casemark/managing-economic-scenario-development

Managing Economic Scenario Development

Structures macroeconomic scenario design with consistent variable paths and probability assessment.

When To Use

• Building a scenario set for strategic planning, capital allocation, or risk management
• Designing stress test scenarios for regulatory submissions (CCAR, DFAST, ICAAP) or internal risk frameworks
• Creating baseline, upside, and downside macro forecasts for investment committees or board presentations
• Developing conditional forecasts around specific policy actions (rate decisions, fiscal stimulus, trade policy shifts)
• Coordinating multi-analyst scenario exercises where variable consistency across teams is critical

Inputs To Gather

• Forecast horizon: short-term (1-4 quarters), medium-term (1-3 years), or long-term (5-10 years)
• Geographic scope: single-country, regional bloc, or global multi-economy
• Core macro variables required: GDP growth, inflation (CPI/PCE), unemployment, policy rates, yield curves, credit spreads, FX rates, commodity prices, housing prices [VERIFY which variables are mandated by the specific regulatory or internal framework]
• Number of scenarios: typically 3 (base/upside/downside) or 5+ for full distribution analysis
• Probability weighting approach: subjective expert assignment, model-derived, or hybrid
• Narrative anchors: the key shock or theme driving each non-base scenario (e.g., "prolonged stagflation," "rapid disinflation with rate cuts," "geopolitical supply disruption")
• Existing constraints: regulatory scenario parameters, board-mandated severity thresholds, or prior-period scenario continuity requirements

Workflow

1. Define the scenario architecture

   • Set the number of scenarios, horizon, and periodicity (quarterly, annual)
   • Assign each scenario a narrative label and a 1-2 sentence thesis describing the primary economic mechanism
   • Establish probability weights summing to 100%; document the rationale for each weight

2. Specify variable paths for the baseline

   • Start with the consensus or internal house view for each macro variable
   • Ensure internal consistency: e.g., if GDP growth is above trend, unemployment should decline and capacity utilization should rise
   • Cross-check against current data releases and central bank forward guidance [VERIFY latest data vintage and release calendar]

3. Build alternative scenario paths

   • For each non-base scenario, identify the primary shock and trace its transmission through the macro system
   • Apply directional logic: a demand shock affects GDP and unemployment first, then inflation with a lag; a supply shock hits inflation and output simultaneously
   • Calibrate severity using historical analogues (e.g., magnitude of 2008 GDP decline, 1970s inflation spike, 2020 labor market shock) — state which analogue is referenced
   • Ensure variable paths are mutually consistent within each scenario; flag any deliberate deviations from standard macro relationships

4. Validate cross-scenario consistency

   • Check that the probability-weighted average of all scenarios is close to the baseline (it need not match exactly, but large deviations signal imbalance)
   • Verify that downside scenarios are sufficiently severe relative to the stated probability — a 10% probability scenario should reflect a genuinely adverse outcome, not a mild slowdown
   • Compare scenario spreads to historical realized volatility for each variable [VERIFY relevant historical sample period]

5. Build the variable path tables

   • Create a matrix: rows = variables, columns = time periods, layers = scenarios
   • Include quarter-over-quarter and year-over-year changes alongside levels where applicable
   • Add peak-to-trough metrics for stress scenarios (max drawdown in GDP, peak unemployment, widest credit spread)

6. Document assumptions and limitations

   • List every material assumption (e.g., "assumes no change in trade policy," "central bank follows forward guidance through Q2")
   • Note model dependencies: which variables are model-driven vs. judgment-based
   • Flag tail risks not captured in the scenario set

7. Coordinate review and sign-off

   • Route baseline to economics team lead; route stress scenarios to risk management
   • Resolve inter-team inconsistencies (e.g., equity research using different GDP assumptions than credit)
   • Lock the scenario set with a version number and effective date

Output

The deliverable is a Scenario Design Report containing:

• Executive summary: scenario count, horizon, key themes, and probability weights
• Narrative descriptions: 1-paragraph thesis for each scenario explaining the driving mechanism and key risks
• Variable path tables: full numeric paths for all specified macro variables across all scenarios and time periods
• Historical calibration exhibit: table comparing scenario severity to selected historical episodes
• Probability-weighted summary statistics: expected values and ranges for key variables
• Assumption log: complete list of stated assumptions, judgment overrides, and data vintage references
• Limitations and exclusions: risks or variables deliberately excluded from the scenario set

Quality Checks

• Every macro variable path is internally consistent within its scenario (no conflicting directional signals without explicit justification)
• Probability weights are documented with rationale and sum to 100%
• At least one downside scenario meets or exceeds the severity of a relevant historical stress episode
• Variable paths cover the full specified horizon with no gaps in periodicity
• All data sources and vintages are cited; stale inputs are flagged with [VERIFY]
• The probability-weighted mean is compared to the baseline and any material divergence is explained
• Scenario narratives match the numeric paths — if the narrative says "deep recession," GDP paths must reflect contraction, not mild slowdown
• Cross-team variable consistency is confirmed (same GDP, rate, and inflation assumptions used by all downstream consumers)