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The paper considers high frequency sampled multivariate continuous-time ARMA(MCARMA) models, and derives the asymptotic behavior of the sampleautocovariance function to a normal random matrix. Moreover, we obtain theasymptotic behavior of the cross-covariances between different components ofthe model. We will see that the limit distribution of the sample autocovariancefunction has a similar structure in the continuous-time and in thediscrete-time model. As special case we consider a CARMA (one-dimensionalMCARMA) process. For a CARMA process we prove Bartlett's formula for the sampleautocorrelation function. Bartlett's formula has the same form in both models,only the sums in the discrete-time model are exchanged by integrals in thecontinuous-time model. Finally, we present limit results for multivariate MAprocesses as well which are not known in this generality in the multivariatesetting yet.

Dependence Estimation for High Frequency Sampled Multivariate CARMA Models