Credit Stress Testing FrameworkFree Financial Model Download

Name: Credit Stress Testing Framework
Brand: Finamodel
Availability: InStock

Model credit portfolio stress tests to see tail risk and expected loss without treating stress as an exercise separate from risk management. Covers probability of default, loss given default, exposure at default, and scenario analysis in one framework.

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About this model

Model credit portfolio stress tests to see tail risk and expected loss without treating stress as a separate exercise from risk management. The model applies macroeconomic shock scenarios (Base / Adverse / Severely Adverse) with explicit GDP, rate, unemployment, and house price parameters. It then transmits those shocks to loan-level probability of default (PD) via stress multipliers, calculates expected credit losses (ECL) by segment (corporate, retail, mortgage), and outputs portfolio loss reserve (PCL). The model generates three-scenario outputs: NII (net interest income under rate shocks), PCL (provisions under credit stress), PPOP (pre-provision operating profit), and CET1 capital ratio (regulatory capital).

Key mechanics: NII is sensitive to rate repricing speed (assets reprice faster than liabilities in rate rises, creating NIM compression); PCL multipliers are scenario-linked (corporate ECL at 4.5x base rate under severe stress, mortgages at 3.0x due to collateral); RWA increases under stress (risk weights move 10-20% higher); and capital (CET1) falls as both losses reduce retained earnings and RWA growth consumes available capital. The model includes covenant tracking (DSCR 1.2x+, leverage limits) and dividend suspension in stressed scenarios (regulatory requirement). Sensitivity tables show CET1 ratio across a range of ECL multipliers, giving regulators confidence in capital adequacy.

Use cases: bank capital planning, regulatory DFAST/EBA submissions, stress testing for corporate credit committees, and evaluating concentration risk in large loan books. Works with scenario publishing (Fed, ECB, PRA parameters).

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Brand-ready
Institutional grade
Fully auditable

What's included

Counterparty ratings and default probability curves
Exposure at default (EAD) and facility-level risk
Loss given default (LGD) and recovery assumptions
Expected loss (EL) by counterparty and portfolio
Portfolio loss distribution and Value at Risk (VaR)

Probability of default curves

PD is modeled as a function of credit rating, industry, and macroeconomic conditions to capture rating migration and credit cycle risk across the portfolio.

Concentration and correlation

Counterparty correlation is modeled explicitly to show portfolio concentration risk and the diversification benefit of spreading exposure across industries and geographies.

Recession and tail scenario analysis

Recession, financial crisis, and tail scenarios show portfolio loss under stress and identify the key risk drivers most likely to threaten capital adequacy.

Frequently asked

What is probability of default (PD)?+

PD is the likelihood a counterparty will default over a one-year period, typically estimated from historical default rates by credit rating and economic cycle.

What is loss given default (LGD)?+

LGD is the percentage of exposure lost when a counterparty defaults, accounting for collateral recovery and seniority. Secured loans have lower LGD; unsecured exposure has higher LGD.

How do you model counterparty correlation?+

Asset-value correlation models such as Vasicek or Merton, or historical default correlations by industry and geography, capture concentration risk that simple expected loss calculations miss.

What regulations require credit stress testing?+

Dodd-Frank CCAR, the Basel ICAAP framework, and CECL reserve methodology all require institutions to model expected and stressed credit losses. This model supports each use case.

Who uses credit stress testing models?+

Risk officers, credit analysts, portfolio managers, and regulators use them for capital adequacy reporting, provision setting, and credit limit management.