Probabilistic and statistical methods in commodity risk management

The vast landscape of the financial sector is characterized by the continuous introduction of innovative financial instruments, providing investors with diverse opportunities for an efficient capital allocation. In this dynamic context, commodities emerge as a cornerstone, encapsulating an ancient financial exchange mechanism that carries profound contemporary implications. The historical significance of commodities as tradable assets intertwines with their crucial role in modern financial markets. Investments in commodities and their associated derivatives stand out as fundamental subjects in both academic and applied financial literature. The framework surrounding these investments has witnessed continual evolution, shaping the ongoing development of sophisticated models and methods to understand and predict their behavior. Within the expansive theme of commodities market modeling, two closely interlinked aspects accentuate its specific relevance within the broader scope of investment theory and practical applications. First, from a quantitative perspective, the intricacies of commodity price dynamics present a motivating challenge. The complexities arise from the distinctive features inherent in these prices, encompassing elements such as seasonality, the occurrence of pronounced peaks, and the dynamic nature of volatility over time. Consequently, the development of effective stochastic models for accurate price forecasting demands a specific approach. These models must exhibit a dual quality, being not only sufficiently comprehensive to encapsulate the nuanced idiosyncrasies of commodity price behavior but also maintaining a level of mathematical tractability that facilitates practical application and interpretation. Striking this delicate balance is imperative to ensure that the models not only accurately capture the multifaceted nature of commodity markets but also remain practical tools for decision-makers navigating the intricacies of financial decision-making. Therefore, building effective stochastic models for price prediction becomes a truly conceptual effort that requires models to be simultaneously suitable and mathematically tractable. Some of the papers within this Special Issue employ sophisticated stochastic models to describe the dynamics of the phenomena of interest. In addition to traditional statistical approaches, the profound influence ofmachine learning methods on the financial landscape is unmistakable. Notably—as we will see in detail below—methodologies such as Reinforcement Learning are prominently featured in some of the presented papers, underscoring the transformative role these advanced techniques play in the realm of finance. Second, froma more business perspective, commodities offer a rational alternative to conventional financial securities. This is particularly noteworthy in the aftermath of the recent financial crisis, where the vulnerabilities of traditional securities have been thoroughly examined. Commodities, by contrast, have gained prominence in the global financial industry, not only for their potential to enhance inflation-adjusted returns but also for the diversification benefits they can provide in comparison to more traditional fixed-income and equity investments. This underscores the multifaceted significance of commodities within the broader context of investment strategy and risk management. The contributions in the special issue can be clustered into some relevant categories in a no unique way, according to different criteria.We here advance a detailed discussion of them in the light of a meaningful classification.