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Special Issue "Multivariate Modelling of Fossil Fuel and Carbon Emission Prices"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Energy and Environment".

Deadline for manuscript submissions: 31 July 2019

Special Issue Editors

Guest Editor
Prof. Chia-Lin Chang

Department of Applied Economics, College of Agriculture and Natural Resources, National Chung Hsing University, Taiwan
Website | E-Mail
Interests: applied econometrics, financial econometrics, financial economics, finance, energy economics and finance, time series analysis, forecasting, empirical industrial organisation, risk management
Guest Editor
Prof. Dr. Michael McAleer

University Chair Professor, Department of Finance, College of Management, Asia University, Wufeng 41354, Taiwan
Website | E-Mail
Phone: +04-2332-3456 (ext. 1837)
Interests: theoretical and applied econometrics; financial econometrics; financial economics; finance, theoretical and applied statistics; time series analysis; forecasting; risk management; energy economics and finance; applied mathematics

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to provide statistically-valid prices, financial returns, and volatility of fossil fuels, simultaneously with carbon emission prices; include fossil fuel and carbon emissions as financial commodities in financial portfolios; use fossil fuel and carbon emissions in optimal hedging (or insurance) of financial portfolios; evaluate the impacts on the environment and sustainability of pricing fossil fuel and carbon emissions; and evaluate the effects on health and medical care costs of pricing fossil fuel and carbon emissions.

The scope of this Special Issue is to analyze the following topics: 

(i) international pricing of fossil energy sources, namely oil, coal, gas and nuclear;
(ii) domestic pricing of fossil energy sources, namely oil, coal, gas and nuclear;
(iii) modelling international and domestic fossil fuel emission prices;
(iv) modelling international and domestic carbon emission prices;
(v) estimation multivariate financial returns and volatility;
(vi) use of alternative multivariate volatility models, including conditional, stochastic and realized volatility models;
(vii) inclusion of fossil fuel and carbon emissions as financial commodities in financial portfolios;
(viii) use of fossil fuel and carbon emissions in optimal hedging (or insurance) of financial portfolios;
(ix) impacts on the environment and sustainability of pricing fossil fuel and carbon emissions;
(x) impacts on health and medical care costs of pricing fossil fuel and carbon emissions.

Prof. Chia-Lin Chang
Prof. Michael McAleer
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

