In this problem, we will show that the Ito integral of a deterministic function is a Gaussian Random Variable.

Let \(\phi\) be deterministic elementary functions. In other words there exists a sequence of real numbers \(\{c_k : k=1,2,\dots,N\}\) so that

\[ \sum_{k=1}^\infty c_k^2 < \infty\]

and there exists a partition

\[0=t_0 < t_1< t_2 <\cdots<t_N=T\]

so that

\[ \phi(t) = \sum_{k=1}^N c_k \mathbf{1}_{[t_{k-1},t_k)}(t) \]

- Show that if \(W(t)\) is a standard brownian motion then the Ito integral

\[ \int_0^T \phi(t) dW(t)\]

is a Gaussian random variable with mean zero and variance

\[ \int_0^T \phi(t)^2 dt \] - * Let \(f\colon [0,T] \rightarrow \mathbf R\) be a deterministic function such that

\[\int_0^T f(t)^2 dt < \infty\]

Then it can be shown that there exists a sequence of deterministic elementary functions \(\phi_n\) as above such that

\[\int_0^T (f(t)-\phi_n(t))^2 dt \rightarrow 0\qquad\text{as}\qquad n \rightarrow \infty\]

Assuming this fact, let \(\psi_n\) be the characteristic function of the random variable

\[ \int_0^T \phi_n(t) dW(t)\]

Show that for all \(\lambda \in \mathbf R\), show that

\[ \lim_{n \rightarrow \infty} \psi_n(\lambda) = \exp \Big( -\frac{\lambda^2}2 \big( \int_0^T f(t)^2 dt \big) \Big)\]

Then use the the convergence result here to conclude that

\[ \int_0^T f(t) dW(t)\]

is a Gaussian Random Variable with mean zero and variance

\[\int_0^T f(t)^2 dt \]

by identifying the limit of the characteristic functions above.Note: When Probabilistic say the “characteristic function” of a random distribution they just mean the Fourier transform of the random variable. See here.