Dynamic conditional correlation models for realized covariance matrices

Bauwens, Luc;Storti, Giuseppe;Violante, Francesco
(2012)

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Authors
  • Bauwens, Lucorcid-logoUCLouvain
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  • Storti, GiuseppeUniversità di Salerno, Italy
    Author
  • Violante, FrancescoMaastricht University
    Author
Abstract
New dynamic models for realized covariance matrices are proposed. The expected value of the realized covariance matrix is specified in two steps: one for each realized variance, and one for the realized correlation matrix. The realized correlation model is a scalar dynamic conditional correlation model. Estimation can be done in two steps as well, and a QML interpretation is given to each step, by assuming a Wishart conditional distribution. The model is applicable to large matrices since estimation can be done by the composite likelihood method.
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Bauwens, L., Storti, G., & Violante, F. (2012). Dynamic conditional correlation models for realized covariance matrices (CORE Discussion Paper 2012/60). https://hdl.handle.net/2078.5/204989