Journal of Energy Markets

Quantitative energy finance is strongly influenced by a mixture of advanced econometrics and financial engineering.  The industry is data intensive and the price formations are complex. In this issue of The Journal of Energy Markets  we have three papers – covering applications to oil, gas and power, using advanced quantitative methods – providing a good balance in terms of contexts and methodology.

Quantitative energy finance is strongly influenced by a mixture of advanced econometrics and financial engineering. The industry is data intensive and the price formations are complex. In this issue of The Journal of Energy Markets we see three useful applications of this interaction of methods.

In the issue’s first paper, “Exploration risk in international oil and gas shareholder returns”, Bård Misund, Klaus Mohn and Marius Sikveland examine the association between exploration activity risk and company shareholder returns. Results from monthly returns data from 189 oil and gas companies between 1993 and 2013 indicate that exploration risk contributes significantly to oil company excess returns. This insight into asset values is clearly beneficial to investment managers in the sector.

Our second paper is “Optimal intraday power trading with a Gaussian additive process”. In it, Enrico Edoli, Marco Gallana and Tiziano Vargiolu formulate the situation of a risk-averse financial agent trading intraday. The price of traded hours is assumed to follow an additive Ornstein–Uhlenbeck process, allowing the optimal strategy to be derived from the Hamilton–Jacobi–Bellman equation. The authors recognize that typical power time series are unevenly spaced in time, with more transactions as maturity approaches. They therefore use maximum likelihood estimation and bootstrap bias correction in order to compensate for having few observations at the beginning of the observation frame. The authors conclude by using empirical backtesting to validate their modeling.

Finally, in “Gas storage valuation under Lévy processes using the fast Fourier trans- form”, Mark Cummins, Greg Kiely and Bernard Murphy look at market-consistent valuations and hedging portfolios. This valuation methodology derives the storage asset value via stochastic dynamic programming. The authors present a modification of the fast Fourier transform method that removes the need for a dampening parameter, and this leads to improved convergence. A mean-reverting variance-gamma (MRVG) process is motivated by fitting the implied volatility smile, from which the authors present a forward-curve-consistent, conditional-characteristic function of the implied spot price model. They derive a transform-based swaption formula in order to calibrate their models to market-traded options, and they then use these calibrated models to value a stylized storage asset. They calculate the hedging positions needed to monetize its value and demonstrate how one can perform an informative scenario-based analysis on the relationship between the implied volatility surface and the asset value. Convergence results for the valuation algorithm are presented, along with a discussion of the potential for increasing the computational efficiency of the algorithm. Finally,to provide increased confidence around the fit of the MRVG model, Cummins et al benchmark against a standard mean-reverting jump-diffusion model.

Overall, these three papers – covering applications to oil, gas and power, using advanced quantitative methods – provide a good balance in terms of contexts and methodology for this issue of The Journal of Energy Markets.

Derek W. Bunn
London Business School

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