Recall that the the optional and predictable projections of a process are defined, firstly, by a measurability property and, secondly, by their values at stopping times. Namely, the optional projection is measurable with respect to the optional sigma-algebra, and its value is defined at each stopping time by a conditional expectation of the original process. Similarly, the predictable projection is measurable with respect to the predictable sigma-algebra and its value at each predictable stopping time is given by a conditional expectation. While these definitions can be powerful, and many properties of the projections follow immediately, they say very little about the sample paths. Given a stochastic process *X* defined on a filtered probability space with optional projection then, for each , we may be interested in the sample path . For example, is it continuous, right-continuous, cadlag, etc? Answering these questions requires looking at simultaneously at the uncountable set of times , so the definition of the projection given by specifying its values at each individual stopping time, up to almost-sure equivalence, is not easy to work with. I did establish some of the basic properties of the projections in the previous post, but these do not say much regarding sample paths.

I will now establish the basic properties of the sample paths of the projections. Although these results are quite advanced, most of the work has already been done in these notes when we established some pathwise properties of optional and predictable processes in terms of their behaviour along sequences of stopping times, and of predictable stopping times. So, the proofs in this post are relatively simple and will consist of applications of these earlier results.

Before proceeding, let us consider what kind of properties it is reasonable to expect of the projections. Firstly, it does not seem reasonable to expect the optional projection or the predictable projection to satisfy properties not held by the original process *X*. Therefore, in this post, we will be concerned with the sample path properties which are *preserved* by the projections. Consider a process with constant paths. That is, at all times *t*, for some bounded random variable *U*. This has about as simple sample paths as possible, so any properties preserved by the projections should hold for the optional and predictable projections of *X*. However, we know what the projections of this process are. Letting *M* be the martingale defined by then, assuming that the underlying filtration is right-continuous, *M* has a cadlag modification and, furthermore, this modification is the optional projection of *X*. So, assuming that the filtration is right-continuous, the optional projection of *X* is cadlag, meaning that it is right-continuous and has left limits everywhere. So, we can hope that the optional projection preserves these properties. If the filtration is not right-continuous, then *M* need not have a cadlag modification, so we cannot expect optional projection to preserve right-continuity in this case. However, *M* does still have a version with left and right limits everywhere, which is the optional projection of *X*. So, without assuming right-continuity of the filtration, we may still hope that the optional projection preserves the existence of left and right limits of a process. Next, the predictable projection is equal to the left limits, , which is left-continuous with left and right limits everywhere. Therefore, we can hope that predictable projections preserve left-continuity and the existence of left and right limits. The existence of cadlag martingales which are not continuous, such as the compensated Poisson process, imply that optional projections do not generally preserve left-continuity and the predictable projection does not preserve right-continuity.

Recall that I previously constructed a version of the optional projection and the predictable projection for processes which are, respectively, right-continuous and left-continuous. This was done by defining the projection at each deterministic time and, then, enforcing the respective properties of the sample paths. We can use the results in those posts to infer that the projections do indeed preserve these properties, although I will now more direct proofs in greater generality, and using the more general definition of the optional and predictable projections.

We work with respect to a complete filtered probability space . As usual, we say that the sample paths of a process satisfy any stated property if they satisfy it up to evanescence. Since integrability conditions will be required, I mention those now. Recall that a process *X* is of class (D) if the set of random variables , over stopping times , is uniformly integrable. It will be said to be *locally of class (D)* if there is a sequence of stopping times increasing to infinity and such that is of class (D) for each *n*. Similarly, it will be said to be *prelocally of class (D)* if there is a sequence of stopping times increasing to infinity and such that is of class (D) for each *n*.

Theorem 1LetXbe pre-locally of class (D), with optional projection . Then,

- if
Xhas left limits, so does .- if
Xhas right limits, so does .Furthermore, if the underlying filtration is right-continuous then,

- if
Xis right-continuous, so is .- if
Xis cadlag, so is .