# Criteria for Poisson Point Processes

If S is a finite random set in a standard Borel measurable space ${(E,\mathcal E)}$ satisfying the following two properties,

• if ${A,B\in\mathcal E}$ are disjoint, then the sizes of ${S\cap A}$ and ${S\cap B}$ are independent random variables,
• ${{\mathbb P}(x\in S)=0}$ for each ${x\in E}$,

then it is a Poisson point process. That is, the size of ${S\cap A}$ is a Poisson random variable for each ${A\in\mathcal E}$. This justifies the use of Poisson point processes in many different areas of probability and stochastic calculus, and provides a convenient method of showing that point processes are indeed Poisson. If the theorem applies, so that we have a Poisson point process, then we just need to compute the intensity measure to fully determine its distribution. The result above was mentioned in the previous post, but I give a precise statement and proof here. Continue reading “Criteria for Poisson Point Processes”

# Poisson Point Processes

The Poisson distribution models numbers of events that occur in a specific period of time given that, at each instant, whether an event occurs or not is independent of what happens at all other times. Examples which are sometimes cited as candidates for the Poisson distribution include the number of phone calls handled by a telephone exchange on a given day, the number of decays of a radio-active material, and the number of bombs landing in a given area during the London Blitz of 1940-41. The Poisson process counts events which occur according to such distributions.

More generally, the events under consideration need not just happen at specific times, but also at specific locations in a space E. Here, E can represent an actual geometric space in which the events occur, such as the spacial distribution of bombs dropped during the Blitz shown in figure 1, but can also represent other quantities associated with the events. In this example, E could represent the 2-dimensional map of London, or could include both space and time so that ${E=F\times{\mathbb R}}$ where, now, F represents the 2-dimensional map and E is used to record both time and location of the bombs. A Poisson point process is a random set of points in E, such that the number that lie within any measurable subset is Poisson distributed. The aim of this post is to introduce Poisson point processes together with the mathematical machinery to handle such random sets.

The choice of distribution is not arbitrary. Rather, it is a result of the independence of the number of events in each region of the space which leads to the Poisson measure, much like the central limit theorem leads to the ubiquity of the normal distribution for continuous random variables and of Brownian motion for continuous stochastic processes. A random finite subset S of a reasonably ‘nice’ (standard Borel) space E is a Poisson point process so long as it satisfies the properties,

• If ${A_1,\ldots,A_n}$ are pairwise-disjoint measurable subsets of E, then the sizes of ${S\cap A_1,\ldots,S\cap A_n}$ are independent.
• Individual points of the space each have zero probability of being in S. That is, ${{\mathbb P}(x\in S)=0}$ for each ${x\in E}$.

The proof of this important result will be given in a later post.

We have come across Poisson point processes previously in my stochastic calculus notes. Specifically, suppose that X is a cadlag ${{\mathbb R}^d}$-valued stochastic process with independent increments, and which is continuous in probability. Then, the set of points ${(t,\Delta X_t)}$ over times t for which the jump ${\Delta X}$ is nonzero gives a Poisson point process on ${{\mathbb R}_+\times{\mathbb R}^d}$. See lemma 4 of the post on processes with independent increments, which corresponds precisely to definition 5 given below. Continue reading “Poisson Point Processes”

# Local Time Continuity

The local time of a semimartingale at a level x is a continuous increasing process, giving a measure of the amount of time that the process spends at the given level. As the definition involves stochastic integrals, it was only defined up to probability one. This can cause issues if we want to simultaneously consider local times at all levels. As x can be any real number, it can take uncountably many values and, as a union of uncountably many zero probability sets can have positive measure or, even, be unmeasurable, this is not sufficient to determine the entire local time ‘surface’

 $\displaystyle (t,x)\mapsto L^x_t(\omega)$

for almost all ${\omega\in\Omega}$. This is the common issue of choosing good versions of processes. In this case, we already have a continuous version in the time index but, as yet, have not constructed a good version jointly in the time and level. This issue arose in the post on the Ito–Tanaka–Meyer formula, for which we needed to choose a version which is jointly measurable. Although that was sufficient there, joint measurability is still not enough to uniquely determine the full set of local times, up to probability one. The ideal situation is when a version exists which is jointly continuous in both time and level, in which case we should work with this choice. This is always possible for continuous local martingales.

