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Multivariate Modelling of Fossil Fuel and Carbon Emission Prices

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

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 42622

Special Issue Editors


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Guest Editor
1. Department of Applied Economics and Department of Finance, National Chung Hsing University, Taichung 402, Taiwan
2. Department of Finance, College of Management, Asia University, Taichung 41354, Taiwan
Interests: economics; econometrics; financial econometrics; statistics; quantitative finance; risk and financial management; energy economics and finance; time series analysis; forecasting; technology and innovation; industrial organization; health and medical economics; tourism research and management
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E-Mail Website
Guest Editor
Department of Finance, College of Management, Asia University, Taichung 41354, Taiwan
Interests: economics; financial econometrics; quantitative finance; risk and financial management; econometrics; statistics; time series analysis; energy economics and finance; sustainability; environmental modelling; carbon emissions; climate change econometrics; forecasting; informatics; data mining
Special Issues, Collections and Topics in MDPI journals

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

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Published Papers (10 papers)

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Research

11 pages, 317 KiB  
Article
A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index
by David E. Allen and Michael McAleer
Energies 2020, 13(15), 4011; https://doi.org/10.3390/en13154011 - 4 Aug 2020
Cited by 13 | Viewed by 4322
Abstract
The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag nonlinear autoregressive distributed lag (NARDL) framework. The attraction of NARDL is that it represents the simplest method available of modelling [...] Read more.
The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag nonlinear autoregressive distributed lag (NARDL) framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthly West Texas Intermediate (WTI) crude oil series from Federal Reserve Bank of St Louis (FRED), commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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21 pages, 1068 KiB  
Article
Modeling Latent Carbon Emission Prices for Japan: Theory and Practice
by Chia-Lin Chang and Michael McAleer
Energies 2019, 12(21), 4222; https://doi.org/10.3390/en12214222 - 5 Nov 2019
Cited by 7 | Viewed by 3040
Abstract
Climate change and global warming are significantly affected by carbon emissions that arise from the burning of fossil fuels, specifically coal, oil, and gas. Accurate prices are essential for the purposes of measuring, capturing, storing, and trading in carbon emissions at regional, national, [...] Read more.
Climate change and global warming are significantly affected by carbon emissions that arise from the burning of fossil fuels, specifically coal, oil, and gas. Accurate prices are essential for the purposes of measuring, capturing, storing, and trading in carbon emissions at regional, national, and international levels, especially as carbon emissions can be taxed appropriately when the price is known and widely accepted. This paper uses a novel Capital (K), Labor (L), Energy (E) and Materials (M) (or KLEM) production function approach to calculate the latent carbon emission prices, where carbon emission is the output and capital (K), labor (L), energy (E) (or electricity), and materials (M) are the inputs for the production process. The variables K, L, and M are essentially fixed on a daily or monthly basis, whereas E can be changed more frequently, such as daily or monthly, so that changes in carbon emissions depend on changes in E. If prices are assumed to depend on the average cost pricing, the prices of carbon emissions and energy may be approximated by an energy production model with a constant factor of proportionality, so that carbon emission prices are a function of energy prices. Using this novel modeling approach, this paper estimates the carbon emission prices for Japan using seasonally adjusted and unadjusted monthly data on the volumes of carbon emissions and energy, as well as energy prices, from December 2008 to April 2018. The econometric models show that, as sources of electricity, the logarithms of coal and oil, though not Liquefied Natural Gas (LNG,) are statistically significant in explaining the logarithm of carbon emissions, with oil being more significant than coal. The models generally displayed a high power in predicting the latent prices of carbon emissions. The usefulness of the empirical findings suggest that the methodology can also be applied for other countries where carbon emission prices are latent. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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17 pages, 519 KiB  
Article
The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures
by Manabu Asai, Rangan Gupta and Michael McAleer
Energies 2019, 12(17), 3379; https://doi.org/10.3390/en12173379 - 2 Sep 2019
Cited by 32 | Viewed by 3015
Abstract
This paper investigates the impact of jumps in forecasting co-volatility in the presence of leverage effects for daily crude oil and gold futures. We use a modified version of the jump-robust covariance estimator of Koike (2016), such that the estimated matrix is positive [...] Read more.
