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

30 Pages Posted: 8 Feb 2017 Last revised: 24 Mar 2019

See all articles by Giuseppe Buccheri

Giuseppe Buccheri

Scuola Normale Superiore

Giacomo Bormetti

University of Bologna - Department of Mathematics

Fulvio Corsi

University of Pisa - Department of Economics; City University London

Fabrizio Lillo

Università di Bologna

Date Written: March 1, 2019

Abstract

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.

Keywords: Intraday Correlations; Dynamic Dependencies; Asynchronicity; Microstructure Noise

JEL Classification: C58; D53; D81

Suggested Citation

Buccheri, Giuseppe and Bormetti, Giacomo and Corsi, Fulvio and Lillo, Fabrizio, A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics (March 1, 2019). Available at SSRN: https://ssrn.com/abstract=2912438 or http://dx.doi.org/10.2139/ssrn.2912438

Giuseppe Buccheri (Contact Author)

Scuola Normale Superiore ( email )

Piazza dei Cavalieri, 7
Pisa, 56126
Italy
+39 3341046378 (Phone)

Giacomo Bormetti

University of Bologna - Department of Mathematics ( email )

Piazza di Porta S. Donato , 5
Bologna, Bologna 40126
Italy

Fulvio Corsi

University of Pisa - Department of Economics ( email )

via Ridolfi 10
I-56100 Pisa, PI 56100
Italy

HOME PAGE: http://people.unipi.it/fulvio_corsi/

City University London ( email )

Northampton Square
London, EC1V OHB
United Kingdom

Fabrizio Lillo

Università di Bologna ( email )

Via Zamboni, 33
Bologna, 40126
Italy

Register to save articles to
your library

Register

Paper statistics

Downloads
381
rank
74,258
Abstract Views
1,710
PlumX Metrics