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

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: 31 December 2018

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
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 (2 papers)

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Research

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
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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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