scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Remarks on Taylor series expansions and conditional expectations for Stratonovich SDEs with complete V-commutativity

07 Jan 2003-Stochastic Analysis and Applications (Taylor & Francis Group)-Vol. 21, Iss: 4, pp 865-894
TL;DR: In this article, an infinite series expansion of the conditional expectation was obtained with respect to the concept of mean square convergence under the assumption of "infinite smoothness" of drift a(t, x) and diffusion coefficients b j (t, X) and with finite initial second moments.
Abstract: It may happen that there is not a finite maximum order bound for numerical approximations of stochastic processes X = (X t : 0 ≤ t ≤ T) satisfying Stratonovich stochastic differential equations (SDEs) with some commutative structure along an appropriate functional V(t, X t ). This statement can be proven with respect to the concept of mean square convergence under the assumption of “infinite smoothness” of drift a(t, x) and diffusion coefficients b j (t, x) and with finite initial second moments. As a result, we obtain an infinite series expansion of the conditional expectation 𝔼[V(t, X t )|ℱ t N ] on any fixed finite time interval [0, T], provided that the information is collected by discretized σ‐field ℱ T N = σ{W t 0 , W t 1 , …, W t N−1 , W T } at N + 1 given time instants t i ∈ [0, T] with t 0 ≤ t 1 ≤ ··· ≤ t N−1 ≤ t N = T.
Citations
More filters
Book ChapterDOI
01 Jan 1998
TL;DR: In this paper, the authors explore questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties, using diffusion processes as a model of a Markov process with continuous sample paths.
Abstract: We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

2,446 citations

Posted Content
01 Jan 1991
TL;DR: The Stratonovich stochastic Taylor formula for diffusion processes was proved and proved in this article, which has a simpler structure and is a more natural generalization of the deterministic Taylor formula than the Ito Taylor formula.
Abstract: The Stratonovich stochastic Taylor formula for diffusion processes is stated and proved. It has a simpler structure and is a more natural generalization of the deterministic Taylor formula than the Ito stochastic Taylor formula.

45 citations

Book ChapterDOI
01 Jan 2012
TL;DR: In this article, a comprehensive concept of dynamic consistency of numerical methods for (ordinary) stochastic differential equations is presented, which relies on several well-known concepts of numerical analysis to replicate the qualitative behaviour of underlying continuous time systems under adequate discretization.
Abstract: We present the comprehensive concept of dynamic consistency of numerical methods for (ordinary) stochastic differential equations. The concept is illustrated by the well-known class of balanced drift-implicit stochastic Theta methods and relies on several well-known concepts of numerical analysis to replicate the qualitative behaviour of underlying continuous time systems under adequate discretization. This involves the concepts of consistency, stability, convergence, positivity, boundedness, oscillations, contractivity and energy behaviour. Numerous results from literature are reviewed in this context.

6 citations

References
More filters
Book
01 Jun 1992
TL;DR: In this article, a time-discrete approximation of deterministic Differential Equations is proposed for the stochastic calculus, based on Strong Taylor Expansions and Strong Taylor Approximations.
Abstract: 1 Probability and Statistics- 2 Probability and Stochastic Processes- 3 Ito Stochastic Calculus- 4 Stochastic Differential Equations- 5 Stochastic Taylor Expansions- 6 Modelling with Stochastic Differential Equations- 7 Applications of Stochastic Differential Equations- 8 Time Discrete Approximation of Deterministic Differential Equations- 9 Introduction to Stochastic Time Discrete Approximation- 10 Strong Taylor Approximations- 11 Explicit Strong Approximations- 12 Implicit Strong Approximations- 13 Selected Applications of Strong Approximations- 14 Weak Taylor Approximations- 15 Explicit and Implicit Weak Approximations- 16 Variance Reduction Methods- 17 Selected Applications of Weak Approximations- Solutions of Exercises- Bibliographical Notes

6,284 citations

Book ChapterDOI
01 Jan 1985
TL;DR: In this paper, the authors return to the possible solutions X t (ω) of the stochastic differential equation where W t is 1-dimensional "white noise" and where X t satisfies the integral equation in differential form.
Abstract: We now return to the possible solutions X t (ω) of the stochastic differential equation (5.1) where W t is 1-dimensional “white noise”. As discussed in Chapter III the Ito interpretation of (5.1) is that X t satisfies the stochastic integral equation or in differential form (5.2) .

4,144 citations

Book ChapterDOI
01 Jan 1998
TL;DR: In this paper, the authors explore questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties, using diffusion processes as a model of a Markov process with continuous sample paths.
Abstract: We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

2,446 citations


"Remarks on Taylor series expansions..." refers background in this paper

  • ...For more details, see Arnold,([1]) Dynkin,([2]) Gard,([24]) Gikhman and Skorochod,([3]) Øksendal,([4]) and Krylov([5]) among many others....

    [...]

Book ChapterDOI
01 Jan 1986

2,356 citations


Additional excerpts

  • ...4), see Krylov,([7]) Kunita([8]) and Walsh([9]))....

    [...]

  • ...R1, with constant real coefficients ai, bi 2 R1 and a2 > 0, scalar space-time-dependent or space-independent white noise Ŵt(x) (we always assume some appropriate conditions of existence and uniqueness for SPDE (1.4), see Krylov,[7] Kunita[8] and Walsh[9])....

    [...]

Book
01 Jan 1988

847 citations