casemark/analyzing-scenario-analysis

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• Historical reference periods: Analogous episodes (e.g., 2008 GFC, 2020 COVID, 2022 rate-hiking cycle) to anchor severity calibration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

1. 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.

2. 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% |

3. 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)

4. 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

5. Identify vulnerabilities and concentrations — Highlight positions or sectors where losses are disproportionate to portfolio weight. Flag scenarios where risk limits are breached.

6. 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