DYNAMIC ASSET CORRELATIONS BASED ON VINES
提出一种基于藤结构（vine）参数化资产收益条件相关矩阵的新方法，允许灵活指定偏相关过程，构建了藤GARCH模型族，并通过模拟和实证与DCC、GAS模型比较。
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 “vine”. Partial correlation processes can be specified separately and arbitrarily, providing a new family of very flexible multivariate GARCH processes, called “vine-GARCH” 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.