CFA Level 2 Exam 2025 – 400 Free Practice Questions to Pass the Test

The key advantage of using Monte Carlo Value at Risk (MC VAR) is that it specifies probability distributions for all parameters involved in the analysis. This approach allows for a more comprehensive capture of the potential variability and uncertainty in those parameters, as opposed to relying solely on historical data or fixed assumptions.

By specifying probability distributions, MC VAR can account for a wide range of possible outcomes and relationships between variables, including non-normally distributed risks or tail events that might not be captured in simpler modeling approaches. This flexibility is particularly useful in assessing risk for portfolios with complex structures or those subject to extreme market movements.

In contrast, other methods might rely on historical averages, linear relationships, or a limited dataset, which could potentially underestimate risk or lead to misleading conclusions. Monte Carlo simulations also facilitate stress testing and scenario analysis, providing a richer framework for understanding the effects of various risks on portfolio value under different conditions.