Energy Market and Energy Finance

Topic Information

Dear Colleagues,

MDPI Topics calls for interdisciplinary research in the fields of energy markets and energy finance. The goal is to gather original (unpublished) scholarly contributions on the dynamics that currently shape the energy field. Among which, we may cite:

• The Russia–Ukraine war and the reshaping of world geopolitics: run on fossil fuels and cereals;
• Climate change and the reply from the industry: greenwashing vs. the Porter hypothesis;
• Cryptocurrency mining and pressure on the electricity grid;
• On the necessary ramping-up of renewable energy to save the world from global warming;
• Rebalancing portfolios from stocks to energy exchange-traded funds;
• Divestment from carbon-intensive to low-carbon industrial processes;
• Logistics of supplying Liquefied Natural Gas to the world;
• Etc.

Any contribution pertaining to this (non-exhaustive) list of topics will be carefully considered by the team of Editors at MDPI Topics in coordination with the participating journals: Economies, Energies, JFRM, Commodities, and FinTech.

We sincerely look forward to reading your piece of research.

With best regards,

Dr. Junhua Zhao
Prof. Dr. Julien Chevallier
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Commodities commodities	-	-	2022	26.8 Days	CHF 1000
Economies economies	2.1	4.0	2013	21.9 Days	CHF 1800
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600
Journal of Risk and Financial Management jrfm	-	4.5	2008	21 Days	CHF 1400
FinTech fintech	-	-	2022	20.4 Days	CHF 1000
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400

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Published Papers (8 papers)

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All Commodities Economies Energies JRFM FinTech Sustainability

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11 pages, 264 KiB  

Open AccessArticle

The Importance of Bitcoin and Commodities as Investment Diversifiers in OPEC and Non-OPEC Countries

by Angham Ben Brayek, Hanen Ben Ameur and Farea Mohammed Alharbi

Economies 2024, 12(12), 351; https://doi.org/10.3390/economies12120351 - 19 Dec 2024

Viewed by 1063

Abstract

The study aims to critically assess the safe-haven properties of Bitcoin and a diverse set of commodities in mitigating stock market risks during periods of extreme financial turbulence. Specifically, this research seeks to evaluate the effectiveness of these assets as hedging tools or [...] Read more.

The study aims to critically assess the safe-haven properties of Bitcoin and a diverse set of commodities in mitigating stock market risks during periods of extreme financial turbulence. Specifically, this research seeks to evaluate the effectiveness of these assets as hedging tools or diversifiers in the portfolios of both OPEC and non-OPEC countries, focusing on their behavior during the COVID-19 pandemic. We employ a wavelet coherence approach to analyze the dynamic relationships between the variables. Portfolio optimization is conducted using CVaR to assess the effectiveness of these assets as safe havens, hedges, or diversification tools in mitigating financial risks during periods of heightened market volatility. The diversification benefits of commodities and Bitcoin in OPEC and non-OPEC stock portfolios decrease over time as their co-movement with stock markets increases. During the COVID-19 period, BTC did not act as a safe haven. However, gold served as a hedge for non-OPEC countries. Using CVaR, we found that BTC provides stronger diversification benefits than commodities, followed by gold. We examine the safe-haven role of Bitcoin and various commodities, specifically within the context of both OPEC and non-OPEC countries. Our study offers a more comprehensive analysis of how BTC and commodities function as portfolio assets during financial stress, providing valuable insights for investors and policymakers. Full article

(This article belongs to the Topic Energy Market and Energy Finance)

18 pages, 11030 KiB  

Open AccessArticle

Analysis of EU’s Coupled Carbon and Electricity Market Development Based on Generative Pre-Trained Transformer Large Model and Implications in China

by Yao Li, Siyuan Ni, Xi Tang, Sizhe Xie and Peng Wang

Sustainability 2024, 16(23), 10747; https://doi.org/10.3390/su162310747 - 7 Dec 2024

Viewed by 945

Abstract

With increasing global attention on carbon emission control and sustainable development, the carbon market has become an important tool for promoting environmental sustainability and carbon emission regulation. As an important part of energy trading, the electricity market is closely related to the carbon [...] Read more.

