ1) 0.6, lambda2(λ2) 0.3
  - simulation 3: credit, lambda1(λ1) 0.7, lambda2(λ2) 0.4
  - simulation 4: credit + funding, lambda1(λ1) 0.5, lambda2(λ2) 0.2, rho(ρ) 0.2, delta(δ) 0.15
  - simulation 5: credit + funding, lambda1(λ1) 0.6, lambda2(λ2) 0.3, rho(ρ) 0.3, delta(δ) 0.2
  - simulation 6: credit + funding, lambda1(λ1) 0.7, lambda2(λ2) 0.4, rho(ρ) 0.4, delta(δ) 0.25

### Domestic interbank contagion — Key findings
- Contagion risks from domestic interbank exposures are limited.
  - As of 2023:Q2, sum of total bank gross exposures relative to total regulatory capital stands at 18.9 percent (down from 24 percent in 2017).
  - No indication that a single failure of a domestic bank would trigger failure of another bank in the network; no cascading effect detected in simulations considered.
- Heterogeneity across bank types:
  - SOBs (state-owned banks) are least vulnerable (lowest vulnerability levels) in both simulations.
  - Private banks and branches of foreign banks exhibit lower vulnerability levels than regional development banks.
  - Regional development banks show higher vulnerability due to higher interbank deposits into and loans from other banks and smaller size in total assets and capital.
  - SOBs appear most contagious (higher average contagion level) in both scenarios.
  - Strong correlation between vulnerability/contagion levels and bank size as measured by KBMI: KBMI 4 banks are generally least vulnerable but most contagious; small banks show the opposite pattern.
- Distributional findings:
  - Approximately 40 percent of banks have vulnerability indices surpassing the average level in both simulations.
  - Private banks with vulnerability indices exceeding average: 42 percent (simulation 2) and 40 percent (simulation 5).
  - Regional development banks exceeding average vulnerability: 41 percent (simulation 2) and 44 percent (simulation 5).
  - Foreign bank branches exceeding average vulnerability: 29 percent (both simulations).
  - SOBs exceeding average vulnerability: 25 percent (both simulations).
  - Contagion index above group average: 50 percent of SOBs, 33 percent of private banks, 29 percent of foreign bank branches, and 26 percent of regional development banks.

### Cross-border interbank contagion — Data, scenarios, and findings
- Cross-border exposures:
  - Indonesian banks’ cross-border claims on United States, Singapore, China, and Japan collectively constitute around 70 percent of total claims.
  - On liabilities side, exposures to Singapore, Hongkong, the United States, and China comprise 72 percent of total liabilities.
  - KBMI 4 banks hold the most cross-border positions; SOBs account for more than 50 percent of cross-border exposure, followed by private banks and foreign bank branches.
- Cross-border simulation setup:
  - Analysis uses BIS locational banking statistics and assumes hypothetical defaults in each foreign banking sector, one at a time.
  - Two simulations:
    - Simulation 1: credit shock with loss given default (λ) set at 40 percent.
    - Simulation 2: compounded credit and funding shock with λ = 40 percent, funding loss fraction ρ = 40 percent, and haircut δ = 50 percent.
  - Analysis conducted on total exposures to the banking sector (claims to banks, non-bank sector, unallocated amount).
  - Hurdle rate: 6 percent tier one (T1) ratio for Indonesian banking sector aggregated T1 breach definition.
- Cross-border results:
  - Moderate level of vulnerability of Indonesian banking sector to external shocks.
  - Aggregate exposures to major economies (e.g., United States) are small relative to those economies, but sector shows greater vulnerability to the United States and the United Kingdom in both simulations.
  - Post-shock T1 ratios of the banking system remain strong in simulations.
  - Important caveat: BIS reporting limitations and missing information on cross-border exposure between Indonesia and other Asian countries, especially ASEAN countries and China.

### Recommendations (interconnectedness exercise)
- Authorities should consider implementing contagion analysis for interbank spillover risk assessment on an ongoing basis.
- FSAP recommends adopting the stress testing methodologies used in this exercise to timely identify vulnerable banks through interbank linkages for ongoing risk monitoring purposes.
- Current practice: authorities perform limited contagion analysis (domestic and cross-border) with findings incorporated only into identification of systemically important banks; recommendation is to expand use for monitoring.

