The time delay restraining the herd behavior with Bayesian approach

Zhong, Guang-Yan; Li, Jiang-Cheng; Jiang, George J.; Li, Haifeng; Tao, Hui-Ming

doi:10.1016/j.physa.2018.05.024

Cited by 12 publications

(7 citation statements)

References 56 publications

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“…The coherent resonance phenomenon of the stock returns and volatilities can be investigated with the Heston model, and the herd behavior of the stock prices influenced by the time delay can also be explored by the delayed Heston model [41] , [42] , [43] . In addition, it is argued that the leverage effect could be explained in terms of a wide class of correlated stochastic volatility models, such as the Heston model [44] .…”

Section: Modelmentioning

confidence: 99%

Empirical study and model simulation of global stock market dynamics during COVID-19

Jin¹,

Zheng²,

Ma³

et al. 2022

Chaos, Solitons & Fractals

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Section: Modelmentioning

confidence: 99%

Empirical study and model simulation of global stock market dynamics during COVID-19

Jin¹,

Zheng²,

Ma³

et al. 2022

Chaos, Solitons & Fractals

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“…Additionally to this upward trend, it is critical to consider that communication between brain areas involves some time delay [20], which is critical for maintaining the brain's healthy or pathological network synchronization [21]. The time delay is a characteristic of realistic systems such as human or animal physiology that is inherent in them [22].…”

Section: Introductionmentioning

confidence: 99%

The Time Lag in Local Field Potential Signals for the Development of its Bayesian Belief Network

Tsukahara,

Junior,

Ruggiero

et al. 2024

Preprint

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Purpose: : The objective is to suggest time as an important variable to consider in the network model, specifically when discussing causality. Methods: : There is a consideration of the context of functional connectivity because of the time importance of observing the feature inside the neuroscience context. Using a set comprised of three rats Local Field Potentials, a network model was constructed from data using the Bayesian Network method, considering and not taking into account the time delay of communication among brain areas recorded in this study. To this end, there was the appliance of the Delayed Mutual Information method to identify the lag between Local Field Potentials and K2 Score to compare the models. Results: : Bayesian Network depicted the probabilistic relationship among rat´s brain areas. Delayed Mutual Information captured the lag among brain areas, and after its appliance on the Bayesian Network model, posed better results. Conclusion: : The main contribution of this study is to incorporate the minor delays inside the Bayesian Network method, achieved by applying the Delayed Mutual Information method before its application. That supports the neurophysiology dynamics considering an essential feature in the suggested methodology to support the functional connectivity analysis among brain areas.

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“…For financial data, it is usually difficult to establish mathematical models. It is a common phenomenon in the field of financial research to study the dynamics of financial market and explore the complexity of financial system with various complex scientific methods [1][2][3][4]. In the past, there have been some different research directions through different methods to study financial problems, such as economic physics, chaos economics, and dynamic economics [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Chaotic Dynamics Analysis Based on Financial Time Series

2021

Complexity

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It is a common phenomenon in the field of financial research to study the dynamic of financial market and explore the complexity of financial system by using various complex scientific methods. In this paper, the chaotic dynamic properties of financial time series are analyzed. Firstly, the nonlinear characteristics of the data are discussed through the empirical analysis of agriculture index data; the daily agriculture index returns can be decomposed into the different scales based on wavelet analysis. Secondly, the dynamic system of some nonlinear characteristic data is established according to the Taylor series expansion form, and the corresponding dynamic characteristics are analyzed. Finally, the bifurcation diagram of the system shows complicated bifurcation phenomena, which provides a perspective for the analysis of chaotic phenomena of economic data.

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The time delay restraining the herd behavior with Bayesian approach

Cited by 12 publications

References 56 publications

Empirical study and model simulation of global stock market dynamics during COVID-19

Empirical study and model simulation of global stock market dynamics during COVID-19

The Time Lag in Local Field Potential Signals for the Development of its Bayesian Belief Network

Chaotic Dynamics Analysis Based on Financial Time Series

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