# Independence of increments of the stochastic process $\frac{1}{t}\int_0^t u dW_u$

Let $$X_t$$ be a stochastic process such that

$$X_{t} =\frac{1}{t}\int_0^t u dW_u$$

I know that for

$$Y_{t} =\int_0^t u dW_u$$ $$Y_t-Y_s$$ is independent of $$Y_s$$ where $$t>s$$. But is this also true for $$X_t$$ which has explicit time dependence in it? Edit The covariance is $$E[X_tX_s] - E[X_s^2]$$

$$E[X_t X_s] =\frac{1}{ts} \cdot E\biggl[\int_t^s u dW_u \int_0^s u dW_u\biggr] +E\biggl[\int_0^s u dW_u \int_0^s dW_u\biggr]$$ The first integral in the first expectation is limit of sequence of normal random variables which are independent of the second one and thus first expectation can be split and using wiener process properties it vanishes.

• Since each increment is normally distributed with mean $0$, you can check if the increments are bivariate normal (check if linear combinations of them are also going to be normal) and then check that the covariance of the increments is $0$. This would imply that the increments are independent since uncorrelated bivariate normal random variables are independent. Nov 21, 2019 at 11:41
Note that, for $$t>s>0$$, \begin{align*} X_t-X_s &= \frac{1}{t}\int_0^t udW_u - \frac{1}{s}\int_0^s udW_u\\ &=\frac{1}{t}\bigg(\int_s^t u dW_u + \int_0^s udW_u \bigg)- \frac{1}{s}\int_0^s udW_u\\ &=\frac{1}{t} \int_s^t u dW_u + \Big(\frac{1}{t} -\frac{1}{s}\Big)\int_0^s udW_u\\ &=\frac{1}{t} \int_s^t u dW_u - \frac{t-s}{t} X_s. \end{align*} Here, $$\int_s^t u dW_u$$ is independent of $$X_s$$. Then \begin{align*} E\big((X_t-X_s) X_s \big) &= -\frac{t-s}{t} E\big(X_s^2\big) \ne 0. \end{align*} That is, $$X_t-X_s$$ is not independent of $$X_s$$.
• @is there a formal way to show that $\int_s^t u dW_u$ is independent of X_s? I mean if both integrals were replaced by finite sums then yes but what about their limit? Nov 21, 2019 at 17:07