Monte Carlo methods are used in corporate finance and mathematical finance to value and analyze (complex) instruments, portfolios and investments by simulating the various sources of uncertainty affecting their value, and then determining the distribution of their value over the range of resultant outcomes.
What does Monte Carlo simulation mean?
Monte Carlo simulation, or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models. Uncertainty in Forecasting Models.
How does a Monte Carlo simulation work?
Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions.
Why is it called Monte Carlo simulation?
Monte Carlo simulations are named after the gambling hot spot in Monaco, since chance and random outcomes are central to the modeling technique, much as they are to games like roulette, dice, and slot machines. The technique was first developed by Stanislaw Ulam, a mathematician who worked on the Manhattan Project.
How accurate are Monte Carlo simulations?
For the sample size 1200, claimed accuracy 95th percentile is 1.0 percent. However, even for a random function with an error factor of 3, the theoretical accuracy of Monte Carlo simulation (see formula 23) is about 4 percent, which is still greater than 1 percent accuracy claimed by SAMPLE.