Mathematical Methods in Energy Economy

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 24086

Special Issue Editors


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Guest Editor
Faculty of Economics, Computer Science and Engineering, 310025 Arad, Romania
Interests: human capital; European economics; sustainable development; globalisation; energy use; economic complexity
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute for Economic Foreasting, Romanian Academy, 50711 Bucharest, Romania
Interests: macroeconomic modelling and forecasting; forecast uncertainty; economic policy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the guest editors of the Special Issue of Mathematics titled “Mathermatical Methods in Energy Economy,” we would like to invite potential authors to submit papers for review and possible publication in the following fields: forecasting, sustainable development, environment and climate, environmetal policy, exploitation, conversion and use of energy, renewable energy, pollution, security of supply, risk analysis, taxation and regulation, markets for energy commodities and derivatives, international trade, energy policy, energy sustainability and competitiveness, economic growth and energy. Contributions to this Special Issue should be based on quantitative methods belonging to econometrics, optimization models, simulation models, analytical models and equilibrium models. Since energy plays a central role in the transition to a climate-neutral economy stated in the European Green Deal, we encourage papers proposing smart practical sollutions to implement energy efficiency principle, secure and modern energy infrastructure, clean energy, cirucular economy, zero pollution, smart mobility, renovating, Green Deal Investment Plan, digital technologies to achieve climate neutrality, and the impact of the COVID-19 pandemic on energy transition.

Prof. Dr. Mihaela Simionescu
Prof. Dr. Olimpia Neagu
Guest Editors

Manuscript Submission Information

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Keywords

  • mathematical modelling
  • energy efficiency
  • energy consumption
  • sustainable development
  • energy policy
  • low carbon economy
  • economic growth
  • European Green Deal

Published Papers (9 papers)

