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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 T>0 and let PT be the predictable sigma-algebra on [0,T]×Ω Progressively measurable and predictable processes, with the finite measure dtP. Then the classes of bounded elementary predictable integrands Elementary predictable Brownian integrands are dense in L2([0,T]×Ω,PT,dtP): for every PT-measurable H with 0T ⁣ ⁣ΩH2dPdt< and every ε>0 there is a bounded elementary predictable integrand G with (0T ⁣ ⁣Ω(HG)2dPdt)1/2<ε. Equalities are equalities of (dtP)-classes, so two elementary integrands whose difference vanishes almost everywhere represent the same approximant.

Facts & Assumptions

Given: AC, T>0, the predictable sigma-algebra PT generated by the sets (s,u]×C with 0s<uT, CFs, and {0}×C with CF0, the finite measure μ:=dtP on it, and the class E of (dtP)-classes of bounded elementary predictable integrands.

[F1]

E 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 H on a refinement is elementary. The constant L2(dtP) class belongs to E, represented by the elementary process 1(0,T] (and its scalar multiples); the literal constant process is not elementary because elementary processes vanish at time zero. Every element of E is bounded, say by G. Elementary predictable Brownian integrands Ito integral of an elementary predictable process

[F2]

The L2 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 UV2=((UV)2dμ)1/2. The Lp norm descends to the quotient and makes Lp a normed space for 1p

[F3]

A finite intersection of generators of PT is empty, or a generator (s,u]×C, or a time-zero generator {0}×C; the family of finite unions of disjoint generators is an algebra generating PT. Progressively measurable and predictable processes

[F4]

If PD is a pi-system of sets and D is a lambda-system, then σ(P)D. Dynkin's pi-lambda theorem

[F5]

Every H0 that is PT-measurable admits nonnegative simple PT-measurable snH pointwise; each sn is a finite nonnegative linear combination of indicators of PT-sets and is bounded, its values being finitely many. Every nonnegative measurable function is the increasing limit of simple measurable functions

[F6]

If measurable functions satisfy fng for a single integrable g and fnf pointwise, then fnf2dμ0 whenever the domination is square-integrable: here 0snH with HL2, and Hsn0 pointwise. Dominated convergence

[F7]

μ({0}×C)=0 for every C: the section at ω is the singleton {0}, which is Lebesgue-null, so Tonelli gives μ({0}×C)=C ⁣ ⁣1{0}(s)dsdP=0. Tonelli's theorem for nonnegative measurable functions on a sigma-finite product

[F8]

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

technique · direct
1.1

Put D:={APT:1AE}, where the closure is taken in the L2(μ) metric of [F2]. By [F1] the set E contains every constant L2(μ) class (represented elementarily by its value on (0,T] and by 0 at time 0) and is closed under finite linear combinations of its elements: if U1,,UkE and a1,,akR, choose GiE with UiGi2<δ and use the triangle inequality to bound iaiUiiaiGi2 by δiai.

F1F2
1.2

Every generator of PT lies in D, and D contains and [0,T]×Ω. Indeed, for (s,u]×C with s>0 the process 1C1(s,u] is elementary on the partition 0<s<u<T when u<T, with coefficients 0,1C,0, and on the partition 0<s<u=T when u=T, with coefficients 0,1C; all coefficients are measurable at the left endpoints since 1CFs and constants are F0-measurable. For a generator (0,u]×C with CF0, use coefficients 1C,0 on 0<u<T when u<T, and the single coefficient 1C when u=T. For {0}×C the zero process is elementary with 1{0}×C022=μ({0}×C)=0 by [F7]. The one-block elementary process 1(0,T] represents 1[0,T]×Ω in L2(μ) because the omitted time-zero slice is null, so the whole space belongs to D; the zero elementary process represents 1, so D.

F1F7given
2.1

D is closed under complements: if 1A is approximated by bounded elementary Gn, then the bounded elementary processes 1(0,T]Gn represent the classes 1Gn and converge to 11A=1Ac in L2(μ) by the linearity of [F1] and the norm axioms of [F2].

F1F2step 1.1
2.2

D is closed under countable unions of pairwise disjoint sets: let A1,A2,D be pairwise disjoint with union A. Given ε>0, choose m so large that (j>mμ(Aj))1/2<ε/2, which is possible because the disjoint additivity of the finite measure μ gives jμ(Aj)=μ(A)T<; since each Aj lies in D, choose for jm a bounded elementary Gj with 1AjGj2<ε/(2m); the finite sum S:=jmGj is bounded and elementary on a common refinement by [F1]; the complement of jmAj inside A is j>mAj, so [F2] and the triangle inequality give 1AS2(μ(j>mAj))1/2+j=1m1AjGj2<ε/2+ε/2=ε, hence AD.

F1F2step 1.1
2.3

The family of finite intersections of generators of PT is the pi-system generated by the generators, and it is contained in D: by [F3] a finite intersection is empty, a generator, or a time-zero generator, and D contains and every generator by step 1.2.

F3step 1.2
3.1

The family D contains , the whole space [0,T]×Ω 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 PT by [F3].

step 2.1step 2.2step 2.3
4.1

By Dynkin's pi-lambda theorem [F4], PT=σ(generators)D. Hence the indicator of every predictable set is an L2(μ) limit of bounded elementary processes.

F4step 3.1
5.1

Consequently every bounded PT-measurable simple function i=1kai1Ai is in E: approximate each indicator 1Ai by bounded elementary processes and combine the finitely many approximants with the coefficients ai using the closure under finite linear combinations from step 1.1.

step 1.1step 4.1
6.1

Let H0 be PT-measurable with HL2(μ). By [F5] choose nonnegative simple PT-measurable snH pointwise; each sn is bounded and lies in E by step 5.1. Since 0snH and HL2(μ), [F6] gives Hsn20, and for ε>0 one takes n with Hsn2<ε/2 and then a bounded elementary G with snG2<ε/2, so that HG2<ε by the triangle inequality.

F5F6step 5.1
7.1

For a general real HL2(μ), decompose H=H+H; both parts are nonnegative and PT-measurable with H±H, so both lie in L2(μ) and each is approximated to within ε/2 by a bounded elementary process by step 6.1; their difference is bounded and elementary on a common refinement by [F1] and is within ε of H by the triangle inequality.

F1step 6.1
8.1

Steps 6.1 and 7.1 prove the density statement for nonnegative and for general predictable L2 classes, with all approximants bounded and elementary; equalities of processes are equalities of (dtP)-classes throughout, which is exactly the qualification "modulo product-almost-everywhere equality". For each fixed m, step 2.2 selects only the finite list G1,,Gm, 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].

step 2.2step 6.1step 7.1F8given

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 L2 approximant. No completeness of L2 is used at this stage; the completion step is item 10.

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