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The simulation data is used to define the scenario index for each simulation run. For example, if an X2 value falls into the low state and X3 is equal to 2, the corresponding scenario, defined in Step 3, is (ii). Color-code the output distribution. When all output values are assigned scenario indices, they are plotted as series in a stacked ...
Monte Carlo method: Pouring out a box of coins on a table, and then computing the ratio of coins that land heads versus tails is a Monte Carlo method of determining the behavior of repeated coin tosses, but it is not a simulation. Monte Carlo simulation: Drawing a large number of pseudo-random uniform variables from the interval [0,1] at one ...
The Monte Carlo method in Excel Prof. André Farber Solvay Business School; Sales Forecasting, vertex42.com; Pricing using Monte Carlo simulation, a practical example, Prof. Giancarlo Vercellino; Personal finance. A Better Way to Size Up Your Nest Egg, Businessweek Online: January 22, 2001
conditional Monte Carlo; and quasi random variables (in Quasi-Monte Carlo method) For simulation with black-box models subset simulation and line sampling can also be used. Under these headings are a variety of specialized techniques; for example, particle transport simulations make extensive use of "weight windows" and "splitting/Russian ...
The antithetic variates technique consists, for every sample path obtained, in taking its antithetic path — that is given a path {, …,} to also take {, …,}.The advantage of this technique is twofold: it reduces the number of normal samples to be taken to generate N paths, and it reduces the variance of the sample paths, improving the precision.
Monte Carlo simulated stock price time series and random number generator (allows for choice of distribution), Steven Whitney; Discussion papers and documents. Monte Carlo Simulation, Prof. Don M. Chance, Louisiana State University; Pricing complex options using a simple Monte Carlo Simulation, Peter Fink (reprint at quantnotes.com)
Monte Carlo is an estimation procedure. The main idea is that if it is necessary to know the average value of some random variable and its distribution cannot be stated, and if it is possible to take samples from the distribution, we can estimate it by taking the samples, independently, and averaging them.
Proponents of Monte Carlo simulation contend that these tools are valuable because they offer simulation using randomly ordered returns based on a set of reasonable parameters. For example, the tool can model retirement cash flows 500 or 1,000 times, reflecting a range of possible outcomes.
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