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Elementary Ito integrals do not depend on step representation
Statement
Assume the Axiom of Choice and the standing hypothesis (H) of Elementary predictable Brownian integrands. Let and be two elementary predictable integrands on whose values agree -almost everywhere, and let , be their defining sums Ito integral of an elementary predictable process for the chosen representations. Then almost surely, and in fact almost surely for every deterministic . In particular, replacing a representation by a deterministic refinement of its partition, or by any other elementary representation of the same -class, does not change the sums; the elementary integral is a function of that class.
Facts & Assumptions
Given: AC, the standing hypothesis (H), a horizon , elementary representations and with bounded coefficients measurable at the left endpoints, agreeing -almost everywhere, and their defining sums.
A -almost-everywhere equality of two elementary processes, each predictable and hence product measurable, may be integrated by Tonelli: . Tonelli's theorem for nonnegative measurable functions on a sigma-finite product Elementary predictable Brownian integrands
For the increment is independent of , has law , mean and second moment . Elementary predictable Brownian integrands Brownian covariance is equivalent to independent stationary normal increments Gaussian even moments for Brownian increments
If is independent of a sub-sigma-algebra , then almost surely: the constant is -measurable, and for the factorization of expectations for the independent pair gives . Independent sigma-algebras and independent events Expectations factor over finite products of independent random variables Conditional expectation is unique almost surely
For integrable and , almost surely. If , is -measurable, and both and are integrable, then . Tower property of conditional expectation Taking out what is known
The defining sums are linear under a common refinement and ; adaptedness and path continuity are part of the definition. Ito integral of an elementary predictable process
AC is declared for the conditional-expectation interface. The Axiom of Choice
Proof
Let be the common refinement of the two partitions, and write and , where is the coefficient of the block containing and similarly for ; then and are bounded and -measurable, because whenever .
On each time interval the difference is the constant random variable : identically on , so Tonelli gives . Every summand is nonnegative, , and therefore , that is, almost surely for each .
On the common refinement the elementary sum of item 5 can be taken over the refined partition: within each original block the increments telescope, , a finite rearrangement of the defining sum. Applying this to and to , the difference of the two terminal sums is identically.
Write and for each . By [F2] the increment is independent of and has law . Hence [F3] gives and almost surely, because these conditional expectations of a variable independent of equal its unconditional mean. Since is bounded and -measurable, [F4] yields and almost surely.
Expanding the square, . If , then is -measurable and integrable, so the tower property and step 3.1 give ; by symmetry every off-diagonal term vanishes. The diagonal terms satisfy by step 3.1.
Combining steps 4.1 and 1.2, , where the last identity is the Tonelli computation of step 1.2. Since is square-integrable, forces almost surely.
The same computation at a deterministic time uses the truncated common partition : its nonempty blocks are with , whose left endpoints are and whose coefficients are -measurable, so the truncated sum is elementary; the difference of the truncated sums is , each increment is independent of with second moment , and the off-diagonal terms vanish by the same tower argument. The diagonal sum is , and this is by the Tonelli identity of step 1.2 restricted to . Hence almost surely for every . A deterministic refinement of one partition is the special case in which the two representations are identically equal, and then the shared coefficients cancel in , recovering that refinement changes nothing. AC enters only through the conditional-expectation facts [F3] and [F4]; the grid, the coefficients and the limits in the argument are all determined by the given representations.
Source notes
Van der Vaart, Definition 5.20 and Lemma 5.22, first defines the integral on step processes and then checks that the definition does not depend on the representation; the isometry computation for the difference is the same conditional-centering expansion used here. The nearly-sure statement at every fixed time is what makes the notation well defined before the completion step of item 10.
Depends on
- Ito integral of an elementary predictable process
- Elementary predictable Brownian integrands
- Brownian covariance is equivalent to independent stationary normal increments
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
- Conditional expectation as an ae class
- Conditional expectation is unique almost surely
- Taking out what is known
- Tower property of conditional expectation
- Expectations factor over finite products of independent random variables
- Independent sigma-algebras and independent events
- Gaussian even moments for Brownian increments
- The Axiom of Choice
- AC supplies countable selections and prescribed serial paths
Used by
- Ito integral for square-integrable predictable processes Definition
- Cross Ito isometry Lemma
- The general Ito integral is well defined Lemma
- Ito isometry for elementary integrands Theorem
Cited to discharge well-definedness by Ito integral of an elementary predictable process.
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Sources
- Aad van der Vaart, Stochastic Integration and Differential Equations, Definition 5.20 and Lemma 5.22 (standard reference, not scraped)