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

49 Pages Posted: 8 Feb 2017 Last revised: 1 Feb 2018

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: December 2017

Abstract

We propose a new multivariate conditional correlation model able to deal with data featuring both observational noise and asynchronicity. When modelling high-frequency multivariate financial time-series, the presence of both problems and the requirement for positive-definite estimates makes the estimation and forecast of the intraday dynamics of conditional covariance matrices particularly difficult. Our approach tackles all these challenging tasks within a new Gaussian state-space model with score-driven time-varying parameters that can be estimated using standard maximum likelihood methods. Similarly to DCC models, large dimensionality is handled by separating the estimation of correlations from individual volatilities. As an interesting outcome of this approach, intra-day patterns are recovered without the need of any cross-sectional averaging, allowing, for instance, to estimate the real-time response of the market covariances to macro-news announcements.

Keywords: asynchronous data, microstructure noise, conditional correlation, score-driven models, Kalman filter, dynamic dependence

JEL Classification: C10, C32, C51, 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 (December 2017). 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

City University London ( email )

Northampton Square
London, EC1V OHB
United Kingdom

Fabrizio Lillo

Università di Bologna ( email )

Via Zamboni, 33
Bologna, 40126
Italy

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