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

Deadline for manuscript submissions: closed (20 May 2021) | Viewed by 17040

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Special Issue Editors

Prof. Dr. Sang Hoon Kang

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Guest Editor

1. Department of Business Adminstration, Pusan National Univerisity, Busan 46241, Republic of Korea
2. UniSA Business School, University of South Australia, Adelaide 5001, Australia
Interests: modelling and forecasting times series analysis; energy finance; volatility spillover; financial market network analysis; risk management

Prof. Cheoljun Eom

E-Mail Website
Guest Editor

School of Business, Pusan National University, Busan 46241, Korea
Interests: asset pricing; portfolio optimization; financial econometrics; empirical finance; econophysics

Special Issue Information

Dear Colleagues,

I would like to invite you to submit a paper for a Special Issue on “Time Series Analysis of Energy Economics” in Energies (impact factor 2.707). This Special Issue focuses on the modelling and forecasting energy time series, with particular emphasis on energy-related data (energy, renewable energy, electricity, ethanol fuel, green bond and futures, etc). The analysis of energy time series has attracted a great deal of attention from acamedic scholars and market participants. Modeling and forecasting energy time series are important inputs into macroeconometric models, risk spillover models, and portfolio selection models. In this Special Issue, we intend to invite authors to submit their original research on exploring the issues and applications of energy time series.

Topics of primary interest include but are not limited to the following:

Modelling and forecasting energy time series;
Connectedness network across energy markets;
Volatility spillover between energy markets and equity markets;
Multivariate GARCH-type models;
Wavelet coherence analysis;
Cointegration, Granger causality, and long-run estimation;
Quantile regression;
High-frequency data analysis;
Time-varying Copula-based CoVaR analysis;
Efficiency test of energy time series.

The Special Issue welcomes quantitative studies, as well as empirical contributions.

Prof. Dr. Sang Hoon Kang
Prof. Cheoljun Eom
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 submissions that pass pre-check are 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 semimonthly 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 2600 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.

Keywords

Energy
Long memory
Structural break
Market spillover
Connectedness network
Multivariate GARCH
Comovement
Market risk
Renewable energy
Ethanol fuel
Electricity
Green bond
High-frequency data

Published Papers (7 papers)

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Research

18 pages, 8394 KiB

Open AccessArticle

On the Relationship between Oil and Exchange Rates of Oil-Exporting and Oil-Importing Countries: From the Great Recession Period to the COVID-19 Era

by Vincenzo Candila, Denis Maximov, Alexey Mikhaylov, Nikita Moiseev, Tomonobu Senjyu and Nicole Tryndina

Energies 2021, 14(23), 8046; https://doi.org/10.3390/en14238046 - 1 Dec 2021

Cited by 60 | Viewed by 2864

Abstract

This paper is dedicated to studying and modeling the interdependence between the oil returns and exchange-rate movements of oil-exporting and oil-importing countries. Globally, twelve countries/regions are investigated, representing more than 60% and 67% of all oil exports and imports. The sample period encompasses economic and natural events like the Great Recession period (2007–2009) and the COVID-19 pandemic. We use the dynamic conditional correlation mixed-data sampling (DCC-MIDAS) model, with the aim of investigating the interdependencies expressed by the long-run correlation, which is a smoother (but always daily observed) version of the (daily) time-varying correlation. Focusing on the advent of the COVID-19 pandemic in 2020, the long-run correlations of the oil-exporting countries (Saudia Arabia, Russia, Iraq, Canada, United States, United Arab Emirates, and Nigeria) and (lagged) WTI crude oil returns strongly increase. For a subset of these countries (that is, Saudia Arabia, Iraq, United States, United Arab Emirates, and Nigeria), the (lagged) correlations turn out to be positive, while for Canada and Russia they remain negative as before the advent of the pandemic. In addition, the oil-importing countries and regions under investigation (Europe, China, India, Japan, and South Korea) experience a similar pattern: before the COVID-19 pandemic, the (lagged) correlations were negative for China, India, and South Korea. After the COVID-19 pandemic, the correlations of these latter countries increased. Full article

(This article belongs to the Special Issue Time Series Analysis of Energy Economics)

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24 pages, 2592 KiB

Open AccessArticle

On the Predictability of China Macro Indicator with Carbon Emissions Trading

by Qian Chen, Xiang Gao, Shan Xie, Li Sun, Shuairu Tian and Shigeyuki Hamori

Energies 2021, 14(5), 1271; https://doi.org/10.3390/en14051271 - 25 Feb 2021

Cited by 4 | Viewed by 2111

Abstract

Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers’ index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers. Full article

(This article belongs to the Special Issue Time Series Analysis of Energy Economics)

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25 pages, 712 KiB

Open AccessArticle

Optimizing the Cooperated “Multi-Countries” Biodiesel Production and Consumption in Sub-Saharan Africa

by Tito Francisco Ianda, Emerson Andrade Sales, Ademar Nogueira Nascimento and Antonio Domingos Padula

