Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series

Jiang, Meihui; Gao, Xiangyun; An, Haizhong; Sun, Bо

doi:10.1038/s41598-017-10759-3

Cited by 35 publications

(38 citation statements)

References 44 publications

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“…In the second step, after extracting the jump volatility of a single financial institution, we investigate whether there is jump volatility spillover between financial institutions according to Granger-causality test [ 57 ]. If the p values of Granger-causality test are smaller than the critical values under the 5% significance level [ 58 ], there exists causality relationships between financial institutions.…”

Section: Methodsmentioning

confidence: 99%

Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

Xin

Zhao

Gong

et al. 2020

Entropy

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The investigation of the systemic importance of financial institutions (SIFIs) has become a hot topic in the field of financial risk management. By making full use of 5-min high-frequency data, and with the help of the method of entropy weight technique for order preference by similarities to ideal solution (TOPSIS), this paper builds jump volatility spillover network of China’s financial institutions to measure the SIFIs. We find that: (i) state-owned depositories and large insurers display SIFIs according to the score of entropy weight TOPSIS; (ii) total connectedness of financial institution networks reveal that Industrial Bank, Ping An Bank and Pacific Securities play an important role when financial market is under pressure, especially during the subprime crisis, the European sovereign debt crisis and China’s stock market disaster; (iii) an interesting finding shows that some small financial institutions are also SIFIs during the financial crisis and cannot be ignored.

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Section: Methodsmentioning

confidence: 99%

Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

Xin

Zhao

Gong

et al. 2020

Entropy

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“…initially cut the whole time series into subseries by means of the sliding time window approach. e advantage of a sliding window is that it can contain historical information and transitivity of the time series [7,28,36]. First, we need to set the window length of the time window w 1 and the step size for each slide l 1 .…”

Section: Calculating Correlation Of Investor Attention Series Andmentioning

confidence: 99%

“…Gao et al [17] proposed an approach to measure the transmission relation of the linear relation between time series. Jiang et al [28] characterized the causality evolution behavior of multivariate series.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks

Guo

Feng

2019

Complexity

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e relationship between investor attention and stock prices has been a topic of interest in economics. Previous studies have shown that the correlation relationship between the two changes with time. However, there are few studies to explore the timevarying evolution of the relationship, as well as the transmission characteristics under important cycles. us, this paper is dedicated to discover the dynamic transmission characteristics of the correlation between investor attention and stock price. We selected the typical stocks of China's energy industry, PetroChina and Sinopec, as the research objects, as they occupy a large market share and are representative. And the transaction data and attention data are used to build investor attention indicator. In order to reproduce the dynamic transmission process of correlation at di erent cycles, sliding time window and complex network are applied. e results show that PetroChina and Sinopec stocks have a weakly negative correlation between investor attention and stock price from 2017 to 2018. However, from the perspective of di erent cycles, the correlation has time-varying characteristics. As the cycle grows, the types of transmission patterns of the ve consecutive days of correlation between the two become less, but the transmission intensity between the modes increases and the transition becomes more regular and inclined. In addition, by mining the important transmission modes and main transmission paths under important periods, we nd that the series modes of uncorrelated or weakly positive correlation for ve consecutive days dominate the transition of modes in the networks. Also, the closed loop formed by these two important modes and related modes is the main transmission path. ese ndings can reveal the rules of the typical stock market in China's energy industry and help investors with di erent investment cycle preferences make sound decisions.

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“…Fertilizer markets are closely linked to each other during low and high regimes. Jiang M [20] proposed an algorithm to transfer this evolution process to a complex network. Causality patterns are considered as nodes and the succeeding sequence relations between patterns as edges.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

Song

2018

Discrete Dynamics in Nature and Society

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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.

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Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series

Cited by 35 publications

References 44 publications

Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks

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

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