Characteristics of the Co-Fluctuation Matrix Transmission Network Based on Financial Multi-Time Series

10 Pages Posted: 23 Sep 2015

See all articles by Huajiao Li

Huajiao Li

China University of Geosciences (CUG) - School of Humanities and Economic Management

An Haizhong

China University of Geosciences, Beijing

Xiangyun Gao

China University of Geosciences (CUG) - School of Humanities and Economic Management

Wei Fang

China University of Geosciences (CUG) - School of Humanities and Economic Management

Date Written: September 2015

Abstract

The co-fluctuation of two time series has often been studied by analysing the correlation coefficient over a selected period. However, in both domestic and global financial markets, there are more than two active time series that fluctuate constantly as a result of various factors, including geographic locations, information communications and so on. In addition to correlation relationships over longer periods, daily co-fluctuation relationships and their transmission features are also important, since they can present the co-movement patterns of multi-time series in detail. To capture and analyse the features of the daily co-movements of multiple financial time series and their transmission characteristics, we propose a new term — “the co-fluctuation relation matrix” — which can reveal the co-fluctuation relationships of multi-time series directly. Here, based on complex network theory, we construct a multi-time series co-fluctuation relation matrix transmission network for financial markets by taking each matrix as a node and the succeeding time sequence as an edge. To reveal the process more clearly, we utilize daily time series data for four well-known stock indices — the NASDAQ Composite (COMP), the S&P 500 Index, the Dow Jones Industrial Average and the Russell 1000 Index — from 22 January 2003 to 21 January 2015, to examine the concentration of the transmission networks and the roles of each matrix — in addition to the transmission relationships between the matrices — based on a variety of coefficients. We then compare our results with the statistical features of the stock indices and find that there are not many discernible patterns of co-fluctuation matrices over the 12-year period, and few of these play important roles in the transmission network. However, the conductibility of the few dominant nodes is different and reveals certain novel features that cannot be obtained by traditional statistical analysis, such as the “all positive co-fluctuation matrix”, which is the most important node, although one stock index has negative correlation with the other three. This research therefore provides a novel method for analysing the co-movement behaviour of multiple financial time series, which can help researchers obtain the roles and relations of co-fluctuation patterns both over short and long terms. The findings also provide an important basis for further investigations into financial market simulations and the fluctuation of multiple financial time series.

Suggested Citation

Li, Huajiao and Haizhong, An and Gao, Xiangyun and Fang, Wei, Characteristics of the Co-Fluctuation Matrix Transmission Network Based on Financial Multi-Time Series (September 2015). Palgrave Communications, Vol. 1, pp. 15023-, 2015, Available at SSRN: https://ssrn.com/abstract=2664575 or http://dx.doi.org/10.1057/palcomms.2015.23

Huajiao Li

China University of Geosciences (CUG) - School of Humanities and Economic Management

Beijing
China

An Haizhong (Contact Author)

China University of Geosciences, Beijing ( email )

NO. 29, Xueyuan Road, Haidian District
Beijing, 100083
China

Xiangyun Gao

China University of Geosciences (CUG) - School of Humanities and Economic Management

Beijing
China

Wei Fang

China University of Geosciences (CUG) - School of Humanities and Economic Management

Beijing
China

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
21
Abstract Views
354
PlumX Metrics