Immediate causality network of stock markets

Zhou, Li; Qiu, Lu; Gu, Changgui; Yang, Huijie

doi:10.1209/0295-5075/121/48002

Cited by 20 publications

(10 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, the lag in Eq. (1) is equal to 1 (Zhou et al, 2018). The effects of the lag are examined in section I of the supplementary materials.…”

Section: Methodsmentioning

confidence: 99%

Disassortative Network Structure Improves the Synchronization between Neurons in the Suprachiasmatic Nucleus

Wang

et al. 2019

J Biol Rhythms

Self Cite

View full text Add to dashboard Cite

In mammals, an endogenous clock located in the suprachiasmatic nucleus (SCN) of the brain regulates the circadian rhythms of physiological and behavioral activities. The SCN is composed of about 20,000 neurons that are autonomous oscillators with nonidentical intrinsic periods ranging from 22 h to 28 h. These neurons are coupled through neurotransmitters and synchronized to form a network, which produces a robust circadian rhythm of a uniform period. The neurons, which are the nodes in the network, are known to be heterogeneous in their characteristics, which is reflected in different phenotypes and different functionality. This heterogeneous nature of the nodes of the network leads to the question as to whether the structure of the SCN network is assortative or disassortative. Thus far, the disassortativity of the SCN network has not been assessed and neither have its effects on the collective behaviors of the SCN neurons. In the present study, we build a directed SCN network composed of hundreds of neurons for a single slice using the method of transfer entropy, based on the experimental data. Then, we measured the synchronization degree as well as the disassortativity coefficient of the network structure (calculated by either the out-degrees or the in-degrees of the nodes) and found that the network of the SCN is a disassortative network. Furthermore, a positive relationship is observed between the synchronization degree and disassortativity of the network, which is confirmed by simulations of our modeling. Our finding suggests that the disassortativity of the network structure plays a role in the synchronization between SCN neurons; that is, the synchronization degree increases with the increase of the disassortativity, which implies that a more heterogeneous coupling in the network of the SCN is important for proper function of the SCN.

show abstract

“…Here, the lag in Eq. (1) is equal to 1 (Zhou et al, 2018). The effects of the lag are examined in section I of the supplementary materials.…”

Section: Methodsmentioning

confidence: 99%

Disassortative Network Structure Improves the Synchronization between Neurons in the Suprachiasmatic Nucleus

Wang

et al. 2019

J Biol Rhythms

Self Cite

View full text Add to dashboard Cite

show abstract

“…Herein an active linkage implies that its error is more than a standard deviation larger than its mean. [20] To display the change of the set of linkages and modes that form the backbone, two statistic properties of each mode are calculated, namely, the degree (amount of its connected neighbors) and the average of the shortest paths from it to all the other modes. [21,22] A node can be selected to be the kernel if it has the largest degree and the smallest average shortest paths.…”

Section: Resultsmentioning

confidence: 99%

Patterns of cross-correlation in time series: A case study of gait trails*

Song¹,

Weng²,

Gu³

et al. 2020

Chinese Phys. B

View full text Add to dashboard Cite

A complex system contains generally many elements that are networked by their couplings. The time series of output records of the system’s dynamical process is subsequently a cooperative result of the couplings. Discovering the coupling structure stored in the time series is an essential task in time series analysis. However, in the currently used methods for time series analysis the structural information is merged completely by the procedure of statistical average. We propose a concept called mode network to preserve the structural information. Firstly, a time series is decomposed into intrinsic mode functions and residue by means of the empirical mode decomposition solution. The mode functions are employed to represent the contributions from different elements of the system. Each mode function is regarded as a mono-variate time series. All the mode functions form a multivariate time series. Secondly, the co-occurrences between all the mode functions are then used to construct a threshold network (mode network) to display the coupling structure. This method is illustrated by investigating gait time series. It is found that a walk trial can be separated into three stages. In the beginning stage, the residue component dominates the series, which is replaced by the mode function numbered M 14 with peaks covering ∼680 strides (∼12 min) in the second stage. In the final stage more and more mode functions join into the backbone. The changes of coupling structure are mainly induced by the co-occurrent strengths of the mode functions numbered as M 11, M 12, M 13, and M 14, with peaks covering 200–700 strides. Hence, the mode network can display the rich and dynamical patterns of the coupling structure. This approach can be extended to investigate other complex systems such as the oil price and the stock market price series.

show abstract

“…Because of the high efficiency of information spreading in financial systems, and due to the stylized fact that the autocorrelation of the index return vanishes in a very short period of time, the choice

is robust against spurious causality due to longer memory effects. A similar choice has been adopted in several studies dealing with transfer entropy, Granger causality and global transfer entropy [ 12 , 14 , 16 , 18 , 19 , 21 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…Transfer entropy [ 11 ] was introduced by Schreiber in 2000, and the first application to finance this information-theoretical measure distinguishing driving and responding elements in subsystems was proposed in [ 12 ]. Other investigations of transfer entropy between pairs of market indices or sector indices have been published in [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. The investigation of information flows in complex systems has also considered the role of global variables in the process of information flow.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Information Transfer in the Global System of Financial Markets

Scagliarini

Faes

Marinazzo

et al. 2020

Entropy

View full text Add to dashboard Cite

Uncovering dynamic information flow between stock market indices has been the topic of several studies which exploited the notion of transfer entropy or Granger causality, its linear version. The output of the transfer entropy approach is a directed weighted graph measuring the information about the future state of each target provided by the knowledge of the state of each driving stock market index. In order to go beyond the pairwise description of the information flow, thus looking at higher order informational circuits, here we apply the partial information decomposition to triplets consisting of a pair of driving markets (belonging to America or Europe) and a target market in Asia. Our analysis, on daily data recorded during the years 2000 to 2019, allows the identification of the synergistic information that a pair of drivers carry about the target. By studying the influence of the closing returns of drivers on the subsequent overnight changes of target indexes, we find that (i) Korea, Tokyo, Hong Kong, and Singapore are, in order, the most influenced Asian markets; (ii) US indices SP500 and Russell are the strongest drivers with respect to the bivariate Granger causality; and (iii) concerning higher order effects, pairs of European and American stock market indices play a major role as the most synergetic three-variables circuits. Our results show that the Synergy, a proxy of higher order predictive information flow rooted in information theory, provides details that are complementary to those obtained from bivariate and global Granger causality, and can thus be used to get a better characterization of the global financial system.

show abstract

Immediate causality network of stock markets

Cited by 20 publications

References 35 publications

Disassortative Network Structure Improves the Synchronization between Neurons in the Suprachiasmatic Nucleus

Disassortative Network Structure Improves the Synchronization between Neurons in the Suprachiasmatic Nucleus

Patterns of cross-correlation in time series: A case study of gait trails*

Synergistic Information Transfer in the Global System of Financial Markets

Contact Info

Product

Resources

About