Forecasting Covariance Matrices: A Mixed Frequency Approach

Forthcoming in Journal of Financial Econometrics published by Oxford University Press.

34 Pages Posted: 15 Jan 2011 Last revised: 13 Jan 2015

See all articles by Roxana Halbleib

Roxana Halbleib

University of Konstanz

Valeri Voev

Aarhus University - CREATES

Date Written: October 12, 2012

Abstract

In this paper we introduce a new method of forecasting covariance matrices of large dimensions by exploiting the theoretical and empirical potential of using mixed-frequency sampled data. The idea is to use high-frequency (intraday) data to model and forecast daily realized volatilities combined with low-frequency (daily) data as input to the correlation model. The main theoretical contribution of the paper is to derive statistical and economic conditions, which ensure that a mixed-frequency forecast has a smaller mean squared forecast error than a similar pure low-frequency or pure high-frequency specification. The conditions are very general and do not rely on distributional assumptions of the forecasting errors or on a particular model specification. Moreover, we provide empirical evidence that, besides overcoming the computational burden of pure high-frequency specifications, the mixed-frequency forecasts are particularly useful in turbulent financial periods, such as the previous financial crisis and always outperforms the pure low-frequency specifications.

Keywords: Multivariate volatility, Volatility forecasting, High-frequency data, Realized variance, Realized covariance

JEL Classification: C32, C53

Suggested Citation

Halbleib, Roxana and Voev, Valeri, Forecasting Covariance Matrices: A Mixed Frequency Approach (October 12, 2012). Forthcoming in Journal of Financial Econometrics published by Oxford University Press. , Available at SSRN: https://ssrn.com/abstract=1740587 or http://dx.doi.org/10.2139/ssrn.1740587

Roxana Halbleib (Contact Author)

University of Konstanz ( email )

Universitaetsstr. 10
Box: D 124
78457 Konstanz
Germany

HOME PAGE: http://econometrics.wiwi.uni-konstanz.de/staff/halbleib.htm

Valeri Voev

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

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