Proving the Exponential Brownian Martingale
Summary
The document studies the process formed by exponentiating a constant multiple of Brownian motion and subtracting half the corresponding variance growth. It corrects a common misconception: Brownian motion at two times is not generally decomposed as a sum using the same starting-time value; the useful decomposition is the earlier value plus an independent increment.
One proof conditions on the filtration at an earlier time, factors out the known Brownian value, and uses the Gaussian moment-generating function of the independent increment to recover the earlier process value. A second approach applies Itô’s lemma to show the process is a local martingale, then addresses integrability to establish the stronger martingale property. The arguments rely on standard Brownian motion and finite-time integrability; the first answer states a positive parameter, though the conditional expectation argument extends to any real constant.
Key ideas
- Brownian motion at a later time equals its earlier value plus an independent increment.
- The exponential of the increment has a Gaussian moment that offsets the variance correction.
- Conditioning on the earlier filtration shows the process has the martingale property.
- Itô’s lemma identifies a stochastic-integral representation and establishes local martingality.
- Integrability conditions are needed to promote a local martingale to a martingale.
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# Proof that $\exp(aW(t)-0.5a^2t)$ is a martingale
# Proof that $\exp(aW(t)-0.5a^2t)$ is a martingale
I'm trying to prove that $Z(t)=\exp(aW(t)-0.5a^2t)$ is a martingale where $W(t)$ is a Wiener process and $a$ is a constant. Here is my attempt:
$$E[Z(t+s)] = E\left[\exp\left(aW(t+s)-0.5a^2(t+s)\right)\right].$$
I was told that we can write $\exp(aW(t+s))$ as $\exp(aW(t)+aW(s))$, could anyone explain why it is the case?
## Answer by Kevin (score 8, accepted)
https://quant.stackexchange.com/a/50259
Let $(W_t)$ be a standard Brownian motion and $a>0$. We define $X_t=e^{aW_t-\frac{1}{2}a^2t}$. Then, the process $(X_t)$ is adapted and integrable which are the first two conditions of being a martingale. Finally, for any $s<t$, \begin{align*} \mathbb{E}\left[ X_t\mid\mathcal{F}_s\right] &= \mathbb{E}\left[ e^{aW_t-\frac{1}{2}a^2t}\mid\mathcal{F}_s\right] \\ &= e^{-\frac{1}{2}a^2(t-s)}\mathbb{E}\left[ e^{aW_t-\frac{1}{2}a^2s}\mid\mathcal{F}_s\right] \\ &= e^{-\frac{1}{2}a^2(t-s)}\mathbb{E}\left[ e^{a(W_t-W_s)}e^{aW_s-\frac{1}{2}a^2s}\mid\mathcal{F}_s\right] \\ &= e^{-\frac{1}{2}a^2(t-s)}\mathbb{E}\left[ e^{a(W_t-W_s)}\right] e^{aW_s-\frac{1}{2}a^2s} \\ &= e^{aW_s-\frac{1}{2}a^2s}\\ &= X_s. \end{align*} Note that the increment $W_t-W_s\sim N(0,t-s)$ is independent of $\mathcal{F}_s$ and hence the conditioning on $\mathcal{F}_s$ can be dropped. Furthermore, $\mathbb{E}\left[ e^{a(W_t-W_s)}\right]=e^{\frac{1}{2}a^2(t-s)}$. On the other hand, $W_s$ is $\mathcal{F}_s$ measurable (i.e. known at time $s$ and thus may be taken out of the conditional expectation.
## Answer by user39119 (score 6)
https://quant.stackexchange.com/a/50258
Set $Z_t := f(W_t,t)$ where $f(x,t) = e^{ax -\frac{1}{2}a^2t}.$ Then applying Ito's lemma we get $$dZ_t = aZ_tdW_t.$$ This means that $Z_t$ is a local martingale. For $Z_t$ to be a martingale you have to prove that $E \int_0^t|a e^{aW_t -\frac{1}{2}a^2u}|^2 du <\infty.$ For that you can use Fubini's theorem. \begin{align*} E \int_0^t|a e^{a W_u - \frac{1}{2}a^2u}|^2 du &= E \int_0^t a^2 e^{2aW_u -a^2u}|^2 du \\ &\leq a^2 E \int_0^t e^{2 a W_u}du \tag*{(since $e^{2 a W_u -a^2u} \leq e^{2 a W_u }$)} \\ &= a^2 \int_0^t E e^{2aW_u}du \tag*{(Fubini's T)} \\ &= a^2 \int_0^t e^{2a^2 u}du \\ &< \infty. \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.