Hi,
I am not entirely sure what is happening and did not have the time to look at this in detail. But Durbin/Koopman (2012) in their book discuss on page 125 that numerical problems may occur if you have missing values at the beginning of your time series. This seems to be what is happening here. It may explain why the univariate must be used. Note that the univariate Kalman filter still correctly deals with covariances across series. See again the book of Durbin/Koopman. I hope to have a deeper look at this soon.