Journal of Energy Markets

This issue marks the start of the tenth volume of The Journal of Energy Markets. It has been an eventful decade for researchers and professionals in energy markets. We have seen the rise and fall of carbon trading, the demise of nuclear energy, the game-changing emergence of US oil and gas, the amazing growth and competitiveness of wind and solar power, and the growing sophistication of energy risk management techniques. Model-based insights into the market implications of all of these and more have featured strongly in The Journal of Energy Markets. The journal’s original intention of fulfilling a need for analytical research in energy markets at the interface of the disciplines of finance, economics and management science has proven to be timely, as we have witnessed a steady growth in submissions and citations since the journal’s inception; it seems that this goal will be just as relevant, if not more so, going forward. The papers included in this issue all show the important contributions that can be made in the distinctive area of quantitative energy finance.

The first paper in this issue, “Calibration of temperature futures by changing the mean reversion” by Fred Espen Benth and Salvador Ortiz-Latorre, reveals the growing importance of weather risk management and the consequent need for more weather-based analytics. The authors formulate continuous-time autoregressive dynamics for deseasonalized temperatures along with a pricing measure that specifies simultaneous changes to the level and speed of mean reversion in the risk-neutral dynamics. They compare this pricing measure with that provided by the classic Girsanov transformation and show that the new pricing measure provides better calibration errors and more realistic risk-premium profiles. Further, their analysis indicates that there is a risk premium not only for temperature level but also for temperature volatility.

In “Modeling energy spreads with a generalized novel mean-reverting stochastic process”, the second paper in the issue, Mir Hashem Moosavi Avonleghi and Matt Davison consider the continuous limit of a mean-reverting random walk with extensions. This is compared with the Vasicek process, and its advantages are discussed. The authors show that this new model for spread dynamics is capable of capturing skewness and kurtosis. Local linearization is used to estimate the model parameters, and an application to the crude oil spread between West Texas Intermediate (WTI) and West Texas Sour (WTS) is described.

Our third paper, “The application of structural electricity models for dynamic hedging” by Cord Harms and Rüdiger Kiesel, focuses on the dynamic hedging of contingent claims on spot electricity prices. The authors specify conditions on the fuels/demand dynamics and the bid stack function under which the electricity forwards have martingale properties, thereby facilitating the derivation of hedging strategies. They also find that for liquid power forward contracts this model implies cointegrated forwards. However, as power forward contracts are not always sufficiently liquid, the authors construct an alternative hedging strategy in which fuels are used to hedge power based on the marginal fuel in the generating market.As an example, the authors compare their strategy with the structural model of Carmona, Coulon and Schwarz. Based on an hourly hedging profile, they show how to progress to a hedging strategy using exchange-traded futures (weekly, monthly, quarterly, etc).

Sofia B. Ramos, Abderrahim Taamouti, Helena Veiga and Chih-Wei Wang move from market prices to asset valuations in “Do investors price industry risk? Evidence from the cross-section of the oil industry”, the fourth and final paper in this issue. Here, the authors look at a cross-section of asset returns over time in the oil industry and show that the inclusion of an oil factor provides a substantial amount of extra explanatory power over traditional factors. Their results suggest that investors demand compensation for the exposure to oil price risk, and this has implications for the cost of equity.

Derek W. Bunn
London Business School

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