Theorem 1 Let X be a continuous local martingale. Then, the local times

 $\displaystyle (t,x)\mapsto L^x_t$

have a modification which is jointly continuous in x and t. Furthermore, this is almost surely ${\gamma}$-Hölder continuous w.r.t. x, for all ${\gamma < 1/2}$ and over all bounded regions for t.

# The Kolmogorov Continuity Theorem

One of the common themes throughout the theory of continuous-time stochastic processes, is the importance of choosing good versions of processes. Specifying the finite distributions of a process is not sufficient to determine its sample paths so, if a continuous modification exists, then it makes sense to work with that. A relatively straightforward criterion ensuring the existence of a continuous version is provided by Kolmogorov’s continuity theorem.

For any positive real number ${\gamma}$, a map ${f\colon E\rightarrow F}$ between metric spaces E and F is said to be ${\gamma}$-Hölder continuous if there exists a positive constant C satisfying

$\displaystyle d(f(x),f(y))\le Cd(x,y)^\gamma$

for all ${x,y\in E}$. Hölder continuous functions are always continuous and, at least on bounded spaces, is a stronger property for larger values of the coefficient ${\gamma}$. So, if E is a bounded metric space and ${\alpha\le\beta}$, then every ${\beta}$-Hölder continuous map from E is also ${\alpha}$-Hölder continuous. In particular, 1-Hölder and Lipschitz continuity are equivalent.

Kolmogorov’s theorem gives simple conditions on the pairwise distributions of a process which guarantee the existence of a continuous modification but, also, states that the sample paths ${t\mapsto X_t}$ are almost surely locally Hölder continuous. That is, they are almost surely Hölder continuous on every bounded interval. To start with, we look at real-valued processes. Throughout this post, we work with repect to a probability space ${(\Omega,\mathcal F, {\mathbb P})}$. There is no need to assume the existence of any filtration, since they play no part in the results here

Theorem 1 (Kolmogorov) Let ${\{X_t\}_{t\ge0}}$ be a real-valued stochastic process such that there exists positive constants ${\alpha,\beta,C}$ satisfying

$\displaystyle {\mathbb E}\left[\lvert X_t-X_s\rvert^\alpha\right]\le C\lvert t-s\vert^{1+\beta},$

for all ${s,t\ge0}$. Then, X has a continuous modification which, with probability one, is locally ${\gamma}$-Hölder continuous for all ${0 < \gamma < \beta/\alpha}$.

# The Ito-Tanaka-Meyer Formula

Ito’s lemma is one of the most important and useful results in the theory of stochastic calculus. This is a stochastic generalization of the chain rule, or change of variables formula, and differs from the classical deterministic formulas by the presence of a quadratic variation term. One drawback which can limit the applicability of Ito’s lemma in some situations, is that it only applies for twice continuously differentiable functions. However, the quadratic variation term can alternatively be expressed using local times, which relaxes the differentiability requirement. This generalization of Ito’s lemma was derived by Tanaka and Meyer, and applies to one dimensional semimartingales.

The local time of a stochastic process X at a fixed level x can be written, very informally, as an integral of a Dirac delta function with respect to the continuous part of the quadratic variation ${[X]^{c}}$,

 $\displaystyle L^x_t=\int_0^t\delta(X-x)d[X]^c.$ (1)

This was explained in an earlier post. As the Dirac delta is only a distribution, and not a true function, equation (1) is not really a well-defined mathematical expression. However, as we saw, with some manipulation a valid expression can be obtained which defines the local time whenever X is a semimartingale.

Going in a slightly different direction, we can try multiplying (1) by a bounded measurable function ${f(x)}$ and integrating over x. Commuting the order of integration on the right hand side, and applying the defining property of the delta function, that ${\int f(X-x)\delta(x)dx}$ is equal to ${f(X)}$, gives

 $\displaystyle \int_{-\infty}^{\infty} L^x_t f(x)dx=\int_0^tf(X)d[X]^c.$ (2)

By eliminating the delta function, the right hand side has been transformed into a well-defined expression. In fact, it is now the left side of the identity that is a problem, since the local time was only defined up to probability one at each level x. Ignoring this issue for the moment, recall the version of Ito’s lemma for general non-continuous semimartingales,

