Time varying correlation structure of Chinese stock market of crude oil related companies greatly influenced by external factors

Xue, Liutang; Chen, Feier; Guo, Siqing; Fu, Guiyuan; Li, Tingyi; Yang, Yinan

doi:10.1016/j.physa.2019.121086

Cited by 6 publications

(2 citation statements)

References 37 publications

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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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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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“…China's oil consumption reached 13% of the world total volume in year 2017, more than 67% depending on import (BP, 2019). Consequently, literature investigating the dynamic interconnections among energy markets has emerged over the last two decades for the changing world energy market (Badel and McGillicuddy, 2015;Baumeister and Kilian, 2016a;Xue et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of the World Energy Trade Structure by Multiscale Embedding

Xue

Chen

et al. 2021

Front. Energy Res.

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This study investigates the dynamic trading network structure of the international crude oil and gas market from year 2012 to 2017. We employed the dynamical similarity analysis at different time scales by inducing a multiscale embedding for dimensionality reduction. This analysis quantifies the effect of a global event on the dependencies and correlation stability at both the country and world level, which covers the top 53 countries. The response of China’s trading structure toward events after the unexpected 2014 price drop is compared with other major traders. China, as the world’s largest importing country, lacks strong stability under global events and could be greatly affected by a supply shortage, especially in the gas market. The trend of multi-polarization on the market share gives a chance for China to construct closer relationships with more stable exporters and join in the trade loop of major countries to improve its position in the energy trading networks. The hidden features of trade correlation may provide a deeper understanding of the robustness of relationship and risk resistance.

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Response of air quality to short-duration high-strength human tourism activities at a natural scenic spot: a case study in Zhangjiajie, China

Juan

Liu

et al. 2021

Environ Monit Assess

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Time varying correlation structure of Chinese stock market of crude oil related companies greatly influenced by external factors

Cited by 6 publications

References 37 publications

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

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

Stability Analysis of the World Energy Trade Structure by Multiscale Embedding

Response of air quality to short-duration high-strength human tourism activities at a natural scenic spot: a case study in Zhangjiajie, China

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