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Topic Editors

Dr. Junhua Zhao
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518100, China
Laboratoire d'Économie Dionysien (LED), University Paris 8, 93526 Saint-Denis, France
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Energy Market and Energy Finance

Abstract submission deadline
30 September 2024
Manuscript submission deadline
31 December 2024
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2286

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 The goal is to gather original (unpublished) scholarly contributions on the dynamics that currently shape the energy field.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

  • geopolitics of energy
  • crude oil
  • natural gas
  • renewables
  • financialization
  • global warming
  • climate finance

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Commodities
commodities 		- - 2022 15.0 days * CHF 1000 Submit
Economies
economies 		2.6 3.2 2013 21.4 Days CHF 1800 Submit
Energies
energies 		3.2 5.5 2008 16.1 Days CHF 2600 Submit
Journal of Risk and Financial Management
jrfm 		- 2.8 2008 20.5 Days CHF 1400 Submit
FinTech
fintech 		- - 2022 22.2 Days CHF 1000 Submit

* Median value for all MDPI journals in the second half of 2023.


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Published Papers (3 papers)

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23 pages, 1324 KiB  
Article
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 139
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  
Article
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
Viewed by 643
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  
Article
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
Viewed by 527
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Yet Another Closed Form Approximation of Spread Option Value and Greeks: Sector Formula
Authors: Roza Galeeva; Zi Wang
Affiliation: Johns Hopkins University
Abstract: The goal of the paper is to derive a closed form approximation formulas for the spread option value and Greeks by using a double integration and investigating the exercise boundary. We find that the straight line approximation 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 {\it sector formula}. Numerical tests show accuracy and supremacy of our sector formula in comparison with the previous methods.

Title: What Insights Do Short Maturity Return Predictive Regressions Offer about Risk Preferences in the Oil Market?
Authors: Gurdip Bakshi; Xiaohui Gao; Zhaowei Zhang
Affiliation: Temple University
Abstract: This study examines the predictive relationship between the short maturity (weekly) returns of oil futures and three higher-order risk-neutral return cumulants. Using data from weekly expiring West Texas Crude Oil futures options, we model-free calculate these cumulants at the beginning of the options expiration cycles. Our results show that the third risk-neutral cumulant is a consistent predictor of oil futures returns. We compare our findings with 14 other predictors and provide a theoretical explanation for the negative coefficient estimated for the third cumulant. This theory links higher-order risk-neutral cumulants to the risk premiums of oil futures.

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