 \displaystyle \begin{aligned} f(X_t)=& f(X_0)+\int_0^t f^{\prime}(X_-)dX+\frac12A_t\\ &\quad+\sum_{s\le t}\left(\Delta f(X_s)-f^\prime(X_{s-})\Delta X_s\right). \end{aligned} (3)

where ${A_t=\int_0^t f^{\prime\prime}(X)d[X]^c}$. Equation (2) allows us to express this quadratic variation term using local times,

 $\displaystyle A_t=\int_{-\infty}^{\infty} L^x_t f^{\prime\prime}(x)dx.$

The benefit of this form is that, even though it still uses the second derivative of ${f}$, it is only really necessary for this to exist in a weaker, measure theoretic, sense. Suppose that ${f}$ is convex, or a linear combination of convex functions. Then, its right-hand derivative ${f^\prime(x+)}$ exists, and is itself of locally finite variation. Hence, the Stieltjes integral ${\int L^xdf^\prime(x+)}$ exists. The infinitesimal ${df^\prime(x+)}$ is alternatively written ${f^{\prime\prime}(dx)}$ and, in the twice continuously differentiable case, equals ${f^{\prime\prime}(x)dx}$. Then,

 $\displaystyle A_t=\int _{-\infty}^{\infty} L^x_t f^{\prime\prime}(dx).$ (4)

Using this expression in (3) gives the Ito-Tanaka-Meyer formula. Continue reading “The Ito-Tanaka-Meyer Formula”

# The Stochastic Fubini Theorem

Fubini’s theorem states that, subject to precise conditions, it is possible to switch the order of integration when computing double integrals. In the theory of stochastic calculus, we also encounter double integrals and would like to be able to commute their order. However, since these can involve stochastic integration rather than the usual deterministic case, the classical results are not always applicable. To help with such cases, we could do with a new stochastic version of Fubini’s theorem. Here, I will consider the situation where one integral is of the standard kind with respect to a finite measure, and the other is stochastic. To start, recall the classical Fubini theorem.

Theorem 1 (Fubini) Let ${(E,\mathcal E,\mu)}$ and ${(F,\mathcal F,\nu)}$ be finite measure spaces, and ${f\colon E\times F\rightarrow{\mathbb R}}$ be a bounded ${\mathcal E\otimes\mathcal F}$-measurable function. Then,

$\displaystyle y\mapsto\int f(x,y)d\mu(x)$

is ${\mathcal F}$-measurable,

$\displaystyle x\mapsto\int f(x,y)d\nu(y)$

is ${\mathcal E}$-measurable, and,

 $\displaystyle \int\int f(x,y)d\mu(x)d\nu(y)=\int\int f(x,y)d\nu(x)d\mu(y).$ (1)

# Semimartingale Local Times

For a stochastic process X taking values in a state space E, its local time at a point ${x\in E}$ is a measure of the time spent at x. For a continuous time stochastic process, we could try and simply compute the Lebesgue measure of the time at the level,

 $\displaystyle L^x_t=\int_0^t1_{\{X_s=x\}}ds.$ (1)

For processes which hit the level ${x}$ and stick there for some time, this makes some sense. However, if X is a standard Brownian motion, it will always give zero, so is not helpful. Even though X will hit every real value infinitely often, continuity of the normal distribution gives ${{\mathbb P}(X_s=x)=0}$ at each positive time, so that that ${L^x_t}$ defined by (1) will have zero expectation.

Rather than the indicator function of ${\{X=x\}}$ as in (1), an alternative is to use the Dirac delta function,

 $\displaystyle L^x_t=\int_0^t\delta(X_s-x)\,ds.$ (2)

Unfortunately, the Dirac delta is not a true function, it is a distribution, so (2) is not a well-defined expression. However, if it can be made rigorous, then it does seem to have some of the properties we would want. For example, the expectation ${{\mathbb E}[\delta(X_s-x)]}$ can be interpreted as the probability density of ${X_s}$ evaluated at ${x}$, which has a positive and finite value, so it should lead to positive and finite local times. Equation (2) still relies on the Lebesgue measure over the time index, so will not behave as we may expect under time changes, and will not make sense for processes without a continuous probability density. A better approach is to integrate with respect to the quadratic variation,

 $\displaystyle L^x_t=\int_0^t\delta(X_s-x)d[X]_s$ (3)

which, for Brownian motion, amounts to the same thing. Although (3) is still not a well-defined expression, since it still involves the Dirac delta, the idea is to come up with a definition which amounts to the same thing in spirit. Important properties that it should satisfy are that it is an adapted, continuous and increasing process with increments supported on the set ${\{X=x\}}$,