Energies 2020, 13(18), 4717; https://doi.org/10.3390/en13184717 - 10 Sep 2020

Cited by 10 | Viewed by 2084

Abstract

Sub-Saharan African countries present chronic energy shortages and heavy reliance on oil imports for diesel. The small demand and high production costs in some countries have compromised the economic feasibility of the biodiesel industry in the region. Therefore, to overcome these limitations a model of “multi-countries” cooperated production and consumption of biodiesel was proposed for a group of seven neighboring countries. The model explored linear programming and simulations to the problem of minimizing biodiesel production costs considering different types of production and demand restrictions. The data processing was realized using the Solver and Linear Interactive Discrete Optimizer software (LINDO). The simulations and scenarios revealed that palm oil is the crop that minimize the production costs (US$0.82/L) and that, although jatropha was classified in the second place (US$1.05/L), it is the crop with the biggest job creation potential (5.0 times that of the palm oil seeds). These results reveal the presence of a trade-off in the strategy and the choice between different oilseeds: (a) to produce biodiesel from the crop with minimal costs (palm oil) or (b) to choose the one that has the biggest potential for job creation (jatropha). Considering the diesel price between US$0.60 and US$1.14/L at service stations in the region in 2016, both the biodiesel from palm oil and jatropha will need subsidies and fiscal incentives (tax reductions) to be competitive in the fuel market (diesel). The volume of biodiesel to supply the B10 demand in 2031 has the potential to reduce US$ 1.98 billion/year of the expenses on oil imports. It is worth observing that this decision-support model adds the “multi-countries” cooperation perspective as a contribution to the methodological and political approaches about biofuels production and consumption and can be exploited as a starting point for the formulation of policies, strategies, and investment decisions for the establishment of biodiesel production programs. Full article

(This article belongs to the Special Issue Time Series Analysis of Energy Economics)

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16 pages, 625 KiB

Open AccessArticle

Effect of Oil Prices on Exchange Rate Movements in Korea and Japan Using Markov Regime-Switching Models

by Suyi Kim, So-Yeun Kim and Kyungmee Choi

Energies 2020, 13(17), 4402; https://doi.org/10.3390/en13174402 - 26 Aug 2020

Cited by 5 | Viewed by 1805

Abstract

We examined the effects of oil prices along with fundamental economic variables on exchange rate movements in the Korean and Japanese foreign exchange markets, using two-regime Markov Regime Switching Models (MRSMs) over the period from January 1991 to March 2019. We selected the best MRSMs explaining their exchange rate movements using the Maximum Log-Likelihood and Akaike Information Criteria, and analyze effects of oil prices on their exchange rates based on the selected best MRSMs. We consider two regimes, regime 1 with high-volatility and regime 2 with low-volatility. In Korea, two apparent regimes are observed, and unstable regime 1 consists of two distinct prolonged periods, the 1997 Asian Financial Crisis and the 2008 Global Financial Crisis. Meanwhile in Japan, no evident prolonged regimes are observed. Rather, the two regimes occasionally alternate. Oil prices influence exchange rate movements in regime 2 with low-volatility in Korea, while they do not influence exchange rate movements in either regimes in Japan. The Japanese foreign exchange market is more resistant to external oil price shocks because the Japanese industry and economy has less dependence on oil than Korea. Full article

(This article belongs to the Special Issue Time Series Analysis of Energy Economics)

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19 pages, 1168 KiB

Open AccessArticle

Dynamic Spillover and Hedging among Carbon, Biofuel and Oil

by Yeonjeong Lee and Seong-Min Yoon

Energies 2020, 13(17), 4382; https://doi.org/10.3390/en13174382 - 25 Aug 2020

Cited by 17 | Viewed by 2143

Abstract

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. Full article

(This article belongs to the Special Issue Time Series Analysis of Energy Economics)

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22 pages, 302 KiB

Open AccessArticle

The Effects of Oil and Gas Risk Factors on Malaysian Oil and Gas Stock Returns: Do They Vary?

by Mohammad Enamul Hoque, Soo-Wah Low and Mohd Azlan Shah Zaidi

Energies 2020, 13(15), 3901; https://doi.org/10.3390/en13153901 - 31 Jul 2020

Cited by 8 | Viewed by 2098

Abstract

This study explores Malaysian oil and gas stocks’ exposure to oil and gas risk factors, paying special attention to subindustry classification, stock size, book-to-market value, and volatility state. The study employs firm-level weekly frequency data of oil and gas firms and several multi-asset pricing models within a GARCH (1,1)-X and Markov-switching framework. The empirical findings reveal that oil price, gas price, and exchange rate exhibit positive effects on the stock returns of all oil and gas sub-industries, but they exhibit negative effects on gas utilities sub-industry stock returns. The empirical findings also reveal that the extent of this effect varies across sub-industry, stock size, book-to-market value, and volatility states. Thus, the findings suggest the existence of asymmetric, heterogeneous, and non-linear exposures. Full article

(This article belongs to the Special Issue Time Series Analysis of Energy Economics)

14 pages, 2608 KiB

Open AccessArticle

Analysis of the Informational Efficiency of the EU Carbon Emission Trading Market: Asymmetric MF-DFA Approach

by Yun-Jung Lee, Neung-Woo Kim, Ki-Hong Choi and Seong-Min Yoon

Energies 2020, 13(9), 2171; https://doi.org/10.3390/en13092171 - 1 May 2020

Cited by 18 | Viewed by 2957

Abstract

This study explores the degree and change of informational efficiency of the European Union (EU) carbon emission trading market using an asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method, which allows asymmetry. For this purpose, we analysed the daily price series of the European Emissions Market, which is operated according to the European Union Emissions Trading Scheme. This carbon market is the most active and has the largest trading volume. The data covers the period (from 4 August 2005 to 31 December 2019). The main results are summarised as follows. First, there is a multifractal feature in the price return movements of the EU carbon trading market, which behaves differently in the upward and downward periods of the market. Second, the informational efficiency of the carbon emission market has changed over time, with Phase I having the lowest informational efficiency and Phase III having the highest informational efficiency. These results indicate that informational efficiency has increased as the carbon emission market matures. Third, from the result of the market deficiency measure (MDM), Phase I showed the lowest market efficiency, whereas Phase III showed the highest efficiency. During Phase III, the MDM values of the upward period were higher than that of the downward period, implying higher market inefficiency during the upward period. Full article

(This article belongs to the Special Issue Time Series Analysis of Energy Economics)

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