Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-14
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Optional stopping under uniform integrability

Statement

Assume AC. Let M be a uniformly integrable martingale and let σ≤τ be pointwise ordered, almost-surely finite stopping times. Define Mσ and Mτ using the fixed cemetery value 0 on the respective null events where the stopping time is infinite. Then Mσ,Mτ∈L1 and E[Mτ∣Fσ]=Mσa.s.,EMτ=EMσ.

Facts & Assumptions

Given: The hypotheses, objects, and conventions in the Statement.

[F1]

Closed martingale characterization supplies M∞∈L1 with Mn=E[M∞∣Fn] and makes conditional expectations of this fixed variable uniformly integrable.

[F2]

Optional sampling for bounded stopping times handles every truncated time.

[F3]

Stopped random variable and stopped process gives pointwise stabilization at an almost-surely finite time.

[F4]

Sigma-algebra at a stopping time gives the stopped-event tests, and Tower property of conditional expectation applies to nested sigma-algebras.

[F5]

The Axiom of Choice records the background AC hypothesis. The conditional-expectation and representative properties used here are supplied by [F1], [F2], and [F4].

[F6]

The stopping-time sigma-algebra is a sigma-algebra proves Fσ⊆Fτ for pointwise σ≤τ.

Proof

1.1F1F2

Fix a stopping time ρ equal to either σ or τ. For r≥n, F2 applied to ρ∧n≤r gives Mρ∧n=E[Mr∣Fρ∧n]. Let r→∞ in L1 using F1 and conditional contraction to obtain Mρ∧n=E[M∞∣Fρ∧n].

2.1F1F3step 1.1

The sigma-algebras Fρ∧n increase with n. Thus the sequence in step 1.1 is a closed, hence uniformly integrable, martingale by F1. Since ρ<∞ almost surely, F3 gives Mρ∧n→Mρ almost surely; F1's UI convergence implication upgrades this to L1, proving Mρ∈L1.

3.1F1F4step 1.1step 2.1

For A∈Fρ, the event A∩{ρ≤n} belongs to Fρ∧n (check the defining finite-level intersections). Apply the conditional identity in step 1.1 on this event, then let n→∞. The left side converges by step 2.1; the right side converges by dominated convergence for M∞1A∩{ρ≤n}. Hence ∫AMρ dP=∫AM∞ dP, so Mρ=E[M∞∣Fρ].

4.1F1F4F5F6step 3.1∎

Since σ≤τ pointwise, F6 gives Fσ⊆Fτ. Apply the tower property in F4 to the two representations from step 3.1: E[Mτ∣Fσ]=E[E[M∞∣Fτ]∣Fσ]=E[M∞∣Fσ]=Mσ. Taking expectations finishes. AC is the background hypothesis recorded in F5; the conditional-expectation identities come from F1 and F4.

Depends on

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Sources