With increasing global attention on carbon emission control and sustainable development, the carbon market has become an important tool for promoting environmental sustainability and carbon emission regulation. As an important part of energy trading, the electricity market is closely related to the carbon market and their effective coupling is critical for achieving sustainable energy transitions. Power generation can effectively link carbon prices and electricity prices. Through the promulgation of reasonable policies, we can promote the coupling of the carbon market and the electricity market, and then, through macro-control, promote the steady and joint development of the carbon and electricity markets, improve the construction of the carbon rights market, reduce greenhouse gas emissions, and create a sustainable future for humanity. Given the relative maturity of the EU carbon market and electricity market, this paper identifies key points of market volatility through the coupling coordination model analysis and uses the GPT large model analysis to analyze policy factors that have a greater impact on carbon market and electricity market transactions. The research results show that market-regulating policies and expectation management policies have the greatest impact on the degree of coupling between the carbon and electricity markets. The impact of secondary policies such as international cooperation policies and structural adjustment policies is relatively insignificant. Although the development of China’s carbon market faces many challenges, reasonable policy interventions are expected to achieve significant results in the domestic carbon market. Finally, this paper also draws on analogy analysis to draw corresponding implications for China from the EU’s carbon–electricity coupling policy promulgation. Full article

(This article belongs to the Topic Energy Market and Energy Finance)

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19 pages, 1004 KiB  

Open AccessArticle

Cost of Capital in the Energy Sector, in Emerging Markets, the Case of a Dollarized Economy

by Victor Aguilar, Freddy Naula and Fanny Cabrera

Energies 2024, 17(19), 4782; https://doi.org/10.3390/en17194782 - 25 Sep 2024

Viewed by 1579

Abstract

This article estimates the weighted average cost of capital (WACC) for the energy sector in Ecuador, a country with a dollarized economy and illiquid stock markets. Thus, reference companies in the region were taken, and at the same time combined with characteristics of [...] Read more.

This article estimates the weighted average cost of capital (WACC) for the energy sector in Ecuador, a country with a dollarized economy and illiquid stock markets. Thus, reference companies in the region were taken, and at the same time combined with characteristics of national companies, establishing a useful methodology, which makes sense with the acceptable discount rates in the Ecuadorian economy. For the above, four estimation alternatives were used. In method one, the traditional WACC formula was applied using interest rates and risk premiums from the U.S. market, which resulted in an overestimation due to the double penalty of the country risk and the U.S. market premium. Method two adjusted the market risk premium to consider only the Ecuador-specific risk premium, thus avoiding the double penalty. In method three, the credit default swap (CDS) was used to calculate the country risk premium, and the CDS was excluded from the nominal interest rate, avoiding redundancies. Finally, method four combined the U.S. interest rate with the CDS directly to calculate the market risk premium, more accurately reflecting local economic conditions in a dollarized economy. The WACC results range from 12.63% to 29.70%. In addition, a dummy variable was controlled for during the pandemic period. This article highlights the need for methodologies adapted to emerging markets, since traditional approaches would overestimate the WACC. Full article

(This article belongs to the Topic Energy Market and Energy Finance)

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18 pages, 8013 KiB  

Open AccessArticle

The Impact of a Market Maker in an Electricity Market

by Sebastián Arias, Adriana M. Santa-Alvarado and Harold Salazar

Energies 2024, 17(16), 4042; https://doi.org/10.3390/en17164042 - 15 Aug 2024

Cited by 1 | Viewed by 1443

Abstract

Electricity retailers in an electricity market use over-the-counter (OTC) contracts, or bilateral, and spot market purchases to meet the energy demands of their users. In some markets, OTC contracts face issues with price discrimination and accessibility. This study reveals some inefficiencies of OTC [...] Read more.

Electricity retailers in an electricity market use over-the-counter (OTC) contracts, or bilateral, and spot market purchases to meet the energy demands of their users. In some markets, OTC contracts face issues with price discrimination and accessibility. This study reveals some inefficiencies of OTC contracts in Colombia that expose regulated users—approximately 70% of the national demand—to market risk. This risk is aggravated by the current tariff design. To mitigate these inefficiencies, this article proposes the incorporation of a market maker that will improve the liquidity of existing energy futures in the country. These futures are mechanisms that the retailers could implement to hedge their demand and reduce the adverse effects of market risk. The characteristics of the market maker and a quantitative analysis of its impact are developed in this paper. While the characterization of the problem with its solution is developed with Colombian data, the conceptual framework could be extended to other countries that are concerned about how energy users are being affected by increases in tariffs due to high exposure to spot market price volatility. Full article

(This article belongs to the Topic Energy Market and Energy Finance)

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33 pages, 1879 KiB  

Open AccessArticle

Sector Formula for Approximation of Spread Option Value & Greeks and Its Applications

by Roza Galeeva and Zi Wang

Commodities 2024, 3(3), 281-313; https://doi.org/10.3390/commodities3030017 - 26 Jul 2024

Viewed by 1497

Abstract

The goal of this paper is to derive closed-form approximation formulas for the spread option value and Greeks by using double integration and investigating the exercise boundary. We have found that the straight-line approximation suggested in previous research does not perform well for [...] Read more.

