论文标题
使用R-Inla建模多元正相值时间序列
Modeling Multivariate Positive-Valued Time Series Using R-INLA
论文作者
论文摘要
在本文中，我们描述了对向量正值时间序列的快速贝叶斯统计分析，并应用于有趣的财务数据流。我们讨论一个灵活的相关模型（LCM）框架，用于构建矢量正值时间序列的层次模型。 LCM允许我们将边缘伽马分布组合成积极值的组件响应，同时考虑到潜在级别的组件之间的关联。我们使用集成的嵌套拉普拉斯近似（INLA）通过R-Inla软件包快速近似贝叶斯建模，构建自定义功能来处理此设置。我们使用所提出的方法来对几个股票指数实现的波动率指标之间的相互依赖性进行建模。
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.
