Set out the strengths and weaknesses of sensitivity and scenario risk measures
The idea
Sensitivity measures are simple, precise for small moves and easy to hedge against, but they are local: duration and delta ignore curvature for large moves, each covers only one factor, and none gives a probability. Historical scenarios are objective and easy to explain, but the next crisis is unlikely to repeat the last, current positions or instruments may not have existed then, and past correlations may not recur. Hypothetical scenarios can explore new risks and correlation breakdowns, but need judgement, are hard to calibrate and carry no probability, so it is hard to decide how much weight to give them. The trap is assuming that surviving a set of scenarios shows a portfolio is safe.
In the app: 2 practice questions on this outcome, a narrated walkthrough with diagrams, spaced review that brings it back before you forget it.
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- Explain how value at risk (VaR) is applied to gauge the risk of a portfolio
- Contrast three ways to estimate VaR: Monte Carlo simulation, historical simulation, and the parametric (variance–covariance) method
- Estimate VaR by Monte Carlo simulation, by historical simulation, and by the parametric method, and explain what each result means
- Set out the strengths and weaknesses of VaR
- Set out the ways VaR has been extended
- Explain sensitivity and scenario risk measures and weigh them against VaR
- Show how exposure measures for equities, fixed income, and options can help gauge and control market risk and volatility risk
- Set out how sensitivity and scenario risk measures are applied
- Explain the limits used to control market risk, among them stop-loss limits, scenario limits, position limits, and risk budgeting
- Explain how risk measures can guide decisions on allocating capital
- Set out the risk measures relied on by insurers, pension funds, asset managers, and banks