 $\displaystyle L^x_t=\int_0^t1_{\{X_s=x\}}dL^x_s.$

Local times are a very useful and interesting part of stochastic calculus, and finds important applications to excursion theory, stochastic integration and stochastic differential equations. However, I have not covered this subject in my notes, so do this now. Recalling Ito’s lemma for a function ${f(X)}$ of a semimartingale X, this involves a term of the form ${\int f^{\prime\prime}(X)d[X]}$ and, hence, requires ${f}$ to be twice differentiable. If we were to try to apply the Ito formula for functions which are not twice differentiable, then ${f^{\prime\prime}}$ can be understood in terms of distributions, and delta functions can appear, which brings local times into the picture. In the opposite direction, which I take in this post, we can try to generalise Ito’s formula and invert this to give a meaning to (3). Continue reading “Semimartingale Local Times”

# Properties of the Dual Projections

In the previous post I introduced the definitions of the dual optional and predictable projections, firstly for processes of integrable variation and, then, generalised to processes which are only required to be locally (or prelocally) of integrable variation. We did not look at the properties of these dual projections beyond the fact that they exist and are uniquely defined, which are significant and important statements in their own right.

To recap, recall that an IV process, A, is right-continuous and such that its variation

 $\displaystyle V_t\equiv \lvert A_0\rvert+\int_0^t\,\lvert dA\rvert$ (1)

is integrable at time ${t=\infty}$, so that ${{\mathbb E}[V_\infty] < \infty}$. The dual optional projection is defined for processes which are prelocally IV. That is, A has a dual optional projection ${A^{\rm o}}$ if it is right-continuous and its variation process is prelocally integrable, so that there exist a sequence ${\tau_n}$ of stopping times increasing to infinity with ${1_{\{\tau_n > 0\}}V_{\tau_n-}}$ integrable. More generally, A is a raw FV process if it is right-continuous with almost-surely finite variation over finite time intervals, so ${V_t < \infty}$ (a.s.) for all ${t\in{\mathbb R}^+}$. Then, if a jointly measurable process ${\xi}$ is A-integrable on finite time intervals, we use

$\displaystyle \xi\cdot A_t\equiv\xi_0A_0+\int_0^t\xi\,dA$

to denote the integral of ${\xi}$ with respect to A over the interval ${[0,t]}$, which takes into account the value of ${\xi}$ at time 0 (unlike the integral ${\int_0^t\xi\,dA}$ which, implicitly, is defined on the interval ${(0,t]}$). In what follows, whenever we state that ${\xi\cdot A}$ has any properties, such as being IV or prelocally IV, we are also including the statement that ${\xi}$ is A-integrable so that ${\xi\cdot A}$ is a well-defined process. Also, whenever we state that a process has a dual optional projection, then we are also implicitly stating that it is prelocally IV.

From theorem 3 of the previous post, the dual optional projection ${A^{\rm o}}$ is the unique prelocally IV process satisfying

$\displaystyle {\mathbb E}[\xi\cdot A^{\rm o}_\infty]={\mathbb E}[{}^{\rm o}\xi\cdot A_\infty]$

for all measurable processes ${\xi}$ with optional projection ${{}^{\rm o}\xi}$ such that ${\xi\cdot A^{\rm o}}$ and ${{}^{\rm o}\xi\cdot A}$ are IV. Equivalently, ${A^{\rm o}}$ is the unique optional FV process such that

$\displaystyle {\mathbb E}[\xi\cdot A^{\rm o}_\infty]={\mathbb E}[\xi\cdot A_\infty]$

for all optional ${\xi}$ such that ${\xi\cdot A}$ is IV, in which case ${\xi\cdot A^{\rm o}}$ is also IV so that the expectations in this identity are well-defined.

I now look at the elementary properties of dual optional projections, as well as the corresponding properties of dual predictable projections. The most important property is that, according to the definition just stated, the dual projection exists and is uniquely defined. By comparison, the properties considered in this post are elementary and relatively easy to prove. So, I will simply state a theorem consisting of a list of all the properties under consideration, and will then run through their proofs. Starting with the dual optional projection, the main properties are listed below as Theorem 1.

