## 1ausea2019004

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### Executive summary — key findings and vulnerabilities
- Bank capital and supervision:
  - Total regulatory capital ratio is 14.7 percent (as of December 2017).
  - CET1 ratio is 10.6 percent (requirement of 8 percent).
  - For the subset of 10 banks in the stress test exercise the CET1 ratio was 10.5 percent.
  - Capital conservation buffer: 3.5 percent for D-SIBs and 2.5 percent for other banks; D-SIBs are subject to a 1 percent surcharge (part of the 3.5 percent buffer).
  - Asset quality: average NPLs of 1 percent; provisions about 40 percent of NPLs.
- Structural vulnerabilities:
  - Household debt-to-income is nearly 200 percent (noted elsewhere as some 190 percent of disposable income).
  - The four largest banks hold some 80 percent of banking system assets.
  - Wholesale funding comprises around one-third of total (non-equity) liabilities; approximately two-thirds of wholesale funding is from international sources.
- Housing and mortgage metrics:
  - House prices rose by about 70 percent over a 10-year period through mid-2017, before a modest house price decline of about 3 percent in the 12 months ending in July 2018.
  - Price appreciation higher in Sydney and Melbourne where prices doubled over the past 10 years; recent corrections of about 5 percent and 3 percent respectively.
  - Loan-to-Value (LTV) on the stock of mortgage loans average just over 50 percent.
- Banking sector concentration and exposures:
  - Residential mortgages form over half of bank lending; CRE about 10 percentage points of the loan book.
  - Off-balance-sheet exposures between major banks, foreign banks, and nonbanks (mainly CCPs) appear sizeable.
  - Domestic interbank balance sheet exposures are relatively small (~5 percent of each large institution’s asset base).

### Stress-testing framework, scenarios, and implementation
- Methodology and scope:
  - Solvency stress tests: top-down balance-sheet approach covering the 10 largest banks (~90 percent of system assets); Baseline and Adverse scenarios; three-year profit/loss and capital evolution; starting positions Q4 2017.
  - Liquidity stress tests: LCR, NSFR, and a cash flow-based liquidity stress test; aggregate and AUD-only cash flows; same bank set.
  - Systemic risk: network analysis and market-based contagion analysis (Diebold-Yilmaz).
  - Corporate sector analysis: sample of 250 large corporates with combined assets around A$1 trillion.
- Time horizon and baseline macro path:
  - Simulations for 2018 Q1–2020 Q4; Baseline GDP growth path: first year close to 3 percent, second year 3.1 percent, third year 2.9 percent.
  - Baseline unemployment declines from 5.6 percent (2017) to 5 percent by end of scenario.
  - USD exchange rate assumed stable at 1.32 throughout the Baseline scenario.
- Adverse scenario — driving shocks:
  - Combination of: Shock 1 (sharp decline in real estate prices), Shock 2 (significant slowdown in China and global growth), Shock 3 (global financial market turmoil and sharper-than-expected tightening of global financial conditions).
  - Real GDP assumed to decline at annual rates of 2.5 percent and 0.5 percent in the first two years, and recover to 1.6 percent in the third year (cumulative GDP shock of about 10 percentage points relative to Baseline).
  - China adverse GDP growth shocks of 2.6 percent, 3.9 percent, and 1.7 percent in the three years relative to the baseline.
  - Unemployment increases from 5.6 percent to 8.8 percent and 9.9 percent in the first and second years, respectively, and declines to 9.7 percent in the final year.
  - AUD loses 25 percent of its value against the USD in the first year; cumulative depreciations of 20 percent and 16 percent by end of second and third years.
  - House prices decline by 15 percent in year 1 and 10 percent in each of years 2 and 3 (cumulative decline slightly over 31 percent at end of three years).
  - Monetary policy: 100-basis points reduction in the RBA Cash Rate in the first year to 50 basis points; further reduction of 25 basis points in the second year.
  - Spread and funding dynamics: Bank Bill spread widens from 30 basis points to 130 basis points at end-2018, then to 70 basis points end-2019 and 50 basis points end-2020.

### Solvency stress test results and drivers
- Aggregate outcomes (Baseline vs Adverse):
  - Baseline: average CET 1 increases from 10.5 percent in 2017 to 10.8 percent in 2020; all banks remain comfortably above regulatory capital requirements.
  - Adverse: system-wide average CET 1 decreases from 10.5 percent in 2017 to 7.2 percent in 2020; all 10 banks in the ST sample remain above the 4.5 percent CET 1 hurdle.
  - Largest 5 banks: CET 1 decreases from 10.6 percent in 2017 to 7.2 percent in 2020 (decline of 3.3 percentage points).
  - Mid-sized banks: CET 1 decreases from 9.7 percent in 2017 to 7.0 percent in 2020.
- Major drivers of capital changes:
  - Loan loss provisions (large increase due to higher defaults and higher provisioning rates).
  - Increases in risk-weighted assets (notably for IRB banks).
  - Declines in net interest margins due to funding shocks.
- NPL projections:
  - Mortgage NPL ratio projected to rise from 0.8 percent at year-end 2017 to just over 3 percent at year-end 2020 (system-wide average, Adverse).
  - Regional evidence: WA mortgage NPL rose from 0.5 percent at year-end 2013 to around 1.6 percent at year-end 2017; national mortgage NPL increased by less than 0.2 percentage point over same period.
  - Provisioning rates in the Adverse scenario: corporate loans between 35 and 50 percent; consumer loans up to 90 percent; mortgage loans in the 10 to 17 percent range given low average LVRs.

### Liquidity analysis and maturity transformation risks
- Funding composition and maturity transformation:
  - Wholesale funding remains about one-third of total funding; two-thirds of wholesale funding is sourced internationally.
  - Largest five banks funding composition (approximate): wholesale deposits ~33 percent, secured/unsecured wholesale debt ~35 percent, retail deposits ~32 percent.
  - Mid-sized banks: retail deposits close to 46 percent of total non-equity funding; wholesale deposits ~32 percent.
  - Loan portfolios have a very long-dated maturity profile due to mortgage dominance and slow amortization (interest-only loans contribute to back-dated cash inflows).
- LCR and NSFR outcomes:
  - Baseline average LCR across ST sample = 136 percent at December 2017; mid-sized banks average 131 percent.
  - NSFR average at system = 113 percent; all banks above 100 percent.
  - Under Wholesale stress scenario: aggregate LCR falls to 91 percent; six banks fall below 100 percent; largest five banks average LCR falls from 136 percent to 91 percent.
- Cash flow-based liquidity stress test:
  - Several banks experience cash shortfalls after using existing CLF amounts; three banks still experience shortfalls in simulation.
  - All banks maintain positive cash balances in first 30 days.
  - Shortfalls extend beyond 30 days for three banks in different maturity windows; total shortfall peaks at less than 0.5 percent of banking system assets during the “3 months to 6 months” window.
- Policy implications:
  - Reliance on wholesale funding and mismatch with long-dated loan cash flows create short-term liquidity vulnerabilities.
  - Suggested authorities’ actions include encouraging maturity extension, reducing dependence on overseas wholesale funding, and enhancing data collection for cash flows and off-balance-sheet positions.

### Interconnectedness, network analysis, and contagion findings
- Global and cross-border network results:
  - Main global contagion sources: U.S., U.K., French banking systems; vulnerabilities distributed globally.
  - Australian system: neither a significant source nor a significant recipient of contagion among global banking systems in the sample.
  - Australian banks vulnerable to external funding shocks due to overseas wholesale funding dependence; large liability positions vis-à-vis the United States and the United Kingdom.
- Domestic interbank and off-balance-sheet exposures:
  - On-balance-sheet domestic interbank exposures small: around 4–5 percent of assets for the aggregate system/major institutions.
  - Off-balance-sheet gross notional amounts large: reported A$36 trillion (ten-year increase); RBA trade repository: OTC IR derivatives gross market value around A$900 billion; Australian banks counterparties on A$363 billion of these trades.
  - Cross-border consolidated international claims ~20 percent of aggregate banking system assets; locational international claims ~8 percent of banking assets.
- Market-based contagion (Diebold-Yilmaz) results:
  - Relatively low degree of interconnectedness between major Australian banks and G-SIBs; Australian banks’ net contribution to global systemic risk negligible in sample.
  - Mid-sized Australian banks show net inward spillover risk (greater vulnerability).

