Unexpected Information Demand and Volatility Clustering of Chinese Stock Returns: Evidence from Baidu Index

Chu, Gang; Li, Xiao; Shen, Dehua; Zhang, Yongjie

doi:10.3390/e22010044

Cited by 5 publications

(7 citation statements)

References 23 publications

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“…Battiston et al [15,16] introduce degree centrality in networks to compare different financial institutions and propose a new centrality measure DebtRank, which further extends the idea of centrality in networks, the impact of different levels of nodes on the network can be seen more clearly. Vodenska et al [17][18][19] proposed a BankRank centrality metric based on the DebtRank idea and study the debt crisis providing evidence of the contagion of the 2007 financial crisis in equity and bond markets in emerging economies around the world.…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

Yang

Javaid

2022

Entropy

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The interdependence of financial institutions is primarily responsible for creating a systemic hierarchy in the industry. In this paper, an Adaptive Hierarchical Network Model is proposed to study the problem of hierarchical relationships arising from different individuals in the economic domain. In the presented dynamically evolving network model, new directed edges are generated depending on the existing nodes and the hierarchical structures among the network, and these edges decay over time. When the preference of nodes in the network for higher ranks exceeds a certain threshold value, the equality state in the network becomes unstable and rank states emerge. Meanwhile, we select four real data sets for model evaluation and observe the resilience in the network hierarchy evolution and the differences formed by different patterns of hierarchy preference mechanisms, which help us better understand data science and network dynamics evolution.

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Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

Yang

Javaid

2022

Entropy

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“…The hierarchical clustering algorithm is a methodology that robustly explores the clustering of a dataset to mine this information for connectedness visualization. It is worth nothing that GARCH-based models, entropy, and hierarchical clustering have successfully been applied to model volatility [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ], to evaluate randomness in financial and economic data [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ], and to cluster financial data [ 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic

Lahmiri

Bekiros

2020

Entropy

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The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.

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“…Furthermore, Internet search depicts the personal behavior of netizens in the online society, which becomes one of the important sources of Internet information flow. Therefore, we can reveal the information flow among cities through the information generated by the public search attention [ 9 , 10 ], which further reflects the evolution of the intercity network.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Spatiotemporal Evolution and Sustainable Driving Factors of Information Flow Network: A Public Search Attention Perspective

Zhu

Yuen

et al. 2022

IJERPH

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The promotion of information flow reinforces the interactive cooperation and evolutionary process among cities. In the information age, public online search is a typical behavior of Internet society, which is the key to information flow generation and agglomeration. In this study, we attempt to explore the evolutionary characteristics of intercity networks driven by public online social behavior in the information age and construct an information flow network (IFN) from the perspective of public search attention. We also explore the evolution of the IFN in terms of the whole network, node hierarchy, and subgroup aggregation. Meanwhile, we also discuss the impact of the sustainable driving factors on the IFN. Finally, an empirical study was conducted in Guanzhong Plain Urban Agglomeration (GPUA). Our results show that: (1) the information flow in GPUA fluctuating upward in the early study period and gradually decreasing in the later study period. However, the agglomeration degree of information flow in the urban agglomeration continues to increase. (2) The hierarchical structure of urban nodes in GPUA presents a trend of “high in the middle and low on both sides”, and the formation of subgroups is closely related to geographic location. (3) The driving factors all impacting the IFN include public ecology, resource investment, information infrastructure, and economic foundation. This study provides theoretical and practical support for exploring the intercity network and promotes the sustainable urban development.

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Unexpected Information Demand and Volatility Clustering of Chinese Stock Returns: Evidence from Baidu Index

Cited by 5 publications

References 23 publications

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic

Exploring the Spatiotemporal Evolution and Sustainable Driving Factors of Information Flow Network: A Public Search Attention Perspective

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