Variance of the product of a random matrix and a random vector

If $X$ and $Y$ are independent random variables, then the variance of the product $XY$ is given by

$\mathbb{V}\left(XY\right)=\left\{ \mathbb{E}\left(X\right)\right\} ^{2}\mathbb{V}\left(Y\right)+\left\{ \mathbb{E}\left(Y\right)\right\} ^{2}\mathbb{V}\left(X\right)+\mathbb{V}\left(X\right)\mathbb{V}\left(Y\right)$

If $\mathbf{X}$ and $\mathbf{y}$ are independent matrix and vector of $m\times m$ and $m\times1$ dimension respectively, then what would be the variance of the product $\mathbf{X}\mathbf{y}$?

My Attempt

$\mathbb{V}\left(\mathbf{X}\mathbf{y}\right)=\mathbb{E}\left(\mathbf{X}\right)\mathbb{V}\left(\mathbf{y}\right)\left\{ \mathbb{E}\left(\mathbf{X}\right)\right\} ^{\prime}+\left\{ \mathbb{E}\left(\mathbf{y}\right)\otimes\mathbf{I}_{m}\right\} ^{\prime}\mathbb{V}\left\{ \textrm{vec}\left(\mathbf{X}\right)\right\} \left\{ \mathbb{E}\left(\mathbf{y}\right)\otimes\mathbf{I}_{m}\right\} +\mathbb{V}\left\{ \textrm{vec}\left(\mathbf{X}\right)\right\} \left\{ \mathbb{V}\left(\mathbf{y}\right)\otimes\mathbf{I}_{m}\right\}$

I know this is not right, at least the last term is wrong. I'd highly appreciate if you give me the right identity or point out any reference. Thanks in advance for your help and time.

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Are you interested in the full covariance matrix or just the variances of the elements of the resultant vector (i.e., the diagonal of the covariance matrix)? – jbowman May 11 '12 at 0:26
Interested in full covariance matrix. – MYaseen208 May 11 '12 at 0:27
Thanks @jbowman for your notice. I'm interested in the full covariance matrix. Looking forward to your answer. Thanks – MYaseen208 May 11 '12 at 0:42
What's wrong with the answer you received on Stats.SE? You seem to have not accepted that answer, and are now opening a bounty on this one. It would help if you edited the question to specify what more you want here. – Willie Wong May 14 '12 at 7:51

I'll assume that the elements of $\mathbf{y}$ are i.i.d. and likewise for the elements of $\mathbf{X}$. This is important, though, so be forewarned!
1. The diagonal elements of the covariance matrix equal the sum of $m$ products of i.i.d. random variates, so the variance will equal $m \mathbb{V}(x_{ij}y_j)$, which variance you have above in your first row.
2. The off-diagonal elements all equal zero, as the rows of $\mathbf{X}$ are independent. To see this, without loss of generality assume $\mathbb{E}x_{ij} = \mathbb{E}y_i = 0 \space \forall\thinspace i,j$. Define $\mathbf{x}_i$ as the $i^{\text{th}}$ row of $\mathbf{X}$, transposed to be a column vector. Then:
$\text{Cov}(\mathbf{x_i^\text{T}y},\mathbf{x_j^\text{T}y}) = \mathbb{E}(\mathbf{x_i^\text{T}y})^\text{T}(\mathbf{x_j^\text{T}y}) = \mathbb{E}\mathbf{y}^{\text{T}}\mathbf{x}_i\mathbf{x}_j^\text{T}\mathbf{y}=\mathbb{E}_y\mathbb{E}_x \mathbf{y}^{\text{T}}\mathbf{x}_i\mathbf{x}_j^\text{T}\mathbf{y}$
Note that $\mathbf{x}_i\mathbf{x}_j^\text{T}$ is a matrix, the $(p,q)^\text{th}$ element of which equals $x_{ip}x_{jq}$. When $i \ne j$, the expectation with respect to $x$ of $\mathbf{y}^{\text{T}}\mathbf{x}_i\mathbf{x}_j^\text{T}\mathbf{y}$ equals 0 for any $\mathbf{y}$, as each element is just the expectation of the product of two independent r.v.s with mean 0 times $y_py_q$. Consequently, the entire expectation equals 0.