### Corporate risk analysis — Data and descriptive findings
- Sample: 460 NFCs from Capital IQ (S&P Global Market Intelligence), annual data up to end-2022: 441 publicly listed, 19 private companies, including 21 state-owned enterprises.
- Sample coverage: debt held by sample firms and their total revenue in 2022 account for about 52.7 percent of total outstanding NFC debt in the economy and 23.1 percent of GDP.
- Corporate metrics improved since 2020:
  - Median debt-to-equity ratios decreased from 40 in 2020 to 30 in 2022.
  - Median return on assets increased from 2.5 in 2020 to 3.5 in 2022.
  - Median interest coverage ratios increased from 2 in 2020 to 4 in 2022.
- Debt-at-risk concentration:
  - Bottom decile of firms by size: share of debt for firms with an ICR<1 is over 60 percent.
  - Top decile: share of debt for firms with an ICR<1 is around 10 percent.
  - Variation across industries in share of debt in vulnerable firms.

### Corporate stress testing — Approach and variables
- Focus: firms’ debt service capacity proxied by interest coverage ratio (ICR = EBIT / interest payment).
- Regression-based approach to forecast ROA, effective interest rate, and leverage for baseline and adverse scenarios.
- Explanatory variables:
  - Domestic macroeconomic variables: Real GDP growth, Exchange rate growth (relative to USD), Short-term rate.
  - Global macroeconomic variables: World GDP growth, Commodity prices, Oil prices, US short-term rate, US GDP growth, China GDP growth, VIX.
  - Firm-specific variables: Total assets, Tangible assets-to-total assets ratio, Return on assets, Debt-to-assets ratio, Effective interest rate.
- Global forecasts: October 2023 IMF WEO forecasts and October 2023 GFSR global stress test.
- Regression sample: 2,532 NFCs from 6 major ASEAN economies as of end-2022 (including Indonesia).
- Baseline and adverse ICR forecasts constructed using predicted ROA, effective interest rate, and leverage (formulas and regression specification preserved in source).

*Source: Excerpt from IMF PDF chapter content unit 1idnea2025003-print-pdf - 69.*

### 91.      The stress test indicates that there may be heightened debt at risk in the corporate

### The stress test indicates that there may be heightened debt at risk in the corporate sector

### Corporate sector stress test: key findings
- In the baseline scenario, the share of debt for firms with an ICR<1 is 17 percent.
- The share increases to 27 percent in the recession scenario.
- The share increases to 31 percent in the stagflation scenario.
- Caveats:
  - The analysis does not consider the effects of policy support measures under the stress scenarios.
  - Sample firms account for only a small share of non-financial firms in Indonesia and do not include micro-sized firms in the informal sector, which tend to suffer disproportionately in the face of negative economic shocks.
  - To the extent informal and micro-sized firms are excluded, the estimated impact of the shocks on Indonesia’s NFC sector in the economy is likely to be downward biased.

### Systemwide liquidity and NBFI analysis — Introduction and scope
- Objective: assess resilience and identify vulnerabilities associated with system-wide liquidity by tracing funding flows across agents and combining domestic and cross-border networks.
- The analysis:
  - Includes NBFI subsectors: Insurance Companies, Pension Funds, Finance Companies, Investment Funds.
  - Includes other market agents: central bank, government, commercial banks, non-financial corporations, households, and foreign investors.
  - Leverages aggregate balance-sheet level data by agent type and bilateral exposures informed by who-to-whom holdings, collected via an IMF staff data template.
- Intended uses:
  - Understand interconnectedness and system-wide liquidity conditions.
  - Assess each agent’s contribution to system-wide liquidity stress and transmission channels.
  - Inform micro- and macroprudential liquidity measures.

