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Euler–Maruyama one-step moment and increment estimates
Statement
Let , , and with . Let be an -measurable -valued random variable with . Assume is independent of with law . Let and be measurable and satisfy Set . There is a constant , independent of , such that and The first estimate controls a grid step and, with , the interpolated value on one grid interval. The second estimate is the interpolation increment bound. The global state-Lipschitz and time-Hölder assumptions of Euler–Maruyama recursion are not needed for these one-step bounds; the uniform linear-growth bound is enough.
Facts & Assumptions
Given: The Axiom of Choice, finite dimensions , , , the coefficient growth bound in the Statement, and an -measurable .
A standard -dimensional Brownian motion has increments with law for a time increment of length . -dimensional Brownian motion
The shared coefficient bound is . Euler–Maruyama recursion
A multivariate normal law is realized using independent standard-normal coordinates. Multivariate normal law, including singular covariance
A standard normal coordinate has density . Standard normal and normal laws
The increment is independent of ; this filtration-relative independence is an explicit hypothesis, as required for conditional estimates.
The Axiom of Choice (AC) asserts that every family of nonempty sets has a choice function. The Axiom of Choice
Proof
The law in [F1] has the representation with realized as in [F3]. By [F4] each coordinate has density . Its th absolute moment is finite: the integral on is finite, and for the function is bounded, so . The finite-dimensional inequality then gives and . For the increment is zero.
For , direct differentiation gives for . Apply the one-dimensional Taylor formula with integral remainder to , where . The triangle inequality and for yield .
The drift term in is -measurable and the Brownian increment is conditionally centered with covariance . Therefore the cross term has conditional expectation zero and . This also holds for .
The coefficient growth assumption [F2] and give . Conditional centering gives . By the growth bound and , . For the th moment, and step 1.1 give , since and . Also . Substituting these bounds into step 1.2 and using proves . The AC assumption is inherited from the cited Brownian and normal-law interfaces; these finite-dimensional estimates make no choice.
Depends on
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Sources
- Simo Särkkä and Arno Solin, Applied Stochastic Differential Equations, Algorithm 8.1 (standard reference, not scraped)
- Desmond J. Higham, Xuerong Mao, and Andrew M. Stuart, Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations, §2 (standard reference, not scraped)
- Tiejun Li, Lecture 16: Numerical SDEs: Basics, §1 and §2 (standard reference, not scraped)