Which method is used to calculate contingency for risk by multiplying probability by impact?

Which method is used to calculate contingency for risk by multiplying probability by impact?

• A. Expected monetary value
• B. Cost-benefit analysis
• C. Monte Carlo simulation
• D. Sensitivity analysis

Answer: (A)

Expected monetary value is the method that converts a risk into a single monetary amount by multiplying the probability of the event by its potential impact. This yields the average expected loss (or gain) if the risk could occur many times, which is exactly what you need to set aside a contingency reserve. To use it for contingency, compute the EMV for each identified risk and then sum those values. For example, a risk with a 20% probability and a $50,000 potential cost has an EMV of 0.20 × 50,000 = $10,000. Add up all such EMVs across identified risks to determine the total contingency. Cost-benefit analysis weighs costs and benefits of decisions but isn’t the direct tool for sizing risk reserves by probability times impact. Monte Carlo simulation builds a distribution of outcomes through random sampling, not a simple probability × impact calculation. Sensitivity analysis explores how changes in inputs affect outcomes, rather than producing a single EMV figure used for contingency sizing.

Expected monetary value is the method that converts a risk into a single monetary amount by multiplying the probability of the event by its potential impact. This yields the average expected loss (or gain) if the risk could occur many times, which is exactly what you need to set aside a contingency reserve.

To use it for contingency, compute the EMV for each identified risk and then sum those values. For example, a risk with a 20% probability and a $50,000 potential cost has an EMV of 0.20 × 50,000 = $10,000. Add up all such EMVs across identified risks to determine the total contingency.

Cost-benefit analysis weighs costs and benefits of decisions but isn’t the direct tool for sizing risk reserves by probability times impact. Monte Carlo simulation builds a distribution of outcomes through random sampling, not a simple probability × impact calculation. Sensitivity analysis explores how changes in inputs affect outcomes, rather than producing a single EMV figure used for contingency sizing.