Dynamic conditional correlation models for realized covariance matrices
Dynamic conditional correlation models for realized covariance matrices
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.
covariance targeting, realized covariance; dynamic conditional correlations; covariance targeting; Wishart distribution; composite likelihood, composite likelihood, realized covariance, dynamic conditional correlations, covariance targeting, Wishart distribution, composite likelihood, realized covariance, Wishart distribution, dynamic conditional correlations, jel: jel:C13, jel: jel:C32, jel: jel:C58
covariance targeting, realized covariance; dynamic conditional correlations; covariance targeting; Wishart distribution; composite likelihood, composite likelihood, realized covariance, dynamic conditional correlations, covariance targeting, Wishart distribution, composite likelihood, realized covariance, Wishart distribution, dynamic conditional correlations, jel: jel:C13, jel: jel:C32, jel: jel:C58
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