### Static analysis: structure and exposures
- NBFI size and composition:
  - NBFIs make up 24 percent of the overall financial sector.
  - Largest NBFI subsectors by assets: Insurance Companies (13 percent), Finance Companies (3.5 percent), Investment Funds (3.5 percent), Pension Funds (2.5 percent).
  - FinTech lending companies: assets and lending rose by 51 percent and 63 percent, respectively.
- Interconnections and exposures:
  - Commercial banks rely mostly on retail and wholesale deposits; major debtors are non-financial corporates and households.
  - Pension funds, investment funds and insurance companies are creditors with very small liability exposures to commercial banks; finance companies have both significant bilateral asset and liability exposures.
  - NBFIs have large asset exposures to domestic banks and non-financial corporates through deposits, debt securities, and equities.
  - Non-financial corporate sector debt obligations to foreign investors more than double the size of those to domestic banks.
- Holdings of sovereign and corporate securities:
  - Commercial banks hold over 1400 trillion IDR in sovereign securities, tripling the amount held by insurance companies, investment funds, pension funds and finance companies together.
  - Commercial banks hold almost 60 percent in corporate securities, more than double the size of those held by investment funds.
  - Pension funds and insurance companies each hold around 8 percent of the corporate securities market share.
  - Share of sovereign securities on NBFI balance sheets: around 30 percent for investment funds and pension funds, and 21 percent for insurance companies.
- Maturities:
  - Banks hold securities with shorter duration at around 3.5 years on average.
  - Percent of sovereign securities with maturities exceeding 10 years: 68 percent for insurance companies, 48 percent for pension funds, 38 percent for investment funds.

### Methodology: systemwide liquidity analysis and shock design
- Approach:
  - Systemwide liquidity analysis is performed on total currency level using Monte Carlo simulations with compoundable layers of liquidity stress scenarios.
  - Correlation factor is assumed at 0.9 across all types of liquidity shocks.
  - A pecking order of market clearing is specified; agents prefer highly liquid assets and are assumed initially able to freely withdraw liquidity from other agents after exhausting their own cash buffers.
- Shock layers and calibrated ranges (Table 6):
  - Layer 1 Sovereign bond yield shock: [0,4] with avg shock of 200bps
  - Layer 1 Corporate bond yield shock: [0,8] with avg shock of 400bps
  - Layer 2 NFC FX funding shock: [0,50]
  - Layer 3 Wholesale deposit run-off: [0,50]
  - Layer 4 Resident retail deposit run-off: [0,20]
  - Layer 4 Non-resident retail deposit run-off: [0,50]
  - Layer 5 Share redemption rate: [0,50]
  - Layer 5 Insurance reserves outflow: [0,50]
  - Layer 5 Pension withdrawal rate: [0,50]
- Layer narratives:
  - Layer 1: Sovereign and corporate securities devaluation affects liquidity buffers.
  - Layer 2: NFC FX funding shock—firms seek domestic funding from commercial banks when external funding tightens.
  - Layer 3: Wholesale deposit run-off—firms move deposits outside banks/financial system.
  - Layer 4: Retail deposit run-off—non-residents move deposits abroad; residents shift to cash-like instruments.
  - Layer 5: NBFI liabilities shocks—share redemptions, insurance surrenders, pension withdrawals.
- Valuation method for securities shocks:
  - Shocks follow a modified duration approach using granular holdings by maturity buckets, with duration D at midpoint of each bucket and calibrated yield shocks to derive market valuation impacts.

### Results of systemwide liquidity analysis
- Overall resilience:
  - The system remains resilient against the five narratives with commercial banks backstopping liquidity needs of all sectors.
  - Under the most severe test with combined shocks, commercial banks show only marginal liquidity shortfalls (a thin negative tail in their liquidity distribution).
  - In about 2 percent of simulations with all shocks, commercial banks have a small negative net liquidity position.
  - Despite large shocks to pension funds, investment funds and insurance companies, the overall liquidity impact from the NBFI sector is limited due to their relatively small size and because withdrawals by households and NFCs are deposited at commercial banks.
- Drivers of bank outflows:
  - The largest contribution to changes in the net liquidity position of commercial banks comes from the non-financial corporate sector.
  - Liquidity outflows from corporates contribute the most to the decline in net liquidity position of commercial banks, particularly given corporates’ high exposure to wholesale deposits.
- Caveats on results:
  - Analysis only considers exposure at sector level which may mask entity-level vulnerabilities.
  - Analysis considers total currency exposure without a direct split between domestic and foreign currency, which may understate the impact in the absence of currency frictions.
  - Analysis does not consider policy support measures under stress scenarios.
  - Sample excludes firms (typically MSMEs) in the informal sector.

