Forecasting Covariance Matrices: A Mixed Frequency Approach

Roxana Halbleib () and Valeri Voev ()

Working Papers ECARES from ULB -- Universite Libre de Bruxelles

Abstract: This paper proposes a new method for forecasting covariance matrices of financial returns. the model mixes volatility forecasts from a dynamic model of daily realized volatilities estimated with high-frequency data with correlation forecasts based on daily data. This new approach allows for flexible dependence patterns for volatilities and correlations, and can be applied to covariance matrices of large dimensions. The seperate modeling of volatility and correlation forecasts considerably reduces the estimation and measurement error implied by the joint estimation and modeling of covariance matrix dynamics. Our empirical results show that the new mixing approach provides superior forecasts compared to multivariate volatility specifications using single sources of information.

Pages: 38 p.
Date: 2011-01
New Economics Papers: this item is included in nep-mst and nep-rmg
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Citations: View citations in EconPapers (11)

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Related works:
Working Paper: Forecasting Covariance Matrices: A Mixed Frequency Approach (2012)
Working Paper: Forecasting Covariance Matrices: A Mixed Frequency Approach (2011)
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