scholarly journals Detecting High-Dimensional Causal Networks using Randomly Conditioned Granger Causality

2021 ◽  
Vol 2 (4) ◽  
pp. 680-696
Author(s):  
Huanfei Ma
Keyword(s):  
Granger Causality ◽  
High Dimensional ◽  
Causal Networks

scholarly journals Dynamic Contagion of Systemic Risks on Global Main Equity Markets Based on Granger Causality Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Qiuhong Zheng ◽  
Liangrong Song
Keyword(s):  
Granger Causality ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Critical Role ◽  
Stock Index ◽  
Causality Test ◽  
Complex Network Theory ◽  
Significant Difference ◽  
Causal Networks ◽  
Global Stock Markets

A total of 156 Granger causal networks of stock markets are constructed by using the Granger causality test and time series sliding window based on stock index data of 34 major stock markets in the world from 2004 to 2017. The topological structures and evolution characteristics of the Granger causal networks are analyzed from the perspective of complex network theory. Empirical results demonstrate that the network topology has a significant difference during the global financial crisis and other periods. The causal relationships among different global stock markets exhibit a jump growth when each major crisis occurs. The contagion path is also short. A causal relationship between any two stock markets can usually be established with one stock market on average, not by using more than five stock markets. For risk contagion, the American stock markets exerted the largest influence in 12 years, followed by the European stock markets. Stock markets with high intermediate contagion ability play an important role in systemic risk contagion. Despite the crucial markets in Europe and America (e.g., USA, Brazil, and Mexico), stock markets with weak network correlation and strong media ability (e.g., the markets of Japan, Korea, Australia, and New Zealand) play a critical role in risk contagion.

scholarly journals Yule–Walker Equations Using a Gini Covariance Matrix for the High-Dimensional Heavy-Tailed PVAR Model

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 614
Author(s):  
Jin Zou ◽  
Dong Han
Keyword(s):  
Granger Causality ◽  
Real Data ◽  
Vital Role ◽  
High Dimensional ◽  
Asymptotic Results ◽  
Vector Autoregressive ◽  
Matrix Estimation ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Gini Covariance

Gini covariance plays a vital role in analyzing the relationship between random variables with heavy-tailed distributions. In this papaer, with the existence of a finite second moment, we establish the Gini–Yule–Walker equation to estimate the transition matrix of high-dimensional periodic vector autoregressive (PVAR) processes, the asymptotic results of estimators have been established. We apply this method to study the Granger causality of the heavy-tailed PVAR process, and the results show that the robust transfer matrix estimation induces sign consistency in the value of Granger causality. Effectiveness of the proposed method is verified by both synthetic and real data.

scholarly journals Evaluation of Granger Causality Measures for Constructing Networks from Multivariate Time Series

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1080 ◽  
Author(s):  
Elsa Siggiridou ◽  
Christos Koutlis ◽  
Alkiviadis Tsimpiris ◽  
Dimitris Kugiumtzis
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Financial Markets ◽  
Dimension Reduction ◽  
Granger Causality ◽  
Multivariate Time Series ◽  
High Dimensional ◽  
Information Measures ◽  
Model Based ◽  
The Time Domain

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. These measures are in the time domain, such as model-based and information measures, the frequency domain, and the phase domain. The study aims also to compare bivariate and multivariate measures, linear and nonlinear measures, as well as the use of dimension reduction in linear model-based measures and information measures. The latter is particular relevant in the study of high-dimensional time series. For the performance of the multivariate causality measures, low and high dimensional coupled dynamical systems are considered in discrete and continuous time, as well as deterministic and stochastic. The measures are evaluated and ranked according to their ability to provide causality networks that match the original coupling structure. The simulation study concludes that the Granger causality measures using dimension reduction are superior and should be preferred particularly in studies involving many observed variables, such as multi-channel electroencephalograms and financial markets.

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