Four Methods for Computing a Brownian Exponential Moment
Summary
The document solves the expectation of a Brownian motion value multiplied by its exponential. It presents several approaches: differentiating the Gaussian moment-generating function, changing probability measure with the exponential martingale, applying Itô’s formula and solving the resulting ordinary differential equation, completing the square in the Gaussian density, and using Malliavin integration by parts. Each route gives the same expression, proportional to time times the exponential of half the time.
The examples show how the result can be derived using elementary probability or more advanced stochastic calculus. The Itô approach also illustrates why taking expectations of the differential requires care: the stochastic integral has zero expectation under appropriate integrability conditions, while the drift contributes an integral equation. The discussion is a mathematical exercise rather than a trading strategy, and it does not address extensions to other processes, assumptions beyond Brownian motion, or financial applications.
Key ideas
- The Gaussian moment-generating function can be differentiated to obtain moments weighted by an exponential.
- An exponential change of measure turns the weighted expectation into an expectation under a shifted Brownian motion.
- Applying Itô’s formula leads to an integral equation that becomes an ordinary differential equation for the expectation.
- Completing the square in the Gaussian density provides a direct integration-based derivation.
- Malliavin integration by parts offers another derivation using the Brownian path derivative.
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# How to compute $E[W(T)\exp(W(T)]$
# How to compute $E[W(T)\exp(W(T)]$
I have got this interview question twice. Does anyone know from which interview question book or another source this question comes from? It may be some well known source as two different interviewers asked the same question, but unfortunately I don't know the source.
Here is what I did during the interviews:
Apply Itô to $f(W)=W\exp(W)$
$\frac{\partial f}{\partial W}=\exp(W)+W \exp(W)$
$\frac{\partial^2 f}{\partial W^2}=2\exp(W)+W \exp(W)$
$df=(\exp(W)+W \exp(W))dW + (\exp(W)+1/2 W \exp(W)) dt$
Given that we will compute the expected value, we ignore the stochastic integral term:
$df= (\exp(W)+1/2f) dt$
## Answer by Thomasunny (score 9)
https://quant.stackexchange.com/a/66432
Hereunder is how I would solve that. I would say this is some sort recurring exercice in probability classes at university.
Solution based on the derivation of the characteristic function $e^{\lambda W_T}$, as $W_T$ is a gaussian random variable of mean 0 and standard deviation $\sqrt{T}$. Then, $E[e^{\lambda W_T}] = e^{\lambda^2 \times T/2}$
Deriving the rhs expression once in $\lambda$ gives $2\lambda\frac{T}{2} \times e^{\lambda^2 \frac{T}{2}} $.
Finally, observing that $E[W_T e^{W_T}]$ corresponds to the derivation of $E[e^{\lambda W_T}]$ evaluated in $\lambda=1$, we can conclude that $E[W_T e^{W_T}] = Te^{T/2}$
## Answer by emot (score 7)
https://quant.stackexchange.com/a/66442
