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The Quasi-Monte Carlo method recently became popular in the area of mathematical finance or computational finance. [1] In these areas, high-dimensional numerical integrals, where the integral should be evaluated within a threshold ε, occur frequently. Hence, the Monte Carlo method and the quasi-Monte Carlo method are beneficial in these ...
The purpose was to explain the remarkable success of quasi-Monte Carlo (QMC) in approximating the very-high-dimensional integrals in finance. They argued that the integrands are of low effective dimension and that is why QMC is much faster than Monte Carlo (MC). The impact of the arguments of Caflisch et al. [21] was great. A number of papers ...
The "quasi" modifier is used to denote more clearly that the values of a low-discrepancy sequence are neither random nor pseudorandom, but such sequences share some properties of random variables and in certain applications such as the quasi-Monte Carlo method their lower discrepancy is an important advantage.
A Monte Carlo simulation shows a large number and variety of possible outcomes, including the least likely as well … Continue reading → The post Understanding How the Monte Carlo Method Works ...
Monte Carlo methods are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes: [2] optimization, numerical integration, and generating draws from a probability distribution.
The Monte Carlo approach involves generating a sequence of randomly distributed points inside the unit hypercube (strictly speaking these will be pseudorandom). In practice, it is common to substitute random sequences with low-discrepancy sequences to improve the efficiency of the estimators. This is then known as the quasi-Monte Carlo method.
The main variance reduction methods are common random numbers; antithetic variates; control variates; importance sampling; stratified sampling; moment matching; 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 ...
In statistics, Halton sequences are sequences used to generate points in space for numerical methods such as Monte Carlo simulations. Although these sequences are deterministic, they are of low discrepancy, that is, appear to be random for many purposes. They were first introduced in 1960 and are an example of a quasi-random number sequence.