What is Monte Carlo Simulation

monte carlo simulation is mathematical technique which is used to estimate the possible outcomes of an uncertain event it's chance to see into the future and while actual time travel is still beyond us let's address three questions about monte carlo simulations to get you on your way to making better decisions come on in guys so number one how do they work multi-color simulation works by modeling the probability of different outcomes in process or system that cannot easily be predicted due to the intervention of random variables and it uses something called random sampling and rambling random sampling is used to generate multiple possible outcomes and calculate the average result so take for example the calculation of the probability of rolling two standard dice well if you wanted to calculate this probability the brute force way you would have to roll the dice whole bunch say 000 times if we consider that there are six sides to dice we have two of them and we want to run this thousand times to get good sample size but with monte carlo simulation we can reduce the number of roles by randomly sampling the possible outcomes knowing there are 36 combination of dice rolls and calculating the percentage of times that we get say seven now number two who uses them there are number of common applications for monte carlo simulations and perhaps the most well known of those is in the area of just portfolio management and also in the area of investment planning by running thousands or even millions of simulations investors can get better idea of how their portfolio might perform under different market conditions and other common applications are things like risk analysis option pricing and planning for spare capacity but monte carlo simulation is applied in all sorts of fields from medicine all the way through to astrophysics all the way through to figuring out what today's wordle might actually be okay number three how to run one monte carlo techniques involve three basic steps first you set up the predictive model and this is identifying both the dependent variable to be predicted and the independent variables also known as the input risk of predictive variables that will drive the predictions secondly you specify the probability distribution and that's the probability distribution of the independent variables you can use historical data or an analyst's subjective judgment to define range of likely values and assign probability weights for each and then number three we can run simulations repeatedly generating random values of the independent variables do this until enough results are gathered to make up representative sample of the near infinite number of possible combinations you can run as many monte carlo simulations as you wish by modifying the underlying parameters you use to simulate the data however you'll also want to compute the range of variation within sample by calculating the variance and the standard deviation which are commonly used measures of spread the more you sample the more accurate your sampling range and then the better your estimation and while you may not be able to travel into the future with monte carlo simulation you'll have much better idea about the possibilities that the future holds if you have any questions please drop us line below and if you want to see more videos like this in the future please like and subscribe thanks for watching