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Density of elementary predictable processes in predictable L2
Statement
Assume the Axiom of Choice and the standing hypothesis (H) of Elementary predictable Brownian integrands. Fix and let be the predictable sigma-algebra on Progressively measurable and predictable processes, with the finite measure . Then the classes of bounded elementary predictable integrands Elementary predictable Brownian integrands are dense in : for every -measurable with and every there is a bounded elementary predictable integrand with Equalities are equalities of -classes, so two elementary integrands whose difference vanishes almost everywhere represent the same approximant.
Facts & Assumptions
Given: AC, , the predictable sigma-algebra generated by the sets with , , and with , the finite measure on it, and the class of -classes of bounded elementary predictable integrands.
is a real vector space: on a common refinement of two elementary representations the coefficients of a sum, difference or scalar multiple are bounded and measurable at the left endpoints of the refinement, and the representation of on a refinement is elementary. The constant class belongs to , represented by the elementary process (and its scalar multiples); the literal constant process is not elementary because elementary processes vanish at time zero. Every element of is bounded, say by . Elementary predictable Brownian integrands Ito integral of an elementary predictable process
The norm on the quotient space of almost-everywhere classes is well defined and satisfies the norm axioms, including the triangle inequality; distances are computed by . The norm descends to the quotient and makes a normed space for
A finite intersection of generators of is empty, or a generator , or a time-zero generator ; the family of finite unions of disjoint generators is an algebra generating . Progressively measurable and predictable processes
If is a pi-system of sets and is a lambda-system, then . Dynkin's pi-lambda theorem
Every that is -measurable admits nonnegative simple -measurable pointwise; each is a finite nonnegative linear combination of indicators of -sets and is bounded, its values being finitely many. Every nonnegative measurable function is the increasing limit of simple measurable functions
If measurable functions satisfy for a single integrable and pointwise, then whenever the domination is square-integrable: here with , and pointwise. Dominated convergence
for every : the section at is the singleton , which is Lebesgue-null, so Tonelli gives . Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
AC is declared for uniformity with the ambient probability interfaces; the density argument itself is choice-free, and the implication bridge records the inherited obligations. The Axiom of Choice
Proof
Put , where the closure is taken in the metric of [F2]. By [F1] the set contains every constant class (represented elementarily by its value on and by at time ) and is closed under finite linear combinations of its elements: if and , choose with and use the triangle inequality to bound by .
Every generator of lies in , and contains and . Indeed, for with the process is elementary on the partition when , with coefficients , and on the partition when , with coefficients ; all coefficients are measurable at the left endpoints since and constants are -measurable. For a generator with , use coefficients on when , and the single coefficient when . For the zero process is elementary with by [F7]. The one-block elementary process represents in because the omitted time-zero slice is null, so the whole space belongs to ; the zero elementary process represents , so .
is closed under complements: if is approximated by bounded elementary , then the bounded elementary processes represent the classes and converge to in by the linearity of [F1] and the norm axioms of [F2].
is closed under countable unions of pairwise disjoint sets: let be pairwise disjoint with union . Given , choose so large that which is possible because the disjoint additivity of the finite measure gives ; since each lies in , choose for a bounded elementary with ; the finite sum is bounded and elementary on a common refinement by [F1]; the complement of inside is , so [F2] and the triangle inequality give , hence .
The family of finite intersections of generators of is the pi-system generated by the generators, and it is contained in : by [F3] a finite intersection is empty, a generator, or a time-zero generator, and contains and every generator by step 1.2.
The family contains , the whole space and is closed under complements (step 2.1) and under countable disjoint unions (step 2.2), so it is a lambda-system; step 2.3 shows that it contains the pi-system of finite intersections of generators, whose generated sigma-algebra is by [F3].
By Dynkin's pi-lambda theorem [F4], . Hence the indicator of every predictable set is an limit of bounded elementary processes.
Consequently every bounded -measurable simple function is in : approximate each indicator by bounded elementary processes and combine the finitely many approximants with the coefficients using the closure under finite linear combinations from step 1.1.
Let be -measurable with . By [F5] choose nonnegative simple -measurable pointwise; each is bounded and lies in by step 5.1. Since and , [F6] gives , and for one takes with and then a bounded elementary with , so that by the triangle inequality.
For a general real , decompose ; both parts are nonnegative and -measurable with , so both lie in and each is approximated to within by a bounded elementary process by step 6.1; their difference is bounded and elementary on a common refinement by [F1] and is within of by the triangle inequality.
Steps 6.1 and 7.1 prove the density statement for nonnegative and for general predictable classes, with all approximants bounded and elementary; equalities of processes are equalities of -classes throughout, which is exactly the qualification "modulo product-almost-everywhere equality". For each fixed , step 2.2 selects only the finite list , so the density argument itself uses no choice principle; the declared AC is inherited from the standing hypothesis (H) and ambient probability interfaces as recorded in [F8].
Source notes
Van der Vaart, Lemmas 5.21--5.23, obtains density by a monotone-class argument on the predictable rectangles followed by simple approximation. The version here separates the two steps: the lambda-system argument produces indicator approximants for every predictable set, and the increasing simple approximation plus dominated convergence produces the general approximant. No completeness of is used at this stage; the completion step is item 10.
Depends on
- Progressively measurable and predictable processes
- Elementary predictable Brownian integrands
- Ito integral of an elementary predictable process
- Every nonnegative measurable function is the increasing limit of simple measurable functions
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
- Dominated convergence
- Dynkin's pi-lambda theorem
- The $L^p$ norm descends to the quotient and makes $L^p$ a normed space for $1 \le p \le \infty$
- The Axiom of Choice
- AC supplies countable selections and prescribed serial paths
Used by
- Ito integral for square-integrable predictable processes Definition
- The general Ito integral is well defined Lemma
- Ito isometry and linearity in predictable L2 Theorem
- Localized Ito integral Theorem
- Multidimensional Ito formula for Brownian-driven processes Theorem
- One-dimensional Ito formula Theorem
- Quadratic covariation of Brownian Ito processes Theorem
- Quadratic variation of an Ito integral Theorem
- The Ito integral process has a continuous martingale version Theorem
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Sources
- Aad van der Vaart, Stochastic Integration and Differential Equations, Lemmas 5.21-5.23 (standard reference, not scraped)