Let $\xi \sim \mathcal{N}(0, \Sigma)$ be a Gaussian random vector in $\mathbb{R}^n$. I would like to calculate the covariance matrix of the normalized vector $\frac{\xi}{\lVert \xi \rVert}$, i.e.: $$ C = \mathbb{E}\left[\frac{\xi \otimes \xi}{\lVert \xi \rVert^2} \right] $$ where $\lVert \cdot \rVert$ denotes the Euclidean $l_2$-norm.
What we know:
- If $\Sigma = I$, then $C = n^{-1} I$, where $I$ is the identity matrix, according to this post, but it is not clear how to generalise.
- Using the spectral decomposition $\Sigma = V\Lambda V^\mathtt{T}$, it is enough to estimate $C$ for $\xi \sim \mathcal{N}(0, \Lambda)$, where $\Lambda$ is a diagonal matrix, since $\mathbb{E}\left[\frac{(V \xi) \otimes (V \xi)}{\lVert V\xi \rVert^2}\right] = VCV^\mathtt{T}$.
Any help would be appreciated. In particular, I am interested in cases where $n\to\infty$ and $\xi$ lies in a (separable) Hilbert space, which is well defined when $\Sigma$ is trace class and positive definite.