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MethodIntermediate03.07
Uncertainty & Stochastic Systems

Risk Analysis

Quantify downside, tail events, and risk appetite.

Overview

Risk Analysis focuses on quantify downside, tail events, and risk appetite. In the map of OR, it connects Expected shortfall, VaR, Stress tests to decisions that must be modeled, solved, explained, and revised as evidence changes.

Risk analysis combines probability models, simulation, optimization, and decision analysis to compare exposure, mitigation, and resilience. The practical use case is clearest in Finance, Energy, Public safety, Supply chains, where the method helps turn constraints and tradeoffs into a decision artifact someone can inspect.

Core ideas

Expected shortfall

Expected shortfall is a core checkpoint for Risk Analysis: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

VaR

VaR is a core checkpoint for Risk Analysis: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

Stress tests

Stress tests is a core checkpoint for Risk Analysis: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

Scenario planning

Scenario planning is a core checkpoint for Risk Analysis: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

Risk measures

Risk measures is a core checkpoint for Risk Analysis: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

How to use it

  1. 1Start with Finance: write the decision, time horizon, actors, and objective in operational language.
  2. 2Translate the problem into Expected shortfall, VaR, and Stress tests; define units and data sources for each one.
  3. 3Build a small instance of Risk Analysis that can be solved or simulated by hand inspection before using full production data.
  4. 4Compare the recommendation against a baseline policy, not just against mathematical optimality.
  5. 5Document assumptions, sensitivity results, and the conditions under which the recommendation should be revisited.

Applications

FinanceEnergyPublic safetySupply chains
  • Finance: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
  • Energy: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
  • Public safety: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
  • Supply chains: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.

Common pitfalls

  • Applying Risk Analysis because the label sounds appropriate while leaving the actual decision boundary vague.
  • Treating Expected shortfall as a technical detail instead of a modeling choice that affects the recommendation.
  • Reporting one answer without showing sensitivity to demand, capacity, costs, or behavioral assumptions.
  • Ignoring implementation details such as data quality, explainability, ownership, and how users will override bad recommendations.

Resources