Systemic risk and spatiotemporal dynamics of the US housing market

Meng, Hao; Xie, Wen-Jie; Jiang, Zhi‐Qiang; Podobnik, Boris; Zhou, Wei‐Xing; Stanley, H. Eugene

doi:10.1038/srep03655

Cited by 93 publications

(79 citation statements)

References 38 publications

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“…Recently, the largest eigenvalue has been successfully used as a predicting index for sudden changes in complex systems, such as the financial crisis 21,53 and the housing market. 22 Previous studies have suggested that characterizing the largest eigenvalue of an adjacency matrix of complex networks is useful for better understanding the network behavior. In our experiment, the largest eigenvalue provided the most information about the contribution of different brain regions to the dynamic brain network.…”

Section: Discussionmentioning

confidence: 99%

“…25 The RMT was proposed initially to measure the spectral fluctuation and has found widespread applications in different real systems. [21][22][23][24][25][26][27][28][29][30][31][32][33] Therefore, we used the RMT method to further explore the dynamic brain network. NNSD is the distribution of spacing between eigenvalues and only accounts for the relationship between neighboring eigenvalues.…”

Section: Discussionmentioning

confidence: 99%

“…The RMT was proposed to deal with the following limitation: a deterministic model cannot describe the complex interaction between different energy levels because of the chaotic properties of a nuclear system; 19 RMT characterizes the statistical properties of the energy spectrum in unclear systems 19 and has been successfully used in quantum graphing. 20 Recent studies have indicated that RMT is also useful for characterizing the spectral properties of an adjacency matrix corresponding to a complex network and provides important information about network structure and dynamics; for example, RMT has been widely applied in different real systems, such as price fluctuations in the stock market, [21][22][23] atmosphere, 24 human EEG data, 25 biological networks, [26][27][28] and various other model networks. [29][30][31][32][33] Compared to the complex network method, RMT has a great advantage in its ability to separate the complex system-dependent data from the random universal data, which results from the complexity of the system.…”

Section: Introductionmentioning

confidence: 99%

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Spectral properties of the temporal evolution of brain network structure

Wang

Zhang

et al. 2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems. The temporal evolution of brain network structure is crucial to revealing brain functions at different brain states, such as learning, memory, and visual stimulation. The sliding window method is effective for the construction of a dynamic brain network, and complex network analysis has been widely used to characterize these networks. Recently, the spectral property analysis of complex networks has attracted increasing attention based on its ability to measure more intrinsic properties of networks than complex network analysis. In particular, the dynamic brain network constructed from human electroencephalogram (EEG) data has been successfully studied by applying random matrix theory (RMT), which aims to identify the spectral fluctuation properties of networks. However, little is known about the spectral properties of the dynamic brain network based on human functional magnetic resonance imaging (fMRI) data at different brain states, and how spectral properties reflect changes in the dynamic brain network must be explained in greater detail. Here, we addressed these issues and investigated the spectral properties of a dynamic brain network constructed from human fMRI data. We show that the global and local properties of the eigenvalue of the brain network are effective for evaluating the changes in the dynamic brain network caused by visual stimulation. However, the differences between global properties...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectral properties of the temporal evolution of brain network structure

Wang

Zhang

et al. 2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Varsha Kulkarni analyzed the volatility of the Indian stock market based on stochastic matrix theory [5]. Based on the knowledge of stochastic matrix, Meng and Xie used absorption rate, linear regression and clustering methods to study the correlation degree and division of the American real estate market, and found that the risk of the American estate market was very high and the whole market was unstable [6,7]. Tian, et al investigated the information and characteristics of the international crude oil market (1999-2015) based on the random matrix theory [8].…”

Section: Literature Review Of Stochastic Matricesmentioning

confidence: 99%

“…In order to further study the influence of the eigenvalues of the correlation coefficient matrices C(t) on the natural gas import market in OECD countries, we define R n (t ) = u T n (t )r(t ) (6) where, t = t − L + 1, · · · , t, r(t ) = [r 1 (t ), r 2 (t ), · · · , r 7 (t )] T . We construct a regression model…”

Section: Feature Combination Algorithmmentioning

confidence: 99%

The Spatiotemporal Dynamics of Natural Gas Imports in OECD Countries

Yang

et al. 2017

Sustainability

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The natural gas receives universal attention and priority use around the world because of its high quality and cleanness. The Organisation for Economic Co-operation and Development (OECD) countries' data on imports of natural gas are rich in market information and have foresight. We quantify the role of the market in resource allocation. Based on the random matrix method, this article studies the correlation coefficient matrix of OECD countries' natural gas imports by average correlation coefficients and explores the information that the natural gas imports market contains. At the same time, this article analyzes the risk and correlation in the OECD countries' natural gas imports market by eigenvalues, regression analysis and absorption. Finally, combined with the clustering method, we synchronize and regionalize the OECD countries' natural gas import market. We can find that the OECD countries' natural gas imports are related by analyzing the correlation coefficient and the risk. Correlations in OECD countries' natural gas imports market are high. We find that different eigenvalues have different effects on the OECD countries' natural gas imports market by researching on the entropy of the eigenvalues. We can correctly predict the turning points of OECD countries' natural gas imports and find the correlation coefficient matrix contains advanced market information. Through the method of absorption rate, the risk of the gas import market in OECD countries is further studied. It is found that the import of natural gas in OECD countries is large and the whole market is unstable. Through the method of dynamic clustering, it is found that the whole OECD countries have high synchronism in the market of natural gas import, except for Germany.

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Tail dependence networks of global stock markets

2018

Self Cite

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The Pearson correlation coefficient is used by many researchers to construct complex financial networks. However, it is difficult to capture the structural characteristics of financial markets that have extreme fluctuations. To solve this problem, we resort to tail dependence networks. We first build the edge information of the stock network by adopting Pearson's correlation coefficient and the symmetrized Joe–Clayton copula model, respectively. By using the planar maximally filtered graph method, we filter the edge information, obtain Pearson's correlation coefficient and tail dependence network, and compare their efficiencies. The community structure of the constructed networks is investigated. We find that the global efficiency of tail‐dependent networks is higher than that of the Pearson correlation networks. Further analysis of the nodes in the upper‐ and lower‐tail dependence networks reveals that the European markets are more influential than Asian and African markets during a booming market and a recession market. In addition, different cliques are found in the two tail dependence networks. The finding indicates that financial risks will impact geographically adjacent markets.

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Systemic risk and spatiotemporal dynamics of the US housing market

Cited by 93 publications

References 38 publications

Spectral properties of the temporal evolution of brain network structure

Spectral properties of the temporal evolution of brain network structure

The Spatiotemporal Dynamics of Natural Gas Imports in OECD Countries

Tail dependence networks of global stock markets

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