Jointly measurable and progressively measurable stochastic processes

Jordan Bell
June 18, 2015

1 Jointly measurable stochastic processes

Let E=d with Borel , let I=0, which is a topological space with the subspace topology inherited from , and let (Ω,,P) be a probability space. For a stochastic process (Xt)tI with state space E, we say that X is jointly measurable if the map (t,ω)Xt(ω) is measurable I.

For ωΩ, the path tXt(ω) is called left-continuous if for each tI,

Xs(ω)Xt(ω),st.

We prove that if the paths of a stochastic process are left-continuous then the stochastic process is jointly measurable.11 1 cf. Charalambos D. Aliprantis and Kim C. Border, Infinite Dimensional Analysis: A Hitchhiker’s Guide, third ed., p. 153, Lemma 4.51.

Theorem 1.

If X is a stochastic process with state space E and all the paths of X are left-continuous, then X is jointly measurable.

Proof.

For n1, tI, and ωΩ, let

Xtn(ω)=k=01[k2-n,(k+1)2-n)(t)Xk2-n(ω).

Each Xn is measurable I: for B,

{(t,ω)I×Ω:Xtn(ω)B}=k=0[k2-n,(k+1)2-n)×{Xk2-nB}.

Let tI. For each n there is a unique kn for which t[kn2-n,(kn+1)2-n), and thus Xtn(ω)=Xkn2-n(ω). Furthermore, kn2-nt, and because sXs(ω) is left-continuous, Xkn2-n(ω)Xt(ω). That is, XnX pointwise on I×Ω, and because each Xn is measurable I this implies that X is measurable I.22 2 Charalambos D. Aliprantis and Kim C. Border, Infinite Dimensional Analysis: A Hitchhiker’s Guide, third ed., p. 142, Lemma 4.29. Namely, the stochastic process (Xt)tI is jointly measurable, proving the claim. ∎

2 Adapted stochastic processes

Let I=(t)tI be a filtration of . A stochastic process X is said to be adapted to the filtration FI if for each tI the map

ωXt(ω),ΩE,

is measurable t, in other words, for each tI,

σ(Xt)t.

For a stochastic process (Xt)tI, the natural filtration of X is

σ(Xs:st).

It is immediate that this is a filtration and that X is adapted to it.

3 Progressively measurable stochastic processes

Let I=(t)tI be a filtration of . A function X:I×ΩE is called progressively measurable with respect to the filtration FI if for each tI, the map

(s,ω)X(s,ω),[0,t]×ΩE,

is measurable [0,t]t. We denote by 0(I) the set of functions I×ΩE that are progressively measurable with respect to the filtration I. We shall speak about a stochastic process (Xt)tI being progressively measurable, by which we mean that the map (t,ω)Xt(ω) is progressively measurable.

We denote by Prog(I) the collection of those subsets A of I×Ω such that for each tI,

([0,t]×Ω)A[0,t]t.

We prove in the following that this is a σ-subalgebra of I and that it is the coarsest σ-algebra with which all progressively measurable functions are measurable.

Theorem 2.

Let I=(t)tI be a filtration of .

  1. 1.

    Prog(I) is a σ-subalgebra of I, and is the σ-algebra generated by the collection of functions I×ΩE that are progressively measurable with respect to the filtration I:

    Prog(I)=σ(0(I)).
  2. 2.

    If X:I×ΩE is progressively measurable with respect to the filtration I, then the stochastic process (Xt)tI is jointly measurable and is adapted to the filtration.

Proof.

If A1,A2,Prog(I) and tI then

([0,t]×Ω)n1An=n1(([0,t]×Ω)An),

which is a countable union of elements of the σ-algebra [0,t]t and hence belongs to [0,t]t, showing that n1AnProg(I). If A1,A2Prog(I) and tI then

([0,t]×Ω)(A1A2)=(([0,t]×Ω)A1)(([0,t]×Ω)A2),

which is an intersection of two elements of [0,t]t and hence belongs to [0,t]t, showing that A1A2Prog(I). Thus Prog((t)tI) is a σ-algebra.

If X:I×ΩE is progressively measurable, B, and tI, then

([0,t]×Ω)X-1(B)={(s,ω)[0,t]×Ω:X(s,ω)B}.

Because X is progressively measurable, this belongs to [0,t]t. This is true for all t, hence X-1(B)Prog(I), which means that X is measurable Prog(I).

If X:I×ΩE is measurable Prog(I), tI, and B, then because X-1(B)Prog(I), we have ([0,t]×Ω)X-1(B)[0,t]t. This is true for all B, which means that (s,ω)X(s,ω), [0,t]×ΩE, is measurable [0,t]t, and because this is true for all t, X is progressively measurable. Therefore a function I×ΩE is progressively measurable if and only if it is measurable Prog(I), which shows that Prog(I) is the coarsest σ-algebra with which all progressively measurable functions are measurable.

If X:I×ΩE is a progressively measurable function and B,

X-1(B)=k1(([0,k]×Ω)X-1(B)).

Because X is progressively measurable,

([0,k]×Ω)X-1(B)[0,k]kI,

thus X-1(B) is equal to a countable union of elements of I and so itself belongs to I. Therefore X is measurable I, namely X is jointly measurable.

Because Prog(I) is the σ-algebra generated by the collection of progressively measurable functions and each progressively measurable function is measurable I,

Prog(I)I,

and so Prog(I) is indeed a σ-subalgebra of I.