This paper investigates the impact of jumps in forecasting co-volatility in the presence of leverage effects for daily crude oil and gold futures. We use a modified version of the jump-robust covariance estimator of Koike (2016), such that the estimated matrix is positive definite. Using this approach, we can disentangle the estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation matrix. Empirical results show that more than 80% of the co-volatility of the two futures contains jump variations and that they have significant impacts on future co-volatility but that the impact is negligible in forecasting weekly and monthly horizons. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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24 pages, 1441 KiB  
Article
Modeling and Testing Volatility Spillovers in Oil and Financial Markets for the USA, the UK, and China
by Chia-Lin Chang, Michael McAleer and Jiarong Tian
Energies 2019, 12(8), 1475; https://doi.org/10.3390/en12081475 - 18 Apr 2019
Cited by 8 | Viewed by 3039
Abstract
The main purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility spillovers between international crude oil and associated financial markets. The prices of oil and its interactions with financial markets make it possible to determine the associated prices [...] Read more.
The main purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility spillovers between international crude oil and associated financial markets. The prices of oil and its interactions with financial markets make it possible to determine the associated prices of financial derivatives, such as carbon emission prices. The approach taken in the paper is different from others in the literature; the purpose is to examine the usefulness of modeling and testing volatility spillovers in the oil and financial markets. The paper investigates co-volatility spillovers (namely, the delayed effect of a returns shock in one physical or financial asset on the subsequent volatility or co-volatility in another physical or financial asset) between the oil and financial markets. The oil industry has four major regions, namely North Sea, the USA, Middle East, and South-East Asia. Associated with these regions are two major financial centers, namely the UK and the USA. For these reasons, the data to be used are the returns on alternative crude oil markets, returns on crude oil derivatives, specifically futures, and stock index returns in the UK and the USA. Given the importance of the Chinese financial and economic systems, the paper also analyzes Chinese financial markets, where the data are more recent. The USA and China are the world’s two largest economies and the UK is the world’s sixth largest economy (and second in the existing EU) behind the USA, China, Japan, Germany, and India. Moreover, the USA and the UK are associated with WTI and Brent oil, respectively. One of the purposes of the paper is to examine how China might be different from the USA and the UK, which seems to be borne out in the empirical analysis. Based on the conditional covariances to test the co-volatility spillovers, dynamic hedging strategies will be suggested to analyze market fluctuations in crude oil prices and associated financial markets. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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41 pages, 7065 KiB  
Article
Modeling the Relationship between Crude Oil and Agricultural Commodity Prices
by Duc Hong Vo, Tan Ngoc Vu, Anh The Vo and Michael McAleer
Energies 2019, 12(7), 1344; https://doi.org/10.3390/en12071344 - 8 Apr 2019
Cited by 44 | Viewed by 5785
Abstract
The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to [...] Read more.
The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities. Full article
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
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24 pages, 4621 KiB  
Article
Moving Average Market Timing in European Energy Markets: Production Versus Emissions
by Chia-Lin Chang, Jukka Ilomäki, Hannu Laurila and Michael McAleer
Energies 2018, 11(12), 3281; https://doi.org/10.3390/en11123281 - 25 Nov 2018
Cited by 10 | Viewed by 3533
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 [...] Read more.
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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18 pages, 3533 KiB  
Article
Measuring the Time-Frequency Dynamics of Return and Volatility Connectedness in Global Crude Oil Markets
by Yuki Toyoshima and Shigeyuki Hamori
Energies 2018, 11(11), 2893; https://doi.org/10.3390/en11112893 - 24 Oct 2018
Cited by 34 | Viewed by 3746
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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19 pages, 4819 KiB  
Article
The Relationship Between Prices of Various Metals, Oil and Scarcity
by József Popp, Judit Oláh, Mária Farkas Fekete, Zoltán Lakner and Domicián Máté
Energies 2018, 11(9), 2392; https://doi.org/10.3390/en11092392 - 11 Sep 2018
Cited by 41 | Viewed by 6116
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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19 pages, 1143 KiB  
Article
Theoretical and Empirical Differences between Diagonal and Full BEKK for Risk Management
by David E. Allen and Michael McAleer
Energies 2018, 11(7), 1627; https://doi.org/10.3390/en11071627 - 22 Jun 2018
Cited by 9 | Viewed by 4347
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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19 pages, 285 KiB  
Article
Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice
by Chia-Lin Chang, Yiying Li and Michael McAleer
Energies 2018, 11(6), 1595; https://doi.org/10.3390/en11061595 - 19 Jun 2018
Cited by 27 | Viewed by 4346
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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