/ifrs9-scenarios

name: ifrs9-scenarios
description: >
  Activate for: macro overlay, macroeconomic scenarios, PIT PD,
  point-in-time PD, credit cycle adjustment, scenario weighting,
  forward-looking information, satellite model, GDP, unemployment,
  house price index, IFRS 9 scenarios, scenario probability.
  NOT for: ECL calculation mechanics (use ifrs9-ecl), staging assessment
  (use ifrs9-staging), stress testing for capital adequacy (use stress-testing).
metadata:
  version: "1.0"
  author: "Panaversity — The AI Agent Factory"
  standard: "IFRS 9.5.5.17 (Forward-looking information)"

IFRS 9 SCENARIO FRAMEWORK REQUIREMENTS

IFRS 9.5.5.17 requires: reasonable and supportable information about future economic conditions, including forward-looking information. This is not optional. Single-scenario ECL is non-compliant with IFRS 9.

SCENARIO STRUCTURE — MINIMUM AND BEST PRACTICE

Minimum (IFRS 9): base + 1 upside + 1 adverse Best practice: 4–5 scenarios with explicit probability weights

| Scenario | Typical Weight | Key Feature | | -------- | -------------- | -------------------------------------------- | | Upside | 10–20% | Above-trend growth, falling unemployment | | Base | 35–50% | Central forecast, moderate conditions | | Adverse | 25–35% | Mild recession, rising unemployment | | Severe | 10–20% | Deep recession, sharply falling asset prices |

Weights must: sum to 1.0; reflect management's genuine probability assessment; be documented and approved by the IFRS 9 Governance Committee. Equal weighting (25% each) is RARELY defensible and will be challenged by auditors.

KEY MACROECONOMIC VARIABLES BY ASSET CLASS

Retail mortgages: House Price Index (HPI), unemployment rate, base rate Consumer loans: Unemployment rate, disposable income index, base rate SME loans: GDP growth, SME default index, unemployment rate Corporate loans: GDP growth, corporate default rates, sector-specific indices Commercial Real Estate: CRE capital value index, vacancy rates, GDP growth

CREDIT CYCLE ADJUSTMENT (CCA) — CONVERTING TTC TO PIT PD

PIT PD = TTC PD x CCA CCA is estimated from a satellite model. Typical satellite model form: ln(CCA) = a + b1(GDP_growth) + b2(Unemployment) + b3(HPI_growth) + e

Example CCA values: Severe recession: CCA = 1.8–2.5 (PDs 80–150% above long-run average) Adverse: CCA = 1.2–1.5 Base: CCA ~ 1.0 (by definition — TTC PD already reflects long-run average) Upside: CCA = 0.7–0.9 (PDs below long-run average)

SATELLITE MODEL DETAIL

Model Structure

The satellite model links macroeconomic variables to credit risk parameters. Typical specification for a UK mortgage portfolio:

ln(Default Rate*t) = a + b1 * Unemployment_t + b2 * HPI_growth_t + b3 * Base_Rate_t + b4 * ln(Default Rate*{t-1}) + e_t

Key requirements for the satellite model:

• Estimated on a sufficiently long time series (minimum 1 full cycle, ideally 2+)
• Must include at least one recession period in calibration data
• Coefficients must have economically intuitive signs (e.g., higher unemployment

increases default rates)

• Out-of-sample validation required (typically holdout the most recent 2–3 years)
• R-squared typically 0.6–0.85 for well-specified models

Model Validation Requirements

• Annual independent validation by Model Risk Management
• Backtesting: compare predicted vs actual default rates over rolling windows
• Sensitivity testing: how much does ECL change for a 1pp change in each variable?
• Benchmarking: compare against external provider models (Moody's, Oxford Economics)

WEIGHTED ECL CALCULATION

Step 1: Calculate PIT PD for each scenario using scenario-specific CCA Step 2: Calculate ECL for each scenario: ECL_s = PD_PIT_s x LGD x EAD (Stage 1) or ECL_s = Sum_t [PD_marginal_t_s x LGD_t x EAD_t x DF_t] (Stage 2/3) Step 3: Weighted ECL = Sum_s (Weight_s x ECL_s)

NON-LINEAR EFFECTS

Weighted ECL != ECL at weighted-average PD (due to non-linearity in ECL formula). Always calculate ECL for each scenario separately, then probability-weight the results. The difference between these approaches (non-linear adjustment) is material for portfolios with high LGD or long remaining maturities.

Non-Linearity Worked Example

Portfolio: 1,000M gross carrying amount, LGD = 40%

| Scenario | Weight | PIT PD | ECL (PD x LGD x EAD) | | -------- | ------ | ------ | -------------------- | | Upside | 15% | 0.8% | 3.2M | | Base | 40% | 1.5% | 6.0M | | Adverse | 30% | 3.0% | 12.0M | | Severe | 15% | 6.0% | 24.0M |

Correct: Weighted ECL = 0.15 x 3.2 + 0.40 x 6.0 + 0.30 x 12.0 + 0.15 x 24.0 = 10.08M Wrong: Weighted PD = 0.15 x 0.8 + 0.40 x 1.5 + 0.30 x 3.0 + 0.15 x 6.0 = 2.52% ECL at weighted PD = 2.52% x 40% x 1,000 = 10.08M (linear case — same)

For lifetime ECL with compounding and discounting, the non-linear effect becomes material (typically 5–15% higher ECL when correctly scenario-weighted).

SCENARIO EXPLAINABILITY FOR GOVERNANCE COMMITTEE

For each quarterly scenario update, prepare:

1. Scenario name and narrative description 2. Key macroeconomic variables for each scenario (3-year forward path) 3. Scenario weights and rationale for any weight changes from prior quarter 4. ECL under each scenario individually 5. Probability-weighted ECL (reported figure) 6. Sensitivity: ECL if severe scenario were weighted 100% (IFRS 7 required) 7. Changes from prior quarter: which scenarios/weights/variables changed and why

FORWARD-LOOKING HORIZON

Explicit forecast horizon: typically 2–5 years (period with supportable forecasts) Mean reversion: beyond explicit horizon, variables revert to long-run average over a reversion period (typically 2–5 additional years) Perpetuity: beyond reversion period, variables held at long-run average

GOVERNANCE OF SCENARIOS

Scenarios must be:

• Approved by the IFRS 9 Governance Co