We are independent & ad-supported. We may earn a commission for purchases made through our links.
Advertiser Disclosure
Our website is an independent, advertising-supported platform. We provide our content free of charge to our readers, and to keep it that way, we rely on revenue generated through advertisements and affiliate partnerships. This means that when you click on certain links on our site and make a purchase, we may earn a commission. Learn more.
How We Make Money
We sustain our operations through affiliate commissions and advertising. If you click on an affiliate link and make a purchase, we may receive a commission from the merchant at no additional cost to you. We also display advertisements on our website, which help generate revenue to support our work and keep our content free for readers. Our editorial team operates independently of our advertising and affiliate partnerships to ensure that our content remains unbiased and focused on providing you with the best information and recommendations based on thorough research and honest evaluations. To remain transparent, we’ve provided a list of our current affiliate partners here.
Technology

Our Promise to you

Founded in 2002, our company has been a trusted resource for readers seeking informative and engaging content. Our dedication to quality remains unwavering—and will never change. We follow a strict editorial policy, ensuring that our content is authored by highly qualified professionals and edited by subject matter experts. This guarantees that everything we publish is objective, accurate, and trustworthy.

Over the years, we've refined our approach to cover a wide range of topics, providing readers with reliable and practical advice to enhance their knowledge and skills. That's why millions of readers turn to us each year. Join us in celebrating the joy of learning, guided by standards you can trust.

What is the Monte Carlo Simulation?

By Toni Henthorn
Updated: May 17, 2024
Views: 7,644
Share

A Monte Carlo simulation is a mathematical model for calculating the probability of a specific outcome by randomly testing or sampling a wide variety of scenarios and variables. First utilized by Stanilaw Ulam, a mathematician who worked on the Manhattan Project during World War II, the simulations provide analysts an avenue for making difficult decisions and solving complex problems that have multiple areas of uncertainty. Named after the casino-populated resort in Monaco, the Monte Carlo simulation uses historical statistical data to generate millions of different financial outcomes by randomly inserting components in each run that can influence the end result, such as account returns, volatility, or correlations. Once the scenarios are formulated, the method calculates the odds of reaching a particular outcome. Unlike standard financial planning analyses that use long-term averages and estimates of future growth or savings, the Monte Carlo simulation, available in software and web applications, can provide a more realistic means of handling variables and measuring the probabilities of financial risk or reward.

Monte Carlo methods are often used for personal financial planning, portfolio evaluation, valuation of bonds and bond options, and in corporate or project finance. Although probability computations are not new, David B. Hertz first pioneered them in finance in 1964 with his article, “Risk Analysis in Capital Investment,” published in the Harvard Business Review. Phelim Boyle applied the method to derivative valuation in 1977, publishing his paper, “Options: A Monte Carlo Approach,” in the Journal of Financial Economics. The technique is harder to use with American options, and with the results being dependent on the underlying assumptions, there are some events that the Monte Carlo simulation cannot predict.

Simulation offers several distinct advantages over other forms of financial analysis. In addition to generating the probabilities of the possible endpoints of a given strategy, the method of data formulation facilitates the creation of graphs and charts, fostering better communication of the findings to investors and shareholders. Monte Carlo simulation highlights the relative impact of each variable to the bottom line. Using this simulation, analysts can also see exactly how certain combinations of inputs affect and interplay with each other. Understanding of the positive and negative interdependent relationships between variables affords a more accurate risk analysis of any instrument.

Risk analysis by this method involves the use of probability distributions to describe the variables. A well-known probability distribution is the normal or bell curve, with users specifying the expected value and a standard deviation curve defining the variation. Energy prices and inflation rates may be depicted by bell curves. Lognormal distributions portray positive variables with unlimited potential to increase, such as oil reserves or stock prices. Uniform, triangular, and discrete are examples of other possible probability distributions. Values, which are randomly sampled from the probability curves, are submitted in sets called iterations.

Share
WiseGeek is dedicated to providing accurate and trustworthy information. We carefully select reputable sources and employ a rigorous fact-checking process to maintain the highest standards. To learn more about our commitment to accuracy, read our editorial process.

Editors' Picks

Discussion Comments
Share
https://www.wisegeek.net/what-is-the-monte-carlo-simulation.htm
Copy this link
WiseGeek, in your inbox

Our latest articles, guides, and more, delivered daily.

WiseGeek, in your inbox

Our latest articles, guides, and more, delivered daily.