Let tI. That X is progressively measurable means that

(s,ω)X(s,ω),[0,t]×Ω

is measurable [0,t]t. This implies that for each s[0,t] the map ωX(s,ω) is measurable t.33 3 Charalambos D. Aliprantis and Kim C. Border, Infinite Dimensional Analysis: A Hitchhiker’s Guide, third ed., p. 152, Theorem 4.48. (Generally, if a function is jointly measurable then it is separately measurable in each argument.) In particular, ωX(t,ω) is measurable t, which means that the stochastic process (Xt)tI is adapted to the filtration, completing the proof. ∎

We now prove that if a stochastic process is adapted and left-continuous then it is progressively measurable.44 4 cf. Daniel W. Stroock, Probability Theory: An Analytic View, second ed., p. 267, Lemma 7.1.2.

Theorem 3.

Let (t)tI be a filtration of . If (Xt)tI is a stochastic process that is adapted to this filtration and all its paths are left-continuous, then X is progressively measurable with respect to this filtration.

Proof.

Write X(t,ω)=Xt(ω). For tI, let Y be the restriction of X to [0,t]×Ω. We wish to prove that Y is measurable [0,t]t. For n1, define

Yn(s,ω)=k=02n-11[kt2-n,(k+1)t2-n)(s)Y(kt2-n,ω)+1{t}(s)Y(t,ω).

Because X is adapted to the filtration, each Yn is measurable [0,t]t. Because X has left-continuous paths, for (s,ω)[0,t]×Ω,

Yn(s,ω)Y(s,ω).

Since Y is the pointwise limit of Yn, it follows that Y is measurable [0,t]t, and so X is progressively measurable. ∎

4 Stopping times

Let I=(t)tI be a filtration of . A function T:Ω[0,] is called a stopping time with respect to the filtration FI if

{Tt}t,tI.

It is straightforward to prove that a stopping time is measurable [0,]. Let

=σ(t:tI).

We define

T={A:if tI then A{Tt}t}.

It is straightforward to check that T is measurable T[0,], and in particular {T<}T.

For a stochastic process (Xt)tI with state space E, we define XT:ΩE by

XT(ω)=1{T<}(ω)XT(ω)(ω).

We prove that if X is progressively measurable then XT is measurable T.55 5 Sheng-wu He and Jia-gang Wang and Jia-An Yan, Semimartingale Theory and Stochastic Calculus, p. 86, Theorem 3.12.

Theorem 4.

If I=(t)tI is a filtration of , (Xt)tI is a stochastic process that is progressively measurable with respect to I, and T is a stopping time with respect to I, then XT is measurable T.

Proof.

For tI, using that T is a stopping time we check that ωT(ω)t is measurable t[0,t], and then ω(T(ω)t,ω), Ω[0,t]×Ω, is measurable t[0,t]t.66 6 Charalambos D. Aliprantis and Kim C. Border, Infinite Dimensional Analysis: A Hitchhiker’s Guide, third ed., p. 152, Lemma 4.49. Because X is progressively measurable, (s,ω)Xs(ω) is measurable [0,t]t. Therefore the composition

ωXT(ω)t(ω),ΩE,

is measurable t, and a fortiori it is measurable . We have

XT(ω)=limn1{Tn}(ω)XT(ω)n(ω),

and because ω1{Tn}(ω)XT(ω)n(ω) is measurable , it follows that ωXT(ω) is measurable . For B,

{XTB}{Tt}={ωΩ:XT(ω)t(ω)B}{Tt}t,

therefore {XTB}T. This means that XT is measurable T. ∎

For a stochastic process (Xt)tI, a filtration I=(t)tI, and a stopping time T with respect to the filtration, we define

XtT(ω)=XT(ω)t(ω),

and (XtT)tI is a stochastic process. We prove that if X is progressively measurable with respect to I then the stochastic proces XT is progressively measurable with respect to I.77 7 Ioannis Karatzas and Steven Shreve, Brownian Motion and Stochastic Calculus, p. 9, Proposition 2.18.

Theorem 5.

If (Xt)tI is a stochastic process that is progressively measurable with respect to a filtration I=(t)tI and T is a stopping time with respect to I, then XT is progressively measurable with respect to I.

Proof.

Let tI. Because T is a stopping time, for each s[0,t] the map ωT(ω)s is measurable s[0,t] and a fortiori is measurable t[0,t]. Therefore (s,ω)T(ω)s is measurable [0,t]t[0,t],88 8 Charalambos D. Aliprantis and Kim C. Border, Infinite Dimensional Analysis: A Hitchhiker’s Guide, third ed., p. 152, Theorem 4.48. and (s,ω)ω is measurable [0,t]tt. This implies that

(s,ω)(T(ω)s,ω),[0,t]×Ω[0,t]×Ω,

is measurable [0,t]t[0,t]t.99 9 Charalambos D. Aliprantis and Kim C. Border, Infinite Dimensional Analysis: A Hitchhiker’s Guide, third ed., p. 152, Lemma 4.49. Because X is progressively measurable,

(s,ω)Xs(ω),[0,t]×ΩE,

is measurable [0,t]t. Therefore the composition

(s,ω)XT(ω)s(ω),[0,t]×ΩE,

is measurable [0,t]t, which shows that XT is progressively measurable. ∎