### Nonfinancial corporate (NFC) sector resilience and risks
- Sector structure and exposure:
  - NFC assets total around 250 percent of GDP.
  - Bank loans comprise around 15 percent of aggregate liabilities.
  - Sectoral debt concentration: Materials 25%, Industrials 20%, Utilities 11%, Real Estate 11%, Energy 8%, Telecom Services 7%, Consumer Discretionary 7%, Healthcare 5%, Consumer Staples 5%, IT 1%.
  - Sample of 250 firms has combined debt around A$1 tn; four cyclical sectors (industrials, materials, energy, real estate) have combined debt over 65 percent of total.
  - Corporate debt is about 60 percent of equity for sample.
- Sensitivity results:
  - Under a 1 standard deviation shock to earnings and interest expense, firms exposed to China and the domestic real estate market show weaker debt repayment capacity.
  - Sectors with notable deterioration in interest coverage: Energy, Industrials, Utilities, Real Estate.
  - Materials sector relatively resilient.

### Policy recommendations and priorities
- Data and analytics:
  - Commission and implement a comprehensive forward-looking review of potential data needs over the next 5 years; improve quantity, quality, granularity and consistency of data available to CFR agencies to support financial supervision, systemic risk oversight, and policy formulation. — Time: ST; Responsibility: APRA, RBA.
  - Enhance monitoring, modelling, and stress testing framework for solvency, liquidity, and contagion risk. — Time: ST; Responsibility: APRA.
  - Strengthen integration of systemic risk analysis and stress testing into supervisory processes. — Time: I; Responsibility: APRA, RBA.
- Funding and liquidity:
  - Encourage further maturity extension and lowering of dependence on overseas wholesale funding. — Time: I; Responsibility: APRA.
  - Enhance collection of cash flow information, including flows from derivatives and off-balance-sheet positions, to support liquidity risk analysis.
- Stress-testing framework improvements:
  - Use uniform data sources across banks; develop in-house models to cross-validate bank estimates; monitor exposures in major foreign jurisdictions; conduct regular network analysis.

### Key macrofinancial variable paths (Baseline vs Adverse; percent unless noted)
- Australia Real GDP growth:
  - Baseline: 2017 2.3, 2018 3.0, 2019 3.1, 2020 2.9
  - Adverse: 2018 -2.5, 2019 -0.5, 2020 1.6
- China Real GDP growth:
  - Baseline: 2017 6.9, 2018 6.6, 2019 6.4, 2020 6.3
  - Adverse: 2018 2.9, 2019 3.1, 2020 3.8
- Unemployment rate:
  - Baseline: 2017 5.6, 2018 5.3, 2019 5.2, 2020 5.0
  - Adverse: 2018 8.8, 2019 9.8, 2020 9.7
- USD Exchange rate (level):
  - Baseline: 2017 1.31, 2018 1.32, 2019 1.32, 2020 1.32
  - Adverse: 2018 1.75, 2019 1.63, 2020 1.55
- House price index (2017=100):
  - Baseline: 2017 100, 2018 106, 2019 112, 2020 117
  - Adverse: 2018 85, 2019 77, 2020 69
- RBA cash rate:
  - Baseline: 2017 1.50, 2018 1.75, 2019 2.25, 2020 2.75
  - Adverse: 2018 0.50, 2019 0.25, 2020 0.25
- Bank Bill Rate/Cash Rate Spread:
  - Baseline: 2017 0.30, 2018 0.30, 2019 0.30, 2020 0.30
  - Adverse: 2018 1.30, 2019 0.70, 2020 0.50
- 3-mo Bank Bill Rate:
  - Baseline: 2017 1.80, 2018 2.05, 2019 2.55, 2020 3.05
  - Adverse: 2018 1.80, 2019 0.95, 2020 0.75
- CPI inflation:
  - Baseline: 2017 1.9, 2018 2.1, 2019 2.2, 2020 2.3
  - Adverse: 2018 0.6, 2019 1.2, 2020 1.3

*Source: EXECUTIVE SUMMARY and FSAP technical notes (content unit: 1ausea2019004) — IMF staff summaries and stress-testing, liquidity, and network analysis findings.*

### EXECUTIVE SUMMARY __________________________________________________________________________ 5

### EXECUTIVE SUMMARY

### Key findings
- Bank capital levels have been strengthened and funding risks have also been reduced in recent years; financial supervision and systemic risk oversight have been enhanced.
- Authorities have taken successful policy actions to moderate rapid growth in riskier segments of the mortgage market.
- Notable vulnerabilities:
  - Household debt-to-income is nearly 200 percent.
  - The four largest banks hold some 80 percent of banking system assets.
  - Wholesale funding comprises around one-third of total (non-equity) liabilities.
  - Approximately two-thirds of wholesale funding is from international sources.
- Banking sector metrics (as of December 2017 and related statements):
  - Total regulatory capital ratio is 14.7 percent.
  - CET1 ratio is 10.6 percent (requirement of 8 percent).
  - For the subset of 10 banks in the stress test exercise the CET1 ratio was 10.5 percent.
  - Capital conservation buffer: 3.5 percent for D-SIBs and 2.5 percent for other banks.
  - D-SIBs are subject to a 1 percent surcharge (part of the 3.5 percent buffer).
  - Asset quality: average NPLs of 1 percent; provisions about 40 percent of NPLs.
  - Residential mortgages form over half of bank lending; commercial residential (CRE) about 10 percentage points of the loan book.
- Housing market developments:
  - House prices rose by about 70 percent over a 10-year period through mid-2017, before a modest house price decline of about 3 percent in the 12 months ending in July 2018.
  - Price appreciation has been higher in Sydney and Melbourne, where prices have doubled over the past 10 years; these two cities experienced price corrections of about 5 percent and 3 percent, respectively, over the past year.
  - Loan-to-Value (LTV) on the stock of mortgage loans average just over 50 percent.
- Nonfinancial corporate (NFC) sector:
  - NFC assets total around 250 percent of GDP; bank loans comprise around 15 percent of aggregate liabilities (sample-based).
  - Industrials, materials, energy, and real estate sectors have a combined debt of over 65 percent (sample of 250 firms from S&P Capital IQ).
  - Sensitivity analysis indicates firms exposed to China and the domestic real estate market are particularly vulnerable under an adverse scenario.

### Bank vulnerabilities and structural risks
- Major vulnerabilities stem from homogeneous business models of the four major banks and reliance on offshore wholesale funding.
- Key structural points:
  - Residential assets comprise over half the loan book; CRE a further 10 percentage points.
  - Wholesale funding remains about one-third of total funding for the banking system.
  - Nearly two-thirds of wholesale funding is sourced internationally, raising vulnerability to global liquidity shocks.
  - Banks have increased average duration of wholesale funding and use cross currency swaps matching average durations to hedge currency risk.
- Off-balance-sheet exposures between the four major Australian banks, and between major banks, foreign banks, and nonbanks (mainly CCPs), appear sizeable.
- Domestic interbank balance sheet exposures are relatively small (~5 percent of each large institution’s asset base).

### Stress test and liquidity analysis results
- Solvency stress test:
  - A combined stress (significant slowdown in China, sharp correction in real estate valuations, and marked tightening of global financial conditions) revealed pressures on capital but the 10 banks in the stress test sample would all still meet regulatory minima.
- Liquidity stress tests:
  - Cash flow-based liquidity stress test indicates that a stress in the funding markets would lead to multiple banks experiencing cash shortfalls and raise reliance on central bank funding.
  - The cash flow-based test adopted assumptions generally more severe than LCR assumptions for the first 30-day period and assumed stress conditions continue beyond 30 days.
- Interest rate and spread sensitivity analyses were performed, with implications for profit and capital under various shock scenarios (details in full report).

### Systemic risk and interconnectedness
- Systemic analysis shows a low degree of interconnectedness between the largest Australian banks and their global counterparts.
- Cross-border and interbank exposures data corroborate:
  - The systemic importance of the four largest banks.
  - The particular vulnerability of Australian banks to external funding shocks.
- Interbank spillovers from on- and off-balance sheet exposures and interconnectedness between major Australian banks and Global Systemically Important Banks (G-SIBs) were analyzed; off-balance-sheet linkages are notable.

### Corporate sector resilience and risks
- Corporates appear generally resilient under baseline conditions, with relatively strong gross operating profits recently.
- Sensitivity analysis (1 standard deviation shock to earnings and interest expense for corporates) indicates:
  - Debt repayment capacity is particularly weak for sectors exposed to China and the domestic real estate market.
  - Sharp deterioration in interest coverage ratios for some sectors reflects higher leverage and increased sensitivity to interest rate shocks.
- Insolvency counts have normalized after earlier spikes but remain above pre-GFC levels in some regions.

