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Uniqueness of continuous predictable brackets

Statement

Let X be an adapted process on [0,T]. Suppose C,D are continuous, predictable finite-variation processes with C0=D0=0, E[Var⁡(C)T+Var⁡(D)T]<∞, and both X−C and X−D are martingales. Then C and D are indistinguishable.

Facts & Assumptions

Given: The hypotheses of the Statement and the continuous predictable compensator convention of Optional and predictable brackets for counting-process martingales.

[F1]

A continuous finite-variation martingale with integrable total variation and initial value zero is indistinguishably zero. (A continuous finite-variation martingale is constant)

[F2]

A martingale is an adapted integrable process satisfying the all-pairs conditional-expectation identity. (Continuous-time adapted processes and martingales)

[F3]

Conditional expectation is linear, so differences of martingales are martingales. (Basic algebra and order properties of conditional expectation)

[F4]

Indistinguishability means equality for all times outside one probability-one event. (Process law, modification, and indistinguishability)

[A1]

AC is inherited from the conditional-expectation interface in [F2]. (The Axiom of Choice)

Proof

1.1A1F1F2F3given

The difference D−C=(X−C)−(X−D) is a martingale by [F3]. It is continuous, has finite variation, starts at zero, and EVar⁡(D−C)T≤EVar⁡(D)T+EVar⁡(C)T<∞, so it satisfies [F1].

2.1F1F4step 1.1∎

By [F1], D−C is zero outside one probability-one event, which is indistinguishability by [F4]. Hence C=D indistinguishably, proving uniqueness for both scalar and cross-bracket compensators in the stated continuous class.

Source notes

The cited sources formulate predictable brackets through predictable compensators. The local uniqueness proof reduces that convention to the finite-variation martingale lemma, rather than assuming a continuous-time Doob–Meyer uniqueness result not otherwise supplied on this page.

Depends on

Used by

Cited to discharge well-definedness by Optional and predictable brackets for counting-process martingales.

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Sources