Commodity Markets

We examine how categorical economic policy uncertainty (EPU) is linked to extreme downside risk in U.S. energy futures markets across contract maturities. Using data from 1994 to 2024, we document maturity-dependent spillovers between EPU indices and Value-at-Risk (VaR) for crude oil and natural gas. Crude oil VaR exhibits increasing connectedness with maturity, while natural gas shows a flatter pattern. The composition of policy-driven spillovers also varies across maturities and time. Storage capacity constraints, which may hinder carry arbitrage, intensify spillovers to longer-term contracts, especially in the oil market. This highlights the role of physical market frictions in policy risk transmission. 

Keywords: Crude Oil, Natural Gas, Extreme Risk, Economic Policy Uncertainty, Maturity Effects, Storage Capacity Constraints.

JEL Codes: E31, E37, Q41, Q43.

This study evaluates the effectiveness of state-level policies on renewable energy (RE) generation capacity in the United States. Using a comprehensive panel dataset of 3017 counties from 2009 to 2019, we account for county-level variations in policy implementation and employ spatial panel models to capture spillover effects among neighboring counties. Our findings reveal that Renewable Portfolio Standards, expressed as percentage targets, significantly increase RE generation capacity across all sources. Property tax reductions boost solar and wind capacity but do not affect hydro and geothermal energy. In contrast, Energy Efficiency Resource Standards are negatively associated with hydro and geothermal capacity. Other policies, such as the Mandatory Green Power Option, sales tax incentives, and market deregulation, have shown no significant linkage with RE capacity. The results further indicate that spatial spillover effects are more pronounced for solar and wind generation capacity than for hydro and geothermal generation capacity. These findings highlight the need for tailored policies that consider the unique characteristics of each renewable energy source and account for regional interdependence in policy design.. 

Keywords: Renewable energy; Renewable portfolio standards; Energy efficiency resource standards; Mandatory green power; Market deregulation; Financial incentive policies

JEL Codes: C01, C21, O14, O33, P28, Q42, Q48.

Applying a no-arbitrage term structure model, we analyze how risk premiums in crude oil, corn, and ethanol futures have evolved amid their increasingly synchronized price movements. Specifically, the model estimates a common factor that summarizes the information driving the three futures prices simultaneously and one idiosyncratic factor that captures distinct information in each market. The common risk prices are more strongly linked to macroeconomic observables, whereas market-specific factors Granger cause the risk prices of both common and idiosyncratic components. We find that financialization negatively impacts the overall level of risk premiums. The risk premiums for crude oil, corn, and ethanol risk premiums all increased from the financialization period to the post-financialization period. While financialization significantly affected the level of risk premiums, its influence on their comovement across markets may have been limited. In contrast, uncertainty surrounding biofuel policy may have affected the linkage between corn and ethanol risk premiums. 

Keywords: Risk premium, common factor, latent idiosyncratic factor, futures, term structure, crude oil, corn, ethanol, speculation, biofuel policy.

JEL Codes: G12, G13

The advent of COVID-19 ended an era of stable US retail food prices that followed the world food price crisis of 2010–2012. Pandemic-related disruptions, avian influenza outbreaks, and the Russia-Ukraine war drove 2022 food-at-home inflation to its highest rate since 1974 (11.4%). In 2023, U.S. Department of Agriculture (USDA) economists responded to these changes by updating food price forecasts using statistical learning protocols to select time series models and prediction intervals to convey their uncertainty. We characterise the public good provided by these “adaptive” inflation forecasts and enhance them by incorporating exogenous variables to improve their precision and explanatory power. COVID-19’s arrival highlighted the value of adapting to the growing relevance of the all-items-less-food-and-energy ("core”) index, the money supply, and wages in predicting food prices. The strong relationships between food prices and core prices and the money supply indicate the sensitivity of food markets to macroeconomic forces and government policy decisions.

Keywords: Food inflation, forecasting, adaptive, external drivers

JEL Codes: Q33, Q41, Q54

Recent fertilizer price spikes in 2021–2022, coupled with government interventions by major exporting countries, have raised global concerns about food security. This study investigates the key drivers of urea prices in China—the world’s largest producer and a major exporter. First, we analyze price transmission between China’s domestic spot and export urea markets, finding no cointegration even prior to the export restrictions introduced in October 2021. Next, we employ a structural vector autoregression model to examine domestic urea price spikes, using a heteroskedasticity-based identification strategy that allows for smoothly transitioning covariances. We decompose urea prices into four structural shocks: supply shocks driven by energy price changes, demand shocks linked to crop price fluctuations, export demand shocks, and market-specific idiosyncratic shocks unrelated to the first three. The findings reveal that rising energy costs and idiosyncratic factors were the main drivers of urea prices in China from 2018 to 2023, while shocks to corn prices and export demand had a smaller impact. Overall, the results indicate that recent export restrictions have had limited influence on domestic urea prices, calling into question the effectiveness of China’s October 2021 export restrictions as a price stabilization measure.