### Recommendations
- Enhance data collection and risk monitoring on NBFI statistics and their interconnection with banks at both sectoral and institutional levels.
- Improve granularity of data, including entity-level data, to better assess NBFI importance.
- Regularly collect Bank-NBFI exposure matrices at both sectoral and institutional levels to facilitate systemwide risk analysis.
- Enhance data collection on conglomerates (Banks and NBFIs), including assets of sub-entities, ownership structure, and bilateral balance sheet exposures within group structures to identify large and opaque intra-group exposures and potential risk transfer mechanisms.

### Systemwide FX liquidity analysis — highlights
- As of end-2022:
  - Indonesian corporates had about 161 billion USD external debt, 30 billion of which was short-term.
  - Additional 59.5 billion USD domestic borrowing in foreign currencies.
  - NFCs hold about 52 billion USD in FX deposits in the domestic system.
  - Banking system’s FX CBC stood at over 40 billion USD, indicating scope to meet corporate deposit outflows.

*Source: Capital IQ and IMF staff; BI, OJK, and IMF; IMF staff.*

### 111.      A granular NFC-bank FX liquidity stress test assesses both systemwide resilience and

### A granular NFC-bank FX liquidity stress test assesses both systemwide resilience and institutional vulnerabilities from FX liquidity mismatch

### Scope, data, and methodology
- Coverage: analysis focuses on 15 firms with the largest reported FX debt in a publicly available dataset (Based on Capital IQ).
- Data sources:
  - Capital IQ for outstanding debt instruments (including currency and maturity), FX revenue and total revenue for 2022.
  - Company annual reports for deposits by currency and bank (restricted deposits not included).
  - Supervisory data for institution-level FX liquid assets and their composition.
  - BI and IMF staff inputs.
- Stress test design:
  - Granular, institution-level stress test to identify shortfalls at individual firms and banks even when aggregate liquidity remains.
  - Tests joint resilience of banking and NFC sectors by assuming no policy support.
  - Extreme shocks imposed on sample firms to partly compensate for limited granular data on the entire network.
- Firm financing pecking orders and options considered:
  - FX revenue, offshore FX deposits, domestic FX deposits, conversion of domestic IDR deposits to FX, and new domestic FX financing (if domestic system’s remaining FX liquidity permits).
  - New FX financing from the domestic banking system placed at bottom of firms’ pecking order.
- Common assumptions across scenarios:
  - No inflows assumed to replenish banks’ CBC over the 30-day stress period (standard liquidity stress testing approach).
  - BI’s haircut assumptions applied when liquidating banks’ FX assets.
  - IDR-denominated debt assumed to face no rollover issues.
  - No policy interventions.

### Scenario designs and calibration
- Macro shock:
  - Uses the same stagflation scenario as the bank solvency stress test and the corporate debt-at-risk stress test.
  - Firm-level FX debt refinancing shocks are calibrated based on predicted ICR for each firm in the corporate debt-at-risk stress test for the stagflation scenario.
  - Rollover assumptions by predicted ICR:
    - Predicted ICR above 2: assumed to face no rollover pressures.
    - Predicted ICR between 1 and 2: assumed to face rating downgrade that dries up external financing to rollover maturing debt over the stress test horizon (1 month).
    - Predicted ICR below 1: treated as technically in default and need to repay their entire debt immediately.
  - Firms use the following pecking order in results discussed: first, available FX revenues (assumed to be 1/12th of annual 2022 FX revenue); second, offshore FX deposits; third, domestic FX deposits; fourth, domestic IDR deposits.
  - Note: The option of firms to use cash buffers to pay interest and defer default is assumed not available.
- Proportional shock scenarios:
  - Impose refinancing need for uniform shares of firms’ short-term and total FX debt.
  - Severe assumptions to test resilience:
    - A 50 percent haircut is applied to FX revenue.
    - Pecking order altered in some scenarios so firms use domestic FX deposits before offshore ones.
  - Scenarios include extreme cases (e.g., proportion of all external debt needs to be rolled over) to compensate for limited data coverage.
- Sensitivity and behavior:
  - Scenarios assess ability of firms to tap banking system’s remaining liquidity by comparing corporate shortfalls against banking system’s remaining FX CBC.

