Practical Applications of Monte Carlo Simulation in Financial Modeling

In Monte Carlo Simulation in Financial Modeling, from the September 2023 issue of The Journal of Portfolio Management, Koray Simsek of Rollins College Crummer Graduate School of Business explains how the Monte Carlo method provides a powerful tool for analyzing situations that are too complex to be reduced to ordinary mathematical formulas. Although the Monte Carlo method can be applied to virtually any situation where uncertainty can be expressed through random variables, it is especially valuable for analyzing problems where random variables are defined in terms of non-normal distributions or where the input random variables influence each other. Monte Carlo simulations use a computer to simulate hundreds or thousands of scenarios (iterations), with different randomly selected values of the input variables in each one. The output of the process is a distribution of simulation results (e.g., portfolio returns) that can be described with basic statistics (e.g., mean, median, standard deviation). These, in turn, figure into the user's real-world decision making. Simsek describes three examples where financial professionals use Monte Carlo simulation: pricing options, comparing portfolio insurance strategies, and estimating portfolio risk.