Note that the first three statements are saying that the dual projection is indeed a linear projection from the prelocally IV processes onto the linear subspace of optional FV processes. As explained in the previous post, by comparison with the discrete-time setting, the dual optional projection can be expressed, in a non-rigorous sense, as taking the optional projection of the infinitesimal increments,

 $\displaystyle dA^{\rm o}={}^{\rm o}dA.$ (2)

As ${dA}$ is interpreted via the Lebesgue-Stieltjes integral ${\int\cdot\,dA}$, it is a random measure rather than a real-valued process. So, the optional projection of ${dA}$ appearing in (2) does not really make sense. However, Theorem 1 does allow us to make sense of (2) in certain restricted cases. For example, if A is differentiable so that ${dA=\xi\,dt}$ for a process ${\xi}$, then (9) below gives ${dA={}^{\rm o}\xi\,dt}$. This agrees with (2) so long as ${{}^{\rm o}(\xi\,dt)}$ is interpreted to mean ${{}^{\rm o}\xi\,dt}$. Also, restricting to the jump component of the increments, ${\Delta A=A-A_-}$, (2) reduces to (11) below.

We defined the dual projection via expectations of integrals ${\xi\cdot A}$ with the restriction that this is IV. An alternative approach is to first define the dual projections for IV processes, as was done in theorems 1 and 2 of the previous post, and then extend to (pre)locally IV processes by localisation of the projection. That this is consistent with our definitions follows from the fact that (pre)localisation commutes with the dual projection, as stated in (10) below.

Theorem 1

1. A raw FV process A is optional if and only if ${A^{\rm o}}$ exists and is equal to A.
2. If the dual optional projection of A exists then,
 $\displaystyle (A^{\rm o})^{\rm o}=A^{\rm o}.$ (3)
3. If the dual optional projections of A and B exist, and ${\lambda}$, ${\mu}$ are ${\mathcal F_0}$-measurable random variables then,
 $\displaystyle (\lambda A+\mu B)^{\rm o}=\lambda A^{\rm o}+\mu B^{\rm o}.$ (4)
4. If the dual optional projection ${A^{\rm o}}$ exists then ${{\mathbb E}[\lvert A_0\rvert\,\vert\mathcal F_0]}$ is almost-surely finite and
 $\displaystyle A^{\rm o}_0={\mathbb E}[A_0\,\vert\mathcal F_0].$ (5)
5. If U is a random variable and ${\tau}$ is a stopping time, then ${U1_{[\tau,\infty)}}$ is prelocally IV if and only if ${{\mathbb E}[1_{\{\tau < \infty\}}\lvert U\rvert\,\vert\mathcal F_\tau]}$ is almost surely finite, in which case
 $\displaystyle \left(U1_{[\tau,\infty)}\right)^{\rm o}={\mathbb E}[1_{\{\tau < \infty\}}U\,\vert\mathcal F_\tau]1_{[\tau,\infty)}.$ (6)
6. If the prelocally IV process A is nonnegative and increasing then so is ${A^{\rm o}}$ and,
 $\displaystyle {\mathbb E}[\xi\cdot A^{\rm o}_\infty]={\mathbb E}[{}^{\rm o}\xi\cdot A_\infty]$ (7)

for all nonnegative measurable ${\xi}$ with optional projection ${{}^{\rm o}\xi}$. If A is merely increasing then so is ${A^{\rm o}}$ and (7) holds for nonnegative measurable ${\xi}$ with ${\xi_0=0}$.

