Jointly measurable and progressively measurable stochastic processes
1 Jointly measurable stochastic processes
Let with Borel , let , which is a topological space with the subspace topology inherited from , and let be a probability space. For a stochastic process with state space , we say that is jointly measurable if the map is measurable .
For , the path is called left-continuous if for each ,
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 is a stochastic process with state space and all the paths of are left-continuous, then is jointly measurable.
Proof.
For , , and , let
Each is measurable : for ,
Let . For each there is a unique for which , and thus . Furthermore, , and because is left-continuous, . That is, pointwise on , and because each is measurable this implies that is measurable .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 is jointly measurable, proving the claim. ∎
2 Adapted stochastic processes
Let be a filtration of . A stochastic process is said to be adapted to the filtration if for each the map
is measurable , in other words, for each ,
For a stochastic process , the natural filtration of is
It is immediate that this is a filtration and that is adapted to it.
3 Progressively measurable stochastic processes
Let be a filtration of . A function is called progressively measurable with respect to the filtration if for each , the map
is measurable . We denote by the set of functions that are progressively measurable with respect to the filtration . We shall speak about a stochastic process being progressively measurable, by which we mean that the map is progressively measurable.
We denote by the collection of those subsets of such that for each ,
We prove in the following that this is a -subalgebra of and that it is the coarsest -algebra with which all progressively measurable functions are measurable.
Theorem 2.
Let be a filtration of .
-
1.
is a -subalgebra of , and is the -algebra generated by the collection of functions that are progressively measurable with respect to the filtration :
-
2.
If is progressively measurable with respect to the filtration , then the stochastic process is jointly measurable and is adapted to the filtration.
Proof.
If and then
which is a countable union of elements of the -algebra and hence belongs to , showing that . If and then
which is an intersection of two elements of and hence belongs to , showing that . Thus is a -algebra.
If is progressively measurable, , and , then
Because is progressively measurable, this belongs to . This is true for all , hence , which means that is measurable .
If is measurable , , and , then because , we have . This is true for all , which means that , , is measurable , and because this is true for all , is progressively measurable. Therefore a function is progressively measurable if and only if it is measurable , which shows that is the coarsest -algebra with which all progressively measurable functions are measurable.
If is a progressively measurable function and ,
Because is progressively measurable,
thus is equal to a countable union of elements of and so itself belongs to . Therefore is measurable , namely is jointly measurable.
Because is the -algebra generated by the collection of progressively measurable functions and each progressively measurable function is measurable ,
and so is indeed a -subalgebra of .
Let . That is progressively measurable means that
is measurable . This implies that for each the map is measurable .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, is measurable , which means that the stochastic process 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 be a filtration of . If is a stochastic process that is adapted to this filtration and all its paths are left-continuous, then is progressively measurable with respect to this filtration.
Proof.
Write . For , let be the restriction of to . We wish to prove that is measurable . For , define
Because is adapted to the filtration, each is measurable . Because has left-continuous paths, for ,
Since is the pointwise limit of , it follows that is measurable , and so is progressively measurable. ∎
4 Stopping times
Let be a filtration of . A function is called a stopping time with respect to the filtration if
It is straightforward to prove that a stopping time is measurable . Let
We define
It is straightforward to check that is measurable , and in particular .
For a stochastic process with state space , we define by
We prove that if is progressively measurable then is measurable .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 is a filtration of , is a stochastic process that is progressively measurable with respect to , and is a stopping time with respect to , then is measurable .
Proof.
For , using that is a stopping time we check that is measurable , and then , , is measurable .66 6 Charalambos D. Aliprantis and Kim C. Border, Infinite Dimensional Analysis: A Hitchhiker’s Guide, third ed., p. 152, Lemma 4.49. Because is progressively measurable, is measurable . Therefore the composition
is measurable , and a fortiori it is measurable . We have
and because is measurable , it follows that is measurable . For ,
therefore . This means that is measurable . ∎
For a stochastic process , a filtration , and a stopping time with respect to the filtration, we define
and is a stochastic process. We prove that if is progressively measurable with respect to then the stochastic proces is progressively measurable with respect to .77 7 Ioannis Karatzas and Steven Shreve, Brownian Motion and Stochastic Calculus, p. 9, Proposition 2.18.
Theorem 5.
If is a stochastic process that is progressively measurable with respect to a filtration and is a stopping time with respect to , then is progressively measurable with respect to .
Proof.
Let . Because is a stopping time, for each the map is measurable and a fortiori is measurable . Therefore is measurable ,88 8 Charalambos D. Aliprantis and Kim C. Border, Infinite Dimensional Analysis: A Hitchhiker’s Guide, third ed., p. 152, Theorem 4.48. and is measurable . This implies that
is measurable .99 9 Charalambos D. Aliprantis and Kim C. Border, Infinite Dimensional Analysis: A Hitchhiker’s Guide, third ed., p. 152, Lemma 4.49. Because is progressively measurable,
is measurable . Therefore the composition
is measurable , which shows that is progressively measurable. ∎