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Research

22 pages, 673 KiB  
Article
Innovating and Pricing Carbon-Offset Options of Asian Styles on the Basis of Jump Diffusions and Fractal Brownian Motions
by Yue Qi and Yue Wang
Mathematics 2023, 11(16), 3614; https://doi.org/10.3390/math11163614 - 21 Aug 2023
Viewed by 815
Abstract
Due to CO2 emissions, humans are encountering grave environmental crises (e.g., rising sea levels and the grim future of submerged cities). Governments have begun to offset emissions by constructing emission-trading schemes (carbon-offset markets). Investors naturally crave carbon-offset options to effectively control risk. [...] Read more.
Due to CO2 emissions, humans are encountering grave environmental crises (e.g., rising sea levels and the grim future of submerged cities). Governments have begun to offset emissions by constructing emission-trading schemes (carbon-offset markets). Investors naturally crave carbon-offset options to effectively control risk. However, the research and practice for these options are relatively limited. This paper contributes to the literature in this area. Specifically, according to carbon-emission allowances’ empirical distributions, we implement fractal Brownian motions and jump diffusions instead of traditional geometric Brownian motions. We contribute to extending the theoretical model based on carbon-offset option-pricing methods. We innovate the carbon-offset options of Asian styles. We authenticate the options’ stochastic differential equations and analytically price the options in the form of theorems. We verify the parameter sensitivity of pricing formulas by illustrations. We also elucidate the practical implications of an emission-trading scheme. Full article
(This article belongs to the Special Issue Mathematical Methods in Energy Economy)
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20 pages, 714 KiB  
Article
A Mutual Information-Based Network Autoregressive Model for Crude Oil Price Forecasting Using Open-High-Low-Close Prices
by Arash Sioofy Khoojine, Mahboubeh Shadabfar and Yousef Edrisi Tabriz
Mathematics 2022, 10(17), 3172; https://doi.org/10.3390/math10173172 - 03 Sep 2022
Cited by 5 | Viewed by 1235
Abstract
The global financial markets are greatly affected by crude oil price movements, indicating the necessity of forecasting their fluctuation and volatility. Crude oil prices, however, are a complex and fundamental macroeconomic variable to estimate due to their nonlinearity, nonstationary, and volatility. The state-of-the-art [...] Read more.
The global financial markets are greatly affected by crude oil price movements, indicating the necessity of forecasting their fluctuation and volatility. Crude oil prices, however, are a complex and fundamental macroeconomic variable to estimate due to their nonlinearity, nonstationary, and volatility. The state-of-the-art research in this field demonstrates that conventional methods are incapable of addressing the nonlinear trend of price changes. Additionally, many parameters are involved in this problem, which adds to the complexity of such a prediction. To overcome these obstacles, a Mutual Information-Based Network Autoregressive (MINAR) model is developed to forecast the West Texas Intermediate (WTI) close crude oil price. To this end, open, high, low, and close (OHLC) prices of crude oil are collected from 1 January 2020 to 20 July 2022. Afterwards, the Mutual Information-based distance is utilized to establish the network of OHLC prices. The MINAR model provides a basis to consider the joint effects of the OHLC network interactions, the autoregressive impact, and the independent noise and establishes an intelligent tool to estimate the future fluctuations in a complex, multivariate, and noisy environment. To measure the accuracy and performance of the model, three validation measures, namely, RMSE, MAPE, and UMBRAE, are applied. The results demonstrate that the proposed MINAR model outperforms the benchmark ARIMA model. Full article
(This article belongs to the Special Issue Mathematical Methods in Energy Economy)
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20 pages, 480 KiB  
Article
Forecasting Crude Oil Future Volatilities with a Threshold Zero-Drift GARCH Model
by Tong Liu and Yanlin Shi
Mathematics 2022, 10(15), 2757; https://doi.org/10.3390/math10152757 - 03 Aug 2022
Cited by 1 | Viewed by 1185
Abstract
The recent price crash of the New York Mercantile Exchange (NYMEX) crude oil futures contract, which occurred on 20 April 2020, has caused history-writing movements of relative prices. For instance, the West Texas Intermediate (WTI) experienced a negative price. Explosive heteroskedasticity is also [...] Read more.
The recent price crash of the New York Mercantile Exchange (NYMEX) crude oil futures contract, which occurred on 20 April 2020, has caused history-writing movements of relative prices. For instance, the West Texas Intermediate (WTI) experienced a negative price. Explosive heteroskedasticity is also evidenced in associated products, such as the Intercontinental Exchange Brent (BRE) and Shanghai International Energy Exchange (INE) crude oil futures. Those movements indicate potential non-stationarity in the conditional volatility with an asymmetric influence of negative shocks. To incorporate those features, which cannot be accommodated by the existing generalized autoregressive conditional heteroskedasticity (GARCH) models, we propose a threshold zero-drift GARCH (TZD-GARCH) model. Our empirical studies of the daily INE returns from March 2018 to April 2020 demonstrate the usefulness of the TZD-GARCH model in understanding the empirical features and in precisely forecasting the volatility of INE. Robust checks based on BRE and WTI over various periods further lead to highly consistent results. Applications of news impact curves and Value-at-Risk (VaR) analyses indicate the usefulness of the proposed TZD-GARCH model in practice. Implications include more effectively hedging risks of crude oil futures for policymakers and market participants, as well as the potential market inefficiency of INE relative to WTI and BRE. Full article
