A Monte Carlo Simulation is a computational technique that uses repeated random sampling often thousands or millions of iterations to model the probability distribution of possible outcomes for a financial variable, such as portfolio returns, retirement corpus, option prices, or credit default probabilities. By running a large number of simulated scenarios based on historical return distributions, volatility, and correlations, Monte Carlo simulations provide a probabilistic range of outcomes rather than a single point estimate. In personal finance, they are used to stress-test retirement plans by simulating thousands of possible return sequences and showing the probability of running out of money. In trading, they help evaluate the robustness of a strategy across a wide range of market conditions. In risk management, they underpin Value-at-Risk calculations and portfolio stress testing frameworks.
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