Similarity and heterogeneity of price dynamics across China’s regional carbon markets: A visibility graph network approach

Fan, Xinghua; Li, Xuxia; Yin, Jiuli; Li, Tian; Liang, Jiaochen

doi:10.1016/j.apenergy.2018.11.007

Cited by 48 publications

(17 citation statements)

References 32 publications

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“…Network topology can be used to indicate the current status and behavior of system which may not be shown in the time series explicitly (Fan et al, 2019 ). In Lacasa et al ( 2008 ) found a new algorithm to map time series into complex networks, which is a key problem in the research on time series, namely, visibility graph ( VG ).…”

Section: Methodology and Datamentioning

confidence: 99%

Difference and Cluster Analysis on the Carbon Dioxide Emissions in China During COVID-19 Lockdown via a Complex Network Model

Chen

Zhu

et al. 2022

Front. Psychol.

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The continuous increase of carbon emissions is a serious challenge all over the world, and many countries are striving to solve this problem. Since 2020, a widespread lockdown in the country to prevent the spread of COVID-19 escalated, severely restricting the movement of people and unnecessary economic activities, which unexpectedly reduced carbon emissions. This paper aims to analyze the carbon emissions data of 30 provinces in the 2020 and provide references for reducing emissions with epidemic lockdown measures. Based on the method of time series visualization, we transform the time series data into complex networks to find out the hidden information in these data. We found that the lockdown would bring about a short-term decrease in carbon emissions, and most provinces have a short time point of impact, which is closely related to the level of economic development and industrial structure. The current results provide some insights into the evolution of carbon emissions under COVID-19 blockade measures and valuable insights into energy conservation and response to the energy crisis in the post-epidemic era.

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Section: Methodology and Datamentioning

confidence: 99%

Difference and Cluster Analysis on the Carbon Dioxide Emissions in China During COVID-19 Lockdown via a Complex Network Model

Chen

Zhu

et al. 2022

Front. Psychol.

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“…Due to the nonlinear and discrete properties, a bunch of analyzing approaches have been proposed [2]. Afterwards, complex network theory is developing rapidly [3] and applied to the analysis of time series data [1][2][3][4][5][6][7][8][9]. Hence, a technique, i.e., time series data visualization, and some improved versions, are developed by constructing complex networks from the initial data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Stock Price Data: Determinition of The Optimal Sliding-Window Length

et al. 2021

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Over the recent years, the study of time series visualization has attracted great interests. Numerous scholars spare their great efforts to analyze the time series using complex network technology with the intention to carry out information mining. While Visibility Graph and corresponding spin-off technologies are widely adopted. In this paper, we try to apply a couple of models derived from basic Visibility Graph to construct complex networks on one-dimension or multi-dimension stock price time series. As indicated by the results of intensive simulation, we can predict the optimum window length for certain time series for the network construction. This optimum window length is long enough to the majority of stock price SVG whose data length is 1-year. The optimum length is 70% of the length of stock price data series.

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“…Analyses of financial time series via a VG approach have been studied intensively [19][20][21][22][23][24][25][26][27][28][29]. For example, Long Yu discovered small-world characteristics in visibility-graph networks converted from the time series of the price of gold and its returns [24].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Stock Price Motion Asymmetry via Visibility-Graph Algorithm

Liu

Chen

2020

Front. Phys.

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This paper is the first to differentiate between concave and convex price motion trajectories by applying visibility-graph and invisibility-graph algorithms to the analyses of stock indices. Concave and convex indicators for price increase and decrease motions are introduced to characterize accelerated and decelerated stock index increases and decreases. Upon comparing the distributions of these indicators, it is found that asymmetry exists in price motion trajectories and that the degree of asymmetry, which is characterized by the Kullback-Leibler divergence between the distributions of rise and fall indictors, fluctuates after a change in time scope. Moreover, asymmetry in price motion speeds is demonstrated by comparing conditional expected rise and fall returns on the node degrees of visibility and invisibility graphs.

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Similarity and heterogeneity of price dynamics across China’s regional carbon markets: A visibility graph network approach

Cited by 48 publications

References 32 publications

Difference and Cluster Analysis on the Carbon Dioxide Emissions in China During COVID-19 Lockdown via a Complex Network Model

Difference and Cluster Analysis on the Carbon Dioxide Emissions in China During COVID-19 Lockdown via a Complex Network Model

Analysis of Stock Price Data: Determinition of The Optimal Sliding-Window Length

Analysis of Stock Price Motion Asymmetry via Visibility-Graph Algorithm

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