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Scenario analysis

Scenario analysis builds a coherent adverse narrative, translates factors and paths into impacts on a selected scope, and exposes assumptions and vulnerabilities. It is a stress-testing method, not a forecast.

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Who this entry is for — Anyone who needs to understand how an economic, financial or operational story propagates through factors, positions and resources without turning it into a certain forecast.

Scenario analysis describes a coherent set of conditions and, where needed, their evolution over time. It connects a narrative to paths for risk factors — prices, rates, volatility, spreads, currencies, liquidity or operational variables — and estimates effects on the selected scope.

In the Basel Committee framework it is one methodology in the stress-testing family, alongside sensitivity analysis and reverse stress testing. A scenario may be historical, hypothetical or hybrid. It is not necessarily the most likely forecast and does not always require a numerical probability.

In plain terms — A sound analysis does not say “this will happen”. It says “if these coherent conditions occurred, this is how they would propagate and where the system would be vulnerable”.

From question to scenario—not from forecast to certainty A useful test exposes scope, shocks, transmission, results and limitations From question to scenario—not from forecast to certainty A useful test exposes scope, shocks, transmission, results and limitations A useful test exposes scope, shocks, transmission, results and limitations 1 · OBJECTIVE Scope, horizon, metric anddecision to be informed 2 · DESIGN Sensitivity, historical orhypothetical scenario, revers… 3 · TRANSMISSION Coherent factors,dependencies, non-linearity,… 4 · RESULTS Losses, resources,concentrations,… NOT A FORECAST Passing a test does not prove safety; failing it does not automaticallyassign a probability to the scenario. Cyclepedia · source-checked visual explainer
Consistency among narrative, factors, horizon and model makes the result interpretable.

Boundaries with adjacent methods

Method Structure Main use
Sensitivity analysis Changes one or a few inputs, even without a full narrative Identify exposures and non-linearities
Scenario analysis Combines coherent factors and, when needed, paths Explore joint transmission and impacts
Reverse stress testing Starts from a breaking outcome and seeks conditions that could cause it Find vulnerabilities and viability thresholds
Post-entry plan Conditional rules for managing one trade Prepare operational trade decisions

The final row is covered separately in Scenario. It may use an “if X, then Y” structure, but it does not replace risk scenario analysis.

Structure of a traceable analysis

  1. Objective and scope — Define the decision, entity, portfolio, positions, date, currency and resources under review.
  2. Horizon and dynamics — Distinguish an instantaneous shock, a multi-period path and when losses or constraints are recognised.
  3. Narrative — Explain the mechanism linking events and why it is relevant to vulnerabilities in the scope.
  4. Factors and paths — Specify levels, changes and relationships among rates, prices, volatility, spreads, currencies, liquidity and other drivers.
  5. Consistency — Check that factors are not an arbitrary collection of incompatible shocks, and document any exceptions.
  6. Transmission — Revalue linear and non-linear payoffs, margin, collateral, funding, counterparties and potential second-round effects.
  7. Results and limitations — Separate impacts, contributions, excluded risks, sensitivity to assumptions and model uncertainty.
  8. Use and challenge — Connect findings to possible decisions, checking that actions remain feasible under the stated conditions.

There is no universal number of scenarios. One focused story can answer a specific question; a broader set may be needed to cover different vulnerabilities. The choice follows the purpose, not a fixed “three to five” rule.

Severity, plausibility and probability

Basel principles call for scenarios that are sufficiently severe and varied for the purpose and, for adverse scenarios, severe but plausible. “Plausible” does not mean “forecast” or “frequent”. Historical events can inform calibration, while hypothetical scenarios represent emerging risks or combinations absent from the data.

Assigning a probability is useful only where the method supports it. A number without an empirical or model basis creates false precision. The Federal Reserve, for example, states that its stress scenarios are hypothetical paths, not forecasts.

Interpretation errors

  • turning the scenario into a forecast or price target;
  • selecting dramatic but mutually inconsistent shocks;
  • ignoring liquidity, funding, non-linearity or counterparty responses;
  • assuming historical correlations remain unchanged under stress;
  • prescribing generic actions — such as “limit orders only” — without testing execution, costs and effects;
  • reporting precise outputs without exposing model risk.

Review frequency depends on purpose, portfolio change, environment and risk materiality. No monthly or quarterly schedule is valid for every analysis.

Typical mistake — Treating the narrative as certainty and the modelled number as observable precision. Both are conditional inputs that require challenge.

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