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

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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Gao X,An H,Fang W,Huang X,Li H,Zhong W

doi

10.1038/srep06290

subject

Has Abstract

pub_date

2014-09-05 00:00:00

pages

6290

issn

2045-2322

pii

srep06290

journal_volume

4

pub_type

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