7. If A has dual optional projection ${A^{\rm o}}$ and ${\xi}$ is an optional process such that ${\xi\cdot A}$ is prelocally IV then, ${\xi}$ is ${A^{\rm o}}$-integrable and,
 $\displaystyle (\xi\cdot A)^{\rm o}=\xi\cdot A^{\rm o}.$ (8)
8. If A is an optional FV process and ${\xi}$ is a measurable process with optional projection ${{}^{\rm o}\xi}$ such that ${\xi\cdot A}$ is prelocally IV then, ${{}^{\rm o}\xi}$ is A-integrable and,
 $\displaystyle (\xi\cdot A)^{\rm o}={}^{\rm o}\xi\cdot A.$ (9)
9. If A has dual optional projection ${A^{\rm o}}$ and ${\tau}$ is a stopping time then,
 $\displaystyle \setlength\arraycolsep{2pt} \begin{array}{rl} &\displaystyle(A^{\tau})^{\rm o}=(A^{\rm o})^{\tau},\smallskip\\ &\displaystyle(A^{\tau-})^{\rm o}=(A^{\rm o})^{\tau-}. \end{array}$ (10)
10. If the dual optional projection ${A^{\rm o}}$ exists, then its jump process is the optional projection of the jump process of A,
 $\displaystyle \Delta A^{\rm o}={}^{\rm o}\!\Delta A.$ (11)
11. If A has dual optional projection ${A^{\rm o}}$ then
 $\displaystyle \setlength\arraycolsep{2pt} \begin{array}{rl} &\displaystyle{\mathbb E}\left[\xi_0\lvert A^{\rm o}_0\rvert + \int_0^\infty\xi\,\lvert dA^{\rm o}\rvert\right]\le{\mathbb E}\left[{}^{\rm o}\xi_0\lvert A_0\rvert + \int_0^\infty{}^{\rm o}\xi\,\lvert dA\rvert\right],\smallskip\\ &\displaystyle{\mathbb E}\left[\xi_0(A^{\rm o}_0)_+ + \int_0^\infty\xi\,(dA^{\rm o})_+\right]\le{\mathbb E}\left[{}^{\rm o}\xi_0(A_0)_+ + \int_0^\infty{}^{\rm o}\xi\,(dA)_+\right],\smallskip\\ &\displaystyle{\mathbb E}\left[\xi_0(A^{\rm o}_0)_- + \int_0^\infty\xi\,(dA^{\rm o})_-\right]\le{\mathbb E}\left[{}^{\rm o}\xi_0(A_0)_- + \int_0^\infty{}^{\rm o}\xi\,(dA)_-\right], \end{array}$ (12)

for all nonnegative measurable ${\xi}$ with optional projection ${{}^{\rm o}\xi}$.

12. Let ${\{A^n\}_{n=1,2,\ldots}}$ be a sequence of right-continuous processes with variation

$\displaystyle V^n_t=\lvert A^n_0\rvert + \int_0^t\lvert dA^n\rvert.$

If ${\sum_n V^n}$ is prelocally IV then,

 $\displaystyle \left(\sum\nolimits_n A^n\right)^{\rm o}=\sum\nolimits_n\left(A^n\right)^{\rm o}.$ (13)

# Dual Projections

The optional and predictable projections of stochastic processes have corresponding dual projections, which are the subject of this post. I will be concerned with their initial construction here, and show that they are well-defined. The study of their properties will be left until later. In the discrete time setting, the dual projections are relatively straightforward, and can be constructed by applying the optional and predictable projection to the increments of the process. In continuous time, we no longer have discrete time increments along which we can define the dual projections. In some sense, they can still be thought of as projections of the infinitesimal increments so that, for a process A, the increments of the dual projections ${A^{\rm o}}$ and ${A^{\rm p}}$ are determined from the increments ${dA}$ of A as

 $\displaystyle \setlength\arraycolsep{2pt} \begin{array}{rl} &\displaystyle dA^{\rm o}={}^{\rm o}(dA),\smallskip\\ &\displaystyle dA^{\rm p}={}^{\rm p}(dA). \end{array}$ (1)

Unfortunately, these expressions are difficult to make sense of in general. In specific cases, (1) can be interpreted in a simple way. For example, when A is differentiable with derivative ${\xi}$, so that ${dA=\xi dt}$, then the dual projections are given by ${dA^{\rm o}={}^{\rm o}\xi dt}$ and ${dA^{\rm p}={}^{\rm p}\xi dt}$. More generally, if A is right-continuous with finite variation, then the infinitesimal increments ${dA}$ can be interpreted in terms of Lebesgue-Stieltjes integrals. However, as the optional and predictable projections are defined for real valued processes, and ${dA}$ is viewed as a stochastic measure, the right-hand-side of (1) is still problematic. This can be rectified by multiplying by an arbitrary process ${\xi}$, and making use of the transitivity property ${{\mathbb E}[\xi\,{}^{\rm o}(dA)]={\mathbb E}[({}^{\rm o}\xi)dA]}$. Integrating over time gives the more meaningful expressions

$\displaystyle \setlength\arraycolsep{2pt} \begin{array}{rl} &\displaystyle {\mathbb E}\left[\int_0^\infty \xi\,dA^{\rm o}\right]={\mathbb E}\left[\int_0^\infty{}^{\rm o}\xi\,dA\right],\smallskip\\ &\displaystyle{\mathbb E}\left[\int_0^\infty \xi\,dA^{\rm p}\right]={\mathbb E}\left[\int_0^\infty{}^{\rm p}\xi\,dA\right]. \end{array}$

