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This paper uses a novel dataset of commodity-linked notes (CLNs) to examine the impact of the flows of financial investors on commodity futures prices. Investor flows into and out of CLNs are passed to and withdrawn from the futures markets via issuers' trades to hedge their CLN liabilities. The flows are not based on information about futures price movements but nonetheless cause increases and decreases in commodity futures prices when they are passed through to and withdrawn from the futures markets. These finding are consistent with the hypothesis that non-information-based financial investments have important impacts on commodity prices.

This paper develops a model with a tractable log-linear equilibrium to analyze the effects of informational frictions in commodity markets. By aggregating dispersed information about the strength of the global economy among goods producers whose production has complementarity, commodity prices serve as price signals to guide producers' production decisions and commodity demand. Our model highlights important feedback effects of informational noise originating from supply shocks and futures market trading on commodity demand and spot prices. Our analysis illustrates the weakness common in empirical studies on commodity markets of assuming that different types of shocks are publicly observable to market participants.

How do income shocks affect armed conflict? Theory suggests two opposite effects. If labour is used to appropriate resources violently, higher wages may lower conflict by reducing labour supplied to appropriation. This is the opportunity cost effect. Alternatively, a rise in contestable income may increase violence by raising gains from appropriation. This is the rapacity effect. Our article exploits exogenous price shocks in international commodity markets and a rich dataset on civil war in Colombia to assess how different income shocks affect conflict. We examine changes in the price of agricultural goods (which are labour intensive) as well as natural resources (which are not). We focus on Colombia's two largest exports, coffee and oil. We find that a sharp fall in coffee prices during the 1990s lowered wages and increased violence differentially in municipalities cultivating more coffee. This is consistent with the coffee shock inducing an opportunity cost effect. In contrast, a rise in oil prices increased both municipal revenue and violence differentially in the oil region. This is consistent with the oil shock inducing a rapacity effect. We also show that this pattern holds in six other agricultural and natural resource sectors, providing evidence that price shocks affect conflict in different directions depending on the type of the commodity.

The large inflow of investment capital to commodity futures markets in the past decade has generated a heated debate about whether financialization distorts commodity prices. Rather than focusing on the opposing views concerning whether investment flows caused a price bubble, we critically review academic studies through the perspective of how financial investors affect risk sharing and information discovery in commodity markets. We argue that financialization has substantially changed commodity markets through these mechanisms.

The authors found that, concurrent with the rapidly growing index investment in commodity markets since the early 2000s, prices of non-energy commodity futures in the United States have become increasingly correlated with oil prices; this trend has been significantly more pronounced for commodities in two popular commodity indices. This finding refiects the financialization of the commodity markets and helps explain the large increase in the price volatility of non-energy commodities around 2008.

We introduce a return predictor related to the slope and curvature of the futures term structure: basis-momentum. Basis-momentum strongly outperforms benchmark characteristics in predicting commodity spot and term premiums in both the time series and the cross section. Exposure to basis-momentum is priced among commodity-sorted portfolios and individual commodities. We argue that basis-momentum captures imbalances in the supply and demand of futures contracts that materialize when the market-clearing ability of speculators and intermediaries is impaired, and that it represents compensation for priced risk. Our findings are inconsistent with alternative explanations based on storage, inventory, and hedging pressure.

We propose and test a theory of using commodities as collateral for financing. Under capital control and collateral constraint, investors import commodities and pledge them as collateral to earn higher expected returns. Higher collateral demands increase commodity prices and make the inventory-convenience yield relation less negative. Our model illustrates these equilibrium effects and suggests that the violation of covered interest-rate parity is a proxy for collateral demands. Evidence from eight commodities in China and developed markets supports the theoretical predictions. Our findings complement the theory of storage and provide new insights into the financialization of commodity markets.

Agent-based models are a flexible and scalable modeling approach employed to study and describe the evolution of complex systems in different fields, such as social sciences, engineering, and economics. In the latter, they have been largely employed to model financial markets with a bottom-up approach, with the aim of understanding the price formation mechanism and to generate market scenarios. In the last few years, they have found application in the analysis of energy markets, which have experienced profound transformations driven by the introduction of energy policies to ease the penetration of renewable energy sources and the integration of electric vehicles and by the current unstable geopolitical situation. This review provides a comprehensive overview of the application of agent-based models in energy commodity markets by defining their characteristics and highlighting the different possible applications and the open-source tools available. In addition, it explores the possible integration of agent-based models with machine learning techniques, which makes them adaptable and flexible to the current market conditions, enabling the development of dynamic simulations without fixed rules and policies. The main findings reveal that while agent-based models significantly enhance the understanding of energy market mechanisms, enabling better profit optimization and technical constraint coherence for traders, scaling these models to highly complex systems with a large number of agents remains a key limitation.

Information contained in high-frequency financial market data reveals that a 10 basis-point surprise increase in interest rates causes commodity prices to fall immediately by approximately 0.6%. This is similar to the estimated responses of both the Standard and Poor's 500 and a United States trade weighted exchange rate index, and approximately five times larger than the response in a standard vector autoregression, even twelve months after the shock. Metals prices tend to respond more than agricultural commodities. The point estimate for oil prices is similar to other commodities, but is estimated less precisely.

The run-up in oil prices since 2004 coincided with growing investment in commodity markets and increased price co-movement among different commodities. We assess whether speculation in the oil market played a role in driving this salient empirical pattern. We identify oil shocks from a large dataset using a dynamic factor model. This method is motivated by the fact that a small-scale vector autoregression is not informationally sufficient to identify the shocks. The main results are as follows. (i) While global demand shocks account for the largest share of oil price fluctuations, speculative shocks are the second most important driver. (ii) The increase in oil prices over the last decade is mainly driven by the strength of global demand. However, speculation played a significant role in the oil price increase between 2004 and 2008 and its subsequent collapse. (iii) The co-movement between oil prices and the prices of other commodities is mainly explained by global demand shocks. Our results support the view that the recent oil price increase is mainly driven by the strength of global demand but that the financialization process of commodity markets also played a role.