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
Date Written: October 12, 2012
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: Suggested Citation