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Distribution and Variance of an Exponential Wiener Integral

Article Quant Q&A · Author: NC520

Summary

The document studies the stochastic integral of an exponential deterministic function against a Wiener process over a finite time interval. It identifies the integral as a Wiener integral, which is normally distributed with mean zero. Its variance follows from Ito isometry: integrate the square of the deterministic integrand over time, yielding the stated closed-form expression when the constant parameter is nonzero.

An accepted response also applies Ito's lemma to a product involving the process and exponential time factor, then rearranges to express the integral. That route is more cumbersome and contains a derivative error in the displayed intermediate calculation; the direct Gaussian and isometry argument is the clearer method. The result supports distributional or numerical calculations, with the zero-parameter case obtained by taking the limiting variance, equal to the interval length.

Key ideas

  • A Wiener integral with deterministic integrand is a centered Gaussian random variable.
  • Ito isometry gives its variance by integrating the squared integrand over time.
  • The exponential integrand produces a closed-form variance, with a continuous limiting case when its parameter is zero.
  • The Ito lemma derivation shown is less direct and includes an incorrect intermediate expression.

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Full text
# Calculate value of Integral of Wiener process $\int_{0}^t e^{\lambda u } dZ_u$


# Calculate value of Integral of Wiener process $\int_{0}^t e^{\lambda u } dZ_u$












I am not quite sure how to solve this integral to be able to do numerical calculations with it. $\lambda$ is a constant, $u$ is time, and $Z_u$ is a wiener process. Can anyone provide some direction on how to solve it please?

$f = \int_{0}^t e^{\lambda u } dZ_u$

## Answer by Jan Stuller (score 2, accepted)

https://quant.stackexchange.com/a/71083

I'll try to use Ito's Lemma to come up with a solution. Ito's lemma states that:

$$F(Z_t,t)=\int_0^t\left(\frac{\partial F}{\partial u}+\frac{\partial F}{\partial Z}a+0.5\frac{\partial^2 F}{\partial Z^2}b^2\right)du+\int_0^t\frac{\partial F}{\partial Z}bdZ_u$$

We have $a=0$ and $b=1$ (because $Z_t=\int_0^t0du+\int_0^t1 dZ_u$).

My strategy is to find a function so that the integral $\int_0^te^{\lambda u}dZ_u$ appears in the expression of Ito's Lemma under $\int_0^t\frac{\partial F}{\partial Z}dZ_u$: therefore, I will try $F(Z_t,t):=Z_te^{\lambda t}$. Then we get:

$$Z_te^{\lambda t}=\\=\int_0^t\left(\frac{\partial F}{\partial u}+\frac{\partial F}{\partial Z}a+0.5\frac{\partial^2 F}{\partial Z_t^2}b^2\right)du+\int_0^t\frac{\partial F}{\partial Z}bdZ_u=\\=\int_0^tuZ_ue^{\lambda u}du+\int_0^te^{\lambda u}dZ_u$$

Now we can isolate the term of interest on the RHS and write:

$$Z_te^{\lambda t}-\int_0^tuZ_ue^{\lambda u}du=\int_0^te^{\lambda u}dZ_u$$.

As per @siou0107's answer, the above is a normally distributed random variable with:

$$\mathbb{E}\left[Z_te^{\lambda t}-\int_0^tuZ_ue^{\lambda u}du\right]=\\=\mathbb{E}\left[Z_t\right]_{=0}e^{\lambda t}-\int_0^tu\mathbb{E}\left[Z_u\right]_{=0}e^{\lambda u}du=0$$

$$Var\left(Z_te^{\lambda t}-\int_0^tuZ_ue^{\lambda u}du\right)=Var\left(\int_0^te^{\lambda u}dZ_u\right)=\\=\mathbb{E}\left[\left(\int_0^te^{\lambda u}dZ_u\right)^2\right]-\left(\mathbb{E}\left[\int_0^te^{\lambda u}dZ_u\right]_{=0}\right)^2$$

Using Ito Isometry, we get:

$$\mathbb{E}\left[\left(\int_0^te^{\lambda u}dZ_u\right)^2\right]=\mathbb{E}\left[\int_0^t\left(e^{\lambda u}\right)^2du\right]=\mathbb{E}\left[\int_0^te^{2\lambda u}du\right]=\int_0^te^{2\lambda u}du$$

PS: we can see that using Ito's lemma was not the smart way of solving the problem, because instead of simplifying, it made it more complicated. Directly taking the expectation and variance of the Ito integral, as per @siou0107's answer, is the best way to approach the problem.

## Answer by siou0107 (score 9)

https://quant.stackexchange.com/a/71084

This is just a Wiener integral (stochastic integral with respect to a Brownian motion and deterministic integrand), hence a centered Gaussian random variable with variance $$ \int_0^t{e^{2\lambda u}\mathrm{d}u} = \left[\frac{e^{2\lambda u}}{2\lambda}\right]_0^t = \frac{e^{2\lambda t} - 1}{2 \lambda} $$

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.