### Policy recommendations (from Table 1)
- Recommendations, timing and responsibility:
  - "Commission and implement results of a comprehensive forward-looking review of potential data needs over the next 5 years. Improve the quantity, quality, granularity and consistency of data available to the CFR agencies to support financial supervision, systemic risk oversight, and policy formulation." — Time: ST; Responsibility: APRA, RBA.
  - "Enhance the authorities’ monitoring, modelling, and stress testing framework for assessing solvency, liquidity, and contagion risk. Draw on the results to inform policy formulation and evaluation." — Time: ST; Responsibility: APRA.
  - "Strengthen the integration of systemic risk analysis and stress testing into supervisory processes." — Time: I; Responsibility: APRA, RBA.
  - "Encourage further maturity extension and lowering of dependence on overseas wholesale funding." — Time: I; Responsibility: APRA.
- Timing legend provided in the report:
  - I Immediate (within 1 year); ST Short-term (within 1–2 years); MT Medium-Term (within 3–5 years).

*Source: EXECUTIVE SUMMARY (1ausea2019004) — IMF Financial Sector Assessment excerpt.*

### 6.      The financial stability analysis undertaken as part of the FSAP primarily focused on

### 6.      The financial stability analysis undertaken as part of the FSAP primarily focused on

### Methodology and Modules
- Solvency stress tests
  - Top-down solvency ST based on the balance sheet approach covering the 10 largest banks, accounting for nearly 90 percent of total banking system assets.
  - Utilized a Baseline scenario and an Adverse scenario.
  - Generated three-year estimates for profit and losses, and the evolution of risk-weighted assets and capital.
  - Bank balance sheets, income statements, and prudential data as of Q4 2017 were used as the starting point.
  - Several sensitivity tests were conducted.
- Liquidity stress tests
  - Employed three methods: the LCR, the NSFR, and a cash flow-based liquidity stress test.
  - Tests carried out on aggregate cash flows and separately for AUD-only cash flows in the case of LCR.
  - Conducted for the same set of banks as the solvency ST.
- Systemic risk analysis
  - Two methodologies: bank network analysis and market-based contagion analysis.
- Network analysis
  - Captures contagion risks from cascading defaults via cross-border and domestic interbank exposures.
  - Includes credit shock simulation (counterparty default eroding capital) and funding shock simulation (default of a funding counterparty inducing liquidity shortfall).
  - Envisages potential fire sales with haircuts on illiquid assets for extreme funding shocks.
  - Based on APRA data on individual bank exposures and country-level locational banking statistics.
  - Coverage includes the ten largest Australia-incorporated banks.
  - Cross-border exposure data as of end-June 2018; locational banking statistics as of December 2017.
- Market-based contagion analysis
  - Sample includes 25 of the 30 global systemically important banks (G-SIBs) identified by the FSB in 2017 and the nine largest publicly traded Australian banks or banking groups.
  - Based on the Diebold-Yilmaz (2014) methodology using daily data from January 2003 through July 2018.
- Corporate sector vulnerability analysis
  - Sensitivity analysis to gauge resilience of nonfinancial corporates to interest rate risk and macroeconomic shocks.
  - Sample of 250 large Australian corporates with a combined asset base of around A$1 trillion.
  - Interest coverage ratios estimated at the firm-level under an adverse scenario and mapped to debt-at-risk measures for each sector.
  - Adverse scenario for corporate sensitivity assumes a 1 standard deviation shock to earnings as well as interest expense; the standard deviation is estimated over a 10-year time frame.
  - Data limitations prevent deeper analysis on corporate sector risks and bank–NFC linkages.

### Overview of Systemic Risk
- Systemic risks depend heavily on the health of the four major banks; their stability is crucial for the stability of the whole financial system.
- Similarity of balance sheets and interlinkages between the large banks and between the large banks and NBFIs imply a severe shock to any of the four major banks could propagate rapidly.
- Potential losses to the four major banks from direct exposures to domestic counterparties appear manageable given strong capital levels; large capital buffers imply ability to absorb external and domestic shocks to the rest of the system.
- Second-round and contagion effects to smaller institutions could still be significant and are investigated via interconnectedness and contagion analysis.
- Contagion index estimates (average percent of capital impaired in the system in the event of an institution’s failure) reveal failure of any of the four major banks would significantly deplete overall system capital buffers.
- Cross-border and consolidated exposures
  - Around 55 percent of cross-border asset exposures for banks located in Australia are through their local subsidiaries abroad, while the rest is via cross-border lending.
  - International claims amount to around 20 percent of the aggregate banking system assets on a consolidated basis.
  - Claims on global banks amount to roughly 3 percent of total assets for Australian banks, with largest exposures to banks in the United States, China, and the United Kingdom.
  - At the individual bank level, these exposures are concentrated among the big four banks.
  - Banks’ liabilities to global counterparties amount to around 22 percent of system liabilities, with liabilities to international banks totaling roughly 13 percent of system liabilities.
  - On a locational basis, interbank claims total around 7 percent of system assets (note: consolidated BIS basis reported above).

### Key Risks and Vulnerabilities (basis for FSAP analysis)
- Real estate valuations are stretched; household leverage is high.
  - Until the recent national house price correction (prices declining about 3 percent over the year ending July 2018), house prices had risen by about 70 percent over a 10-year period.
  - Price appreciation was even higher in Sydney and Melbourne, where prices had doubled on average over 10 years; these two cities experienced price corrections of about 5 percent and 3 percent respectively over the past year.
  - Commercial real estate prices, particularly for office space, have risen sharply in major cities and have not shown signs of cooling.
  - Housing affordability linking incomes to prices is near all-time lows.
  - Household debt stands at some 190 percent of disposable income (some 20–25 percentage points above the level in the 2012 FSAP).
  - Elevated debt levels are counterbalanced by housing and financial assets and substantial pension savings through the mandatory superannuation system.
- Australia–China linkages via trade are strong.
  - One-third of Australian goods exports, including 40 percent of commodities, go to China.
  - A slowdown in China could have broad macrofinancial impact on Australia.
- Reliance on wholesale funding markets
  - Banks’ reliance on wholesale funding remains high at about one-third of total liabilities.
  - Approximately two-thirds of wholesale funding is from international sources.
- Identified macrofinancial risks (Appendix I, RAM)
  - a. Significant slowdown in China and weak growth in advanced economies leading to lower exports, deterioration in terms of trade, reduced GDP growth, weaker consumption, lower corporate profits and investment, higher unemployment, and higher bank losses.
  - b. Tightening of global financial conditions due to abrupt change in risk appetite leading to higher funding costs for Australian banks, decline in net capital inflows, repricing of country risk, lower asset valuations, AUD depreciation, and lower credit growth.
  - c. Sharp housing market correction leading to lower confidence, weaker housing demand, lower residential investment, higher nonperforming loans, tightened lending standards, and amplification of the downturn.

### Scenarios Underpinning the Financial Stability Analysis
- Time horizon and data
  - Stress tests used two scenarios, Baseline and Adverse, over a three-year horizon.
  - Simulations use balance sheet data as of 2017 Q4 and simulate profit and loss, balance sheets, and evolution of risk-weighted assets and capital for 2018 Q1–2020 Q4.
- Baseline scenario
  - Reflects the IMF World Economic Outlook (WEO) projections as of April 2018.
  - Main macrofinancial variables: annual GDP growth rate, unemployment rate, house price index, RBA Cash Rate, and the AUD/USD exchange rate.
  - Baseline GDP growth path: first year close to 3 percent, second year 3.1 percent, third year 2.9 percent.
  - Unemployment rate declines from beginning level of 5.6 percent as of 2017 to 5 percent by end of scenario.
  - USD exchange rate assumed stable at 1.32 throughout the scenario period.
- Adverse scenario (driving shocks)
  - Driven by a combination of: Shock 1 (sharp decline in real estate prices), Shock 2 (significant slowdown in China and global growth), Shock 3 (global financial market turmoil and sharper-than-expected tightening of global financial conditions).
  - The stress test analysis excludes certain categories of risk such as operational or legal risk and unexpected costs from ongoing inquiries into financial sector misconduct.

### Adverse Scenario — Quantified Assumptions and Paths
- Real economy and unemployment
  - Real GDP assumed to decline at annual rates of 2.5 percent and 0.5 percent in the first two years, and to recover to 1.6 percent in the third year.
  - This corresponds to a cumulative GDP shock of about 10 percentage points relative to the Baseline, or 3.75 times the standard deviation of three-year cumulative GDP growth rates observed in Australia in the past 30 years.
  - China experiences adverse GDP growth shocks of 2.6 percent, 3.9 percent, and 1.7 percent in the three years relative to the baseline.
  - Unemployment increases from 5.6 percent to 8.8 percent and 9.9 percent in the first and second years, respectively, and declines to 9.7 percent in the final year.
- Exchange rate and external sector
  - The AUD loses 25 percent of its value against the USD in the first year.
  - Cumulative depreciations of 20 percent and 16 percent by the end of the second and third years (partial recoveries after first-year loss).
- Housing and monetary policy
  - House prices decline by 15 percent in the first year, and by 10 percent in each of the second and third years.
  - Cumulative decline in the house price index is slightly over 31 percent at the end of three years.
  - Monetary policy response: 100-basis points reduction in the RBA Cash Rate in the first year, bringing it down to 50 basis points; further reduction of 25 basis points in the second year.
- Funding costs and spreads
  - Spread between the 3-month Bank Bill rate and the Cash Rate widens from 30 basis points (2017 year-end level) to 130 basis points at end-2018.
  - The spread is assumed to decline to 70 basis points by end-2019 and to 50 basis points by end-2020.
  - Short-term bank borrowing rates remain largely unchanged in the first year despite the decline in the Cash Rate due to spread widening.

