lev-os Analyzing Scenario Analysis
Structures forward-looking scenario analysis by defining macroeconomic assumptions, modeling transmission channels, and quantifying portfolio-level impact across base, adverse, and tail scenarios.
When To Use
- Stress-testing a portfolio against potential macroeconomic shifts (recession, rate shocks, geopolitical disruption)
- Evaluating how changes in GDP growth, inflation, unemployment, or credit spreads cascade through asset classes
- Preparing risk committee or board-level scenario summaries for regulatory or internal governance purposes
- Assessing vulnerability of concentrated exposures under plausible-but-severe conditions
- Comparing portfolio resilience across multiple forward-looking narratives (e.g., soft landing vs. stagflation vs. hard landing)
Inputs To Gather
- Portfolio composition: Asset class weights, sector/geography breakdown, top exposures, duration/convexity profile
- Baseline macro assumptions: Current consensus forecasts for GDP, CPI, fed funds rate, unemployment, credit spreads, and commodity prices
- Scenario definitions: Number of scenarios (typically 3–5), each with a narrative label, probability weight, and time horizon (6M, 1Y, 3Y)
- Shock parameters per scenario: Specific deviations from baseline for each macro variable (e.g., "GDP −3.5%, CPI +2.0pp, 10Y yield +150bps")
- Transmission mappings: How each macro variable links to asset-class returns—equity beta to GDP, credit spread sensitivity, duration impact on fixed income, FX pass-through [VERIFY: confirm institution-specific factor models or vendor models in use]
- Historical reference periods: Analogous episodes (e.g., 2008 GFC, 2020 COVID, 2022 rate-hiking cycle) to anchor severity calibration
- Risk limits and thresholds: Drawdown tolerances, VaR/CVaR limits, liquidity coverage ratios that define "breach" under stress
Workflow
Define scenario narratives — Write a concise macro story for each scenario (2–3 sentences). Assign probability weights. Ensure scenarios span a range of outcomes: at minimum a base case, a moderate adverse case, and a severe/tail case.
Set macro variable paths — For each scenario, specify the trajectory of key variables across the time horizon. Present in a table:
Variable Base Adverse Severe Real GDP growth +2.1% −0.5% −3.5% CPI (YoY) 2.4% 4.0% 6.5% Fed funds rate 4.25% 5.50% 3.00% IG credit spread 110bps 200bps 350bps Unemployment 4.0% 5.8% 8.2% Map transmission channels — For each asset class or sub-portfolio, document how macro shocks translate to P&L impact:
- Equities: earnings growth sensitivity to GDP, multiple compression under rising rates
- Fixed income: duration × yield change, spread widening on credit
- Alternatives: illiquidity discount, NAV lag adjustments
- FX: carry unwind, safe-haven flows
- Flag any non-linear or threshold effects (e.g., covenant breaches at spread > 300bps)
Compute portfolio impact — Apply shocks through the transmission model. For each scenario, calculate:
- Total portfolio return / P&L change
- Contribution by asset class and top individual positions
- Change in risk metrics (VaR, CVaR, max drawdown estimate)
- Liquidity impact: redemption capacity, margin call exposure
Identify vulnerabilities and concentrations — Highlight positions or sectors where losses are disproportionate to portfolio weight. Flag scenarios where risk limits are breached.
Formulate risk-mitigation options — For each material vulnerability, propose actionable hedges or rebalancing steps (e.g., "Add 2Y put spread on S&P to cap equity tail loss" or "Reduce HY allocation by 5pp to stay within CVaR limit under adverse scenario").
Output
Structure the deliverable as follows:
- Executive Summary: One-paragraph overview of the most decision-relevant finding (e.g., "Under the severe scenario, portfolio drawdown reaches −18%, breaching the −15% board limit, driven primarily by HY credit and EM equity exposure").
- Scenario Definitions Table: Narratives, probability weights, macro variable paths.
- Portfolio Impact Matrix: Scenario × asset class P&L grid with total portfolio row.
- Risk Metric Dashboard: VaR, CVaR, max drawdown, liquidity coverage under each scenario vs. limits.
- Vulnerability Heat Map: Positions or sectors flagged red/amber/green by severity of scenario loss relative to weight.
- Mitigation Recommendations: Prioritized list of hedges or allocation changes with estimated cost and risk-reduction impact.
- Assumptions & Limitations: Explicitly state model limitations (e.g., linear approximation, no credit migration, static portfolio assumption). Mark jurisdiction- or regulation-dependent items with [VERIFY].
Quality Checks
- Each scenario narrative is internally consistent (e.g., a deflationary recession should not pair with rising commodity prices without explanation)
- Macro variable paths are cross-checked against historical analogs for plausibility—no variable exceeds the most extreme observed move without explicit justification
- Portfolio impact sums to total—asset-class contributions reconcile to the aggregate P&L number
- Risk metric breaches are clearly flagged with reference to the specific limit or policy threshold
- Transmission assumptions are documented and source-attributed (vendor model, internal factor model, or regression-based) [VERIFY: confirm which factor/risk model framework the institution uses]
- Probability weights across scenarios sum to approximately 100%
- All data vintage dates are stated—stale inputs are flagged