Multifractal detrended cross-correlation analysis of the relation between price and volume in European carbon futures markets

Zou, Shaohui; Zhang, Tian

doi:10.1016/j.physa.2019.122310

Cited by 29 publications

(12 citation statements)

References 35 publications

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“…For instance, Reference [13] used multifractal analysis on an air pollution index time series, while References [14,15] used six air pollutants for numerical experiments in their multifractal analysis. Fine particulate matter pollutants have been of interest for several research endeavors [16][17][18], while [19], for example, used a multifractal analysis on a time series from the European carbon futures markets. Multifractal analysis is often used for temperature analysis like [20] did for air temperature, or [21] for global methane concentrations and remotely-sensed temperature anomalies.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

Kojić

Mitić

Dimovski³

et al. 2021

Mathematics

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One of the most challenging endeavors of contemporary research is to describe and analyze the dynamic behavior of time series arising from real-world systems. To address the need for analyzing long-range correlations and multifractal properties of multivariate time series, we generalize the multifractal detrended moving average algorithm (MFDMA) to the multivariate case and propose a multivariate MFDMA algorithm (MV-MFDMA). The validity and performance of the proposed algorithm are tested by conducting numerical simulations on synthetic multivariate monofractal and multifractal time series. The MV-MFDMA algorithm is then utilized to analyze raw, seasonally adjusted, and remainder components of five air pollutant time series. Results from all three cases reveal multifractal properties with persistent long-range correlations.

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Section: Introductionmentioning

confidence: 99%

Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

Kojić

Mitić

Dimovski³

et al. 2021

Mathematics

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“…Since the development of multifractal detrended crosscorrelation analysis (MF-DCCA, or called MF-DXA) by Zhou (2008) reveal the multifractal features of two cross-correlated signals, DCCA and MF-DCCA have been widely discussed and used (Aslam et al 2022;Jafari et al 2007;Jiang and Zhou 2011;Kristoufek 2011;Zou and Zhang 2020).…”

Section: Multifractal Detrended Analysis Methodsmentioning

confidence: 99%

“…The conventional econometric methodologies that are frequently employed in literature on carbon prices mostly examine the connection between these markets in a linear perspective. Multifractality is central to the science of complexity and it finds applications in almost all essential areas of scientific activity, including physics (Muzy et al 2008;Subramaniam et al 2008), chemistry Meakin 1988, Udovichenko andStrizhak 2002), biology (Makowiec et al 2009;Rosas et al 2002), hydrology (Telesca et al 2005), environment (Farjah 2019;Sipra et al 2021), linguistics (Drożdż et al 2016), Physiology (Nagy et al 2017), psychology (Kelty-Stephen 2017Stephen et al 2012), behavioral sciences (Ihlen and Vereijken 2013), agriculture (Kim et al 2011, McKenzie andHolt 2002), economics (Drożdż et al 2010), finance (Delbianco et al 2016, Zou andZhang 2020), and even music (Jafari et al 2007). Additionally, using conventional linear analysis techniques to study the financial markets is not reliable and robust.…”

Section: Literature Reviewmentioning

confidence: 99%

On the inner dynamics between Fossil fuels and the carbon market: a combination of seasonal-trend decomposition and multifractal cross-correlation analysis

Aslam

Ali

Amjad

et al. 2022

Environ Sci Pollut Res

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This study examines the inner dynamics of multifractality between the carbon market (EU ETS) and four major fossil fuel energy markets: Brent Crude Oil (BRN), Richards Bay Coal (RBC), UK Natural Gas (NGH2), and FTSE350 electricity index (FTSE350) from January 04, 2016, to March 04, 2022. First, we decompose the daily price changes by applying seasonal and trend decomposition using loess (STL). Then, we examine the inner dynamics of multifractality and cross-correlation by employing multifractal detrended fluctuation analysis (MFDFA) and multifractal detrended cross-correlation analysis (MFDCCA) using the remaining components of the return series. Our findings reveal that all series and the cross-correlations of carbon and fossil fuels markets have multifractal characteristics. We find crude oil to be the most efficient market (lowest multifractal), while coal is the least efficient (highest multifractal). Only coal shows persistent, whereas the other markets exhibit antipersistent behavior. Interestingly, the coal and EU ETS pair demonstrates a higher degree of multifractal patterns. In contrast, the pair of natural gas and EU ETS exhibits the lowest multifractal characteristics among the energy markets. Only the crude oil market shows persistent cross-correlations in the multifractality. These findings have important academic and managerial implications for investors and policymakers.

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“…The first subgroup mainly focused on the relationship between the carbon futures price, trading volume and volatility. Some analyzed the volume and volatility relationship between two main carbon futures products in the EU ETS (Kim, 2014;Rannou and Barneto, 2016), the basic results showed that there existed the lead-lag relationship between them, but it varied at different frequencies (Zou and Zhang, 2020). Apart from the EU ETS, the volatility dynamics for Carbon Financial Instruments traded in the Chicago Climate Exchange were noticed (Sabbaghi and Sabbaghi, 2011), and thin trading effects was found.…”

Section: Introductionmentioning

confidence: 99%

Decarbonization roadmap for China’s energy sector in the context of carbon futures trading: An energy-environment-economy assessment perspective

Li¹,

Li²,

Wei³

et al. 2023

Front. Energy Res.

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By virtue of the hedging and price discovery function, carbon futures trading may help carbon market function more effectively. Is it necessary to establish carbon futures trading in China? The authorities have endorsed the idea since 2015, however, the scheme has not yet started; no antecedent pre-assessment quantitative research has been carried out. Therefore this study that attempts to fill this gap in the literature, could be of real significance. Through deriving a potential decarbonization roadmap, this study tries to give some clues pertaining to the converted mitigation strategy imposed by carbon futures trading in China. A model chain has been proposed, which is composed of the Optimal Production Decision-making Model for Producers, Life Cycle Impact Assessment, Monetization, and Genetic Algorithm based optimization, to quantify the environmental benefits (including improvements to human health, ecosystem damage and increased temperature induced GDP losses) of the assumed mitigation trajectories. By setting the maximization of environmental benefits as the objective, the optimal decarbonization roadmap with carbon futures trading is derived. Results show that the optimal emission reductions for power enterprises (covered by the carbon market) for the next 10 years (2021–2030) are around 3.27 billion tonnes CO2e. If 36% of this amount is assigned to previously discussed mitigation trajectories, it is found that 106.98 GW ultra-super critical units, 160.85 GW mono-Si PV facilities and 167.26 GW doubly-fed induction generator wind capacity should be installed. Overall environmental benefits are 4.6 trillion CNY2018, over 5% of China’s 2018 GDP. Results demonstrate the optimal emission reductions and potential decarbonization roadmap for China’s power enterprises (those covered by the carbon market) under the context of carbon futures trading, which can be an important reference for the authorities and therefore encourage the establishment of the scheme.

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Multifractal detrended cross-correlation analysis of the relation between price and volume in European carbon futures markets

Cited by 29 publications

References 35 publications

Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants

On the inner dynamics between Fossil fuels and the carbon market: a combination of seasonal-trend decomposition and multifractal cross-correlation analysis

Decarbonization roadmap for China’s energy sector in the context of carbon futures trading: An energy-environment-economy assessment perspective

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