### Stress Test Implementation and Coverage
- Solvency ST: top-down, balance-sheet based, 10 largest banks (~90 percent of system assets), baseline and adverse scenarios, three-year profit/loss and capital evolution, starting positions as of Q4 2017.
- Liquidity ST: LCR, NSFR, and cash flow-based tests on aggregate and AUD-only cash flows for same bank set.
- Network analysis: 10 largest Australia-incorporated banks; cross-border exposures as of end-June 2018; locational statistics as of December 2017.
- Market contagion: 25 of 30 G-SIBs plus nine largest publicly traded Australian banks; Diebold-Yilmaz methodology; daily data January 2003–July 2018.
- Corporate sector analysis: sample of 250 large corporates, combined asset base around A$1 trillion; firm-level interest coverage under adverse scenario mapped to debt-at-risk.

*International Monetary Fund — Australia FSAP technical note (chapters and figures summarized above).*

### 21.      In addition to the short-term interbank borrowing markets, the Adverse scenario

### 1ausea2019004 - 21.      In addition to the short-term interbank borrowing markets, the Adverse scenario

### Funding shocks: calibration and modeling
- The Adverse scenario simulated higher risk premiums across the full set of banks’ funding sources, not only short-term interbank borrowing markets.
- Six funding sources analyzed and used for calibration: (i) retail deposits, (ii) wholesale deposits, (iii) onshore short-term debt, (iv) offshore short-term debt, (v) onshore long-term debt, and (vi) offshore long-term debt.
- Funding shocks were modeled in two steps: a systemic shock that applies to all banks, and an idiosyncratic shock that affects banks differentially.
- Quarterly funding cost data from 1999 to 2017 were regressed on benchmark financial variables simulated in the Adverse scenario.
- Benchmark variables for domestic funding sources: Cash Rate, 3-month Bank Bill rate, and 10-year Australian Commonwealth Government bond yield.
- Benchmark variables for offshore funding sources: 3-month USD LIBOR and the 10-year U.S. Treasury bond yield.
- Resulting shocks for the six funding sources are presented in Figure 7 (systemic component).

### Systemic vs idiosyncratic components and their behavior
- Deposit rates (retail and wholesale) largely follow the Cash Rate and decline significantly in the systemic funding shock simulation.
- Short-term debt costs largely follow the 3-month Bank Bill rate; because the 3-month Bank Bill spread to the Cash Rate widens in the Adverse scenario, short-term debt costs:
  - stay largely unchanged in the first year, and
  - decline only gradually as the spread narrows in the second and third years.
- Long-term debt costs see the largest increases, driven by widening of the Bank Bill spread and steepening of the yield curve:
  - Both onshore and offshore long-term debt costs increase by approximately 100 basis points in the first year, and decline gradually as the spread contracts in years two and three.
- Idiosyncratic funding shocks capture a flight-to-safety reallocating funds toward the largest banks; mid-sized and small banks suffer further declines in funding supply.

### Calibration of idiosyncratic shocks and bank-level application
- The FSAP team assumed an idiosyncratic funding shock of 50-basis points for all mid-sized banks in the solvency-testing (ST) sample to capture flight-to-safety effects.
- For mid-sized banks:
  - Non-retail deposit sources received the full 50-basis points idiosyncratic shock.
  - Retail deposit costs were assumed to increase by some 20 basis points (retail suppliers less sensitive due to deposit insurance).
- Final bank-level funding shocks were calculated as weighted averages based on each bank’s share of the six funding sources.
- Banks that rely more on retail funding receive smaller aggregate funding shocks because retail deposits receive the lowest shocks.

### Funding composition of banks (December 2017) and implications
- Significant variation in funding composition across banks:
  - Mid-sized banks: close to 46 percent of total non-equity funding consists of retail deposits, 32 percent wholesale deposits, and the remaining 22 percent long-term and short-term debt (secured and unsecured).
- With systemic and idiosyncratic shocks applied, final funding shocks are shown in Figure 9 (systemic vs systemic + idiosyncratic).

### Impact on net interest margins and pass-through assumptions
- Simulated funding shocks cause direct compression of banks’ net interest margins (NIMs).
- Assumed pass-through behavior:
  - Banks unable to pass higher funding costs to borrowers in the first two years of the scenario.
  - In the third year, as GDP growth turns positive, banks assumed able to pass 75 percent of the increase in funding costs to borrowers.
- As a result, the effect on NIMs starts dissipating in the third year.

### Solvency stress test methodology (scenario and sensitivity testing)
- Solvency stress testing used a top-down balance sheet approach based on data as of 2017 year-end.
- Scenario analysis horizon: three years (2018 Q1–2020 Q4). Sensitivity tests were static, assessing instantaneous impacts as of 2017 Q4.
- Regulatory alignment:
  - Largest five banks: Internal Ratings-Based approach (IRB).
  - Five mid-sized banks: Standardized Approach (STA).
- Hurdle rates used:
  - CET 1 ratio hurdle: 4.5 percent.
  - Total capital adequacy ratio (CAR) hurdle: 8 percent.
- Capital conservation buffers:
  - Standard buffer: 2.5 percent of CET 1.
  - For the four largest banks: capital conservation buffer of 3.5 percent of CET 1 (reflecting D-SIB surcharge).

### Projection approach and balance sheet assumptions
- Balance sheet projections: funding shocks and bank credit losses projected quarterly using satellite models; other balance sheet and income statement items projected annually.
- Passive balance sheet assumption:
  - Growth of gross exposures assumed to follow nominal GDP, with a zero-growth floor.
  - Banks cannot raise new capital during the scenario period; capital can only be built through retained earnings.
  - Banks satisfying capital requirements distribute 50 percent of after-tax profits.
  - Total liabilities adjust with the evolution of assets and equity; banks expected to raise additional funding as needed.

### Components of net income and assumptions
- Net income components: (i) net interest income, (ii) net non-interest income, and (iii) credit loss provisions.
- Net interest income estimation accounts for balance sheet size, NIMs (including funding shocks), and NPLs; assumption that banks do not receive interest income from NPLs.
- Non-interest income assumptions:
  - Net fee and commission income assumed to decline by 30 percent from initial level.
  - Other non-interest income and operational/administrative expenses assumed to grow in line with overall balance sheet size.

### Calculation of loss provisions for standardized banks
- Two main components: projection of additional flow of NPLs and projection of loan provisioning rates.
- FSAP estimated satellite credit risk models to project additional NPL flows for each loan exposure class (mortgages, corporate loans, CRE loans, and retail loans) for all banks (IRB and standardized).
- Provisioning rates:
  - For loans other than mortgages, based on stressed loss rates on defaulted exposures used in Phase 2 of the 2017 APRA stress tests.
  - For mortgages, constructed from the LVR distribution of banks’ mortgage portfolios as of 2017 and simulated house price index declines.

### Calculation of loss provisions for IRB banks
- Expected Loss (EL) approach used: EL = PD x LGD x EAD, requiring point-in-time (PiT) PDs.
- Historical PiT PD series not available; PDs from Phase 2 of the 2017 APRA stress tests were used and benchmarked against through-the-cycle PDs and FSAP-estimated NPL ratios.
- LGDs:
  - For non-mortgage exposures, stressed loss rates from Phase 2 of the 2017 APRA stress tests used.
  - For mortgages, LGDs constructed from LVR distributions and simulated HPI path.
- Provisioning rates in the Adverse scenario:
  - Corporate loans: between 35 and 50 percent.
  - Consumer loans (mostly uncollateralized): reached as high as 90 percent.
  - Mortgage loans: in the 10 to 17 percent range, given low average LVRs at the starting point.

