The Monte Carlo simulation estimates the probability of different outcomes in a process that cannot easily be predicted because of the potential for random variables.

A Monte Carlo simulation helps investors by modeling potential investment outcomes using randomization and computer algorithms.

On a canonical set of stochastic simulation examples including population-based Markov chain Monte Carlo methods and Sequential Monte Carlo methods, we find speedups from 35- to 500-fold over ...

Monte Carlo simulation has been used to value options since Boyle's seminal paper. Monte Carlo simulation, however, has not been used to its fullest extent for option valuation because of the belief ...