Monte Carlo simulation is a mathematical technique used to estimate the possible outcomes of an uncertain event by modeling the probability of different outcomes in a process or system that cannot easily be predicted due to the intervention of random variables. It is a chance to see into the future and is used to understand the impact of risk and uncertainty in prediction and forecasting models. Monte Carlo simulations are mainly used in three problem classes: optimization, numerical integration, and generating draws from a probability distribution. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo simulations assume perfectly efficient markets.
To perform a Monte Carlo simulation, multiple values are assigned to an uncertain variable to achieve multiple results, and then the results are averaged to obtain an estimate. The variables are described by their probability distribution, which can be estimated with historical data or defined using expert opinion. Then, sensitivity analysis can be run to identify which variables have the largest impact on the outcome. This method allows for more accurate forecasting and better decision-making under uncertainty. Monte Carlo simulations are used in many fields, including investing, business, physics, engineering, and risk analysis.
