We review the state of the art of clustering financial time series and the study of their
correlations alongside other interaction networks. The aim of the review is to gather in one …

The structure of return spillovers is examined by constructing Granger causality networks
using daily closing prices of 20 developed markets from 2nd January 2006 to 31st …

Accurately determining dependency structure is critical to understanding a complex system's
organization. We recently showed that the transfer entropy fails in a key aspect of this …

Much research has been done on time series of financial market in last two decades using
linear and non-linear correlation of the returns of stocks. In this paper, we design a method …

Many empirical networks originate from correlational data, arising in domains as diverse as
psychology, neuroscience, genomics, microbiology, finance, and climate science …

Today's asset management academia and practice is dominated by mean-variance thinking.
In consequence, this leads to the quantification of the dependence structure of asset returns …

In this paper, we study data from financial markets, using the normalised Mutual Information
Rate. We show how to use it to infer the underlying network structure of interrelations in the …

复杂网络能够解决许多金融问题, 能够发现金融市场的拓扑结构特征, 反映不同金融主体之间的
相互依赖关系. 相关性度量在金融复杂网络构建中至关重要. 通过将多元金融时间序列符号化 …

Online social networks have been actively developing for the past 20 years, since the
appearance of the first social networks, such as Friendster, Hastag in 2002, and Facebook in …

In this paper we study data from financial markets using an information-theory tool that we
call the normalised Mutual Information Rate and show how to use it to infer the underlying …