A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics

Abstract

The analysis of the intraday dynamics of covariances among high-frequency returns is challenging due to asynchronous trading and market microstructure noise. Both effects lead to significant data reduction and may severely affect the estimation of the covariances 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 the covariances, (ii) market microstructure noise is taken into account, (iii) estimation is performed by standard maximum likelihood. Our empirical analysis, performed on 1-sec 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.

KEYWORDS:

• Asynchronicity
• Dynamic dependencies
• Intraday correlations
• Microstructure noise

Supplementary Materials

The supplementary materials include an online appendix where several model extensions and robustness checks are illustrated, and the Matlab codes necessary to implement the model.

Acknowledgments

We wish to thank the editor, the associate editor, and the Reviewers for extensive comments that greatly strengthened the article. We are particularly grateful for suggestions we have received from Maria Elvira Mancino, Davide Delle Monache, Ivan Petrella, Fabrizio Venditti, Giampiero Gallo, Davide Pirino and participants to the IAAE 2017 conference in Sapporo, the 10th SoFiE conference in New York, the VIECO 2017 conference in Wien and the workshop on “Score-Driven Time-Series models” in Cambridge in March 2019.