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Euler–Maruyama one-step moment and increment estimates

Statement

Let p≥2, 0≤δ≤1, and s≥0 with s+δ≤T. Let U be an Fs-measurable Rd-valued random variable with E∣U∣p<∞. Assume Ws+δ−Ws is independent of Fs with law Nm(0,δIm). Let bs:Rd→Rd and σs:Rd→Rd×m be measurable and satisfy ∣bs(x)∣2+∥σs(x)∥F2≤K2(1+∣x∣2). Set V=U+δbs(U)+σs(U)(Ws+δ−Ws). There is a constant Cp,m,K, independent of s,δ,U, such that E(∣V∣p∣Fs)≤(1+Cp,m,Kδ)∣U∣p+Cp,m,Kδ and E(∣V−U∣2∣Fs)≤C2,m,Kδ(1+∣U∣2). The first estimate controls a grid step and, with δ=t−tk, 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 d,m, p≥2, 0≤δ≤1, the coefficient growth bound in the Statement, and an Fs-measurable U∈Lp.

[F1]

A standard m-dimensional Brownian motion has increments with law Nm(0,δIm) for a time increment of length δ. d-dimensional Brownian motion

[F2]

The shared coefficient bound is ∣b(t,x)∣2+∥σ(t,x)∥F2≤K2(1+∣x∣2). Euler–Maruyama recursion

[F3]

A multivariate normal law Nm(0,Im) is realized using independent standard-normal coordinates. Multivariate normal law, including singular covariance

[F4]

A standard normal coordinate has density e−x2/2/2π. Standard normal and normal laws

[A1]

The increment Ws+δ−Ws is independent of Fs; this filtration-relative independence is an explicit hypothesis, as required for conditional estimates.

[A2]

The Axiom of Choice (AC) asserts that every family of nonempty sets has a choice function. The Axiom of Choice

Proof

1.1F1F3F4A1algebra

The law in [F1] has the representation δZ with Z realized as in [F3]. By [F4] each coordinate has density e−x2/2/2π. Its pth absolute moment is finite: the integral on [−1,1] is finite, and for ∣x∣≥1 the function ∣x∣pe−x2/4 is bounded, so ∣x∣pe−x2/2≤Cpe−x2/4. The finite-dimensional inequality ∣Z∣p≤mp/2−1∑j=1m∣Zj∣p then gives E∣Z∣p<∞ and E(∣σs(U)(Ws+δ−Ws)∣p∣Fs)≤cp,mδp/2∥σs(U)∥Fp. For δ=0 the increment is zero.

1.2givenalgebra

For ϕ(x)=∣x∣p, direct differentiation gives ∥D2ϕ(x)∥≤Cp∣x∣p−2 for p≥2. Apply the one-dimensional Taylor formula with integral remainder to r↦ϕ(U+rR), where R=δbs(U)+σs(U)(Ws+δ−Ws). The triangle inequality and (a+b)q≤Cq(aq+bq) for q≥0 yield ϕ(U+R)≤ϕ(U)+Dϕ(U)R+Cp(∣U∣p−2∣R∣2+∣R∣p).

1.3F1A1givenalgebra

The drift term in V−U is Fs-measurable and the Brownian increment is conditionally centered with covariance δIm. Therefore the cross term has conditional expectation zero and E(∣V−U∣2∣Fs)=δ2∣bs(U)∣2+δ∥σs(U)∥F2≤C2,m,Kδ(1+∣U∣2). This also holds for δ=0.

2.1F2step 1.1step 1.2A2givenalgebra∎

The coefficient growth assumption [F2] and δ≤1 give ∣bs(U)∣p+∥σs(U)∥Fp≤Cp,K(1+∣U∣p). Conditional centering gives E(R∣Fs)=δbs(U). By the growth bound and ∣a+b∣2≤2∣a∣2+2∣b∣2, E(∣R∣2∣Fs)≤2K2(δ2+δ)(1+∣U∣2)≤4K2δ(1+∣U∣2). For the pth moment, ∣a+b∣p≤2p−1(∣a∣p+∣b∣p) and step 1.1 give E(∣R∣p∣Fs)≤Cp,m,K(δp+δp/2)(1+∣U∣p)≤Cp,m,Kδ(1+∣U∣p), since p≥2 and δ≤1. Also ∣Dϕ(U)⋅δbs(U)∣≤Cp,Kδ(1+∣U∣p). Substituting these bounds into step 1.2 and using ∣U∣p−2(1+∣U∣2)≤2(1+∣U∣p) proves E(∣V∣p∣Fs)≤(1+Cp,m,Kδ)∣U∣p+Cp,m,Kδ. The AC assumption is inherited from the cited Brownian and normal-law interfaces; these finite-dimensional estimates make no choice.

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