### Key findings and quantitative outcomes
- Systemwide resilience and institutional vulnerabilities:
  - Stress test based on the macro scenario:
    - Stressed firms are left with a large portion of their FX refinancing need uncovered, indicating mismatches between corporate FX debt and liquid assets.
    - The banking system retains enough FX liquidity to allow new domestic financing to fill corporates’ FX shortfall.
    - Under assumption of no new cash inflows to CBC, pockets of liquidity strains appear in the banking system but could be addressed through reallocating liquidity within the system.
  - Proportional shocks to short-term FX debt:
    - Even if 100 percent of the sample firms’ ST FX debt needs to be refinanced, firms are able to significantly close the external financing gap by drawing on their deposits.
    - The simulated total FX outflow is 17 billion USD, which is over 50 percent of the total short-term debt for all firms.
    - FX shortfalls in the corporate sector, and amongst small banks, could be covered by the remaining FX liquidity in the banking system.
  - Proportional shocks to total FX debt:
    - In extreme cases where 75 to 100 percent of the sample firms’ total FX debt needs to be refinanced, the remaining counterbalancing capacity of the system falls below corporates’ FX shortfall after deposits are used.
    - Several medium to small banks run out of FX liquidity in these extreme scenarios.
    - These extreme scenarios correspond to imposed shocks of over 1.5 to 2 times the short-term debt of the corporate sector.
- Complementarity with bank FX liquidity stress test:
  - Results align with the bank FX liquidity stress test (which shocked proportions of the entire FX deposit base): pockets of limited shortfalls appear only in severe shocks.
  - Partial data in the bank-corporate FX liquidity stress test limits assessment of transmission from NFCs to individual banks; broader linkage data would improve comprehensiveness.

### Policy implications and recommendations
- Data and monitoring:
  - Increase data coverage and monitoring of FX risk exposures for both banks and corporates.
  - Expand coverage of non-listed large firms, integrating data on both domestic and external debt to enhance surveillance of corporate risks to financial stability.
  - Regular bank-corporate liquidity stress tests, with greater coverage, to identify pockets of concentration and changes in FX stress absorption capacity.
- Bank-side monitoring and limits:
  - On liabilities: monitor FX deposit concentration to help calibrate stress tests accordingly.
  - On assets: monitor systemwide and bank-by-bank FX HQLA concentration, and place limits in case of excessive exposures.
- Policy toolkit if FX stress absorption capacity declines:
  - Consider further increasing FX reserve requirements.
  - Consider limiting FX net outflows of in foreign currency LCR.

*Source: Capital IQ, company annual reports, BI and IMF staff; IMF staff analysis.*

### 16. NFSR analysis using volume-based approach.

### 16. NFSR analysis using volume-based approach.

### Risks and Buffers — Liquidity and Asset Price Shocks
- Risks:
  - Short-term liquidity outflows, asset price shocks, and fire-sales.
- Buffers:
  - Counterbalancing capacity in all types of tests includes liquidity obtained from markets through asset sales and from BI’s standing facilities.

### Banking Sector: Liquidity Stress Test
- Size of shocks:
  - Haircuts are calibrated to be consistent with haircuts from BI’s standing facilities and solvency stress test scenarios.
  - Run-off rates are calibrated with hypothetical system-wide runs and dry-up of whole funding and allow for higher outflow rates for uninsured funding than insured funding.
- Regulatory standards:
  - National regulatory framework.
- Output presentation:
  - System-wide and bank-level liquidity ratio, and liquidity shortfalls.

