Financial Risk Manager Part 1

Explanation

John can easily examine the effects on output variables when changing scenarios. Monte Carlo simulations are a powerful tool for risk management and financial forecasting. They allow for the modeling of complex systems and the examination of the effects of different scenarios on the output variables. This is particularly useful in situations where the relationships between variables are complex and non-linear, such as in the case of John's three investments.

Key Points:

• Option A is incorrect: Monte Carlo simulations can incorporate and measure correlations between investments. In fact, John has already identified that A and B are highly correlated while C has zero correlation with both.
• Option B is incorrect: While understanding probability distributions is important for input variables, Monte Carlo simulations don't require accurately determining the probability distribution of the output in advance - the simulation itself generates the output distribution.
• Option C is correct: One of the main advantages of Monte Carlo simulation is the ability to easily test different scenarios and see their impact on output variables.
• Option D is incorrect: Monte Carlo simulations do not require assuming that the output is normally distributed. They can handle any distribution and the output distribution emerges from the simulation process.

Monte Carlo simulations are particularly valuable for analyzing complex investment portfolios with varying correlations, allowing risk managers to understand how different market conditions and investment relationships affect long-term outcomes.