Characteristics of the transmission of autoregressive sub-patterns in financial time series

被引:33
作者
Gao, Xiangyun [1 ,2 ,3 ,4 ]
An, Haizhong [1 ,2 ,3 ]
Fang, Wei [1 ,2 ,3 ]
Huang, Xuan [1 ,2 ,3 ]
Li, Huajiao [1 ,2 ,3 ]
Zhong, Weiqiong [1 ,2 ,3 ]
机构
[1] China Univ Geosci, Sch Humanities & Econ Management, Beijing 100083, Peoples R China
[2] China Univ Geosci, Chinese Acad Land & Resource Econ, Minist Land & Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100083, Peoples R China
[3] China Univ Geosci, Lab Resources & Environm Management, Beijing 100083, Peoples R China
[4] Univ Waterloo, Dept Earth & Environm Sci, Waterloo, ON N2L 3G1, Canada
基金
中国国家自然科学基金;
关键词
VISIBILITY GRAPH; COMPLEX NETWORK; QUANTIFICATION;
D O I
10.1038/srep06290
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
070301 [无机化学]; 070403 [天体物理学]; 070507 [自然资源与国土空间规划学]; 090105 [作物生产系统与生态工程];
摘要
There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.
引用
收藏
页数:9
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