(This article belongs to the Special Issue Mathematical Methods in Energy Economy)
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14 pages, 1804 KiB  
Article
Macroeconomic Effects of Energy Price: New Insight from Korea?
by Yugang He and Moongi Lee
Mathematics 2022, 10(15), 2653; https://doi.org/10.3390/math10152653 - 28 Jul 2022
Cited by 4 | Viewed by 1569
Abstract
Under the double pressure of the Ukrainian–Russian war and the COVID-19 pandemic, the global energy crisis has also engulfed the Korean economy. Based on this context, this article examines the macroeconomic implications of energy prices, using Korea as an example. According to an [...] Read more.
Under the double pressure of the Ukrainian–Russian war and the COVID-19 pandemic, the global energy crisis has also engulfed the Korean economy. Based on this context, this article examines the macroeconomic implications of energy prices, using Korea as an example. According to an empirical study using the impulse response function, the results show that an energy price shock causes a decline in production, labor supply, capital stock, and energy consumption, as well as an increase in consumption, wages, the goods price level, inflation, and the deposit interest rate. Meanwhile, variance decomposition findings indicate that the energy price shock has a greater impact on the Korean macroeconomy than other shocks. In addition, the findings of three types of robustness tests validate the reliability and accuracy of the conclusions reached in this work. In conclusion, the information presented in this study may aid Korean policymakers in implementing appropriate countermeasures against macroeconomic volatility caused by the energy price shock. Full article
(This article belongs to the Special Issue Mathematical Methods in Energy Economy)
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11 pages, 297 KiB  
Article
Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model
by Yingchao Zou and Kaijian He
Mathematics 2022, 10(14), 2413; https://doi.org/10.3390/math10142413 - 11 Jul 2022
Cited by 4 | Viewed by 1166
Abstract
In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network model to incorporate the external influence of financial [...] Read more.
In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network model to incorporate the external influence of financial markets such as stock market and exchange market in a multiscale setting into the modeling of crude oil market risk movement. We propose a multivariate empirical model decomposition to analyze the finer details of interdependence among risk movement of different markets across different time horizons or scales. We also introduce the convolutional neural network to construct a new nonlinear ensemble algorithm to reduce the estimation bias and improve the forecasting accuracy. We used the major crude oil price data, stock market index, and the euro/United States dollar exchange rate data to evaluate the performance of the multivariate empirical model decomposition convolutional neural network model. The combination of both the multivariate empirical model decomposition and the convolutional neural network model in this paper has produced the risk forecasts with significantly improved risk forecasting accuracy. Full article
(This article belongs to the Special Issue Mathematical Methods in Energy Economy)
14 pages, 3527 KiB  
Article
A Novel Intelligent ANFIS for the Dynamic Model of Photovoltaic Systems
by Abdelhady Ramadan, Salah Kamel, I. Hamdan and Ahmed M. Agwa
Mathematics 2022, 10(8), 1286; https://doi.org/10.3390/math10081286 - 12 Apr 2022
Cited by 5 | Viewed by 2457
Abstract
Developing accurate models for photovoltaic (PV) systems has a significant impact on the evaluation of the accuracy and testing of PV systems. Artificial intelligence (AI) is the science of developing machine jobs to be more intelligent, similar to the human brain. Involving AI [...] Read more.
Developing accurate models for photovoltaic (PV) systems has a significant impact on the evaluation of the accuracy and testing of PV systems. Artificial intelligence (AI) is the science of developing machine jobs to be more intelligent, similar to the human brain. Involving AI techniques in modeling has a significant modification in the accuracy of the developed models. In this paper, a novel dynamic PV model based on AI is proposed. The proposed dynamic PV model was designed based on an adaptive neuro-fuzzy inference system (ANFIS). ANFIS is a combination of a neural network and a fuzzy system; thus, it has the advantages of both techniques. The design process is well discussed. Several types of membership functions, different numbers of training, and different numbers of membership functions are tested via MATLAB simulations until the AI requirements of the ANFIS model are satisfied. The obtained model is evaluated by comparing the model accuracy with the classical dynamic models proposed in the literature. The root mean square error (RMSE) of the real PV system output current is compared with the output current of the proposed PV model. The ANFIS model is trained based on input–output data captured from a real PV system under specified irradiance and temperature conditions. The proposed model is compared with classical dynamic PV models such as the integral-order model (IOM) and fractional-order model (FOM), which have been proposed in the literature. The use of ANFIS to model dynamic PV systems achieves an accurate dynamic PV model in comparison with the classical dynamic IOM and FOM. Full article
(This article belongs to the Special Issue Mathematical Methods in Energy Economy)
