Volatility Spillover and Time-Varying Conditional Correlation Between DDGS, Corn, and Soybean Meal Markets

We find distiller\u27s dried grains with solubles (DDGS) prices to be positively correlated with both corn and soybean meal prices in the long run. However, neither corn nor soybean meal prices respond to deviations from this long-run relationship. We also identify strong time-varying dynamic conditional correlations between the markets, with the correlation between DDGS and corn strengthened after the expansion of ethanol production. There also appear to exist significant volatility spillovers from both the corn and soybean meal markets to the DDGS market, with the impact from corn shocks much larger compared to soybean meal shocks

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Financialization of Agricultural Commodity Markets: Do Financial Data Help to Forecast Agricultural Prices?

The dramatic rise in commodity index investment have made many market analysts and researchers believe that commodity markets have undergone a financialization process that forged a closer link between commodity and financial markets. I empirically test whether this hypothesis is true in a forecasting context by using high-frequency financial data to forecast monthly US corn prices. Specific financial series examined include the Baltic Dry Index, the US exchange rate, the Standard and Poor’s 500 market index, the 3-month US Treasury bill interest rate, and crude oil futures prices. Using a recently developed statistical model that deals with mixed-frequency data, I find that while some improvements may be made when including high-frequency financial data in the forecasting model, the improvements in mean-squared prediction error and directional accuracy using such models are minimal, and that models generated from random walk and autoregressive models perform satisfactory well compared to more complicated models

Projecting Corn and Soybean Crops: How Accurate Are Crop Forecasts?

Commodity prices are determined by the dynamics of supply and demand and they oscillate over time according to expectations of market participants, who form and update their outlooks based on new information available in the market. As new information about supply and demand of commodities becomes available, buyers and sellers review their beliefs and trade in the market accordingly. This process of price discovery is crucial for various business decisions in the agricultural sector, such as production, marketing and risk management. For instance, if new information on supply and demand suggests that corn prices will decrease, grain producers might want to choose a risk management strategy that protects them against falling prices

How Efficient Is Maize Production among Smallholder Farmers in Zimbabwe?

In this paper, we estimate the efficiency of resource use for maize production among smallholder farmers in Zimbabwe. We collect a total of 176 questionnaires from Mazowe South district, consisting of both A1 (less than 10 hectares of land) and A2 (greater than 10 hectares of land) farms. Findings based on parametric Stochastic Frontier models show that smallholder farmers in Zimbabwe are not efficiently utilizing their available agricultural resources when producing maize. The average technical efficiencies are only 36.75% and 38.6% for A1 and A2 farms, respectively. About 60% of A1 and all A2 farmers have technical efficiency scores between 0.3 and 0.5. In the non-parametric analysis, however, we find a much higher technical efficiency for both types of farms, over 60% and 75% for A1 and A2 farms, respectively. Still, these numbers are lower than those found in other countries. Among other factors examined, attaining tertiary education and access to extension services by the head of the household can significantly improve production efficiency

Inverse price spread and illiquid trading in Korea-ETS

The Korea Emissions Trading Scheme was formally launched in January 2015, aiming for a 30% reduction in South Korea's carbon emissions by 2030 from the business-as-usual levels. Under this trading mechanism, carbon allowance credits are traded on the Korea Exchange, while offset credits generated from emission-reduction projects are mostly traded over the counter. Exchange trading under the Korea Emissions Trading Scheme faced immediate liquidity problems a few days after its launch. Additionally, offset credits have been consistently traded at a much higher price on the over-the-counter market versus the exchange-traded allowance credits, a scenario opposite to the conventional wisdom. This article examines the causes of this inverse price relationship between these two types of credits as well as the lack of liquidity in exchange trading under the Korea Emissions Trading Scheme. Lessons learned from the Korean carbon market could have important implications for other countries seeking to adopt a similar system