Keywords: Fertilizer prices, export restriction, China, structural vector autogressions

JEL Codes: Q33, Q41, Q54

This paper presents liquidity measures in high resolution and investigates the impact of trading volume on market liquidity and prices in China's soybean complex markets. We document a U-shaped distribution of volume and spreads over the course of a trading day. Quantile regression results show that trading volume tends to tighten bid-ask spreads but widen spreads in the lower tail. We further find that the impact of trading volume on prices is significantly more pronounced during the opening hours, possibly indicating a higher prevalence of informed trading. Additionally, smaller-sized transactions have a disproportionately larger impact on prices compared to large-sized orders, a possible indication that stealth trading, that is, shaving large orders into smaller slices to conceal private information, presents in China's soybean complex. 

Keywords: Informed trading, market liquidity, soybean complex futures, stealth trading

JEL Codes: Q33, Q41, Q54

Two focus groups were conducted in Idaho to explore wheat producers' marketing preferences and factors affecting their decision-making. Participating producers have used a variety of tools, including cash sales, forward contracts, futures and options contracts, and the store-and-sell-later strategy. Beyond financial and operational considerations, participants emphasized the influence of social, behavioral, and external factors on their marketing decisions. This is likely due to the region’s heavy reliance on export markets and the recent unprecedented volatility caused by geopolitical events. The findings highlight the need for extension programs to improve information sharing, marketing education, and stress management, which will help enhance producers’ ability to navigate market volatility.

Keywords: wheat; marketing; risk management; focus group; Idaho

JEL Codes: Q33, Q41, Q54

Recent fertilizer price booms have raised concerns about whether prices reflect market fundamentals. This study examines urea fertilizer price explosiveness (‘bubbles’) in China, a major global producer and exporter, from 2016 to 2023. We identified six episodes of explosiveness, with five occurring during the 2021–2022 boom. Regression analysis reveals that rising coal and corn prices significantly increase the likelihood of price bubbles, while China’s export restrictions significantly reduce it. Additionally, bubble likelihood ratios were lower during the Black Sea Initiative but increased following the implementation of agricultural subsidies. However, the magnitudes of these changes were not economically meaningful.

Keywords: Bubbles, fertilizer prices, Black Sea Initiative, export restrictions

JEL Codes: Q13, G14

This paper investigates the dynamic relationship between crude oil, ethanol, and corn markets across various quantiles of return distributions, as well as at higher statistical moments. Using a quantile vector autoregression model and data from 2007 to 2022, we find that the cross-market linkages are quantile dependent, with the strongest connections observed in the tails of the distribution. A shock to the oil market significantly impacts ethanol and corn returns under extreme bearish and bullish conditions. Positive shocks to the corn market reduce ethanol returns when the ethanol market is highly bullish, but this effect becomes positive in the left tail of the distribution. We also identify significant co-movement in higher statistical moments between these markets. Extreme excess kurtosis in the food-fuel nexus is more likely to occur with high financial market uncertainty, a bullish stock market, contracting industrial production, and a strong US dollar. In addition to these variables, credit spreads, futures market liquidity, futures term structure, and hedging pressure also influence kurtosis in individual markets within the nexus.. 

Keywords: Crude oil market, corn market, biofuels, price distribution, time-varying moments, quantile vector autoregression

JEL Codes: C31, Q11, Q41

Historically, natural gas prices in the United States are closely linked to supply disruptions in the Gulf Coast due to the dominance of offshore and Gulf states production. Over the past decade, the combined share of natural gas production from the states in shale-rich areas has increased dramatically, while Gulf region production has become less important. One reasonable assumption is then that the supply disruptions due to weather events in the Gulf Coast exert a much smaller effect on natural gas prices compared to the pre-shale era. This paper uses panel distributed lag models to empirically test this hypothesis using state-level natural gas prices from 1995 to 2016. Property losses due to natural hazards in Texas and Louisiana are used to represent supply shocks in the natural gas market from the Gulf area. Results show that natural gas prices in both importing and exporting states have become less responsive to natural hazards in Texas but more sensitive to hazard events in Louisiana since the shale boom. These results are robust to the break dates used, the geographical location of states considered, and the empirical specifications employed. The increasing importance of Louisiana in natural gas pricing is perhaps due to its role as the benchmark pricing location for US natural gas and its expansive pipeline networks. 

Keywords: Fixed-effects panel distributed lag model, natural gas, natural hazards, price fluctuations, property damage, supply disruptions

JEL Codes: Q33, Q41, Q54