A risk estimation method that uses stochastic (randomized) simulations to model potential future market conditions and calculate Value at Risk (VaR). Monte Carlo VaR evaluates the impact of thousands (or millions) of hypothetical scenarios on a portfolio by simulating changes in key market factors such as interest rates, exchange rates, equity prices, and volatility. 

This method is particularly useful for portfolios with non-linear instruments, such as options or complex derivatives, where analytical or historical VaR methods may fall short. It accounts for correlations between variables and can accommodate a wide variety of risk factors, making it one of the most flexible and accurate VaR techniques. 

For example, a risk manager might use Monte Carlo simulation to project one-day potential losses for a portfolio containing foreign exchange forwards and interest rate swaps. By modeling how these instruments behave under thousands of random market paths, the firm can estimate the worst expected loss at a given confidence level (e.g., 99%). 

Monte Carlo VaR requires significant computational resources and relies on robust statistical modeling and data inputs. Its accuracy depends on the quality of assumptions made about the distribution of market returns, volatility, and correlations.

Despite its complexity, it is favored by institutions with advanced risk management frameworks and portfolios with significant exposure to market dynamics.