Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system

Gao, Xiangyun; Huang, Shupei; Sun, Xiaoqi; Hao, Xiaoqing; An, Feng

doi:10.1098/rsos.172092

Cited by 28 publications

(16 citation statements)

References 45 publications

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“…Complex network is an effective method by which to understand the essence of real economic systems [14][15][16][17][18][19][20][21]. The main idea is to consider the relationships among various parts of real complex systems as a complex network.…”

Section: Complex Network Methodsmentioning

confidence: 99%

Pattern and Influencing Factors of Foreign Direct Investment Networks between Countries along the “Belt and Road” Regions

Qian

Cao

2019

Sustainability

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With the in-depth implementation of the “Belt and Road” initiative (BRI), the investment patterns between Belt and Road countries have also become more complicated. The impact of this complex investment network on regional economic development is also growing. To reveal the complexity of this investment pattern, and to better promote the sustainable development of the region’s economy, this paper used the complex network method to study the foreign direct investment(FDI) network of 50 countries along the Belt and Road from 2003 to 2017, revealing its structural and behavioral characteristics and evolution process. The results showed that the imbalance of the investment network structure is outstanding, and preferential selection behavior is obvious. The Central and Eastern European countries show significant clustering behavior. In addition, the network evolved slowly and followed the “Pareto rule” in the early stages of its evolution. The BRI was a turning point in the evolution process. On this basis, the quadratic assignment procedure (QAP) regression analysis method was used to further study the factors affecting the formation process of this investment pattern. It found that economic development level, geographical distance, and bilateral trade were the main influencing factors. Among them, bilateral trade had the greatest impact on the pattern of network.

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Section: Complex Network Methodsmentioning

confidence: 99%

Pattern and Influencing Factors of Foreign Direct Investment Networks between Countries along the “Belt and Road” Regions

Qian

Cao

2019

Sustainability

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“…The normalized betweenness centrality [8], [33] and [22] is useful to determine important vertices within the network, which can be considered as information/shock transmission mediums. It is computed by counting the number of shortest paths between a pair of vertices ( , ) that pass through a given vertex , denoted ( ), and the total number of shortest paths between ( , ):…”

Section: Topology and Spanning Treesmentioning

confidence: 99%

Untitled

2021

TUS

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Network based methods are suitable for the analysis of large number of financial time series and the better understanding of their interdependencies. Known approaches to reveal the underlying information about the complex structure of these interdependencies include network-wise and vertexwise measures of the topology, as well as filtering techniques relying on minimum spanning trees, planar graphs, or spectral analysis. The aim of this study is to review relevant graph theoretical and statistical models and techniques for generating and examining the properties of financial networks, obtained by computing time series correlations or causality relationships. In particular, this study reviews literature discussing the time evolution of the observed phenomena from a network perspective, as well as applications in economy and finance, ranging from risk and diversification, through policy making and better understanding crisis impact, to forecasting. The information synthesized in this paper can be useful to gain further insights into this relatively new research area.

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“…The most common analyses for constructing financial networks were based on the linear Granger causality [ 18 , 54 ], e.g., [ 55 , 56 , 57 ], and on correlation analysis [ 58 , 59 , 60 ]. For comparative reasons, we also report the exported results based on standard linear approaches.…”

Section: Applicationmentioning

confidence: 99%

Non-Uniform Embedding Scheme and Low-Dimensional Approximation Methods for Causality Detection

Papana

2020

Entropy

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Information causality measures have proven to be very effective in uncovering the connectivity patterns of multivariate systems. The non-uniform embedding (NUE) scheme has been developed to address the “curse of dimensionality”, since the estimation relies on high-dimensional conditional mutual information (CMI) terms. Although the NUE scheme is a dimension reduction technique, the estimation of high-dimensional CMIs is still required. A possible solution is the utilization of low-dimensional approximation (LA) methods for the computation of CMIs. In this study, we aim to provide useful insights regarding the effectiveness of causality measures that rely on NUE and/or on LA methods. In a comparative study, three causality detection methods are evaluated, namely partial transfer entropy (PTE) defined using uniform embedding, PTE using the NUE scheme (PTENUE), and PTE utilizing both NUE and an LA method (LATE). Results from simulations on well known coupled systems suggest the superiority of PTENUE over the other two measures in identifying the true causal effects, having also the least computational cost. The effectiveness of PTENUE is also demonstrated in a real application, where insights are presented regarding the leading forces in financial data.

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Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system

Cited by 28 publications

References 45 publications

Pattern and Influencing Factors of Foreign Direct Investment Networks between Countries along the “Belt and Road” Regions

Pattern and Influencing Factors of Foreign Direct Investment Networks between Countries along the “Belt and Road” Regions

Untitled

Non-Uniform Embedding Scheme and Low-Dimensional Approximation Methods for Causality Detection

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