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Levy characterization of Brownian motion

Statement

Assume the Axiom of Choice. Let (Ω,F,P) be a probability space with a continuous-time filtration (Ft)t≥0, and let M be a real continuous local martingale relative to (Ft) with M0=0 almost surely and quadratic variation [M]t=t for every t≥0 in the sense of Quadratic covariation of Brownian Ito processes. Then M is a standard Brownian motion Brownian motion and satisfies the standing hypothesis (H) of Elementary predictable Brownian integrands relative to (Ft): M is adapted, has continuous paths, and for all 0≤s<t the increment Mt−Ms is independent of Fs with law N(0,t−s).

Facts & Assumptions

Given: AC, a filtered probability space with continuous-time filtration (Ft), a real continuous local martingale M with M0=0 almost surely and [M]t=t for all t, and times 0≤s<t.

[F1]

Characteristic exponential. The lemma Characteristic exponential for a continuous local martingale with deterministic clock gives, for every real θ, the conditional identity E[eiθ(Mt−Ms)∣Fs]=e−θ2(t−s)/2 almost surely, together with the complex martingale property of exp⁡(iθMt+θ2t/2). Characteristic exponential for a continuous local martingale with deterministic clock Continuous-time adapted processes and martingales

[F2]

Gaussian law and its characteristic function. For σ2>0 the law N(0,σ2) is defined as the pushforward of N(0,1) under x↦σx, and its characteristic function is ϕ(θ)=e−θ2σ2/2: the computation is the direct Gaussian density computation ∫eiθx(2πσ2)−1/2e−x2/(2σ2)dx=e−θ2σ2/2; the value θ=0 gives 1. Standard normal and normal laws The standard normal density has total mass one

[F3]

Uniqueness from characteristic functions. Two Borel probability laws on R with equal characteristic functions are equal. Uniqueness of a law from its characteristic function

[F4]

Conditional expectations and test events. Conditional expectations are unique almost-sure classes, and for A∈Fs the identity E[1AX∣Fs]=1AE[X∣Fs] holds; the tower property gives E[1AE[X∣Fs]]=E[1AX]. Conditional expectation as an ae class Tower property of conditional expectation

[F5]

AC bookkeeping. Choice is declared for the conditional-expectation interface. The Axiom of Choice

Proof

technique · direct
1.1F2F3F4

The conditional law of the increment is N(0,t−s): by [F1], E[eiθ(Mt−Ms)∣Fs]=e−θ2(t−s)/2 for every real θ; for each A∈Fs, [F4] gives E[1Aeiθ(Mt−Ms)]=E[1A]e−θ2(t−s)/2. Let QA(Γ):=E[1A1{Mt−Ms∈Γ}] for Borel Γ, a finite measure of total mass P(A); its Fourier transform is QA's transform E[1Aeiθ(Mt−Ms)]=P(A)ϕt−s(θ) with ϕt−s the characteristic function of N(0,t−s) by [F2]. Since QA and P(A)N(0,t−s)(⋅) are finite Borel measures with equal Fourier transforms, [F3] applied after normalization (or to the differences) gives QA(Γ)=P(A)N(0,t−s)(Γ) for all Borel Γ.

2.1F3step 1.1

Independence: taking A=Ω in step 1.1 gives the marginal law P(Mt−Ms∈Γ)=N(0,t−s)(Γ). For general A∈Fs and Borel Γ, step 1.1 therefore gives E[1A1{Mt−Ms∈Γ}]=P(A)P(Mt−Ms∈Γ), which is exactly the independence of Mt−Ms from Fs, together with the stated law.

3.1F4step 2.1

Finite lists of increments: for 0=t0<t1<⋯<tn the increments Mtj−Mtj−1 are independent with laws N(0,tj−tj−1). Induction on n: for n=1 this is step 2.1; given the claim for n increments, the conditional law of the increment at tn+1 given Ftn is N(0,tn+1−tn) by step 2.1 and is independent of Ftn, hence independent of the sigma-algebra generated by the previous increments (which is contained in Ftn), and the tower property [F4] multiplies the joint law.

4.1F1F5step 3.1∎

Conclusion and boundary cases: M is adapted, has continuous paths and M0=0 almost surely, and steps 2.1 and 3.1 verify clauses 2 and 3 of the definition of a standard Brownian motion and the increment condition (H) relative to (Ft); hence M is a standard Brownian motion with the stated filtration property. At s=t the increment is 0 with law N(0,0) and independence is trivial; at s=0 the identity gives the law of Mt; for θ=0 the conditional identity is the trivial constant-1 identity; if the clock were c t with c>0, rescaling would give the Gaussian factor e−cθ2(t−s)/2 and the same argument with variance c(t−s); a non-continuous local martingale is not covered, since continuity is used both from the lemma and in the definition of Brownian motion; and AC enters only through [F5].

Source notes

Van der Vaart, Theorem 6.1, characterizes Brownian motion by the characteristic exponential of a continuous local martingale with quadratic variation t. The conditional-law argument of steps 1.1--2.1 is the standard characteristic-function uniqueness route; the conditional expectation is used only through event-testing, so no regular conditional distribution is introduced.

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