Notice that high dimensions and mixed frequencies go together in time series. (If you're looking at a huge number of series, it's highly unlikely that all will be measured at the same frequency, unless you arbitrarily exclude all frequencies but one.) So high-dim MIDAS vector autoregression (VAR) will play a big role moving forward. The MIDAS literature is starting to go multivariate, with MIDAS VAR's appearing; see Ghysels (2015, in press) and Mikosch and Neuwirth (2016 w.p.).
But the multivariate MIDAS literature is still low-dim rather than high-dim. Next steps will be:
(1) move to high-dim VAR estimation by using regularization methods (e.g. LASSO variants),
(2) allow for many observational frequencies (five or six, say),
(3) allow for the "rough edges" that will invariably arise at the beginning and end of the sample, and
(4) visualize results using network graphics.