  The analysis of the intraday dynamics of correlations among high-frequency
returns is challenging due to the presence of asynchronous trading and market
microstructure noise. Both effects may lead to significant data reduction and
may severely underestimate correlations if traditional methods for
low-frequency data are employed. We propose to model intraday log-prices
through a multivariate local-level model with score-driven covariance matrices
and to treat asynchronicity as a missing value problem. The main advantages of
this approach are: (i) all available data are used when filtering correlations,
(ii) market microstructure noise is taken into account, (iii) estimation is
performed through standard maximum likelihood methods. Our empirical analysis,
performed on 1-second NYSE data, shows that opening hours are dominated by
idiosyncratic risk and that a market factor progressively emerges in the second
part of the day. The method can be used as a nowcasting tool for high-frequency
data, allowing to study the real-time response of covariances to macro-news
announcements and to build intraday portfolios with very short optimization
horizons.
