DYNAMIC ASSET CORRELATIONS BASED ON VINES
Benjamin Poignard and
Econometric Theory, 2019, vol. 35, issue 1, 167-197
We develop a new method for generating dynamics of conditional correlation matrices of asset returns. These correlation matrices are parameterized by a subset of their partial correlations, whose structure is described by a set of connected trees called â€œvineâ€ . Partial correlation processes can be specified separately and arbitrarily, providing a new family of very flexible multivariate GARCH processes, called â€œvine-GARCHâ€ processes. We estimate such models by quasi-maximum likelihood. We compare our models with DCC and GAS-type specifications through simulated experiments and we evaluate their empirical performances.
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