In this paper we describe fast Bayesian statistical analysis of vector positive-valued time series, with application to interesting financial data streams. We discuss a flexible level correlated model (LCM) framework for building hierarchical models for vector positive-valued time series. The LCM allows us to combine marginal gamma distributions for the positive-valued component responses, while accounting for association among the components at a latent level. We use integrated nested Laplace approximation (INLA) for fast approximate Bayesian modeling via the R-INLA package, building custom functions to handle this setup. We use the proposed method to model interdependencies between realized volatility measures from several stock indexes.
翻译:在本文中,我们描述了对矢量正值时间序列的快速贝叶西亚统计分析,并应用于有趣的金融数据流。我们讨论了为矢量正值时间序列建立等级模型的灵活水平相关模型框架。 LCM使我们能够将边际伽马分布用于正值部分反应,同时在潜值层面考虑各组成部分之间的关联。我们使用综合巢式拉瓦近比(INLA)来通过R-INLA软件包建立快速近似贝叶西亚的模型,建立用于处理这一设置的定制功能。我们使用拟议方法从若干股票指数中模拟已实现的波动措施之间的相互依存性。