  We present several refinements on the fluctuations of sequences of random
vectors (with values in the Euclidean space $\mathbb{R}^d$) which converge
after normalization to a multidimensional Gaussian distribution. More precisely
we refine such results in two directions: first we give conditions under which
one can obtain bounds on the speed of convergence to the multidimensional
Gaussian distribution, and then we provide a setting in which one can obtain
precise moderate or large deviations (in particular we see at which scale the
Gaussian approximation for the tails ceases to hold and how the symmetry of the
Gaussian tails is then broken). These results extend some of our earlier works
obtained for real valued random variables, but they are not simple extensions,
as some new phenomena are observed that could not be visible in one dimension.
Even for very simple objects such as the symmetric random walk in
$\mathbb{Z}^d$, we observe a loss of symmetry that we can quantify for walks
conditioned to be far away from the origin. Also, unlike the one dimensional
case where the Kolmogorov distance is natural, in the multidimensional case
there is no more such a canonical distance. We choose to work with the
so-called convex distance, and as a consequence, the geometry of the Borel
measurable sets that we consider shall play an important role (also making the
proofs more complicated). We illustrate our results with some examples such as
correlated random walks, the characteristic polynomials of random unitary
matrices, or pattern countings in random graphs.
