Exact Calculation of the Mean-Square Error in the Method of Expansion of Iterated Ito Stochastic integrals Based on Generalized Multiple Fourier Series

Abstract

The article is devoted to the developement of the method of expansion and mean-square approximation of iterated Ito stochastic integrals based on generalized multiple Fourier series converging in the sense of norm in the space $L_2([t,T{]}^k)$ ($k$ is the multiplicity of the iterated Ito stochastic integral). We obtain the exact and approximate expressions for the mean-square approximation error of iterated Ito stochastic integrals of multiplicity $k$ ($k\in \mathbb{N}$) from the stochastic Taylor-Ito expansion. As a result, we do not need to use redundant terms of expansions of iterated Ito stochastic integrals that complicate the numerical methods for Ito stochastic differential equations. Moreover, we proved the convergence with propability 1 for the method of expansion of iterated Ito stochastic integrals based on generalized multiple Fourier series for the cases of multiple Fourier-Legendre series and multiple trigonometric Fourier series. Mean-square approximation of iterated Stratonovich stochastic integrals is also considered in the article. The results of the article can be applied to the high-order strong numerical methods for Ito stochastic differential equations as well as for non-commutative semilinear stochastic partial differential equations with multiplicative trace class noise.

arXiv comment71 pages. Minor changes in Sect. 5, 8, 9. http://diffjournal.spbu.ru/EN/numbers/2018.4/article.2.1.html arXiv admin note: substantial text overlap with arXiv:1712.08991, arXiv:1712.09746, arXiv:1801.00231, arXiv:1712.09516

Cited by in corpus (1)

• Optimization of the Mean-Square Approximation Procedures for Iterated Ito Stochastic Integrals of Multiplicities 1 to 5 from the Unified Taylor-Ito Expansion Based on Multiple Fourier-Legendre Series