### Banking Sector: Contagion Analysis
- Institutions included:
  - All regulated 105 commercial banks according to KBMI and ownership classification.
- Data and starting position:
  - Supervisory and market data at the reference date (e.g., 2023Q2).
- Methodology:
  - Interbank and cross-border network model by Espinosa-Vega and Solé (2010).
- Risks:
  - Credit and funding losses related to bilateral exposures, default of large borrowers.
- Buffers:
  - Institution’s own capital and liquidity buffers.
- Size of shocks:
  - Default of institutions or largest borrowers.
- Output presentation:
  - System-wide and bank-level capital shortfalls and other risk measures.

### Climate Risk Analysis
- Institutions included:
  - All regulated 105 commercial banks according to KBMI and ownership classification.
- Data and starting position:
  - Supervisory data at the reference date (e.g., 2023Q2).
- Methodology:
  - Analysis of transition and physical risks using “Macro” approach.
  - Physical risk: Scenarios based on climate-related damage estimates from World Bank catastrophe risk modelling are simulated using the MCM macro-financial model.
  - Transition risk: Based on climate transition scenarios sectoral output pathways, simulated with a World Bank macro model, are used to generate firm-level probabilities of default which are then used to assess aggregated banking exposures.
- Risks and scenario analysis:
  - Physical risk scenarios based on NGFS climate scenarios.
  - Transition risk scenarios to be determined jointly with World Bank and authorities.
- Output presentation:
  - System-wide and bank-level capital shortfalls and other risk measures.

### Nonfinancial Corporate Sector Vulnerability Analysis
- Institutions included:
  - About 460 listed and non-listed Indonesian firms.
- Data and starting position:
  - Capital IQ—S&P Global Market Intelligence, as of end-2022.
- Methodology:
  - Solvency risk analysis using ICR as the main indicator of interest.
  - Cash flow analysis.
- Risks and scenario analysis:
  - Baseline scenario based on the October 2023 WEO forecasts.
  - Adverse macroeconomic scenario consistent with other analyses in the FSAP team.

### Systemwide Liquidity Analysis
- Institutions included:
  - Banks, NBFIs and Corporates.
- Data and starting position:
  - Supervisory and market data (Capital IQ) at the latest reference date (e.g., 2023Q2).
- Methodology:
  - Sectoral (e.g., bank, NBFI, corporates, households) network analysis and systemwide liquidity shock simulations.
  - Simulation of FX outflows from large corporates and spillover to the banks at entity level.
- Risks:
  - Liquidity outflow risks on banks and NBFIs.
  - FX debt rollover shocks for corporates and spillover to the banking sector.
- Buffers:
  - Available liquid assets (or counter-balancing capacities) in domestic and foreign currencies for banks, NBFIs and corporates.
- Size of shocks:
  - Range of run-off rates calibrated on bank deposit outflows, NBFI share redemptions, Insurance companies’ policyholder surrenders, lapses, and withdrawals, and FX debt rollover shocks on nonfinancial corporates.
- Output presentation:
  - Net liquidity position and liquidity shortfalls for banks, NBFIs, Corporates.

### Appendix III — Growth-at-Risk (GaR)
- Purpose:
  - GaR framework quantifies macro-financial risks to growth by assessing how macro-financial factors affect the entire probability distribution of future GDP growth and supports preemptive policies to mitigate downside risks.
- Model design:
  - A large set of variables aggregated into three partitions using principal component analysis; data coverage starts in 1990 at quarterly frequency.
  - Partitions:
    - Financial conditions partition: term premiums; interbank spread; corporate spreads; sovereign spreads; bond returns; equity returns; real long term interest rate; VIX; exchange rate (relative to USD); foreign reserves.
    - Macrofinancial vulnerabilities partition: credit growth; Credit-to -GDP gap; house price growth; growth of construction activity; external debt; current account; corporate debt; household debt; government debt; liquid assets ratio; equity to assets; capital adequacy ratio.
    - Other factors partition: non-energy commodity prices; energy prices; China GDP growth; US GDP growth; Japan GDP growth; India GDP growth; Malaysia GDP growth; Singapore GDP growth; consumer confidence index.
- Quantile regression specification:
  - Quantile regressions estimated for q in {5%, 25%, 50%, 75%, 90%} using predictors corresponding to the three partitions and lagged dependent quantiles.
- Density estimation:
  - A skewed t distribution is fitted on the empirical conditional quantile function for each time horizon to estimate tail risks; a probability density function for future GDP growth is derived from the skewed t-fitted curve.
- Key result:
  - Based on financial conditions at 2023:Q2, a severely adverse outcome (the 5 percent left tail) is annualized GDP growth falling below -3.1 percent over the two-year horizon. This suggests moderate downside risks to growth given tightening global financial conditions and deterioration in key trading partners.

