Multifractal detrended cross-correlation analysis of carbon emission allowance and stock returns

Fang, Sheng; Lu, Xinsheng; Li, Jianfeng; Qu, Ling

doi:10.1016/j.physa.2018.05.136

Cited by 24 publications

(10 citation statements)

References 63 publications

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“…A rise in carbon price shows a higher spillover effect on the stock market than a decrease in the carbon price. Fang et al (2018) find that in different countries the correlation of carbon and stock returns are significantly multifractal. This paper is related to two strands of literature.…”

Section: Literature Reviewmentioning

confidence: 91%

“…The relationship between the carbon price and stock markets has been widely discussed since carbon trading becomes the most cost-effective emission reduction tool to deal with climate change (see Moreno and Silva, 2016;Fang et al, 2018;Reboredo and Ugolini, 2018;Mejdoub and Ghorbel, 2018;Pereira, 2019;Krokida et al, 2020;Wen et al, 2020;Batten et al, 2020;Duan et al, 2021). Researches find that key energy prices, including coal, gas, oil, and electricity, explain 12% of carbon price variation (Batten et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

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The Dynamics of Carbon on Green Energy Equity Investment: Utilizing the Quantile-on-Quantile and Quantile Coherency Approach

Meng

2021

Preprint

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we analyze the dynamic correlation between the carbon price and the stock returns of green energy companies and calculate the hedging effect of the carbon price on stock returns in green energy sectors. The results show that the coefficients of the carbon price change with time and are vulnerable to extreme events like the COVID-19. The quantile-on-quantile (QQ) model results reveal a dynamic effect from the carbon price to the stock returns of green energy sectors. The quantile coherency (QC) approach results show that investors can benefit more in the short term with high-frequency trading to hedge between carbon trading and the green energy stock market. What’s more, the hedging effects are heterogenetic and investors should adjust their hedging strategies in different quantiles.

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Section: Literature Reviewmentioning

confidence: 91%

Section: Literature Reviewmentioning

confidence: 99%

The Dynamics of Carbon on Green Energy Equity Investment: Utilizing the Quantile-on-Quantile and Quantile Coherency Approach

Meng

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The relationship between the carbon price and stock markets has been widely discussed since carbon trading becomes the most cost-effective emission reduction tool to deal with climate change (see Moreno and Silva 2016 ; Fang et al 2018 ; Reboredo and Ugolini 2018 ; Mejdoub and Ghorbel 2018 ; Pereira and Pereira 2019 ; Krokida et al 2020 ; Wen et al 2020a , b ; Batten et al 2020 ; Duan et al 2021 ). Researches find that key energy prices, including coal, gas, oil, and electricity, explain 12% of carbon price variation (Batten et al 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

The dynamics of carbon on green energy equity investment: quantile-on-quantile and quantile coherency approaches

Meng

2021

Environ Sci Pollut Res

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We analyze the dynamic correlation between the carbon price and the stock returns of green energy companies and calculate the hedging effect of the carbon price on stock returns in green energy sectors. The results show that the coefficients of the carbon price change with time and are vulnerable to extreme events like the COVID-19. The quantile-on-quantile (QQ) model results reveal a dynamic effect from the carbon price to the stock returns of green energy sectors. The quantile coherency (QC) approach results show that investors can benefit more in the short term with high-frequency trading to hedge between carbon trading and the green energy stock market. What is more, the hedging effects are heterogenetic and investors should adjust their hedging strategies in different quantiles.

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“…Subsequently, scholars proposed MFDMA [39], MFADCCA [40], DMF-ADCCA [41], and MFDCCA-MODWT [42] according to different purposes and achieved certain results [43]. Nowadays, correlation analysis based on multifractal has now been widely used in energy [44,45], meteorology [46], and financial markets [47][48][49]. MFDCCA-MODWT performs better than MFDCCA when measuring long memory features of sequences of different lengths and different Hurst exponents because it does not need to select different polynomial orders to fit to eliminate trend items in the time series.…”

Section: Mfdcca-modwt (Multifractal Detrended Cross-correlation Analysis-maximal Overlap Discrete Wavelet Transformmentioning

confidence: 99%

A Causal and Correlation Analysis between China Energy Futures and China Energy‐Related Companies Stock Market

Liu

Zhang

2021

Complexity

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Taking the opportunity of China’s launch of Shanghai crude oil futures (INE), this study empirically examined the information transmission in this immature financial market, investigating this issue from a new perspective. To identify the impact of INE on the related stock market, we collected high-frequency trading data of oil futures and 22 stocks owned by listed companies in the upstream and downstream of China’s oil-related industry chains, constructed a causal chain through Directed Acyclic Graph, and used MFDCCA-MODWT to perform multifractal analysis on the chain. Research shows that INE does have a causal relationship with the stock market of the related industry chain, and there is a multifractal correlation between its transaction time series. Subsequently, the source of fractal correlation was analysed with shuffled and surrogated sequences. We conclude that long memory plays a leading role and is the main reason for multifractal features.

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Multifractal detrended cross-correlation analysis of carbon emission allowance and stock returns

Cited by 24 publications

References 63 publications

The Dynamics of Carbon on Green Energy Equity Investment: Utilizing the Quantile-on-Quantile and Quantile Coherency Approach

The Dynamics of Carbon on Green Energy Equity Investment: Utilizing the Quantile-on-Quantile and Quantile Coherency Approach

The dynamics of carbon on green energy equity investment: quantile-on-quantile and quantile coherency approaches

A Causal and Correlation Analysis between China Energy Futures and China Energy‐Related Companies Stock Market

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