Energy markets are one of the fastest growing and most complex sectors. From the basic role that oil has in the global economy, to the essential services that gas and electricity provide, energy is an area of geopolitical concern as well as financial activities. The Journal of Energy Markets serves as a major research outlet for new empirical and model-based work in this sector, and publishes original papers on the evolution and behaviour of electricity, gas, oil, carbon and other energy markets, both wholesale and retail.
The Journal of Energy Markets considers submissions in the form of research papers on the following, but not limited to, topics:
- Econometric analyses of prices, volatilities and across particular energy markets
- Model-based simulation of price and investment behaviour
- Theoretical and applied analyses of energy derivatives
- High frequency nonlinear models of price formation
- Longer-term geo-political analyses of energy market globalization
- Forward curve and risk premia
- Strategic behaviour by companies
- Financial aspects of new investment
- Relationship of energy and carbon markets to climate change policies
- Renewable energy financing and policy analysis
Abstracting and Indexing: Scopus; EconLit; EconBiz; and Cabell’s Directory
Empirical research on the relationship between renewable energy consumption, foreign direct investment and economic growth in South Asia
This paper scrutinizes the link between renewable energy consumption, foreign direct investment (FDI) and economic progress in South Asian countries.
Dynamics of biofuel prices on the European market: the impact of EU environmental policy on resources markets
This paper explains the major drivers of biodiesel market prices by examining agricultural resource prices and gasoil prices for automotive fuels in the context of European Union environmental policy.
This paper proposes the GARCH model combined with the Cornish–Fisher expansion for the oil VaR forecast.
Directional predictability between returns and trading volume in the futures markets of energy: insights into traders’ behavior
This papers aims to test for directional predictability between returns and volume (and vice versa) in the energy futures markets, employing a cross-quantilogram approach that enables the assessment of the temporal association between two stationary time…
In this paper the author's develop theoretical concepts of optimal injecting and withdrawing for a capacitated commodity storage and give case studies in natural gas.
This paper present a novel systematic commodity trading model utilizing a time series momentum strategy.
In this paper, different machine learning approaches are applied to forecasting future yearly price trends in the natural gas Title Transfer Facility market in the Netherlands.
Using equity, index and commodity options to obtain forward-looking measures of equity and commodity betas and idiosyncratic variance
One-week-ahead electricity price forecasting using weather forecasts, and its application to arbitrage in the forward market: an empirical study of the Japan Electric Power Exchange
This paper constructs a model using weekly weather forecasts for forecasting week-ahead average electricity prices and applies it to an arbitrage strategy in the forward market.
Impact of changes in the global environment on price differentials between the US crude oil spot markets for the periods before and after 2008–9
This paper uses threshold cointegration to examine price differentials between crude oil spot markets in the US for the periods before (2000–2007) and after (2010–17) the advent of major technological and other changes impacting the oil sector.
A fractional Brownian–Hawkes model for the Italian electricity spot market: estimation and forecasting
This paper proposes a new model for the description and forecast of gross prices of electricity in the liberalized Italian energy market via an additive two-factor model.
This paper introduces two models: the first analyzes the impacts of global economic policy uncertainty, gold prices and three-month US Treasury bill rates on oil prices between 1997 and 2020, and the second examines the effects of oil prices and US…
Dynamic behavior of hydro/thermal electrical operators under an environmental policy targeting the preservation of ecosystem integrity and air quality
This paper analyzes the effect of an environmental policy targeting the enhancement of ecosystem integrity as well as air quality in the wholesale electricity market.
Addressing competitiveness of emissions-intensive and trade-exposed sectors: a review of Alberta's carbon pricing system
This paper assesses mechanisms used under the CCIR to address competitiveness-driven carbon leakage for emissions-intensive and trade-exposed sectors with a focus on Alberta’s oil and gas industry.
Zone-wide prediction of generating unit-specific power outputs for electricity grid congestion forecasts
This paper explores various statistical and statistical learning methods, with the goal of adequately predicting the on/off status and power output levels of all power plants within a control zone.
This study examines the causal link between the crude oil price and the exchange rate in five major oil-exporting countries (Saudi Arabia, Russia, Canada, the United Arab Emirates and the United States) that have recently adopted different exchange rate…
The authors model a hierarchical Stackelberg game in a competitive power market under high behind-the-meter photovoltaics penetration and demand-side uncertainty, with emphasis on the feedback loop between distributed generation via photovoltaics and…
The authors model the supply and demand curves of electricity day-ahead auctions in a parsimonious way by building an appropriate algorithm to present the information about electricity prices and demand with far fewer parameters than the existing…
This paper provides a method to identify the best predictive variables and the appropriate predictive indexes for an aggregate hydropower storage forecasting model. To this end, we use an entropy-based approach.
The authors propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature.