# Expected payoff at future time

Let $$a$$, $$b$$, $$c$$, and $$e$$ be constants, $$W_1$$ and $$W_2$$ be Brownian motions with correlation $$\rho$$, and $$f(t)$$ and $$g(t)$$ be deterministic functions of time. Let $$X$$ satisfy $$d(X(t))=(aX(t)+ef(t)g(t))dt+f(t)X(t)dW_1(t)+g(t)X(t)dW_2(t).$$ Compute the expected value of $$X(T)^2$$ given $$X(t)$$ for some $$0\le t\le T$$.

If $$e=0$$, we can use Ito's rule to write $$d(\log X)$$ as an expression independent of $$X$$. Integrating gives that $$X(T)|X(t)$$ is log-normal. If $$e\neq 0$$, $$d(\log X)$$ is no longer independent of $$X$$. I can't think of a way around this issue.

• Where does $c$ come in? Just use Ito's lemma on $X(t)^2$ and integrate. Feb 7, 2019 at 13:34
• $b$ and $c$ are irrelevant. $d(X(t)^2)$ depends on $X(t)$. You have to compute $\mathbb{E}(X(T)^2)$ given $X(t)$ for one value of $t$. Feb 7, 2019 at 13:47
• Then where does $b$ and $c$ come from? I think your question is missing a critical piece. You'll see why if you just use Ito's lemma on $X(t)^2$. Feb 8, 2019 at 12:44

Based on ideas from this question, let \begin{align*} M_t = e^{-at+\frac{1}{2}\int_0^t (f^2+g^2+2\rho fg)ds -\int_0^t(f dW_1(s)+gdW_2(s))}. \end{align*} Then \begin{align*} dM_t = M_t\Big[\big(-a + f^2+g^2 + 2\rho fg \big)dt - f dW_1(t)- gdW_2(t)\Big]. \end{align*} Moreover, \begin{align*} d(M_tX_t) &= M_t dX_t + X_t dM_t + d\langle M, X\rangle_t\\ &=e M_t f g dt. \end{align*} Then, \begin{align*} X_T = \frac{M_t}{M_T}X_t + e\int_t^T\frac{M_s}{M_T} f(s)g(s)ds. \end{align*} Now, you should be able to compute the conditional expectation.