COPARmultivariate time series modeling using the copula autoregressive model
COPARmultivariate time series modeling using the copula autoregressive model
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DOI：
10.1002/asmb.2043
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发表时间：
2015-07-01
影响因子：
1.4
通讯作者：
Czado, Claudia
中科院分区：
文献类型：
--
作者：
Brechmann, Eike Christian;Czado, Claudia
多元时间序列分析是金融、经济等领域的一个常见问题。用于此目的的经典工具是向量自回归模型。然而，这些仅限于线性和对称依赖的建模。我们提出了一种新的基于Copula的模型，允许非线性和非对称建模的串行以及系列之间的依赖关系。该模型充分利用了vine copula的灵活性，vine copula仅由二元copula建立。我们描述了新模型的统计推断技术，并讨论了如何将其用于测试格兰杰因果关系。最后，我们使用该模型来研究通货膨胀对工业生产，股票收益率和利率的影响。此外，样本外的预测能力与相关的基准模型进行了比较。版权所有（c）2014约翰威利父子有限公司
The analysis of multivariate time series is a common problem in areas like finance and economics. The classical tools for this purpose are vector autoregressive models. These however are limited to the modeling of linear and symmetric dependence. We propose a novel copula-based model that allows for the non-linear and non-symmetric modeling of serial as well as between-series dependencies. The model exploits the flexibility of vine copulas, which are built up by bivariate copulas only. We describe statistical inference techniques for the new model and discuss how it can be used for testing Granger causality. Finally, we use the model to investigate inflation effects on industrial production, stock returns and interest rates. In addition, the out-of-sample predictive ability is compared with relevant benchmark models. Copyright (c) 2014 John Wiley & Sons, Ltd.