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Forecasting Covariance Matrices: A Mixed Frequency Approach

  • Roxana Halbleib
  • Valerie Voev

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.

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File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/73640/1/2011-002-HALBLEIB_VOEV-forecasting.pdf
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Paper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number ECARES 2011-002.

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Length: 38 p.
Date of creation: Jan 2011
Date of revision:
Publication status: Published by:
Handle: RePEc:eca:wpaper:2013/73640
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