Multivariate dependence and spillover effects across energy commodities and diversification potentials of carbon assets

Uddin, Gazi Salah; Hernandez, Jose Areola; Shahzad, Syed Jawad Hussain; Hedström, Axel

doi:10.1016/j.eneco.2018.01.035

Cited by 98 publications

(34 citation statements)

References 34 publications

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“…From a distribution perspective, the carbon prices and returns are not normally distributed, but are characterised by "heavy-tailed" and "high-kurtosis" properties. e result of Jarque-Bera test suggests that the "normally distributed" hypothesis can be rejected at any significance level, which is consistent with most prior studies [42][43][44][45].…”

Section: Datasupporting

confidence: 87%

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO₂ Emission Trading Price Fluctuation

Chai

Chen

et al. 2020

Mathematical Problems in Engineering

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Predicting CO2 emission prices is an important and challenging task for policy makers and market participants, as carbon prices follow a stochastic process of complex time series with nonstationary and nonlinear characteristics. Existing literature has focused on highly precise point forecasting, but it cannot correctly solve the uncertainties related to carbon price datasets in most cases. This study aims to develop a hybrid forecasting model to estimate in advance the maximum or minimum loss in the stochastic process of CO2 emission trading price fluctuation. This model can granulate raw data into fuzzy-information granular components with minimum (Low), average (R), and maximum (Up) values as changing space-description parameters. Furthermore, it can forecast carbon prices’ changing space with Low, R, and Up as inputs to support a vector regression. This method’s feasibility and effectiveness is examined using empirical experiments on European Union allowances’ spot and futures prices under the European Union’s Emissions Trading Scheme. The proposed FIG-SVM model exhibits fewer errors and superior performance than ARIMA, ARFIMA, and Markov-switching methods. This study provides several important implications for investors and risk managers involved in trading carbon financial products.

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Section: Datasupporting

confidence: 87%

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO₂ Emission Trading Price Fluctuation

Chai

Chen

et al. 2020

Mathematical Problems in Engineering

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“…Ji et al [19] revealed that the price of Brent oil is an important element influencing the EUA price fluctuations and risks, and there is feedback from the carbon market to other energy-related markets. Uddin et al [20] found evidence that carbon assets provide diversification benefits for energy asset investments. Chevallier et al [21] investigated the interdependence structure between the EUA and major energy prices and found that carbon prices co-move weakly with energy prices, and their link to oil and gas prices is negative.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Spillover and Hedging among Carbon, Biofuel and Oil

Lee

Yoon

2020

Energies

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In recent years, there has been growing interest in the market interactions between carbon (or clean/renewable energy) and traditional fossil energy such as coal and oil, but few studies have discussed their dynamic volatility spillover and time-varying correlation. To investigate these issues, we used the weekly data of the European Union carbon emission allowance (EUA) futures, biofuel and Brent oil prices from 25 October 2009 to 5 July 2020. We employed the vector autoregressive-generalized autoregressive conditional heteroscedasticity (VAR-GARCH) model with the Baba, Engle, Kraft and Krone (BEKK) specification. Our main findings are summarized as follows: First, we identified the sudden changes and the volatility persistence in the EUA, biofuel, and Brent oil markets, and also confirmed that the volatility of the markets has changed significantly over time. Second, we found a weak volatility spillover effect among the three markets, and a strong spillover effect between the EUA and Brent oil markets. In particular, the effect of volatility spillover from the Brent oil market to the EUA market was the strongest than the other cases. Lastly, in financial market, by holding the EUA and energy sources together as assets, investors can effectively hedge their investment risk. The possibility of hedging is more pronounced between the EUA and biofuel markets.

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“…The co-movement and asymmetric dependence relationship between China's carbon emission allowances and gasoline market are investigated [32,33]. Uddin et al, [34] used c-vine conditional VaR and c-vine copula to analyze the dependence structure and spillover effects across China's carbon market and energy markets. Zhang et al, [35] measure the dependence between EU ETS and international financial markets.…”

Section: Introductionmentioning

confidence: 99%

The tail dependence of the carbon markets: The implication of portfolio management

Zhang

2020

PLoS ONE

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Emission trading scheme (ETS), the most popular market-based instrument, is widely used to solve carbon emission problems in the world. With the development of carbon market, carbon asset has been a popular financial product to invest and the risk management becomes important for government, regulated enterprises and other investors. As carbon prices have tail characteristics, this paper explores the extremal risks between carbon markets in US, Europe and China using tail dependence correlation coefficients. The empirical analysis demonstrates the tail dependence structure between carbon markets in US. and Europe is the same sign, which indicates that it is unwise to hold these two carbon assets as a portfolio. Moreover, the co-movements between European Union Emission Trade Scheme (EU ETS) and China's carbon markets are partly significant, and the operation mechanisms in China should be improved. In addition, the tail dependence test among the carbon pilots in China shows diversity. Hubei carbon trading pilot, located in central China, has extremal dependence with all other selected pilots for its regulatory program operation. The findings give insight to the carbon market regulars to improve the operation mechanism and are also useful for the investors to manage their portfolios, policymakers to make practically applicable regulations, and relevant organizations to develop procedures.

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Multivariate dependence and spillover effects across energy commodities and diversification potentials of carbon assets

Cited by 98 publications

References 34 publications

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO₂ Emission Trading Price Fluctuation

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO₂ Emission Trading Price Fluctuation

Dynamic Spillover and Hedging among Carbon, Biofuel and Oil

The tail dependence of the carbon markets: The implication of portfolio management

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Multivariate dependence and spillover effects across energy commodities and diversification potentials of carbon assets

Cited by 98 publications

References 34 publications

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO2 Emission Trading Price Fluctuation

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO2 Emission Trading Price Fluctuation

Dynamic Spillover and Hedging among Carbon, Biofuel and Oil

The tail dependence of the carbon markets: The implication of portfolio management

Contact Info

Product

Resources

About

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO₂ Emission Trading Price Fluctuation

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO₂ Emission Trading Price Fluctuation