indicate that conclusions may critically hinge on a selectedordering of variables. The dynamic correlation Cholesky …

-frequency intraday returns. It disentangles covariance estimation into variance and correlation components. This allows to estimate …

Conditional Correlation GARCH models (DCC-GARCH). The proof is based on Tweedie's (1988) criteria, after having rewritten DCC …

We address some issues that arise with the Dynamic Conditional Correlation (DCC) model. We prove that the DCC large … system estimator (DCC estimator) can be inconsistent, and that the traditional interpretation of the DCC correlation … parameters can lead to misleading conclusions. We then suggest a more tractable dynamic conditional correlation model (cDCC model …

We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation … deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as … a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by …

A method capable of estimating richly parametrized versions of the dynamic conditional correlation (DCC) model that go …

this area are the specification tests related to the correlation component, the extension of the general model to allow for … additional correlation regimes, and a detailed exposition of the systematic, improved modelling cycle required for such nonlinear …

Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The … model can be estimated in two steps, the first devoted to the clustering structure, and the second focusing on correlation …

indicate that conclusions may critically hinge on a selected ordering of variables. The dynamic correlation Cholesky …