Journal of Energy Markets

Derek W. Bunn
London Business School

This issue of The Journal of Energy Markets provides four novel research papers showing how new techniques and model-based insights can benefit energy trading.

In the issue’s first paper, “Assessing the potential profitability of automated power market trading using event signals sourced from grid frequency data”, Thomas Bowcutt, Patrick Denvir, Giuseppe Destino, Navesh Kumar and Chris Regan analyze how network events can be identified quickly by real-time frequency monitoring, providing timely information for power trading algorithms. In a low-inertia system it has become increasingly feasible to identify, size, classify and locate grid events, such as an interconnector interruption event, using a suitably sensitive and accurate network of measurement devices. Loss of generation or interconnector capacity, once known, often leads to a significant change in the price stack, creating movement in market prices as traders adjust their positions. The authors demonstrate that a systematic trading strategy using an event-detection signal based on public frequency data and highly accurate measurement devices can be profitable. They also assess the sensitivity of profits to the overall event-detection and trade-execution lead times.

In “New proxy schemes for swing contracts”, the second paper in this issue, Frank Koster, Daniel Oeltz and Angelina Steffens consider the valuation of swing contracts for energy markets using semi-analytical proxies. Accurate Monte Carlo or finitedifference methods are computationally expensive, so various approximations are generally applied in practice. For example, Keppo’s well-known method replicates the swing contract by forwards and call options. Koster et al extend this method further by introducing two new schemes. The first adds the probability of exercising the option to the constraints, while the second, where the quantities depend on the actual spot prices, ensures that the required constraints are satisfied in a weak sense, providing an upper bound for the price. The authors perform several numerical experiments with a one-factor Lucia–Schwartz model to show the improved quality of their numerical results for different contract parameters. Both methods yield more accurate calculated prices than the commonly used approach while retaining its computational advantage.

Our third paper, “Renewable energy generation capacity following the Russian invasion of Ukraine, and the stock market performance of energy firms: evidence from southern European Union countries” by Maria I. Chondrokouki, Andrianos E. Tsekrekos and Konstantinos I. Vasileiadis, starts from the observation that, since the Russian invasion of Ukraine in February 2022, European countries have faced restrictions on natural gas imports from Russia. Consequently, electricity generation from renewable sources has become even more important. The authors first show how generating capacity from renewable energy sources increased in Portugal, Italy, Greece and Spain between 2012 and 2022, and then they investigate the association between generation capacity from renewable energy sources and the energy firms’ stock returns in those countries. Next, they examine whether the investments in renewable energy capacity generation had any effect on the performance of energy firms in these countries following Russia’s invasion of Ukraine. They provide strong evidence that energy firms from countries with higher renewable energy capacity investments have exhibited statistically significant higher stock returns. Moreover, they find that, since the invasion, the positive effect that a high percentage of energy generation capacity from renewable sources has on stock returns has become more pronounced. In general, the authors’ empirical findings suggest that the country-wide investments and policies that accelerated the transition to renewable energy have contributed significantly to the energy sector’s resilience against security-threatening geopolitical risks.

Finally, in “Volatility spillover effects and risk assessment of Indian green stocks: a DCC-GARCH analysis”, the issue’s fourth paper, Ubaid Ahmad Peer, Rupinder Katoch and Arpit Sidhu observe that new investments in Indian green companies are potentially risky. To understand the risks, they employ a dynamic conditional correlation–generalized autoregressive conditional heteroscedasticity model to analyze the volatility spillover effects within Indian sustainability indexes (S&P BSE CARBONEX and S&P BSE GREENEX), which can be predicted based on market volatility information from traditional stocks and crude oil and from economic policy uncertainty. The authors find that there is high persistence in the conditional variances for all pairs, indicating that past shocks to volatility have a long-lasting impact on future volatility. Understanding these dynamics can guide portfolio diversification for investors in this important emerging market.