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Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields. Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon. This state-of-the-art book on Monte Carlo simulation methods is ideal for finance professionals and students. Order your copy today.

A Guide to Monte Carlo Simulations in Statistical Physics

Direct simulation Monte Carlo - Direct simulation Monte Carlo (DSMC) is a computational Monte Carlo algorithm for the stochastic simulation of rarefied gas flows.

Monte Carlo option model - A Monte Carlo model, in its most general description, includes any method of estimating a value by the random generation of numbers and statistical principles. As a way of pricing or valuing options, Monte Carlo option models use a pseudo-random sequence, one that will be random enough the simulate a range of outcomes yet deterministic enough to reproduce when necessary.

Monte Carlo method - Monte Carlo methods are a class of computational algorithms for simulating the behavior of various physical and mathematical systems. They are distinguished from other simulation methods (such as molecular dynamics) by being stochastic, that is nondeterministic in some manner - usually by using random numbers (or more often pseudo-random numbers) - as opposed to deterministic algorithms.

Parallel tempering - Parallel tempering is a simulation method aimed at improving the dynamic properties of Monte Carlo method simulations of physical systems, and of Markov chain Monte Carlo sampling methods more generally. The method originates from Geyer and was later developed, among others, by G.

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