### Changes in Risk-Weighted Assets (RWA)
- For IRB banks:
  - RWA formula uses through-the-cycle (TTC) PDs and downturn LGDs.
  - Δ TTC PD = 0.5 * Δ PiT PD (increase in TTC PDs assumed to be 50 percent of the increase in PiT PDs).
  - Downturn LGDs left unchanged except where APRA Phase 2 stressed LGDs exceeded downturn LGDs; in such cases downturn LGDs were increased by 10 percent (example: initial downturn LGD of 30 percent increased to 33 percent).
- For STA banks:
  - Risk weights determined by risk ratings and LVR of mortgages per local regulations.
  - Projected LVR distributions based on simulated HPI declines; new higher risk weights calculated each year.
  - A floor of 15 percentage point of three-year cumulative increase for risk weights was adopted.
  - Average increase in mortgage risk weights across STA banks in the sample was approximately 16 percentage points.
  - For most non-mortgage exposures initial risk weights were close to 100 percent and increases were minimal.

### NPL projections — mortgage context and regional experience
- Australia delivered 26 years of uninterrupted economic growth; national mortgage NPL ratios have been low and stayed below 1 percent (NPL data start in 2004).
- Lack of major country-wide downturn and low correlation between national mortgage NPL ratios and macroeconomic variables complicates projection under the severe Adverse scenario.
- FSAP used the regional slowdown in Western Australia (WA) starting in 2014 as a modeling analog:
  - WA unemployment increased from 4.4 percent in Q4 2012 to a peak of around 6.5 percent in Q4 2016 and remained elevated thereafter.
  - WA regional HPI declined by about 9 percent between its local peak in Q1 2014 and Q4 2017.
  - During the same period (Q1 2014–Q4 2017), the overall Australian economy experienced annual economic growth close to 2.5 percent, only modest temporary increases in national unemployment, and the national HPI increased by 29 percent (largely driven by activity in major urban areas).

*Source: IMF Staff (content unit: 1ausea2019004 - 21. In addition to the short-term interbank borrowing markets, the Adverse scenario).*

### 0.5 percent at year-end 2013 to around 1.6 percent at year-end 2017. By comparison, the national

### E. Stress Test Results

### Mortgage Non-Performing Loans (NPLs) and Regional Calibration
- Regional mortgage NPL ratio rose from 0.5 percent at year-end 2013 to around 1.6 percent at year-end 2017.
- By comparison, the national mortgage NPL ratio increased by less than 0.2 percentage point during the same period.
- The FSAP team used regional mortgage NPL ratio data (collected by APRA since Q2 2013) and regional economic variables to calibrate the mortgage NPL model.
- Econometric analysis of regional data indicated:
  - Unemployment is the most important driver of increases in the NPL ratio.
  - The decline in HPI also contributes, but to a much smaller extent.
- Model projections for the Adverse Scenario (system-wide average):
  - NPL ratio would rise from 0.8 percent at year-end 2017 to just over 3 percent at year-end 2020.
- Data limitations and risks:
  - Separate default series for interest-only and principal-and-interest loans were not available.
  - Interest-only loans typically switch to principal-and-interest after about five years; a large concentration entering principal payments during severe adverse conditions may lead to defaults higher than historical series suggest.
  - APRA collects regional NPL data only for mortgage loans; CRE NPL ratio data were not available (see Other Loan NPLs).

### Other Loan NPLs (NFC, CRE, Consumer)
- For non-mortgage loan products, the FSAP team used national NPL history to calibrate scenario period NPLs.
- Historical patterns during the Global Financial Crisis (GFC):
  - NFC loans and CRE loans NPL ratios increased significantly, peaking around mid-2010, then declined to pre-GFC levels.
  - CRE NPL ratio increased from less than 0.5 percent in late 2007 to around 7 percent around mid-2010.
  - Consumer loan NPL ratio was relatively muted and less correlated with the economic slowdown during the GFC.
- Implications:
  - Banks with greater exposure to cycle-sensitive nonfinancial corporates (NFC) and CRE loan categories would experience larger increases in overall NPLs under the Adverse scenario.
- CRE NPL proxy construction:
  - FSAP used "impaired loan ratio" data and assumed aggregate CRE NPLs consist of 2/3 impaired loans and 1/3 past due loans; impaired loan ratio series were scaled up to obtain proxy NPL ratio series.

### Aggregate Stress Test Outcomes — Baseline vs. Adverse
- Baseline scenario:
  - Continuing GDP growth of approximately 3 percent per annum during the scenario period.
  - Gradual decline in the unemployment rate.
  - Average CET 1 of the system increases from 10.5 percent in 2017 to 10.8 percent in 2020.
  - Assumed dividend pay-out ratio of 50 percent.
  - Balance sheets grow in line with nominal GDP growth (about 5.3 percent per year).
  - All banks remain comfortably above regulatory capital requirements.
- Adverse scenario — system-wide outcomes:
  - Banks experience significant credit losses, sharp decline in profitability, and reductions in capital levels.
  - Even with significant declines, all banks remain above regulatory threshold of 4.5 percent for the CET 1 ratio.
  - Potential for significant reduction in credit supply by banks experiencing large capital declines; losses at any of the four major banks could reduce total credit provision due to system concentration.
  - Economy likely to experience below-trend growth for a protracted period beyond the three-year scenario if shocks realize.
- Adverse scenario — CET 1 ratio changes:
  - Average CET 1 ratio of the system decreases from 10.5 percent in 2017 to 7.2 percent in 2020.
  - All 10 banks in the ST sample remain above 4.5 percent CET 1.
  - Several banks fall below the top of the capital conservation buffer:
    - Buffer thresholds: 4.5 + 3.5 percent for the four major banks, and 4.5 + 2.5 percent for all other banks.
  - Largest 5 banks: CET 1 decreases from 10.6 percent in 2017 to 7.2 percent in 2020 (decline of 3.3 percentage points).
  - Mid-sized banks: CET 1 decreases from 9.7 percent in 2017 to 7.0 percent in 2020.
- Leverage ratio:
  - Defined as Tier 1 Capital/Total Assets.
  - Average leverage ratio of the sample declines from 5.3 percent in 2017 to just under 4.7 percent in 2017, with all banks staying well above the Basel III guidance of 3 percent.
  - Note: decline in leverage ratio is milder relative to CET 1 ratio because leverage ratio does not utilize risk-weighted assets.

### Major Drivers of Capital Changes in the Adverse Scenario
- Primary drivers:
  - Loan loss provisions (large increase due to higher defaults and provisioning ratios).
  - Increases in risk-weighted assets (RWA), especially for the largest five banks.
  - Declines in net interest rate margins due to funding shocks (interest rate risk).
- Market risk:
  - Losses from mark-to-market (MTM) decline in prices of sovereign and other securities holdings lead to minimal declines in capital ratios overall.
  - For the largest five banks, corporate bond spread widening leads to a decline of about 0.5 percentage point of capital, but a significant decline in the Cash Rate offsets overall MTM impact.
  - For mid-sized banks, MTM impact of spread and Cash Rate changes is minimal.

### Interest Rate Sensitivity Analysis (Static, Maturity Gap)
- Shock simulated: instantaneous change of 100 basis points in the yield curve for one year, affecting assets and liabilities repricing within one year.
- Results on net interest income and CET 1:
  - A 100-basis points increase in the yield curve leads to an increase of 0.3 percentage points in the system-wide CET 1 ratio.
  - A 100-basis points decrease in the yield curve leads to a decline of 0.3 percentage points in the system-wide CET 1 ratio.
- Explanation:
  - Banks on average have a higher amount of assets repricing within one year than liabilities.
  - Most bank loans are variable rate; simultaneous equal repricing benefits banks due to differential interest rate sensitivity.
- Limitations:
  - Analysis assumes interest rates on all assets and liabilities repricing within one year change by the same amount.
  - Does not model separate policy-rate effects on assets vs. liabilities, nor borrower behavioral responses (e.g., prepayments).
- Heterogeneity:
  - Mid-sized banks: estimated change in average capital is under 0.2 percentage points per 100 basis points change in rates.
  - Largest five banks: estimated change is 0.3 percentage points per 100 basis points change in rates.

### Spread Sensitivity Analysis (Non-Sovereign Securities)
- Stand-alone shock: 300 basis points increase in spread on banks’ non-sovereign securities portfolios (HFT and AFS).
- MTM impact:
  - Full sample: decline of 0.5 percentage points in the average CET 1 ratio.
  - Largest five banks: decline is slightly over 0.5 percentage points.
  - Mid-sized banks: decline is about less than 0.3 percentage points (about half the largest banks), reflecting smaller securities portfolio balances.

