Journal of Energy Markets

While advanced techniques for the precise modeling of energy price risk have attracted substantial research interest in recent years, this high level of interest does not simply reflect a challenging set of problems for academic research. This is an area where advanced modeling can also pay off in practice. With complexity coming from both fundamental and financial properties, the tasks of pricing derivatives and trading asset-backed securities require careful analysis in applications where mistakes can be costly. In this issue of The Journal of Energy Markets, we have four papers that make important contributions in this context.

In our first paper, "Practical stochastic modeling of electricity prices" by Michel Culot, Valérie Goffin, Steve Lawford, Sébastien de Menten and Yves Smeers, many of the well-known specification problems of power prices are approached with regime switching to capture the various stylized features of power prices, including mean reversion, seasonal patterns and spikes. Results from jump diffusions combine the spike and diffusive components to provide convenient closed-form solutions for some key power derivatives. The paper also proposes a simple nonparametric model, based on hourly profile sampling from historical data, which can reproduce complicated intraday patterns and enables fast numerical pricing of hourly options. This work is applied to daily and hourly data from the Amsterdam Power Exchange.

In "Variance and volatility swaps in energy markets", Anatoliy Swishchuk also looks at the stochastic complexity of energy products, this time in the gas context, focusing on the pricing of variance and volatility swaps. An explicit variance swap formula and a closed-form volatility swap formula for energy assets with stochastic volatility are derived. An application is presented using the Alberta Energy Company natural gas index.

The theme of stochastic complexity is probed more deeply by Imen Dakhlaoui and Chaker Aloui in their paper "The US oil spot market: a deterministic chaotic process or a stochastic process?" Chaos theory is used to analyze the nonlinear dynamical complexity of the US crude oil market. Using various well-known tests, the authors demonstrate that the US crude oil market is a biased random walk, that it is not weakform efficient, and that its chaotic properties are rather sporadic. For two subperiods - the crises of 1990-91 and 2007-8 - they show temporary low-dimensional chaotic processes. However, from 1986 to 2008 there is no evidence of persistent chaotic dynamics in the US crude oil market outside these two crises. Furthermore, relating to the active ongoing debate, the chaotic signals that are detected are more sensitive to financial variables than to geopolitical events.

Finally, in "Quantifying natural gas storage optionality: a two-factor tree model" Cliff Parsons looks at the topical theme of optimizing gas storage operations. His model utilizes a two-factor tree in which both factors mean revert. For Henry Hub price data spanning the period 1999-2006, simulated trading using the model obtained average values of US$1.244 per million British thermal units over intrinsic value for fast-cycle storage leases and US$0.397 per million British thermal units over intrinsic for slow-cycle leases. This paper contributes to a rapidly growing body of methodological research literature on this topic.

Overall, this issue showcases research and practical applications that are central to the aims of The Journal of Energy Markets, with innovative modeling contributing measurable value to the financial operations of energy assets.

Derek W. Bunn
London Business School

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