Forecasting Covariance Matrices: A Mixed Frequency Approach

Roxana Halbleib (roxana.halbleib@vwl.uni-freiburg.de) and Valeri Voev (valeri.voev@gmail.com)

No 2012-30, Working Paper Series of the Department of Economics, University of Konstanz from Department of Economics, University of Konstanz

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 (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2012-10-12
New Economics Papers: this item is included in nep-ets, nep-for, nep-mst and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Forecasting Covariance Matrices: A Mixed Frequency Approach (2011)
Working Paper: Forecasting Covariance Matrices: A Mixed Frequency Approach (2011)
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