论文标题
多元时间序列的固定藤副群模型
Stationary vine copula models for multivariate time series
论文作者
论文摘要
多元时间序列展示了两种类型的依赖性：跨变量和跨时间点。葡萄藤是依赖性的图形模型，可以方便地捕获同一模型中的两种依赖性。我们得出了最大的图形结构类别，这些结构在自然而可验证的条件下保证平稳性，称为翻译不变性。我们提出了用于估计，模拟，预测和不确定性定量的计算有效方法，并通过渐近结果和模拟显示了它们的有效性。理论上的结果允许拼写错误的模型，即使专门针对IID案例，也超越了文献中的可用内容。他们的证明是基于一般半理学方法的新结果，该结果应具有独立关注。新的型号类别通过用于预测20股投资的投资组合的应用程序来说明，它们显示出极好的预测性能。该论文附有开源软件实施。
Multivariate time series exhibit two types of dependence: across variables and across time points. Vine copulas are graphical models for the dependence and can conveniently capture both types of dependence in the same model. We derive the maximal class of graph structures that guarantee stationarity under a natural and verifiable condition called translation invariance. We propose computationally efficient methods for estimation, simulation, prediction, and uncertainty quantification and show their validity by asymptotic results and simulations. The theoretical results allow for misspecified models and, even when specialized to the iid case, go beyond what is available in the literature. Their proofs are based on new results for general semiparametric method-of-moment estimators, which shall be of independent interest. The new model class is illustrated by an application to forecasting returns of a portfolio of 20 stocks, where they show excellent forecast performance. The paper is accompanied by an open source software implementation.
