A second-order Monte Carlo method for the solution of the Ito stochastic differential equation
作者:
D.C. Haworth,
S.B. Pope,
期刊:
Stochastic Analysis and Applications
(Taylor Available online 1986)
卷期:
Volume 4,
issue 2
页码: 151-186
ISSN:0736-2994
年代: 1986
DOI:10.1080/07362998608809086
出版商: Marcel Dekker, Inc.
数据来源: Taylor
摘要:
A difference approximation that is second-order accurate in the time stephis derived for the general Ito stochastic differential equation. The difference equation has the form of a second-order random walk in which the random terms are non-linear combinations of Gaussian random variables. For a wide class of problems, the transition pdf is joint-normal to second order inh; the technique then reduces to a Gaussian random walk, but its application is not limited to problems having a Gaussian solution. A large number of independent sample paths are generated in a Monte Carlo solution algorithm; any statistical function of the solution (e.g., moments or pdf's) can be estimated by ensemble averaging over these paths
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