### Appendix IV — Satellite Models

- Credit risk satellite models:
  - Historical probability of default (PD) is used in estimating credit risks.
  - Separate satellite models for PDs of household mortgage, household consumption and large corporate since 2006 at quarterly frequency.
  - PDs for MSMEs are proxied by large corporate PD plus a markup based on average historical differences.
  - PDs for sovereign, banks, NBFIs are generated using a Merton approach translating credit spread under stress to PDs.
  - LGDs are modeled using satellite models on coverage ratio at aggregated level and applied to individual bank starting point.
  - Estimation approach:
    - Model selection technique to average over best models in the model class according to information criteria such as R-square.
    - Logit transformation applied to original PDs to ensure predictions between 0 and 1 and capture nonlinearities.
    - Logit-transformed PDs modeled as linear function of exogenous macroeconomic and financial factors with lagged dependent variables.
    - Projected logit PDs transformed back to PD space and attached to each individual bank’s PDs for baseline and adverse scenarios.
  - Drivers:
    - GDP growth, unemployment rate, interest rates, and asset prices are relevant drivers; significance varies across segments.
- Interest rate risk satellite models:
  - Bank funding costs and lending rates estimated based on interest rate for new business (front book) for each bank; data provided by authorities and proxied by lending and funding accounts that start within one quarter, since 2005.
  - Satellite models estimate aggregate funding and lending rates at portfolio level: asset side includes interest rates on loans and securities; liability side includes interest rates on current, saving and time deposits and bond issued.
  - Results:
    - Short-term rate, long-term rate, and unemployment rate play significant roles.
    - Lending rates highly correlated with short-term interest rate due to dominant share of floating rate loans.
    - Cost of saving and current deposits largely determined by short-term rate; time deposits driven by long-term rates.
    - Unemployment rate is a strong determinant of lending rates, saving and current deposits.
    - Pass-through of short-term interest rate on lending rates around 0.36; for saving and current deposits around 0.1.

- Empirical parameter highlights (from satellite PD and interest rate tables):
  - PD models (Logit; R-square and RMSE reported):
    - PD Mortgage: R-square 0.23; Root mean square error 0.23; Number of observations 67.
    - PD Consumption: R-square 0.58; Root mean square error 0.32; Number of observations 67.
    - PD Large Corporate: R-square 0.47; Root mean square error 0.33; Number of observations 67.
    - Coverage Ratio (Percent): R-square 0.29; Root mean square error 6.54; Number of observations 71.
  - Selected coefficients (examples preserved as reported):
    - Real GDP growth, yoy, percent: -0.05 (PD Mortgage), -0.02 (PD Consumption), -0.1* (PD Large Corporate), -0.45 (Coverage Ratio).
    - Unemployment rate, percent: 0.03 (PD Mortgage), 0.2* (PD Consumption).
    - Short-term rate, percent: 0.06* (PD Mortgage), 0.01 (PD Consumption), 0.08* (PD Large Corporate).
    - Pass-through on lending rates (example): short-term rate coefficient around 0.36* on Loan.
    - R-square for interest rate regressions: Loan 0.85; Bond 0.87; Current Deposits 0.78; Saving Deposits 0.88; Time Deposits 0.61; Bond (liability side) 0.04.
- Comparative banking sector indicators (2023 or latest available) noted in figures:
  - Regulatory Capital Ratio (In percent), Tier 1 Capital to Assets (In percent), NPL Ratio (In percent), Liquid Assets to Total Assets (In percent), Liquid Assets to Short-Term Liabilities (In percent), Liquidity Coverage Ratio (In percent), Net Open Position in Foreign Exchange to Capital (In percent), Return on Assets (In percent).
  - Note: For Indonesia, liquid assets exclude central bank required reserves and include only cash and deposits.