### Concentration Risk (Single-Name NFC Exposures)
- Data and methodology:
  - Tested default of largest and five largest NFC borrowers; applied shocks to 2017 Q4 balance-sheet capital.
  - Large exposures to non-financial corporates not part of standard supervisory data; used large exposures data from May 2017 APRA ST.
  - Assumed zero recovery on unsecured portion and full recovery of available collateral (and alternative scenario with 50 percent haircut on collateral).
- Exposure statistics (system-wide averages, relative to total CET1 capital):
  - Largest NFC exposure: 1.1 percent of total CET1 capital.
    - Largest five banks average: 0.9 percent.
    - Mid-size banks average: 3.7 percent (average pulled up by one mid-size bank with significantly higher exposures).
  - Five largest NFC exposures: 2.2 percent system-wide.
    - Largest five banks: 1.5 percent.
    - Mid-sized banks: 12.2 percent (driven by one bank).
- Results of concentration tests:
  - Default of largest NFC exposure for each of the largest five banks implies average decline of 0.1 percentage points in total CET1 for the group; largest bank-level decline close to 0.3 percentage points. All largest five banks remain well above 4.5 percent CET1.
  - For mid-sized banks, default of largest NFC exposure implies decline of 0.3 percentage points in CET1; largest bank-level decline of 1 percentage point. All remain above 4.5 percent CET1.
  - Default of five largest NFC exposures:
    - Largest five banks: decline increases slightly to 0.14 percentage points.
    - Mid-sized banks: decline increases to 1.1 percentage points, with largest bank-level decline to 3.4 percentage points. All remain above 4.5 percent CET1.
  - Results largely unchanged under scenario with zero unsecured recovery and 50 percent haircut on collateral, reflecting that these NFC exposures are generally not collateral-backed.
- Conclusion:
  - Concentration risks are generally small, particularly for the largest five banks; more variation exists among mid-sized banks.

### Banking System Funding Composition and Liquidity Vulnerabilities (Overview)
- Wholesale funding remains significant, especially for the largest five banks.
- Funding composition for largest five banks (approximate shares of total funding):
  - Wholesale deposits: approximately 33 percent.
  - Secured and unsecured debt from wholesale debt markets: 35 percent.
  - Retail deposits: remaining 32 percent.
- Mid-sized banks funding composition:
  - Retail deposits: close to 46 percent of total non-equity funding.
  - Wholesale deposits: an additional 32 percent (mostly nonfinancial corporates and SMEs).
- Implications:
  - Largest five banks are more vulnerable to stress in wholesale funding markets due to higher wholesale reliance.
  - Mid-sized banks’ higher deposit shares provide diversification within the system, but diversification is limited by the substantially higher market share of the largest five banks.

*Source: IMF Staff estimates and APRA data as presented in the FSAP stress testing section.*

### 56.      A closer inspection of the maturity structure of Australian banks indicates that banks’

### 1ausea2019004 - 56. A closer inspection of the maturity structure of Australian banks indicates that banks’ loan portfolios have a very long-dated maturity profile

### Loan portfolio maturity profile
- Loan portfolios have a very long-dated maturity profile, mainly due to the large share of mortgage loans on banks’ loan portfolios.
- Slow amortization profile of mortgage loans, especially interest-only loans which do not amortize for the first several years of their term, leads to a back-dated profile of cash inflows.
- Analysis input data do not reflect account behavioral aspects, such as borrowers prepaying their mortgages earlier than their contractual maturity date.
- Prepayments, which are observed regularly, increase the inflow of loan principal and reduce the average life of cash inflows; hence, actual cash inflows from mortgage loans are likely to have a less back-dated profile.

### Funding maturity profile and maturity transformation
- The maturity structure of funding sources is much more front-loaded compared to that of the loan portfolio.
- Prevalence of sight deposits in Australia are treated as having instantaneous maturity in this analysis.
- Differences between maturity profiles of loans and funding indicate significant maturity transformation in the Australian banking system.

### Methodology: Liquidity risk assessment tools
- The FSAP team used three methods to evaluate liquidity risks:
  - the liquidity coverage ratio (LCR),
  - a cash flow-based liquidity stress test,
  - the net stable funding ratio (NSFR).
- Tests were carried out with aggregate cash-flows, and separately for AUD-only cash flows in the case of LCR.
- Australian banks’ funding consists mostly of deposits while wholesale funding still contributes a significant amount, especially for the largest five banks.
- A large majority of deposits are sight deposits.
- Differences exist between the funding composition of the largest five banks vs. mid-sized banks.

### LCR-based analysis: approach and scenarios
- The LCR measures a bank’s ability to meet liquidity needs under a 30-day liquidity stress scenario using unencumbered high-quality liquid assets (HQLA).
- Banks must maintain an LCR above 100 percent in both aggregate currency version and in AUD-only version.
- Specific deposit run-off rates, roll-off rates for cash inflows and assets haircuts are included to simulate stressed conditions in three scenarios:
  - Baseline LCR: LCR parameters prescribed by Australian regulation.
  - Retail stress: retail deposit withdrawals assumed to be 1.5 to 2 times higher than regulatory LCR assumptions.
  - Wholesale stress: dry-up of wholesale funding; run-off rates assumed 2 to 5 times higher than regulatory LCR assumptions; simulates higher run-off on unsecured wholesale funding, shocks to secured funding via repo and covered bonds, and the commercial paper market.
- Run-off rates: wholesale funding run-off rates higher than retail; unsecured funding run-off rates higher than secured.

### NSFR-based analysis
- NSFR compares relative liquidity of banks’ assets and liabilities over a longer horizon than LCR.
- Average NSFR at the system is 113 percent.
- NSFR is relatively similar between large and small banks; all banks are above the regulatory limit of 100 percent.
- NSFR does not consider full maturity structure of cash flows; e.g., whole stock of mortgage loans that qualify for a 35 percent risk weight and have more than one-year maturity is treated with a required stable funding coefficient of 65 percent.

### Cash flow-based liquidity risk analysis: approach
- Assesses risks due to potential volatility in banks’ funding sources based on net cash balance after funding outflow shocks at various maturity buckets up to one-year horizon.
- Net cash balance at each maturity bucket = existing cash position + net fund flows + counterbalancing capacity.
- Analysis simulates severe funding shocks affecting all funding sources (assumptions in Table 5).
- Net fund flows = cash inflows minus outflows; outflows include all non-equity liabilities and off-balance sheet items; inflows come from maturing loans, securities, and some derivatives.
- Analysis uses contractual flows from APRA (amortization schedules, maturing loans, term deposits); behavioral flows calibrated based on run-off and roll-off rates.
- Only FX-related derivatives cash flows were considered; cash flows of other derivatives (e.g., interest rate swaps) were not available.
- Counterbalancing capacity includes existing cash reserves, RBA standing repo facility, sale of unencumbered liquid assets, and RBA’s Committed Liquidity Facility (CLF).
- Only CLF amounts approved by the RBA as of 2017 year-end were used.
- Analysis incorporated fire-sale dynamics via haircuts on collateral.

### Results — LCR-based analysis
- Baseline (regulatory parameters):
  - Average LCR across banks in the ST sample = 136 percent at December 2017.
  - Average LCR for large banks = 136 percent.
  - Average LCR for mid-sized banks = 131 percent.
  - Every bank passes the 100 percent hurdle rate.
  - CLF buffers play a significant role; a significant portion of banks’ HQLA consists of CLF buffers.
- Retail stress:
  - Higher run-off rates on retail and SME deposits and higher outflow rates for credit and liquidity facilities to retail and SME customers; inflows and haircuts unchanged.
  - Mid-sized banks average LCR falls to 105 percent; only one bank falls below 100 percent.
  - Largest five banks average LCR declines to 117 percent; all remain above 100 percent.
- Wholesale stress:
  - Assumptions include up to 100 percent run-off rates on wholesale deposits (highest for non-operational deposits); run-off rates for funding secured by HQLA2B assets increased to 50-100 percent; outflow rates for credit and liquidity facilities to banks and other financial institutions increased to 80 percent.
  - Aggregate LCR falls to 91 percent; six banks fall below 100 percent.
  - Mid-sized banks average LCR falls to 98 percent.
  - Largest five banks average LCR falls from 136 percent in baseline to 91 percent.
  - Results highlight sensitivity of Australian banks, particularly large banks, to wholesale funding market conditions.

### Results — Cash flow-based liquidity stress test
- Several banks experience shortfalls based on cash inflows/outflows and existing counterbalancing capacity before utilizing CLF.
- After utilizing existing CLF amounts, three banks still experience cash shortfalls in the simulation.
- All banks maintain positive cash balances in the first 30 days (the LCR horizon).
- Under protracted stress beyond 30 days:
  - One bank experiences cash shortfalls in the “1 month to 2 months” window.
  - A second bank in the “3 months to 6 months” window.
  - A third bank in the “6 months to 12 months” window.
  - Total shortfall peaks at less than 0.5 percent of banking system assets during the “3 months to 6 months” window.
- Analysis highlights exposures to short-term liquidity risks from reliance on wholesale funding combined with long-dated loan cash flows.
- Mitigants: banks’ ability to request additional liquidity access utilizing surplus RBA repo-eligible self-securitization capacity.
- Policy considerations suggested:
  - Authorities could consider encouraging banks to continue to reduce reliance on wholesale funding sources and to term out their funding.
  - Authorities could enhance collection of cash flow information, including flows from derivatives and other off-balance sheet positions, to support additional liquidity risk analysis.

