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Volatility Spillovers and Causality of Carbon Emissions, Oil and Coal Spot and Futures for the EU and USA

Authors: Chia-Lin Chang; Michael McAleer; Guangdong Zuo;

Volatility Spillovers and Causality of Carbon Emissions, Oil and Coal Spot and Futures for the EU and USA

Abstract

Recent research shows that the efforts to limit climate change should focus on reducing the emissions of carbon dioxide over other greenhouse gases or air pollutants. Many countries are paying substantial attention to carbon emissions to improve air quality and public health. The largest source of carbon emissions from human activities in some countries in Europe and elsewhere is from burning fossil fuels for electricity, heat, and transportation. The prices of fuel and carbon emissions can influence each other. Owing to the importance of carbon emissions and their connection to fossil fuels, and the possibility of [1] Granger (1980) causality in spot and futures prices, returns, and volatility of carbon emissions, crude oil and coal have recently become very important research topics. For the USA, daily spot and futures prices are available for crude oil and coal, but there are no daily futures prices for carbon emissions. For the European Union (EU), there are no daily spot prices for coal or carbon emissions, but there are daily futures prices for crude oil, coal and carbon emissions. For this reason, daily prices will be used to analyse Granger causality and volatility spillovers in spot and futures prices of carbon emissions, crude oil, and coal. As the estimators are based on quasi-maximum likelihood estimators (QMLE) under the incorrect assumption of a normal distribution, we modify the likelihood ratio (LR) test to a quasi-likelihood ratio test (QLR) to test the multivariate conditional volatility Diagonal BEKK model, which estimates and tests volatility spillovers, and has valid regularity conditions and asymptotic properties, against the alternative Full BEKK model, which also estimates volatility spillovers, but has valid regularity conditions and asymptotic properties only under the null hypothesis of zero off-diagonal elements. Dynamic hedging strategies by using optimal hedge ratios are suggested to analyse market fluctuations in the spot and futures returns and volatility of carbon emissions, crude oil, and coal prices.

Countries
Netherlands, Spain
Keywords

Granger causality and volatility spillovers, Q42, carbon emissions; fossil fuels; crude oil; coal; low carbon targets; green energy; spot and futures prices; Granger causality; volatility spillovers; quasi likelihood ratio (QLR) test; diagonal BEKK; full BEKK; dynamic hedging, P28, 2391 Química Ambiental, quasi likelihood ratio (QLR) test, Renewable energy sources, Diagonal BEKK, 5302 Econometría, fossil fuels, GE1-350, C58, L71, dynamic hedging, Mineralogía, Carbon emissions, coal, Dynamic hedging, volatility spillovers, Environmental effects of industries and plants, Full BEKK, O13, Low carbon targets, Coal, Quasi likelihood ratio (QLR) test, Granger causality, Quasi likelihood ratio (QLR) test of Diagonal, Spot and futures prices, full BEKK, Dynamic hedging., Mineralogía (Química), TJ807-830, TD194-195, Crude oil, diagonal BEKK, Likelihood ration test, low carbon targets, Econometría (Economía), crude oil, Low carbon targets and green energy, Volatility spillovers, Fossil fuels, Green energy, Environmental sciences, green energy, spot and futures prices, Medio ambiente, ESE - E&MS, carbon emissions, Econometría, ddc: ddc:330

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
24
Top 10%
Top 10%
Top 10%
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