In contrast to (1), these equalities can be used to give mathematically rigorous definitions of the dual projections. As usual, we work with respect to a complete filtered probability space ${(\Omega,\mathcal F,\{\mathcal F_t\}_{t\ge0},{\mathbb P})}$, and processes are identified whenever they are equal up to evanescence. The terminology raw IV process‘ will be used to refer to any right-continuous integrable process whose variation on the whole of ${{\mathbb R}^+}$ has finite expectation. The use of the word raw’ here is just to signify that we are not requiring the process to be adapted. Next, to simplify the expressions, I will use the notation ${\xi\cdot A}$ for the integral of a process ${\xi}$ with respect to another process A,

$\displaystyle \xi\cdot A_t\equiv\xi_0A_0+\int_0^t\xi\,dA.$

Note that, whereas the integral ${\int_0^t\xi\,dA}$ is implicitly taken over the range ${(0,t]}$ and does not involve the time-zero value of ${\xi}$, I have included the time-zero values of the processes in the definition of ${\xi\cdot A}$. This is not essential, and could be excluded, so long as we were to restrict to processes starting from zero. The existence and uniqueness (up to evanescence) of the dual projections is given by the following result.

Theorem 1 (Dual Projections) Let A be a raw IV process. Then,

• There exists a unique raw IV process ${A^{\rm o}}$ satisfying
 $\displaystyle {\mathbb E}\left[\xi\cdot A^{\rm o}_\infty\right]={\mathbb E}\left[{}^{\rm o}\xi\cdot A_\infty\right]$ (2)

for all bounded measurable processes ${\xi}$. We refer to ${A^{\rm o}}$ as the dual optional projection of A.

• There exists a unique raw IV process ${A^{\rm p}}$ satisfying
 $\displaystyle {\mathbb E}\left[\xi\cdot A^{\rm p}_\infty\right]={\mathbb E}\left[{}^{\rm p}\xi\cdot A_\infty\right]$ (3)

for all bounded measurable processes ${\xi}$. We refer to ${A^{\rm p}}$ as the dual predictable projection of A.

Furthermore, if A is nonnegative and increasing then so are ${A^{\rm o}}$ and ${A^{\rm p}}$.

# Pathwise Properties of Optional and Predictable Projections

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 ${(\Omega,\mathcal F,\{\mathcal F_t\}_{t\ge0},{\mathbb P})}$ with optional projection ${{}^{\rm o}\!X}$ then, for each ${\omega\in\Omega}$, we may be interested in the sample path ${t\mapsto{}^{\rm o}\!X_t(\omega)}$. For example, is it continuous, right-continuous, cadlag, etc? Answering these questions requires looking at ${{}^{\rm o}\!X_t(\omega)}$ simultaneously at the uncountable set of times ${t\in{\mathbb R}^+}$, 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 ${{}^{\rm o}\!X}$ or the predictable projection ${{}^{\rm p}\!X}$ 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, ${X_t=U}$ 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 ${M_t={\mathbb E}[U\,\vert\mathcal F_t]}$ 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, ${{}^{\rm p}\!X_t=M_{t-}}$, 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 ${(\Omega,\mathcal F,\{\mathcal F_t\}_{t\ge0},{\mathbb P})}$. 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 ${X_\tau}$, over stopping times ${\tau}$, is uniformly integrable. It will be said to be locally of class (D) if there is a sequence ${\tau_n}$ of stopping times increasing to infinity and such that ${1_{\{\tau_n > 0\}}1_{[0,\tau_n]}X}$ is of class (D) for each n. Similarly, it will be said to be prelocally of class (D) if there is a sequence ${\tau_n}$ of stopping times increasing to infinity and such that ${1_{[0,\tau_n)}X}$ is of class (D) for each n.

Theorem 1 Let X be pre-locally of class (D), with optional projection ${{}^{\rm o}\!X}$. Then,

• if X has left limits, so does ${{}^{\rm o}\!X}$.
• if X has right limits, so does ${{}^{\rm o}\!X}$.

Furthermore, if the underlying filtration is right-continuous then,

• if X is right-continuous, so is ${{}^{\rm o}\!X}$.
• if X is cadlag, so is ${{}^{\rm o}\!X}$.