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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 simulations will generally have a polynomial time complexity, and will be faster for large numbers of simulation steps. Monte Carlo simulations are also less susceptible to sampling errors, since binomial techniques use discrete time units. This becomes more true the smaller the discrete units become.
The efficient and exact Monte-Carlo simulation of the Hull–White model with time dependent parameters can be easily performed, see Ostrovski (2013) and (2016). An open-source implementation of the exact Monte-Carlo simulation following Fries (2016) [1] can be found in finmath lib. [2]
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
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 ...
SimDec, or Simulation decomposition, is a hybrid uncertainty and sensitivity analysis method, for visually examining the relationships between the output and input variables of a computational model. SimDec maps multivariable scenarios onto the distribution of the model output. [ 1 ]
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.
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.