The goal of this paper is to derive closed-form approximation formulas for the spread option value and Greeks by using double integration and investigating the exercise boundary. We have found that the straight-line approximation suggested in previous research does not perform well for curved exercise boundaries. We propose a novel approach: to integrate in a sector and find a closed-form formula expressed in terms of the bivariate normal CDF. We call it the sector formula. Numerical tests show the good accuracy of our sector formula. We demonstrate applications of the formula to the market data of calendar spread options for three major commodities, WTI, Natural Gas, and Corn, listed on the CME site as of May, April, and June 2024. Full article

(This article belongs to the Topic Energy Market and Energy Finance)

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23 pages, 1324 KiB  

Open AccessArticle

What Insights Do Short-Maturity (7DTE) Return Predictive Regressions Offer about Risk Preferences in the Oil Market?

by Gurdip Bakshi, Xiaohui Gao and Zhaowei Zhang

Commodities 2024, 3(2), 225-247; https://doi.org/10.3390/commodities3020014 - 28 May 2024

Viewed by 1463

Abstract

In this study, we investigate the ability of three higher-order risk-neutral return cumulants to predict short maturity (weekly) returns of oil futures. Our data includes weekly West Texas Crude Oil futures options that expire in 7 days (7DTE). Using a model-free approach, we [...] Read more.

In this study, we investigate the ability of three higher-order risk-neutral return cumulants to predict short maturity (weekly) returns of oil futures. Our data includes weekly West Texas Crude Oil futures options that expire in 7 days (7DTE). Using a model-free approach, we estimate these risk-neutral return cumulants at the beginning of each options expiration cycle. Our results suggest that the third risk-neutral return cumulant consistently predicts the returns of various oil futures (including WTI, Brent, Dubai, Heating Oil, and RBOB Gasoline). We compare our findings with 14 other predictors and offer a theoretical explanation for the negative coefficient observed for the 7DTE third risk-neutral return cumulant. Our theory connects higher-order risk-neutral return cumulants with the risk premiums of oil futures. Furthermore, our quantitative investment strategy favors the predictability of oil futures returns. Full article

(This article belongs to the Topic Energy Market and Energy Finance)

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29 pages, 3253 KiB  

Open AccessArticle

Forecasting Oil Prices with Non-Linear Dynamic Regression Modeling

by Pedro Moreno, Isabel Figuerola-Ferretti and Antonio Muñoz

Energies 2024, 17(9), 2182; https://doi.org/10.3390/en17092182 - 2 May 2024

Cited by 2 | Viewed by 2002

Abstract

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to [...] Read more.

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes. Full article

(This article belongs to the Topic Energy Market and Energy Finance)

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10 pages, 1518 KiB  

Open AccessArticle

Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM Models

by Mohammed Alhagyan

J. Risk Financial Manag. 2024, 17(5), 182; https://doi.org/10.3390/jrfm17050182 - 28 Apr 2024

Cited by 2 | Viewed by 1329

Abstract

Future index prices are viewed as a critical issue for any trader and investor. In the literature, various models have been developed for forecasting index prices. For example, the geometric Brownian motion (GBM) model is one of the most popular tools. This work [...] Read more.

Future index prices are viewed as a critical issue for any trader and investor. In the literature, various models have been developed for forecasting index prices. For example, the geometric Brownian motion (GBM) model is one of the most popular tools. This work examined four types of GBM models in terms of the presence of memory and the kind of volatility estimations. These models include the classical GBM model with memoryless and constant volatility assumptions, the SVGBM model with memoryless and stochastic volatility assumptions, the GFBM model with memory and constant volatility assumptions, and the SVGFBM model with memory and stochastic volatility assumptions. In this study, these models were utilized in an empirical study to forecast the future index price of the energy sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, the mean square error (MSE) and mean absolute percentage error (MAPE). The results show that the SVGFBM model demonstrates the highest accuracy, resulting in the lowest MSE and MAPE, while the GBM model was the least accurate of all the models under study. These results affirm the benefits of combining memory and stochastic volatility assumptions into the GBM model, which is also supported by the findings of numerous earlier studies. Furthermore, the findings of this study show that GFBM models are more accurate than GBM models, regardless of the type of volatility. Furthermore, under the same type of memory, the models with a stochastic volatility assumption are more accurate than the corresponding models with a constant volatility assumption. In general, all models considered in this work showed a high accuracy, with MAPE ≤ 10%. This indicates that these models can be applied in real financial environments. Based on the results of this empirical study, the future of the energy sector in Saudi Arabia is forecast to be predictable and stable, and we urge financial investors and stockholders to trade and invest in this sector. Full article

(This article belongs to the Topic Energy Market and Energy Finance)

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