Credit Stress Testing Framework

The framework uses three regulatory-style scenarios: Base, Adverse, and Severely Adverse. Each scenario specifies distinct macroeconomic shocks, including GDP paths, unemployment spikes, and interest rate movements.

• For example, the Severely Adverse scenario assumes a GDP contraction of −4.0% in Year 1 and a 200 basis point rate cut, while the Base scenario projects modest growth and a 75 basis point rate rise. These parameters feed into all scenario-sensitive drivers, such as loan growth rates, repricing betas, and credit loss multipliers.
• Loan growth, for instance, halves in the Adverse scenario and turns negative in the Severely Adverse scenario, reflecting reduced credit demand and faster runoff. The scenario toggle on the Assumptions sheet allows users to switch between scenarios, automatically updating the model outputs.

Credit losses are modelled through expected credit loss (ECL) rates per segment—Corporate, Retail, and Mortgage—each with a base ECL rate and a stress multiplier. Base ECL rates are 0.50% for Corporate, 1.50% for Retail, and 0.20% for Mortgage.

• Under stress, these rates are multiplied: the Severely Adverse multipliers are 4.5× for Corporate, 3.5× for Retail, and 3.0× for Mortgage. The provision for credit losses (PCL) is then calculated as the sum of average segment balances multiplied by their stressed ECL rates.
• This PCL charge flows into the income statement and also builds the loan loss reserve on the balance sheet, which is reduced by write-offs. Write-offs follow a lag factor, typically 0.7× ECL in Year 1 and 1.0× thereafter, reflecting the time it takes for delinquencies to crystallise.

The model integrates earnings and capital impacts. Net interest income (NII) is driven by average loan and funding balances, with yields and costs adjusted for rate shocks and repricing betas.

• Asset repricing betas are higher than liability betas, creating asymmetric NIM compression in stress. Non-interest income is scenario-sensitive, with fee growth turning negative in adverse scenarios.
• Operating expenses are largely fixed but include a modest restructuring adjustment in severe stress. Pre-provision operating profit (PPOP) is total revenue minus opex and depreciation, and net income is PPOP minus PCL, wholesale interest expense, and tax.

Net income feeds into CET1 capital, which also adjusts for dividends and AT1 coupons. Dividends are suspended in stressed scenarios to preserve capital.

The framework outputs the CET1 ratio, calculated as CET1 capital divided by risk-weighted assets (RWA). RWA incorporates risk weights per asset class and a stress overlay that increases credit RWA by 10% in Adverse and 20% in Severely Adverse scenarios.

• The model checks compliance with regulatory thresholds, such as a 10.5% target CET1 ratio and a 7.0% minimum distributable amount trigger. A sensitivity table shows how the Year 3 CET1 ratio varies with uniform ECL multipliers, helping users assess loss severity tolerance.
• Validation checks ensure balance sheet integrity, NIM reasonableness, and provision adequacy. This makes the framework suitable for capital planning and risk management teams conducting DFAST or EBA-style assessments.