Open AccessArticle Moving Average Market Timing in European Energy Markets: Production Versus Emissions
Energies 2018, 11(12), 3281; https://doi.org/10.3390/en11123281
Received: 12 November 2018 / Revised: 21 November 2018 / Accepted: 22 November 2018 / Published: 25 November 2018
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Abstract
This paper searches for stochastic trends and returns predictability in key energy asset markets in Europe over the last decade. The financial assets include Intercontinental Exchange Futures Europe (ICE-ECX) carbon emission allowances (the main driver of interest), European Energy Exchange (EEX) Coal ARA
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This paper searches for stochastic trends and returns predictability in key energy asset markets in Europe over the last decade. The financial assets include Intercontinental Exchange Futures Europe (ICE-ECX) carbon emission allowances (the main driver of interest), European Energy Exchange (EEX) Coal ARA futures and ICE Brent oil futures (reflecting the two largest energy sources in Europe), Stoxx600 Europe Oil and Gas Index (the main energy stock index in Europe), EEX Power Futures (representing electricity), and Stoxx600 Europe Renewable Energy index (representing the sunrise energy industry). This paper finds that the Moving Average (MA) technique beats random timing for carbon emission allowances, coal, and renewable energy. In these asset markets, there seems to be significant returns predictability of stochastic trends in prices. The results are mixed for Brent oil, and there are no predictable trends for the Oil and Gas index. Stochastic trends are also missing in the electricity market as there is an ARFIMA-FIGARCH process in the day-ahead power prices. The empirical results are interesting for several reasons. We identified the data generating process in EU electricity prices as fractionally integrated (0.5), with a fractionally integrated Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) process in the residual. This is a novel finding. The order of integration of order 0.5 implies that the process is not stationary but less non-stationary than the non-stationary I(1) process, and that the process has long memory. This is probably because electricity cannot be stored. Returns predictability with MA rules requires stochastic trends in price series, indicating that the asset prices should obey the I(1) process, that is, to facilitate long run returns predictability. However, all the other price series tested in the paper are I(1)-processes, so that their returns series are stationary. The empirical results are important because they give a simple answer to the following question: When are MA rules useful? The answer is that, if significant stochastic trends develop in prices, long run returns are predictable, and market timing performs better than does random timing. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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Open AccessArticle Measuring the Time-Frequency Dynamics of Return and Volatility Connectedness in Global Crude Oil Markets
Energies 2018, 11(11), 2893; https://doi.org/10.3390/en11112893
Received: 10 September 2018 / Revised: 16 October 2018 / Accepted: 17 October 2018 / Published: 24 October 2018
Cited by 1 | PDF Full-text (3533 KB) | HTML Full-text | XML Full-text
Abstract
This study analyzes return and volatility spillovers across global crude oil markets for 1 January 1991 to 27 April 2018, using an empirical technique from the time and frequency domains, and makes four key contributions. First, the spillover tables reveal that the West
[...] Read more.
This study analyzes return and volatility spillovers across global crude oil markets for 1 January 1991 to 27 April 2018, using an empirical technique from the time and frequency domains, and makes four key contributions. First, the spillover tables reveal that the West Texas Intermediate (WTI) futures market, which is a common indicator of crude oil indices, contributes the least to both return and volatility spillovers. Second, the results also show that the long-term factor contributes the most to returns spillover, while the short-term factor contributes the most in terms of volatility. Third, the rolling analyses show that the time-variate connectedness in terms of returns tends to be strong, but there was no noticeable change from 1991 to April 2018 in terms of volatility. Finally, the major events between 1991 and April 2018, namely the Asian currency crisis (1997–1998) and the global financial crisis (2007–2008), caused a rise in the total connectedness of returns and volatility. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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Open AccessArticle The Relationship Between Prices of Various Metals, Oil and Scarcity
Energies 2018, 11(9), 2392; https://doi.org/10.3390/en11092392
Received: 13 August 2018 / Revised: 27 August 2018 / Accepted: 3 September 2018 / Published: 11 September 2018
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Abstract
No consensus has been reached on the problem of solving resource depletion. A recognition of the fact that resources are not endless and the Earth is a finite globe reinforces the idea that the vision of continuous economic growth is not sustainable over
[...] Read more.
No consensus has been reached on the problem of solving resource depletion. A recognition of the fact that resources are not endless and the Earth is a finite globe reinforces the idea that the vision of continuous economic growth is not sustainable over time. The aim of this paper is to examine the efficacy of real prices as an indicator of metals and oil in consideration of growth tendencies in the Consumer Price Indexes. In addition, enhancing the current literature on commodity price interrelationships, the main contribution of this study is the substitution of different proxies in order to justify the effect of scarcity and crude oil changes on the examined metal group prices. In order to demonstrate the usefulness of scarcity as an indicator of real price deviations, the study has been conducted involving various non-renewable metals, i.e., copper, molybdenum, zinc, gold and platinum group metals. The real price indices and metal prices of the US market are constructed between 1913 and 2015. Moreover, additional econometric analyses are also carried out to discover whether prices of various metals associate with oil prices and scarcity, as the proxy of reserves-to-production ratio. The linear regression results seem to suggest that the effects of the R/P ratios are negatively correlated with each of the examined precious (gold, PGMs), mass consumable (copper, zinc) and doping agent (molybdenum) metals from 1991 to 2015. An increase in oil-prices is positively associated with the price levels of each non-renewable resource in the short-run. The findings of multivariate co-integration and Granger causality tests also suggest that pairwise and direct relationships among these variables seem to arise in the long-run. These findings indicate essential questions that must be addressed by future generations in order to appropriately solve scarcity problems. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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Open AccessArticle Theoretical and Empirical Differences between Diagonal and Full BEKK for Risk Management
Energies 2018, 11(7), 1627; https://doi.org/10.3390/en11071627
Received: 1 March 2018 / Revised: 26 May 2018 / Accepted: 31 May 2018 / Published: 22 June 2018
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Abstract
The purpose of the paper is to explore the relative biases in the estimation of the Full BEKK model as compared with the Diagonal BEKK model, which is used as a theoretical and empirical benchmark. Chang and McAleer et al., 2017 show that
[...] Read more.
The purpose of the paper is to explore the relative biases in the estimation of the Full BEKK model as compared with the Diagonal BEKK model, which is used as a theoretical and empirical benchmark. Chang and McAleer et al., 2017 show that univariate GARCH is not a special case of multivariate GARCH, specifically, the Full BEKK model, and demonstrate that Full BEKK, which, in practice, is estimated almost exclusively, has no underlying stochastic process, regularity conditions, or asymptotic properties. Diagonal BEKK (DBEKK) does not suffer from these limitations, and hence provides a suitable benchmark. We use simulated financial returns series to contrast estimates of the conditional variances and covariances from DBEKK and BEKK. The results of non-parametric tests suggest evidence of considerable bias in the Full BEKK estimates. The results of quantile regression analysis show there is a systematic relationship between the two sets of estimates as we move across the quantiles. Estimates of conditional variances from Full BEKK, relative to those from DBEKK are relatively lower in the left tail and higher in the right tail. The BEKK model is a commonly applied multivariate volatility model frequently used in modelling and forecasting volatilities in financial applications. Our results suggest that it is subject to considerable bias and this should be considered by potential users. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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Open AccessArticle Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice
Energies 2018, 11(6), 1595; https://doi.org/10.3390/en11061595
Received: 24 May 2018 / Revised: 11 June 2018 / Accepted: 12 June 2018 / Published: 19 June 2018
Cited by 1 | PDF Full-text (285 KB) | HTML Full-text | XML Full-text
Abstract
Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or
[...] Read more.
Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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