# Monte Carlo Simulation

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Meaning and definition of Monte Carlo Simulation

Monte Carlo Simulation implies a problem solving technique which is used to estimate the possibility of certain outcomes by running several trial runs, known as simulations, through the use of random variables. As stated by Investopedia, Monte Carlo Simulation has obtained its name from the city in Monaco, where the main attractions are casinos that feature games of chance. Gambling games, such as dice, roulette, and slot machines, demonstrate random behavior.

The working of Monte Carlo Simulation

Monte Carlo simulations carry out risk analysis by building models of probable results by substituting a range of values, a probability distribution, for any factor containing inherent uncertainty. Thereafter, the results are calculated over and over, using a different set of random values every time from the probability functions. Based upon the uncertainties’ number and the specified ranges, a Monte Carlo simulation could rivet numerous recalculations before being accomplished. Monte Carlo simulation generates distributions of probable outcome values.

Monte Carlo simulation includes numerous advantages like:

• Probabilistic Result

The results obtained from Monte Carlo simulation not only reveal what could possibly happen but also the extent of possibility for each outcome.

• Graphical Result

Due to the data generated by a Monte Carlo simulation, it becomes easier to create graphs of various outcomes as well as their chances of occurrence. This is imperative for communicating findings to other stakeholders.

With just a few cases, it becomes difficult with deterministic analysis to look for the variables which affect the outcome the most. In Monte Carlo simulation, it is easier to find inputs showing the largest impact on bottom-line results.

• Scenario Analysis

In deterministic analysis, it is quite difficult to model different combinations of values for distinctive inputs to know the effects of totally different scenarios. Through the use of Monte Carlo simulation, analysts can see precisely which inputs had values together when certain outcomes took place. This is very useful for pursuing further analysis.

• Correlation of Inputs
In Monte Carlo simulation, it is possible to form independent relationships between input variables. Moreover, it is important for precision to signify how, actually, when certain factors go up, other go down correspondingly.