  High-frequency sampled multivariate continuous time autoregressive moving
average processes are investigated. We obtain asymptotic expansion for the
spectral density of the sampled MCARMA process $(Y_{n\Delta})_{n \in
\mathbb{Z}}$ as $\Delta \downarrow 0$, where $(Y_t)_{t \in \mathbb{R}}$ is an
MCARMA process. We show that the properly filtered process is a vector moving
average process, and determine the asymptotic moving average representation of
it, thus generalizing the results by Brockwell et al. in the univariate case to
the multivariate model. The determination of the moving average representation
of the filtered process, important for the analysis of high-frequency data, is
difficult for any fixed positive $\Delta$. However, the results established
here provide a useful and insightful approximation when $\Delta$ is very small.