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17 pages, 1753 KiB  
Article
Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings
by Mohsen Ahmadi, Mahsa Soofiabadi, Maryam Nikpour, Hossein Naderi, Lazim Abdullah and Behdad Arandian
Mathematics 2022, 10(8), 1270; https://doi.org/10.3390/math10081270 - 11 Apr 2022
Cited by 16 | Viewed by 2654
Abstract
Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country’s revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development [...] Read more.
Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country’s revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development areas. Energy demand modeling is one of the essential strategies for better managing the energy sector and developing appropriate policies to increase productivity. With the increasing global demand for energy, it is necessary to develop intelligent forecasting methods and algorithms. Different economic and non-economic indicators can be used to estimate the energy demand, including linear and non-linear statistical methods, mathematics, and simulation models. This non-linear relationship between these indicators and energy demand has led researchers to search for intelligent solutions, such as artificial neural networks for non-linear modeling and prediction. The purpose of this study was to use a deep neural network with fuzzy wavelets to predict energy demand in Iran. For the training of the presented components, a hybrid training method incorporating both an inline PSO and a gradient-based algorithm is presented. The provided technique predicts energy consumption in Tehran, Mashhad, Ahvaz, and Urmia from 2010 to 2021. This study shows that the presented method provides high-performance prediction at a lower level of complexity. Full article
(This article belongs to the Special Issue Mathematical Methods in Energy Economy)
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15 pages, 480 KiB  
Article
Impact of Industry-Specific Risk Factors on Stock Returns of the Malaysian Oil and Gas Industry in a Structural Break Environment
by Mohammad Enamul Hoque and Soo-Wah Low
Mathematics 2022, 10(2), 199; https://doi.org/10.3390/math10020199 - 10 Jan 2022
Cited by 3 | Viewed by 1710
Abstract
This study examines the impact of industry-specific risk factors such as oil price, gas price, and exchange rate on stock returns of Malaysian oil and gas firms in a structural break environment by employing the break least square approach of Bai and Perron [...] Read more.
This study examines the impact of industry-specific risk factors such as oil price, gas price, and exchange rate on stock returns of Malaysian oil and gas firms in a structural break environment by employing the break least square approach of Bai and Perron (1998, 2003). Existing studies fall short of providing such empirical evidence. The results document evidence of structural breaks in the relationship between industry risk factors and the stock returns of the oil and gas industry. Industry-specific risk factors are shown to significantly affect the stock returns of oil and gas industry sub-sectors alongside market-based risk factors. The results reveal that the beta values of oil price, gas price, and exchange rate vary across sub-periods hence confirming that exposure of oil and gas stocks to industry risk factors varies over time and across sub-periods. The effects of oil, gas, and exchange rate risk factors also differ across the sub-industry, with impacts and directions largely dependent on the core business activities of the oil and gas sub-industries. The empirical results offer implications for asset managers and investors. Full article
(This article belongs to the Special Issue Mathematical Methods in Energy Economy)
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15 pages, 1861 KiB  
Article
Energy Crisis in Pakistan and Economic Progress: Decoupling the Impact of Coal Energy Consumption in Power and Brick Kilns
by Abdul Rehman, Hengyun Ma, Magdalena Radulescu, Crenguta Ileana Sinisi and Zahid Yousaf
Mathematics 2021, 9(17), 2083; https://doi.org/10.3390/math9172083 - 28 Aug 2021
Cited by 19 | Viewed by 9314
Abstract
This study aims to examine the impact of coal energy consumption on the economic progress in Pakistan by using annual time series data during 1972–2019. Three-unit root tests were employed to rectify the variables’ stationarity. The quantile regression approach with the extension of [...] Read more.
This study aims to examine the impact of coal energy consumption on the economic progress in Pakistan by using annual time series data during 1972–2019. Three-unit root tests were employed to rectify the variables’ stationarity. The quantile regression approach with the extension of cointegration regression test was utilized to check the variables interaction with the economic progress. The outcomes of the quantile regression uncover that coal energy consumption in power sector and coal energy consumption in brick kilns have adverse influence to the economic progress, while total coal energy consumption has a productive association with the economic progress. Similarly, the findings of cointegration regression analysis uncover that via FMOLS (Fully Modified Least Squares) and DOLS (Dynamic Least Squares) that variables coal energy consumption in power sector and brick kilns have an adverse connection with the economic progress, while total coal energy consumption uncover a productive linkage to the economic progress in Pakistan. Pakistan is still facing a deep energy crisis because of the lack of energy production from cheap sources. New possible policies are required in this direction to improve the energy sector by paying more attention to the alternative energy sources to foster the economic progress. Full article
(This article belongs to the Special Issue Mathematical Methods in Energy Economy)
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