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Forecasting Covariance Matrices: A Mixed Frequency Approach

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Author Info

  • Roxana Halbleib

    ()
    (European Center for Advanced Research in Economics and Statistics (ECARES), Université libre de Bruxelles, Solvay Brussels School of Economics and Management and CoFE)

  • Valeri Voev

    ()
    (School of Economics and Management, Aarhus University and CREATES)

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 separate 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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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-03.

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Length: 37
Date of creation: 18 Jan 2011
Date of revision:
Handle: RePEc:aah:create:2011-03

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Volatility forecasting; High-frequency data; Realized variance;

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References

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  7. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Journal of Econometrics, Elsevier, vol. 162(2), pages 149-169, June.
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Citations

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Cited by:
  1. Kevin Sheppard, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.
  2. Hautsch, Nikolaus & Kyj, Lada M. & Malec, Peter, 2011. "The merit of high-frequency data in portfolio allocation," CFS Working Paper Series 2011/24, Center for Financial Studies (CFS).
  3. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Banco de Espa�a Working Papers 1230, Banco de Espa�a.
  4. Bannouh, K. & Martens, M.P.E. & Oomen, R.C.A. & van Dijk, D.J.C., 2012. "Realized mixed-frequency factor models for vast dimensional covariance estimation," ERIM Report Series Research in Management ERS-2012-017-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.

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