Forecasting time series with multivariate copulas
Forecasting time series with multivariate copulas
复制标题
使用多元 copula 预测时间序列
DOI：
10.1515/demo-2015-0005
复制
发表时间：
2013
影响因子：
0.7
通讯作者：
B. Rémillard
中科院分区：
文献类型：
--
作者：
Clarence Simard;B. Rémillard
摘要本文提出了一种使用基于Copula的多元时间序列模型进行时间序列预测的方法。我们研究了当改变不同可能的依赖关系的强度以及依赖关系的结构时，预测的性能如何演变。我们还研究了边际分布的影响。估计误差对预测性能的影响也被考虑。在所有的实验中，我们比较我们的多变量方法的预测与单变量版本，最近在文献中介绍的预测。为了简化实现，一元马尔可夫时间序列之间的独立性测试。最后，我们用一个实际的财务数据来说明这种方法。
Abstract In this paper we present a forecasting method for time series using copula-based models for multivariate time series. We study how the performance of the predictions evolves when changing the strength of the different possible dependencies, as well as the structure of the dependence. We also look at the impact of the marginal distributions. The impact of estimation errors on the performance of the predictions is also considered. In all the experiments, we compare predictions from our multivariate method with predictions from the univariate version which has been introduced in the literature recently. To simplify implementation, a test of independence between univariate Markovian time series is proposed. Finally, we illustrate the methodology by a practical implementation with financial data.