The Implicit Variable Anticipating Shocks
Natural gas is a staple of the European energy market, but its price is erratic. Since gas is versatile, cleaner than other fossil fuels and essential for integrating renewables, it has seen demand grow steadily. However, predicting its market trends poses a challenge. The price spikes recorded in recent years, especially after the pandemic and the war in Ukraine, have severely tested the nerves of investors and operators in the industry. Forward curves have become distorted, forecasting models have proven inadequate, and most tools for hedging against risk have proved ineffective.
The key variable is the convenience yield
This is the context where the research study by Francesco Rotondi of the Bocconi Department of Finance can be placed. His paper, “Seasonality and Spikes in the Natural Gas Market”, published in the journal Energy Economics, proposes an alternative approach to understand the gas market. He says we need to look beyond the spot price and focus on a hidden but revealing variable, the convenience yield. This barely visible but highly informative variable measures the implicit benefit of physically owning metric cubes of gas compared to just holding a financial contract for them. It is a premium that varies markedly over time, often signaling tensions or imbalances in the market in advance. “It’s where the most important information is hidden,” Rotondi explains. “Not in the price, which is just the surface, but in the implicit value that the market attributes to having actual possession of the commodity.”
A model that reads seasonality and shocks
Analyzing historical data from the Dutch TTF gas trading hub, Rotondi highlights three central features of the European market: statistical stationarity of the convenience yield compared to instability of the spot price, the frequency of sudden spikes in the former variable, and a marked seasonality linked to supply and demand cycles. From these observations comes a model that starts from the classic Gibson and Schwartz model to radically transform it: the meaningful spikes are in convenience yields, not prices. A sinusoidal function captures annual cyclicality, while a stochastic jump process simulates sudden discontinuities. The choice of a double exponential distribution to model jumps also enables the derivation of closed formulas for the pricing of futures, making the model applicable in practice.
The most difficult test: the market after 2020
The results of the study speak for themselves. The new model manages to replicate real forward curves with surprising fidelity, including the distorted curves that have emerged in the post-2020 period. Whereas traditional models fail to capture these anomalies, Rotondi's approach explains them naturally. In 2012, for example, seasonality was enough to explain the price trend. Ten years later, in 2022, things have changed: the jumps have become rarer but much more intense. Their variance, i.e. the average size of shocks, has increased almost tenfold, marking a new era for the gas market, which has become more volatile and less predictable.
Implications for investors, regulators and analysts
Rotondi’s model has important practical corollaries. For investors and hedgers in energy markets, it provides a more realistic system for the valuation and pricing of gas futures and options. For regulators, it can offer a useful interpretative key in critical scenarios. And for those who are in charge of power infrastructure and gas provisioning, it becomes a tool to better assess the vulnerability of the energy system. “With this approach we can explain both the ‘normal’ and the ‘extraordinary’ behavior of the market,” says Rotondi. “It’s a flexible model, that is also applicable to daily practice.”
Beyond gas, towards other markets
The model opens new directions for research. Its basic approach can be extended to the pricing of more complex options, and adapted to other major commodities or used to explore the behavior of other markets that are subject to frequent shocks. “We have only just begun,” concludes the researcher. “But it is clear that, in order to truly read a chaotic market, you don’t need to go after the noise. You need to understand what lies underneath.”