### Appendix IV. Table 3 — Macroeconomic Scenarios Used in Stress Test (In percent)
- Real GDP growth (2023Q2, 2024Q2, 2025Q2, 2026Q2, 2027Q2, 2028Q2):
  - Baseline: 5.2 4.9 5.0 5.0 5.0 5.0
  - Adverse - Stagflation: 5.2 -3.0 -3.3 0.2 4.6 8.0
  - Adverse - Recession: 5.2 -3.9 -1.2 6.8 7.5 6.4
- Nominal GDP growth:
  - Baseline: 12.3 7.8 7.5 7.6 7.5 7.0
  - Adverse - Stagflation: 12.3 2.4 1.4 4.5 8.6 10.8
  - Adverse - Recession: 12.3 -2.6 -2.3 5.8 7.8 7.3
- Inflation:
  - Baseline: 3.9 2.5 2.5 2.5 2.3 1.7
  - Adverse - Stagflation: 3.9 5.2 5.1 4.3 2.9 1.5
  - Adverse - Recession: 3.9 0.8 -0.1 1.9 2.6 2.6
- Unemployment rate:
  - Baseline: 5.3 5.2 5.1 5.1 5.1 5.1
  - Adverse - Stagflation: 5.3 8.0 8.8 8.8 7.7 7.0
  - Adverse - Recession: 5.3 8.0 8.8 8.5 6.7 5.3
- 3-month interbank rate:
  - Baseline: 6.8 7.1 6.0 4.9 4.0 3.9
  - Adverse - Stagflation: 6.8 8.6 8.0 6.9 5.5 5.4
  - Adverse - Recession: 6.8 6.4 4.9 4.1 3.7 3.8
- Long term yield:
  - Baseline: 6.3 6.3 6.0 5.9 5.8 5.8
  - Adverse - Stagflation: 6.3 11.5 10.8 9.0 7.8 7.2
  - Adverse - Recession: 6.3 11.7 6.5 5.2 5.2 5.4
- Exchange rate (growth, percent):
  - Baseline: 2.3 -1.1 -0.7 -0.4 -0.6 -0.7
  - Adverse - Stagflation: 2.3 21.6 15.7 5.1 -1.9 -5.4
  - Adverse - Recession: 2.3 19.4 -3.4 -9.7 -9.4 -7.8
- Housing price growth:
  - Baseline: 1.9 1.9 1.9 1.9 1.9 1.9
  - Adverse - Stagflation: 1.9 -3.5 -2.7 -0.9 2.6 4.8
  - Adverse - Recession: 1.9 -7.2 -2.5 2.8 3.7 1.9
- Stock price growth:
  - Baseline: -4.1 9.0 9.0 9.0 8.8 8.0
  - Adverse - Stagflation: -4.1 -27.9 20.3 13.0 12.7 10.7
  - Adverse - Recession: -4.1 -30.6 6.9 10.7 11.1 9.9
- Oil price growth:
  - Baseline: -36.5 3.8 -5.0 -6.8 -6.0 -4.5
  - Adverse - Stagflation: -36.5 77.0 -2.8 -19.6 -26.0 -16.3
  - Adverse - Recession: -36.5 -24.9 6.2 -3.6 5.1 -2.4
- Commodity price growth:
  - Baseline: -6.2 -3.0 -0.2 0.1 0.4 0.7
  - Adverse - Stagflation: -6.2 -27.7 9.1 8.9 9.8 2.4
  - Adverse - Recession: -6.2 -23.6 7.1 7.2 8.0 2.1

*Source: IMF staff.*

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_Source: https://www.imf.org/-/media/files/publications/cr/2025/english/1idnea2025003-print-pdf.pdf_