Monte Carlo simulation is a method by which investors can anticipate the probability of meeting specific financial goals at certain time periods in the future. This is accomplished by generating thousands of possible paths (or scenarios) that investments might take during the years until the investor is ready to retire or cash out.
Computer experiments of complex relationships that simulate and analyse sequences of events using random numbers controlled by a specified distribution function.
method for calculating the probabilities of outcome s by simulation, running a model many time s, using a computer. A Monte Carlo model is an example of a "stochastic" model. [D05026] RAMP The technique used by project management application s to estimate the likely range of outcome s from a complex random process by simulating the process a large number of time [D01051] WST See Monte Carlo Method. [D03623] RMW
A method that estimates possible outcomes from a set of random variables by simulating a process a large number of times and observing the outcomes.
A method of generating values from a known distribution for the purposes of experimentation. This is accomplished by generating uniform random variables and using them in an inverse reliability equation to produce failure times that would conform to the desired input distribution.
Simulation refers to any analytical method meant to imitate a real-life system, especially when other analyses are too mathematically complex or too difficult to reproduce. A Monte Carlo simulation, which is often used in the financial services industry, randomly generates values for uncertain variables over and over to simulate a model. Monte Carlo simulation was named for Monte Carlo, Monaco, where the primary attractions are casinos containing games of chance, which exhibit random behavior.
a method in which randomly produced numbers (typically from a computer) are generated for uncertain variables and analyzed to determine the model and/or assembly performance
a model that calculates the gain or loss from a transaction by analyzing a large number of different market scenarios (e
A Monte Carlo simulation generates thousands of probable performance outcomes, called scenarios, which might occur in the future. An investment simulation incorporates economic data such as a range of potential interest rates, inflation rates, tax rates, and so on, combined in random order. As a result, it's designed to account for the uncertainty and performance variation that's always present in financial markets.
A methodology for solving a problem through generation of a large number of scenarios and analysis of the collective result, which is generally a probability distribution of possible outcomes.
A simulation method used by Relex RBD when calculating complex reliability block diagrams. A Monte Carlo Simulation will perform random tests on the specified system in order to provide an analysis of the overall reliability and availability of the system. The result of these simulations will determine the reliability and availability of the RBD model.
A type of simulation modeling that uses random numbers to capture the effects of uncertainty. Multiple simulations are run, with the value of each uncertain variable in the analysis selected at random from a probability distribution for the value of that variable, for each simulation. The simulation results are compiled, providing a probability distribution for the overall result.
A technique for solving open-form differential equations of a type frequently encountered in finance. Involves generating thousands of 'scenarios' and then grouping their results to determine statistical probability: hence a heavily computer-intensive method generally reserved for calculations that cannot be solved more directly.
A risk analysis technique in which probable future events are simulated on a computer generating estimated rates of return and risk indexes.
a technique used to estimate the likely range of outcomes from a complex process by simulating the process under randomly selected conditions a large number of times.
simulation methods that use random numbers to generate possible molecules or materials and then identify the optimal system, for example through molecular mechanics.
a method of pricing derivatives by simulating the evolution of the underlying variable (or variables) many times over. The average outcome of the simulation is an approximation of the derivativeâ€(tm)s value. Monte Carlo is useful in the valuation of complex derivatives for which exact analytical solutions have not been found, but it can be very computationally intensive. Monte Carlo simulation can also be applied to a portfolio of instruments, rather than a single instrument, to estimate the value-at-risk of that portfolio.
The process of generating random variables to simulate future price scenarios.
A method of calculating significance where the analysis in question is repeated using randomized or permuted sequences, in order to determine the expected scores for unrelated (random) sequences.
A problem solving technique used to approximate the probability of certain outcomes by running multiple trial runs, called simulations, using random variables. ;
A subset of digital simulation models based on random or stochastic processes.
A technique of simulation which uses many randomly or "pseudo-randomly" generated scenarios.
An analytical technique for solving a problem by performing a large number of trail runs, called simulations, and inferring a solution from the collective results of the trial runs. Method for calculating the probability distribution of possible outcomes.
