A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series
A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series
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DOI：
10.1016/j.csda.2019.106810
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
N. Barthel;C. Czado;Yarema Okhrin
中科院分区：
文献类型：
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作者：
N. Barthel;C. Czado;Yarema Okhrin
提出了一种对已实现协方差矩阵进行动态建模和预测的新方法。对实现的方差和实现的相关矩阵进行联合估计。一个正定相关矩阵和它所关联的部分相关集合对应于任何vine规格之间的一对一关系被用于数据转换。因此，模型组件是已实现的方差以及已实现的标准和部分相关，对应于每日对数回报序列。因此，它们有明确的实际解释。介绍了一种选择规则藤蔓结构的方法，该方法可以简化模型组件的时间序列和相关性建模。后者是代数独立的，不受任何代数约束。所提出的模型方法被详细概述，并与六个高流动性股票的真实数据示例一起激励。从统计精度和投资组合优化两方面对预测效果进行了评价。与基于Cholesky分解的基准模型的比较支持了该模型方法出色的预测能力。
A novel approach for dynamic modeling and forecasting of realized covariance matrices is proposed. Realized variances and realized correlation matrices are jointly estimated. The one-to-one relationship between a positive definite correlation matrix and its associated set of partial correlations corresponding to any vine specification is used for data transformation. The model components therefore are realized variances as well as realized standard and partial correlations corresponding to a daily log-return series. As such, they have a clear practical interpretation. A method to select a regular vine structure, which allows for parsimonious time-series and dependence modeling of the model components, is introduced. Being algebraically independent the latter do not underlie any algebraic constraint. The proposed model approach is outlined in detail and motivated along with a real data example on six highly liquid stocks. The forecasting performance is evaluated both with respect to statistical precision and in the context of portfolio optimization. Comparisons with Cholesky decomposition based benchmark models support the excellent prediction ability of the proposed model approach.