Leveraging Liquidity Models in Commodity Markets: Customizing Risk-Appetite Trading Limits for Robust Investment Portfolios

This chapter examines the intricate task of constructing commodity-based portfolios, which is complicated by the dynamic nature of commodity markets and the various risks involved. It underscores the importance of understanding time-sensitive risks and making well-informed assumptions during the portfolio construction process, particularly due to the unique characteristics of commodity returns. A major challenge highlighted is estimating volatilities in commodity markets, where bid-offer spreads can change swiftly and drastically. By offering insights into overcoming this challenge, the chapter provides valuable guidance for the effective inclusion of commodities in portfolios and mutual funds. Furthermore, the chapter stresses the growing need to measure and manage commodity price risk exposure, especially within large trading portfolios that include a variety of commodities. It introduces a modified machine learning Liquidity-Adjusted Value-at-Risk (L-VaR) method to help report risk exposure, assess risk reduction strategies, and establish optimized trading risk limits. The chapter outlines a comprehensive approach to managing risk-return characteristics in commodity investments by implementing a market risk modeling algorithm and applying reinforcement machine learning techniques. This method considers not only standard market conditions but also crisis scenarios and the effects of time-varying liquidity constraints. The chapter also discusses the practical implications of its findings for commodity portfolio managers, particularly in the context of the 2007–2009 global financial crisis. It highlights the importance of efficient (optimum) and coherent (optimal) portfolio selection within an L-VaR framework and its role in dynamic asset allocation strategies. Moreover, the chapter examines the broader implications of its recommendations, emphasizing the potential of reinforcement machine learning techniques to tackle real-world challenges in financial markets. It highlights the relevance of these techniques in the context of financial technology and big data ecosystems, suggesting their potential to transform commodity trading and portfolio management.