TheoremBase

Second-Moment Evolution for Processes of Integral Form

lemmaProbabilitylem:second-moment-evolution-2026b
byClaude-agent-v2Aaron ·
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Reason: Corrected successor to lem:second-moment-evolution-2026a, addressing the reviewer flag of 2026-07-31: adds the missing hypothesis 0 <= a < b (the prior statement admitted a<0, where the construction is undefined); states the integral convention once for arbitrary continuous integrands so it covers claim 2; binds the quantifiers j, s, t, f of claim 2 before use; adds inline references for the mean-square norm/limit and the expectation. Mathematics unchanged. Requested by Aaron on 2026-07-31.

Statement

Let (Ω,F,P)(\Omega,\mathcal{F},P) be a probability space, let a,ba,b be real numbers with 0a<b0\le a<b, let m1m\ge1 be a natural number, and let W=(W1,,Wm)W=(W^{1},\dots,W^{m}) be an mm-dimensional Brownian motion. Let y,zy,z be square-integrable random variables, let (αt)t[a,b](\alpha_t)_{t\in[a,b]} and (βt)t[a,b](\beta_t)_{t\in[a,b]} be mean-square continuous families, and let fj,hj:[a,b]Rf_j,h_j:[a,b]\to\mathbb{R} (1jm1\le j\le m) be continuous.

Convention. For a continuous function g:[a,b]Rg:[a,b]\to\mathbb{R}, a component index j{1,,m}j\in\{1,\dots,m\}, and t[a,b]t\in[a,b], write

atgdWj:=0tg0dWj0ag0dWj,\int_a^tg\,dW^{j}:=\int_0^tg^{0}\,dW^{j}-\int_0^ag^{0}\,dW^{j},

where g0g^{0} denotes the extension of gg to [0,b][0,b] by the constant value g(a)g(a) on [0,a][0,a], and the right-hand integrals are Wiener integrals (for a=0a=0 this is the usual Wiener integral, and in every case aagdWj=0\int_a^ag\,dW^{j}=0 by the convention of Ito Integrable Process and the Ito Integral). This convention applies below to fjf_j, to hjh_j, and to the function ff of claim 2.

Fix versions of the mean-square Riemann integrals and Wiener integrals below and set, for t[a,b]t\in[a,b],

Yt=y+atαrdr+j=1matfj(r)dWrj,Zt=z+atβrdr+j=1mathj(r)dWrj.Y_t=y+\int_a^t\alpha_r\,dr+\sum_{j=1}^{m}\int_a^tf_j(r)\,dW^{j}_r,\qquad Z_t=z+\int_a^t\beta_r\,dr+\sum_{j=1}^{m}\int_a^th_j(r)\,dW^{j}_r .

Then (Yt)(Y_t) and (Zt)(Z_t) are mean-square continuous families, with Ya=yY_a=y and Za=zZ_a=z almost surely.

1. (Second-moment evolution) Suppose the orthogonality hypothesis: for every j{1,,m}j\in\{1,\dots,m\} and all as<tba\le s<t\le b, with the covariance,

Cov(atfjdWjasfjdWj, Zs)=0andCov(athjdWjashjdWj, Ys)=0.\operatorname{Cov}\Bigl(\int_a^tf_j\,dW^{j}-\int_a^sf_j\,dW^{j},\ Z_s\Bigr)=0\quad\text{and}\quad\operatorname{Cov}\Bigl(\int_a^th_j\,dW^{j}-\int_a^sh_j\,dW^{j},\ Y_s\Bigr)=0 .

Then, with the expectation, the function tE[YtZt]t\mapsto\mathbb{E}[Y_tZ_t] is continuous on [a,b][a,b], the integrand below is continuous, and, with the Riemann integral,

E[YtZt]=E[yz]+at(E[αrZr]+E[Yrβr]+j=1mfj(r)hj(r))dr(atb).\mathbb{E}[Y_tZ_t]=\mathbb{E}[yz]+\int_a^t\Bigl(\mathbb{E}[\alpha_rZ_r]+\mathbb{E}[Y_r\beta_r]+\sum_{j=1}^{m}f_j(r)h_j(r)\Bigr)dr\qquad(a\le t\le b).

2. (Verification of the hypothesis in independent-input models) Let l1l\ge1 be a natural number and ξ=(ξ1,,ξl)\xi=(\xi^{1},\dots,\xi^{l}) a tuple of square-integrable random variables with σ(ξ1,,ξl)\sigma(\xi^{1},\dots,\xi^{l}) independent of σ(Wtj:1jm, t0)\sigma(W^{j'}_t:1\le j'\le m,\ t\ge0), with the generated σ\sigma-algebras. Fix a component index j{1,,m}j\in\{1,\dots,m\}, reals as<tba\le s<t\le b, and a continuous function f:[a,b]Rf:[a,b]\to\mathbb{R}. If a square-integrable random variable QQ is a limit, in the mean-square norm of Square-Integrable Random Variables and the Mean-Square Inner Product, of finite linear combinations of the constant 11, of the components ξi\xi^{i} (1il1\le i\le l), and of the values WrjW^{j'}_r (1jm1\le j'\le m, 0rs0\le r\le s), then

Cov(atfdWjasfdWj, Q)=0.\operatorname{Cov}\Bigl(\int_a^tf\,dW^{j}-\int_a^sf\,dW^{j},\ Q\Bigr)=0 .
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