Approximation of Multiple Stochastic Integrals and Its Application to Stochastic Differential Equations
β Scribed by C.W. Li; X.Q. Liu
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 671 KB
- Volume
- 30
- Category
- Article
- ISSN
- 0362-546X
No coin nor oath required. For personal study only.
β¦ Synopsis
As multiple stochastic integrals are not very easy to simulate, we would like to treat them as solutions of systems of stochastic differential equations and solve them successively and recursively approximated by the stochastic Taylor expansion as a Chen series in terms of a Philip Hall basis or Lyndon basis. We can save sufficient values of multiple stochastic integrals with independent sample paths in a look-up table for future use. The table can be used to implement high order schemes to solve stochastic differential equations numerically. A numerical example will be shown to illustrate the efficiency.
π SIMILAR VOLUMES
## Abstract Adaptive timeβstepping methods based on the Monte Carlo Euler method for weak approximation of ItΓ΄ stochastic differential equations are developed. The main result is new expansions of the computational error, with computable leadingβorder term in a posteriori form, based on stochastic