# SDE of conditional expectation

Assume I have an SDE of the form $$dX_t = a(X_t, t) dt + b(X_t, t) dW_t, X_0 = x_0.$$

Let now with $$T>t$$ denote $$f(X_t)$$ the conditional expectation of $$X_T$$, $$f(X_t) = E[X_T|X_t]$$ My questions:

1. Is $$f(\cdot )$$ an Ito-diffusion? If no, why? If yes, always?
2. What form does $$df(X_t)$$ have? I know this seems to be just some application of Itos lemma but I got a bit confused with the derivative of the conditional expectation.

My steps so far: with the martingale representation theorem I get a representation of $$f$$ as the sum of an expectation and an Ito integral. I know have an Ito diffusion constant which I can’t fully link to $$X_t$$. I’ve seen that I can represent that diffusion term with the Clark-Ocone-theorem but before I went down that rabbit hole I wanted to check whether I am really on the right path here. I also know that I can represent the conditional expectation as an integral over the conditional density, here however I was afraid that it will be a lot more difficult to reconstruct a more explicit link to $$a$$ and $$b$$.

In the optimal case I want to represent $$df(X_t)$$ as functions of $$a$$ and $$b$$. I have the feeling it’s simpler if those are differentiable for the beginning, maybe if I get the basic mechanics going I can continue by myself. My main issue right now is that I don’t know what to do about $$\frac{d}{dx} f(x)$$ and how to write down a representation for it.

Possible that this is a simple question - I’d appreciate it if you’d help me a bit with a formal derivation there.

For details see Using Markov Property in solving PDE/SDE.

To be clear the deterministic quantity

$$f(x,t):=E[F(X_T)|X_t=x]$$

is different from the random quantity

$$Y_{t}:=E[F(X_T)|\sigma(X_t)]$$

which is equal to

$$f(X_{t},t):=E[F(X_T)|\mathcal{F}_t]$$ by the Markov property of SDEs (see Schilling 21.24 Corollary).

The $$f(x,t)$$ is a deterministic solution to the backward equation eg. as explained Lesson 5, SDE and PDE and the random $$f(X_{t},t)$$ is a martingale (as shown in Using Markov Property in solving PDE/SDE)

The random quantity $$Z_{t}=f(X_{t},t)$$ does not necessarily satisfy a diffusion, it simply satisfies the follow stochastic pde

$$f(X_{t},t)=\int^{t} f_{x}(X_{s},s) b((X_{s},s)) dW_{s},$$

so it is not an Itô diffusion where the only independent variable is time $$t$$.

For this to return to being an Ito diffusion you would need a relation

$$f_{x}(X_{s},s)=g(f(X_{s},s))$$

for some function $$g$$, which is generally not true. For example it fails even for Brownian motion where $$f(x,t)$$ is in terms of Gaussian since the backward-equation is $$f_{t}+\frac{1}{2}\Delta f=0$$ as done here

$$f(x,t)=E[X_{T}|X_{t}=x]=\int_{\mathbb{R}}y\frac{1}{\sqrt{2\pi (T-t)}}e^{-\frac{(y-x)^{2}}{2(T-t)}}dy.$$

• Might just be a typo but: the backward equation is $f_t+\frac12 \Delta f=0$ (with terminal condition). The forward equation, or Fokker Planck equation is $f_t = \frac12 \Delta f$ (with initial condition) has solution $f=$ a Gaussian (PDF). Aug 13, 2023 at 4:19
• I agree and I upvote. Thanks. Aug 13, 2023 at 4:43
• That goes into a better direction! I’m curious about three things: 1) where does that SPDE come from? Would that even be well-defined? 2) I agree with it not being an Ito diffusion but can it still be a general Ito process? Aug 13, 2023 at 7:31
• 3) if I compare your statement with Clark-Ocone, I don’t see immediately why they should be equivalent. Could you shed some light on that? It goes into a similar direction but I’m not so sure why you’d have an additional $b$ in there. Aug 13, 2023 at 7:39
• @freistil90 in math.stackexchange.com/questions/1124771/…, they go in detail or you can go directly to the Shreve-Karatzas chapter they mention. Aug 13, 2023 at 7:46

Let $$f(t,x) = \mathbb{E}(X_T|X_t=x)$$ for any $$(t,x) \in [0, T]\times \mathbb{R}$$. By Feynman-Kac it follows that the Kolmogorov-Backward PDE is satisfied: $$\partial_t f+a(t, x) \partial_x f+ 0.5 b(t,x)^2 \partial_{xx}^2 f = 0$$ for $$(t,x)\in[0,T)\times \mathbb{R}$$ and $$f(T,x)=x$$ for all $$x$$. If you apply Ito's formula to $$Y_t = f(t, X_t)$$ then you get $$dY_t = \partial_x u(t, X_t) b(t, X_t) dW_t$$ using the PDE. Thus $$Y_t = f(t, X_t)$$ is an Ito process, driftless and a martingale provided $$a$$ and $$b$$ are nice enough. If the solution of the Backward-PDE has a closed form, you can compute $$\partial_x u(t,x)$$ exactly.

By the way, many authors use the term diffusion to refer to coefficients that are time-independent $$a(t,x)=a(x)$$ and $$b(t,x)=b(x)$$ and say Ito processes for the more general case.

• If anyone has corrections or suggestions, I'll gladly address them. Aug 13, 2023 at 20:45