### Interconnectedness and contagion: inter-sector financial linkages
- National accounts data show tight interlinkages between the banking system and the non-bank financial sector, households and nonresident balance sheets.
- Nonresidents hold around 30 percent of bank liabilities, primarily via equity and bond instruments.
- Households and nonbank financials hold a combined 45 percent of banking system liabilities.
- On the asset side:
  - Banks’ largest exposures are to households, at roughly 40 percent.
  - Exposures to nonresidents, corporates and nonbank financials are around 17 percent each.
- Interbank linkages based on balance sheet exposures are relatively small: exposures to all other banks amount to around 4 percent of assets for the aggregate system (this estimate excludes off-balance sheet exposures).
- Cross-border exposures:
  - Consolidated bank-level statistics from APRA reveal international claims for the top 10 Australian banks on their 10 largest counterparty countries amount to roughly 20 percent of banking assets, with 75 percent of these claims based in New Zealand, the United States, and the United Kingdom.
  - Interbank claims amount to around 3 percent of banking assets, largest exposures to the United Kingdom, the United States, Canada, and China.
  - On a locational basis, total international claims of the top 10 banks amount to roughly 8 percent of banking assets, while interbank claims total around 5 percent of banking system assets.
  - Interbank cross-border liabilities amount to around 9 percent of system liabilities for the top 10 lenders.
  - Neither consolidated nor locational statistics include off-balance sheet exposures of the reporting banks.

*Source: 1ausea2019004 - extract (Australia FSAP liquidity and interconnectedness analysis).*

### 75.      The network analysis aims to assess the potential for distress at a financial entity to

### 1ausea2019004 - 75.      The network analysis aims to assess the potential for distress at a financial entity to 

### Purpose and scope
- The network analysis assesses the potential for distress at a financial entity to spillover to the rest of the system and adversely impact financial stability.
- It addresses two missing links of the traditional solvency stress test:
  - Solvency effects emanating from banks facing funding pressures.
  - Potential for default cascades triggered by an insolvent bank on its creditors, transmitting distress throughout the banking sector.
- Interconnectedness is assessed at both the domestic and cross-country levels.

### Transmission channels (assessed)
- Bilateral Exposures:
  - Captured using bilateral matrix of exposures at the institutional level (authorities’ data) and locational data at the country level from the BIS.
  - Stress test assesses solvency impact of liquidity strains from fire sales and rising funding costs, and potential for indirect default cascades within the Australian interbank market and cross-border.
- Balance Sheet Approach:
  - Based on the Espinosa-Vega and Sole (2010) network framework, considering both credit and funding shocks.
  - Initial negative credit or funding shock propagates through bilateral claims (BIS locational banking statistics).
  - Banking system deemed to “fail” if losses exceed its capital base (binary fail/active states).
  - Three simulation sets: (1) BIS reporting banks’ exposure to foreign banks, (2) individual cross-border exposures of the top 10 Australian banks, (3) individual bank exposures to domestic counterparts.
- Market Contagion:
  - Captured using equity prices of banks to reflect market participants’ revised expectations of solvency for systemically important banks with similar business models to the distressed bank.
- Market-prices Approach:
  - Diebold and Yilmaz (2014) methodology: pairwise directional interconnectedness via a VAR of equity returns for 25 G-SIBs (sample) and nine largest publicly traded Australian banks/banking groups.
  - Interconnectedness derived from Generalized Variance Decomposition (Pesaran and Shin, 1998).
  - Measures: inward-spillover (exposure to systemic shocks), outward-spillover (contribution to systemic events), net-degree (inward minus outward).

### Balance-sheet approach: simulation assumptions and calibration
- Credit shock:
  - Loss given default (LGD) rates assumed: 40 percent and 100 percent.
- Funding shock:
  - Global funding shock scenario replicates post-Lehman liquidity stresses.
  - Fraction of lost funding non-replaceable: 35 percent (ρ=0.35) implying 65 percent rollover.
  - Haircut in fire sale assumed to be 50 percent (δ=0.5).
  - Unsecured interbank, FX swap markets and capital markets assumed closed; access to emergency liquidity facilities assumed closed.
  - Banks forced to find new funding or liquidate assets subject to market conditions.
- Parameter calibration (Table 6):
  - λ=0.4 and 1.0: 40 and 100 percent loss given default on exposures
  - ρ=0.35: Share of lost funding that is non-replaceable
  - δ=0.5: 50 percent discount on asset sales
  - capital: Tier 1 capital under Basel III
  - bank default: Tier 1 capital falls to 0 percent

### Data and samples
- Cross-border sample: 42 BIS reporting countries with bilateral exposures with Australian banks; cross-border banking exposure data based on BIS locational statistics; Tier 1 regulatory data from IMF’s FSI Statistics; analysis based on Dec-2017 data.
- Market-prices VAR sample: daily equity returns from Jan 1, 2005 to July 23, 2018; includes 25 of the 30 G-SIBs identified by the FSB in 2017 and nine Australian institutions (ANZ, CBA, WBC, NAB, BoQ, MQG, BEN, SUN, AMP Group).
- Supervisory bank-level data from APRA used for bank-level simulations (as of June 2018).

### Key results — global network (Espinosa-Vega-Sole, BIS data)
- Main sources of contagion concentrated in: U.S., U.K., French banking systems; to a lesser degree Germany and Japan.
- Vulnerabilities are distributed globally; no major banking system stands out as an outlier.
- Australian banking system: neither a significant source of contagion nor a significant recipient of spillover risks in the sample of global banking systems.

### Key results — cross-border and bank-level
- Australian banks particularly vulnerable to external funding shocks due to dependence on wholesale funding from overseas.
- Large asset exposures to New Zealand and, to a smaller degree, to China.
- Large liability positions vis-à-vis the United States and the United Kingdom (wholesale U.S. dollar funding).
- Stylized network analysis under adverse credit and funding shock assumptions (LGD=1.0 and no rollover) finds:
  - The four major Australian banks particularly vulnerable to funding shocks from banking systems in the United States and United Kingdom, and to a lesser degree from Singapore and Hong Kong.
  - Four major banks with higher dependence on overseas wholesale funding see sharper deterioration in capital positions.
  - Some mid-sized banks, both purely-domestic and with global operations, face significant erosion of capital base.
- Anecdotal evidence: funding from Singapore and Hong Kong banks reflects borrowing from international banks operating in these regional centers.

### Key results — domestic interbank and off-balance sheet exposures
- On-balance sheet domestic interbank exposures are relatively small: around 5 percent of each major institution’s total asset base (on-balance exposures to each other).
- Off-balance sheet exposures appear sizeable:
  - Gross notional amount of Australian banks’ off-balance sheet assets reportedly tripled over ten years to around A$36 trillion.
  - RBA trade repository estimates gross market value of OTC IR derivatives traded in Australia around A$900 billion, with Australian banks being counterparties on A$363 billion of these trades.
  - Domestic interbank portion of OTC exposures estimated around A$12 billion.
  - Gross market value of banks’ derivative exposures to foreign banks and CCPs estimated around A$120 billion each.

### Diebold-Yilmaz (DY) market-prices network results
- Relatively low degree of interconnectedness between major Australian banks and G-SIBs.
- Both outward and inward spillovers from and to the Australian banking sector appear relatively moderate vis-à-vis G-SIBs.
- Among global banks studied, U.S., French, and German banks have the highest outward spillover (contagion), consistent with direct-exposure analysis.
- Asian banks appear net recipients of spillovers.
- Australian banks’ net contribution to global systemic risk appears to be negligible; vulnerability metric relatively moderate compared to global peers.
- Mid-sized Australian banks reveal net inward spillover risk (increased vulnerability to external shocks).
- One Australian institution stands out as an outlier due to larger exposure to the global financial system.

### Caveats and limitations noted
- Simulation results sensitive to assumptions (e.g., LGDs, funding rollover, fire-sale haircuts).
- Collateral and hedging instruments not taken into account due to data limitations.
- Fire-sale effects are calibrated exogenously; spiral effects from further price declines due to aggregate supply not modeled explicitly.
- Mark-to-market effects from common exposures to stressed assets by banks holding similar assets are not computed.
- Contagion effects from bear-market sentiment to banks with similar business models to the bank in distress are excluded.

