Journal of Energy Markets

The energy sector has always demonstrated an appetite for innovative modeling techniques. In the analysis of energy markets we likewise see a need for, and the benefits of, new methodological innovations. From this perspective, the four papers in this issue of The Journal of Energy Markets all demonstrate strong research contributions through the application of new analytical techniques.

Thus, in the paper "An analysis of energy futures" by Coleen Pantalone, Joseph McCarthy and H. C. Li, an analysis of the distributions of returns on crude oil, heating oil and natural gas futures uses the innovative approach of wavelets. This is motivated partly by the evidence of excess skewness and kurtosis in the returns, and the wavelet approach is used to investigate the correlations. In all three series, volatility and departures from normality are much more prevalent at shorter frequencies and, as a consequence, so are the trading risks. Methodologically, the results demonstrate the capability of wavelet analysis to model nonstationary data and its ability to reveal causality. The research also cautions against the simple assumption of normality of returns.

In a similar context, Matthew Brigida, in his paper "The determinants of regime switching in the natural gas and crude oil cointegrating relationship", seeks to model the determinants of the endogenous regime-switching process underlying the cointegrating relationship between natural gas and crude oil. Combining regime switching with cointegration is well-known to be methodologically challenging. This paper models the cointegrating equation as a two-state, Markov-switching process with time-varying transition probabilities. Following an extensive search of factors, the regime switching in the cointegrating relationship was identified by (a) natural gas supplies but not crude oil supplies, (b) the deviation of the number of heating degree days from average, and (c) the collapse of Enron as an intervention variable during this data set. Macroeconomic data had no effect on the transition probabilities.

Also looking at gas futures, our third paper, "The convenience yield implied in the European natural gas markets: the impact of storage and weather" by Thomas Kremser and Margarethe Rammerstorfer, determines the convenience yield implied in European natural gas markets and investigates the dynamic driving factors. They approximate the convenience yield via an option-based approach, in which the convenience yield is determined as the difference between two average floating-strike Asian options written on the spot and futures contracts. They then fit an exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model to explain the convenience yield via storage and weather as well as other key driving factors. The empirical analysis gives distinct results for the impact of storage, which indicates that the informative release of natural gas storage levels generates considerable volatility.

Finally, in the paper "Systematic analysis of the evolution of electricity and carbon markets under deep decarbonization" by William Blyth, Derek Bunn, Michail Chronopoulos and Jose Munoz, a computationally intensive, multimethod modeling process is undertaken to address the policy question of whether carbon markets by themselves can offer the desired solution of balancing initiatives for technological change while maintaining a commitment to market liberalization. Despite their theoretical attractions, carbon markets have suffered from considerable doubts over effectiveness. The authors address this question through an integrated modeling framework, stylized for the British power market within the EU emissions trading system, which includes three distinct components:

(a) long-term least-cost capacity planning, similar in functionality to many used in policy analysis, but innovative in providing the endogenous calculation of
carbon prices;

(b) short-term price risk analysis producing hourly dispatch and pricing outputs, which are used to test the annual financial performance metrics implied by the
longer-term investments; and

(c) agent-based computational learning to derive pricing behavior in imperfect markets.

The results indicate that the risk-return profile of electricity markets may deteriorate substantially as a result of decarbonization, reducing the propensity of companies to invest in the absence of increased government support and/or more beneficial market circumstances. Markets may adjust, if they are allowed to do so, by deferring investment until conditions improve, or by consolidating to increase market power, or by operating in a tighter market with reduced spare capacity. To the extent that each of these "market-led" solutions may be politically unpalatable, policy design will need to sustain a delicate regulatory regime, moderating the possible increased market power of companies while maintaining low-carbon subsidies for longer than expected. This paper considers some of the modeling implications for this compromise.

Overall, across the four papers in the issue, researchers should find inspiration for further methodological research, and practitioners involved in understanding the risks in energy markets - whether oil, gas, power or carbon policy related - should gain greater applied insights from the advanced techniques used.


Derek W. Bunn
London Business School

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