Notice that $$e^{W^{Q}(T)}$$ looks almost like Doleans exponential $$e^{W^{Q}(T)-\frac{1}{2}T}$$ Therefore $$E^Q[W^{Q}(T)e^{W^{Q}(T)}]=E^Q[W^{Q}(T)e^{W^{Q}(T)}]e^{-\frac{1}{2}T}e^{+\frac{1}{2}T}$$ $$=E^Q[W^{Q}(T)e^{W^{Q}(T)-\frac{1}{2}T}]e^{\frac{1}{2}T}$$ We now define new probability measure $\bar{Q}$ via the Radon Nikodym derivative: $$\frac{{d\bar{Q}}}{dQ}=e^{W(T)-\frac{1}{2}T}$$ Under $\bar{Q}$ measure $$W^{\bar{Q}}(t)=W^{Q}(t)-t$$ is brownian motion. Therefore: $$E^Q[W^{Q}(T)e^{W^{Q}(T)-\frac{1}{2}T}]e^{\frac{1}{2}T}=E^{\bar{Q}}[W^{\bar{Q}}(T)+T]e^{\frac{1}{2}T}=Te^{\frac{1}{2}T}$$
## Answer by ir7 (score 6)
https://quant.stackexchange.com/a/66455
To continue your thought:
$$f(x)=x{\rm e}^x $$
$$df(W_t) = \left({\rm e}^{W_t} + f(W_t)\right) dW_t + \left({\rm e}^{W_t} + 1/2f(W_t) \right)dt $$
We now integrate from $0$ to $T$ ($W_0=0$):
$$f(W_T) = \int_0^T \left({\rm e}^{W_t} + f(W_t) \right) dW_t + \int_0^T {\rm e}^{W_t} dt + 1/2 \int_0^T f(W_t) dt. $$
Then take expectations on both sides and obtain (commuting integration and expectation for the time integrals):
$$E\left[f(W_T)\right] = \int_0^T {\rm e}^{t/2} dt + 1/2 \int_0^T E\left[ f(W_t)\right] dt $$
Introducing deterministic function:
$$ y(u) := E\left[f(W_u)\right], $$
we get
$$ y(T) = \int_0^T {\rm e}^{t/2} dt + 1/2 \int_0^T y(t) dt. $$
Taking the derivative wrt $T$ on both sides, gives ODE:
$$ y'(T) = {\rm e}^{T/2} + 1/2 y(T), $$
$$y(0) = 0,$$
with solution:
$$y(T) = T{\rm e}^{T/2}.$$
## Answer by Kermittfrog (score 5)
https://quant.stackexchange.com/a/66463
So let‘s add the brute force solution as well:
$W_T\sim N(0,T)$ so
$$ \begin{align} E\left(W_Te^{W_T}\right)&=\int_{-\infty}^{\infty}xe^x\frac{e^{-\frac{x^2}{2T}}}{\sqrt{2\pi T}}dx\\ &= \int_{-\infty}^{\infty}x\frac{e^{-\frac{x^2-2Tx}{2T}}}{\sqrt{2\pi T}}dx \\ &= \int_{-\infty}^{\infty}x\frac{e^{-\frac{x^2-2Tx+T^2-T^2}{2T}}}{\sqrt{2\pi T}}dx\\ &= e^{1/2T}\int_{-\infty}^{\infty}x\frac{e^{-\frac{1}{2}\left(\frac{x-T}{\sqrt{T}}\right)^2}}{\sqrt{2\pi T}}dx \end{align} $$
Now let $z=(x-T)/\sqrt{T}$ and $x=T+z\sqrt{T}$ and $dx=dz\sqrt{T}$ then
$$ \begin{align} E\left(W_Te^{W_T}\right)&=e^{1/2T}\int_{-\infty}^{\infty}x\frac{e^{-\frac{1}{2}\left(\frac{x-T}{\sqrt{T}}\right)^2}}{\sqrt{2\pi T}}dx\\&= e^{1/2T}\int_{-\infty}^{\infty}(T+\sqrt{T}z)\frac{e^{-\frac{1}{2}\left(z\right)^2}}{\sqrt{2\pi}}dz\\ &=Te^{\frac{1}{2}T} \end{align} $$
## Answer by user34971 (score 4)
https://quant.stackexchange.com/a/66465
There are many roads to Rome. Here is the road of overkill, but which nevertheless gives a glimpse of the uses of Malliavin calculus:
Note that $$ E_0 \left[W(T) e^{W(T)} \right] = E_0 \left[e^{W(T)} \int_0^T dW(t) \right] $$ The integration by parts formula of Malliavin calculus reads $$ E_0 \left[ F \int_0^T h(t)dW(t) \right] = E_0 \left[ \int_0^T (D^W_t F) h(t) dt \right] $$ where $D_t^W$ denotes the Malliavin derivative with respect to $W$.
In this problem $F = e^{W(T)}$ and $h(t) = 1$. Furthermore, $$ D^W_t e^{W(T)} = 1_{[0,T]} (t) e^{W(T)} $$ with $1_{[0,T]}(t) = 1$ if $t \in [0,T]$ and $0$ otherwise.
Hence, \begin{align} E_0 \left[ \int_0^T (D^W_t F) h(t) dt \right] &= E_0 \left[ \int_0^T e^{W(T)} dt \right] \\ &= T E_0 \left[e^{W(T)} \right] \\ &= T e^{T/2} \end{align}Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.