Assume a random vector $\mathbf{x}=(x_1,\ldots,x_n)^\top$ that has finite second moments, i.e., $$\int\mathbf{x}\mathbf{x}^\top\rho(\mathbf{x})\,\text{d}\mathbf{x} < \infty.$$ Does it follow that also the random vector $\mathbf{x}_{1:k}\,|\,\mathbf{x}_{k+1:n}$, $1<k<n$, has finite second moments?
I found an interesting answer of Ben in Deriving the conditional distributions of a multivariate normal distribution. However, I am still not sure if that holds for the case described here.
I would be grateful for references and/or short explanations.
Thank you!