Jump processes in energy commodities prices

Energy markets have been liberalized globally over the past two decades. During this time, the importance of such commodity markets that organize the trade and supply of energy, including electricity, oil, gas, coal, temperature, and carbon, has increased significantly. These commodity markets are expected to play an important role in the future due to the continuous increase in global energy demand and understanding how energy prices can be mathematically modeled is essential. In energy markets, it is common to see the trajectory of prices through jump processes, which are sudden and unexpected changes caused by new information. This behavior challenges the notion that energy commodity prices follow a geometric Brownian motion process, as it happens for stocks, where log returns are normally distributed. In other words, relying solely on a GBM process for the data-generating mechanism would not be sufficient to accurately depict the genuine dynamics of energy commodity markets. The discontinuous arrival of information necessitates a stochastic process that incorporates this feature, and as such, jump processes have become an important tool in the analysis of energy markets. Most energy and commodity markets exhibit mean-reversion and occasional distinctive price spikes, which result in demand for derivative products which protect the holder against high prices. This thesis undertakes a comprehensive examination of the statistical properties exhibited by energy commodity prices, focusing on gas and electricity. In particular, the aim of this thesis is to develop and validate models that accurately capture the dynamics of energy prices in order to enhance forecasting accuracy, inform policy decisions, and optimize investment strategies in the energy market.