In the first paper of this issue of The Journal of Energy Markets, “Debt and the oil industry: analysis on the firm and production level” by Johannes Lips, we see an analysis of the relationship between debt and the production decisions of companies active in the exploration and production of oil and gas in the United States. It is well known that the development and application of innovative extraction methods has led to a considerable increase in US oil production in recent years. In parallel with the technological changes, another important economic development has been largely debt-driven investment in the oil sector. This extensive use of debt was fostered by the macroeconomic environment in the aftermath of the financial crisis as well as by the rising price of commodities leading to higher asset valuations. This increase in investment activity, especially in the United States, raised production capacity and, as a consequence, the production of crude oil. This trend continued despite the decline in the oil price in 2014. The paper’s main research question is whether debt and lever- age affect the production decisions of companies. It addresses this question using a novel panel vector autoregressive approach and a data set that combines financial data on publicly listed firms with their production data at well level. One of the most interesting results is that the price of oil has a much bigger impact on companies with high leverage and a high share of production from unconventional sources. These results lend further support to the hypothesis that shale oil-producing companies, in particular, might be able to provide more flexible oil production capacity.
Our second paper, “Brent crude oil spot and futures prices: structural break insights” by Miroslava Zavadska, Lucıa Morales and Joseph Coughlan, looks at oil market price data and the key issue of how oil spot and futures prices interact during times of crisis. Using cointegration and causality analysis, Brent crude spot and futures prices are examined before, during and after two different types of crisis: (1) a supply-led event (the 1990–91 Gulf War) and (2) a demand-led event (the global financial crisis). This study provides evidence that different types of crisis engender different levels of causal relationships between Brent crude spot and futures prices. Evidently, this paper is an important contribution to a heavily researched topic.
Finally, we turn to retail electricity. In “A simulation-based model for optimal demand response load shifting: a case study for the Texas power market” by Jacob R. Schaperow, Steven A. Gabriel, Michael Siemann and Jaden Crawford, a simulation model is used to evaluate retail demand response programs for the Texas power market. The model simulates a direct load control technique, whereby customers’ power consumption is adjusted by smart thermostats that automatically increase
participating customers’ temperature setpoints, thus reducing air-conditioning loads and potentially shielding retail electricity providers from financial losses during times of peak load. This study identifies an optimal load control schedule based on forecasted load, settlement prices and weather variables, taking into account stochastic load and prices as well as gray-box thermodynamic modeling. Its authors demonstrate how much benefit retailers stand to gain by following an optimal load schedule, and how much they stand to lose by intervening at the wrong times.
All three papers address important topics that are actively researched and have important practical implications.
This paper analyzes the relationship between debt and the production decisions of companies active in the exploration and production of oil and gas in the United States.
This study focuses on the analysis of long-run and short-run relationships between Brent crude oil spot and futures prices during the first Gulf War (1990–91) and the global financial crisis.
A simulation-based model for optimal demand response load shifting: a case study for the Texas power market
This paper describes a case study of analyzing DR load-shifting strategies for a retail electric provider for the Texas (ERCOT) market using a Monte Carlo simulation with stochastic loads and settlement prices.