City Research Online

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.

Publication Type:	Article
Additional Information:	This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Business and Economic Statistics on 7 Apr 2020, available online: http://www.tandfonline.com/10.1080/07350015.2020.1739530
Publisher Keywords:	Asynchronicity, Dynamic dependencies, Intraday correlations, Microstructure noise
Subjects:	H Social Sciences > HB Economic Theory
Departments:	School of Policy & Global Affairs > Economics
SWORD Depositor:	Symplectic Administrator

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