FRM Part II · FRM Exam Part II

Market-Driven Scenarios: An Approach for Plausible Scenario Construction

A market-driven scenario is a stress test built from a shock to one or a few risk factors, with the other factors' moves inferred from historical relationships. You choose the shock, estimate conditional expected moves using correlations and volatilities, apply them to the portfolio, and read the loss as a plausible, internally consistent outcome.

What this chapter covers

This chapter is about building stress scenarios that are both severe and believable. A simple stress shocks one risk factor and leaves the rest unchanged. That is rarely realistic, because in real markets factors move together. The market-driven approach picks a shock to a chosen factor, then uses the statistical relationship between factors to estimate how the others would be expected to move.

The core idea is the conditional expected shock. Given a stated shock to one factor, you estimate the expected move in every other factor, using their volatilities and correlations. Under a joint normal (elliptical) assumption, the expected move in factor j given a shock to factor i is the shock × correlation × (σj ÷ σi). That is the same logic as a regression beta. Plausibility is then judged by how likely the combined move is, often with a Mahalanobis-type distance, so you can compare the severity of different scenarios on one scale.

This chapter sits inside Market Risk Measurement and Management. It links to VaR and expected shortfall, since scenarios cover tail events that models may miss. It also connects to stress testing under Basel and supervisory expectations, and to Current Issues topics such as geopolitical risk, where scenarios are the main tool. Expect applied questions: compute a conditional shock, apply it to exposures, and interpret the result.

Scenario analysis is a recurring theme in market risk and in supervisory stress testing, so the ideas here support questions beyond this chapter. The calculations are short and repeatable, which makes them reliable marks once you practise. The interpretation questions are where candidates slip: they ask why a scenario is plausible, what it ignores, or how correlation assumptions change the loss. With 80 questions in 4 hours, a short formula you can apply quickly is worth the effort.

Market-Driven Scenarios: An Approach for Plausible Scenario Construction: topics in the order to study them

1Stress Testing and the Role of Scenario AnalysisStart here to learn what stress tests are for, and why scenarios complement VaR by covering events history or models may understate.
2Plausibility and Construction of Market-Driven ScenariosNext, learn the construction logic and what makes a scenario plausible, before any numbers are used.
3Conditional Expected Shocks and Factor CorrelationsThis is the quantitative core. It needs the earlier concepts so you know what the shocks represent.
4Applying Scenarios to Portfolios and Interpreting ResultsFinish by applying the shocks to exposures and reading the loss, which pulls the earlier topics together.

How to prepare Market-Driven Scenarios: An Approach for Plausible Scenario Construction

This chapter rewards a concept first, calculation second routine. Keep your practice short and frequent, especially if you study on a phone.

Read the purpose of stress testing and write down in one line how scenarios differ from VaR.
Learn the construction steps in order: choose the shocked factor, size the shock, infer the other factors, apply to the portfolio, interpret.
Memorise the conditional shock formula: shock to factor i × correlation(i, j) × (σj ÷ σi), and be able to explain it as a regression slope.
Work at least five small examples with two or three factors, checking the sign and size of each inferred move against the correlation.
Practise applying factor moves to exposures such as equity positions, rate sensitivities and FX holdings, then summing the profit and loss.
Write a short note on the limits of the method: reliance on historical correlations, and correlations that change in crises.
Finish with mixed MCQs that ask you to judge plausibility or explain why one scenario is more severe than another.

Common mistakes in Market-Driven Scenarios: An Approach for Plausible Scenario Construction

Shocking one factor and leaving all others unchanged.
Fix: Ask whether the other factors would plausibly move too, and use the conditional expected shock to infer them.
Swapping the volatility ratio, using σi ÷ σj instead of σj ÷ σi.
Fix: Remember the slope scales the move into the units of the factor being estimated, so the target factor's volatility goes on top.
Ignoring the sign of the correlation.
Fix: Write the sign of the inferred move before you calculate its size, then check the final answer against it.
Treating a more severe scenario as a more plausible one.
Fix: Keep them separate. Severity is the size of the loss; plausibility is how consistent the joint move is with historical relationships.
Assuming correlations stay fixed under stress.
Fix: State that the result depends on historical estimates and that correlations may rise in crises, so results can understate losses.
Mixing units when applying moves to exposures.
Fix: Convert everything to the same unit first, such as a percent move times a USD exposure, then sum.

Last-day revision: Market-Driven Scenarios: An Approach for Plausible Scenario Construction

A stress test examines losses under severe but plausible conditions that VaR may not capture.
A market-driven scenario shocks chosen factors and infers the rest from market relationships.
Conditional expected shock of j given a shock to i = shock × ρij × (σj ÷ σi).
Zero correlation implies an expected move of zero in the other factor, under this approach.
A negative correlation gives an inferred move in the opposite direction to the shock.
Plausibility is about how likely the joint move is, not just the size of one shock.
Larger shocks in more factors need not be more plausible; consistency with correlations matters.
Scenario loss = sum of exposure × factor move across all factors.
The method relies on historical volatilities and correlations, which can break down in a crisis.
Scenarios are a complement to VaR and expected shortfall, not a replacement.
Check units: percent moves, basis points and currency exposures must be consistent.

Market-Driven Scenarios: An Approach for Plausible Scenario Construction practice questions

Market-Driven Scenarios: An Approach for Plausible Scenario Construction in other exams

The same ground in other exams, if you are preparing for more than one or want another angle on it.

Market-Driven Scenarios: An Approach for Plausible Scenario Construction: frequently asked questions

What is a conditional expected shock?

It is the expected move in one risk factor given a stated shock to another. Under the standard assumption it equals the shock × correlation × the ratio of the two volatilities. It works like a regression slope.

How is a market-driven scenario different from a historical scenario?

A historical scenario replays moves from a past event. A market-driven scenario starts from a chosen shock and infers the other moves from estimated relationships, so it can describe events that have not happened exactly before.

Do I need to memorise many formulas for this chapter?

Very few. The conditional shock formula and the loss calculation, which is exposure times factor move summed over factors, cover most calculations. The rest is understanding plausibility and the limits of the method.

Why can scenario results understate risk?

The inferred moves rely on historical volatilities and correlations. In a crisis, correlations can change and relationships can break down, so the real losses may be larger than the scenario suggests.