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Characteristics of the transmission of autoregressive sub-patterns in financial time series
Xiangyun Gao1, Haizhong An2, Wei Fang2
11] School of Humanities and Economic Management, China University of Geosciences, Beijing 100083, China [2] Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resources (Chinese Academy of Land and Resource Economics, China University of Geosciences Beijing), Beijing 100083, China [3] Lab of Resources and Environmental Management, China University of Geosciences, Beijing 100083, China [4] Department of Earth and Environmental Sciences, University of Waterloo, ON N2L 3G1, Canada.
Abstract:
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.
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