*Source: IMF Staff calculations and text from the specified FSAP network analysis section.*

### 85.      While banks are an important source of financing for companies, bank loans comprise

### 1ausea2019004 - 85.      While banks are an important source of financing for companies, bank loans comprise

### Nonfinancial corporate (NFC) sector: structure and exposures
- Bank loans comprise only around 15 percent of aggregate liabilities; the bulk of debt and equity financing comes from nonbank financials and nonresidents.
- NFC sector assets total around 250 percent of GDP.
- The bulk of NFC debt resides in cyclical sectors: industrials, materials, energy and real estate.
- Sectoral debt distribution (percent of total) highlights Materials 25%, Industrials 20%, Utilities 11%, Real Estate 11%, Energy 8%, Telecom Services 7%, Consumer Discretionary 7%, Healthcare 5%, Consumer Staples 5%, IT 1%.
- Based on a sample of 250 firms from S&P Capital IQ, the four cyclical sectors have a combined debt of over 65 percent of total debt among NFCs.
- The sample of 250 firms has a combined debt of around A$1 tn.
- Firm leverage: corporate debt is about 60 percent of equity.
- Gross operating profits have been relatively strong in recent years after volatility during the GFC, the European sovereign debt crisis and the slump in global commodities.
- Debt servicing capacity for some levered sectors (real estate and materials) requires closer monitoring due to high exposures to the housing market and China.

### Corporate funding patterns and bank involvement
- Corporates increasingly borrow from global capital markets or foreign banks, attracted by cheaper offshore funding and as domestic banks pull back from some markets.
- Larger firms in materials, industrial and real estate sectors with strong ratings access international debt capital markets to raise U.S. dollar financing and swap proceeds into Australian dollars.
- Anecdotal evidence: these firms have ample natural and synthetic hedges for currency exposures and have built large cash reserves for liquidity needs.
- Balance sheets of foreign banks have grown nearly 50 percent over the past five years (albeit from a small base), compared to a growth rate of 25 percent for the major lenders.
- Foreign subsidiaries and branches comprise around 15 percent of total banking system assets and are reportedly expanding more aggressively across riskier loan segments such as corporate loans and commercial real estate.

### Sensitivity analysis: adverse shock to earnings and funding costs
- Scenario design: a macroeconomic shock that hurts corporate earnings combined with a tightening in global financial markets raising funding costs for banks and corporates; magnitude assumed to be one standard deviation (estimated over a 10-year time horizon for the variables shocked).
- Under this adverse scenario, there is a wide dispersion in interest coverage ratios (ICR) across the sample.
- Several firms with relatively high starting ICRs witness steep deterioration in debt repayment capacity, possibly reflecting higher leverage and interest-rate sensitivity.
- Sectors with notable deterioration in debt-at-risk: Energy, Industrials, Utilities and Real Estate.
- Materials sector appears relatively resilient, having deleveraged after the global commodity slump.
- The sharp deterioration in ICRs for some sectors reflects relatively higher leverage and increased sensitivity to interest rate shocks.

### Policy recommendations (summary)
- Invest in data and analytical tools to strengthen financial supervision and systemic risk oversight:
  - CFR agencies should conduct a major review of potential data needs and implement improvements, publishing results where feasible.
  - Improved data would facilitate better stress testing and closer integration of results into prudential supervision and policy discussions, and better harness RBA and APRA expertise.
- Enhance stress testing framework:
  - Use uniform data sources across all banks for better comparability of risks.
  - Develop in-house models to estimate potential losses to cross-validate banks’ bottom-up loss estimates.
  - Enhance monitoring of banks’ exposures in major foreign jurisdictions and conduct regular network analysis to identify cross-border spillover channels.
  - Perform liquidity risk analysis based on cash flows at various maturities to complement existing liquidity monitoring.
- Reduce banks’ reliance on wholesale funding and extend funding maturity profiles to lower structural funding risks.

### Stress-testing framework and assumptions (banking sector)
- Institutional perimeter: largest 10 banks (largest five subject to Major Bank Levy and five mid-sized) hold approximately 88 percent of domestic banking sector assets. Baseline date: End-2017. Scope: Level 2 consolidated data.
- Methodology:
  - Balance sheet-based approach with satellite models for PDs, LGDs, NPL ratio, provisioning ratio.
  - Market losses from debt instrument holdings using haircuts based on modified duration.
  - Three-year stress horizon (2018–2020).
- Scenario analysis:
  - Baseline scenario based on April 2018 WEO projections.
  - Adverse Scenario simulated using IMF’s Flexible System of Global Models, driven by three major shocks:
    - Shock 1: Significant slowdown in China and weak growth in advanced economies.
    - Shock 2: Tightening of global financial conditions due to abrupt change in risk appetite.
    - Shock 3: A sharp housing market correction.
  - Under the Adverse Scenario, annual GDP growth shocks are -5.5 percent, -3.6 percent, and -1.3 percent (a cumulative deviation of close to 9 percentage points over the first two years compared to Baseline).
  - Unemployment shocks: 3.5 percent, 4.7 percent, and 4.7 percent.
  - Cumulative house price decline: 31 percent.
  - AU$ depreciation: 25 percent in the first year, cumulative depreciation of 16 percent at end of third year.
- Behavioral assumptions:
  - Passive balance sheet: balance sheet growth linked to nominal GDP growth (with a 0-floor); composition constant; banks build capital only through retained earnings; maturing capital instruments not renewed.
  - Dividends: banks that remain adequately capitalized pay dividends with a payout ratio of 50 percent.
- Reporting outputs: system-wide capital shortfall; number of banks and percentage of banking system assets below capital hurdle; impact decomposition by result drivers.

### Liquidity and interconnectedness stress testing
- Liquidity tests:
  - Cash-flow based liquidity stress test using maturity buckets; Basel III LCR and NSFR ratios; analyses carried out separately for AUD and USD.
  - Risks: funding liquidity outflows and market liquidity (price shocks) and haircuts.
  - Buffers: counterbalancing capacity (HQLA) and central bank facilities.
  - Regulatory standards: LCR: 100 percent; NSFR per Basel Committee (2014).
  - Outputs: system-wide liquidity gaps; survival period by bank; number of banks meeting obligations.
- Interconnectedness:
  - Models: balance sheet-based interbank model (Espinosa-Vega and Solé 2010); market price-based spillover model (Diebold and Yilmaz 2014); cross-border network model (Espinosa-Vega and Solé 2010).
  - Risks: credit and funding losses related to interbank cross-exposures and cross-border banking exposures.
  - Outputs: network analyses, system-wide capital shortfall, number of undercapitalized and failed institutions and asset shares, evolution/direction of spillovers.

### Key macrofinancial variables under Baseline and Adverse scenarios (percent, unless noted)
- Australia Real GDP growth:
  - Baseline: 2017 2.3, 2018 3.0, 2019 3.1, 2020 2.9
  - Adverse: 2018 -2.5, 2019 -0.5, 2020 1.6
- China Real GDP growth:
  - Baseline: 2017 6.9, 2018 6.6, 2019 6.4, 2020 6.3
  - Adverse: 2018 2.9, 2019 3.1, 2020 3.8
- Unemployment rate:
  - Baseline: 2017 5.6, 2018 5.3, 2019 5.2, 2020 5.0
  - Adverse: 2018 8.8, 2019 9.8, 2020 9.7
- USD Exchange rate (level):
  - Baseline: 2017 1.31, 2018 1.32, 2019 1.32, 2020 1.32
  - Adverse: 2018 1.75, 2019 1.63, 2020 1.55
- House price index (2017=100):
  - Baseline: 2017 100, 2018 106, 2019 112, 2020 117
  - Adverse: 2018 85, 2019 77, 2020 69
- RBA cash rate:
  - Baseline: 2017 1.50, 2018 1.75, 2019 2.25, 2020 2.75
  - Adverse: 2018 0.50, 2019 0.25, 2020 0.25
- Bank Bill Rate/Cash Rate Spread:
  - Baseline: 2017 0.30, 2018 0.30, 2019 0.30, 2020 0.30
  - Adverse: 2018 1.30, 2019 0.70, 2020 0.50
- 3-mo Bank Bill Rate:
  - Baseline: 2017 1.80, 2018 2.05, 2019 2.55, 2020 3.05
  - Adverse: 2018 1.80, 2019 0.95, 2020 0.75
- 5-yr A Corporate bond/AGS Spread widening (Adverse):
  - 2018 2.57, 2019 2.01, 2020 1.33
- 5-yr BBB Corporate bond/AGS Spread widening (Adverse):
  - 2018 4.23, 2019 2.97, 2020 1.78
- CPI inflation:
  - Baseline: 2017 1.9, 2018 2.1, 2019 2.2, 2020 2.3
  - Adverse: 2018 0.6, 2019 1.2, 2020 1.3

*Source: IMF FSAP material as provided in the content unit.*

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_Source: https://www.imf.org/-/media/files/publications/cr/2019/1ausea2019004.pdf_