Agricultural Commodity Futures Research Articles

Media influences on corn futures pricing

Abstract Understanding agricultural commodity futures is crucial for efficient business operations. This study employs textual machine learning on 290,271 articles (2009–2020) focusing on corn markets, aiming to model the impact of news on corn futures pricing. Our novel approach enables the identification of seven distinct topics within corn news, offering a comprehensive view of the news coverage spectrum. Soybean biofuel news notably influences corn prices, while exports, weather and wheat news significantly impact pricing uncertainty. These insights deepen our understanding of factors shaping corn futures and highlight machine learning’s potential in agricultural economic analysis, enabling more accurate market predictions and policy decisions.

Agricultural commodity futures prices prediction based on a new hybrid forecasting model combining quadratic decomposition technology and LSTM model

The stability of agricultural futures market is of great significance to social economy and agri-cultural development. In view of the complexity of the fluctuation of agricultural futures prices, it is challenging to make up for the shortcomings of the existing data preprocessing technology so as to improve the prediction accuracy of the model. This paper puts forward a new VMD-SGMD-LSTM model based on improved quadratic decomposition technology and artificial intelligence model. First of all, in the data preprocessing part, VMD is used to decompose the original futures price data, and SGMD is used to further process the remaining components. Secondly, the LSTM model is used to predict a series of modal components, and the final result is obtained by synthesizing the predicted values of different components. Furthermore, based on the futures trading data of wheat, corn and sugar in China agricultural futures market, this paper makes an empirical study in the 1-step, 2-step and 4-step ahead forecasting scenarios, respectively. The results show that compared with other benchmark models, the VMD-SGMD-LSTM hybrid model proposed in this paper has better forecasting ability and robustness for different agricultural futures, which effectively makes up for the shortcomings of existing research.

Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes

AbstractThis study investigated the volatility linkages between energy and agricultural futures, including possible causes for these comovements, such as external macroeconomic and financial shocks during low and high volatility regimes. A combination of Markov‐switching regressions and quadrivariate VAR–DCC–GARCH and VAR–BEKK–GARCH modeling revealed that external shocks have an asymmetric effect on the relationship of these assets with higher cross‐correlations reported during high volatility regimes. This comovement effect outweighs the substitution effect between energy and agricultural products. Furthermore, the quadrivariate VAR–BEKK–GARCH model provides strong evidence of a bidirectional price volatility spillover between the agricultural and energy markets during periods of high volatility. Overall, the results suggest that energy futures can be effectively used for hedging in a portfolio comprising agricultural futures (and vice versa), while a combination of macroeconomic and financial index futures can serve as an effective hedging tool in investment portfolios comprising both energy and agricultural commodities.

Air pollution, weather factors, and realized volatility forecasts of agricultural commodity futures

AbstractThis study investigates the potential effects of environmental factors on fluctuations in agricultural commodity futures markets, by constructing a new category of daily exogenous predictors related to air pollution, weather, climate change, and investor attention. The empirical results from out‐of‐sample analyses suggest that the heterogeneous autoregressive (HAR) model incorporating all these exogenous predictors is more likely to outperform other HAR‐type models. Additionally, economic evaluations demonstrate the superior performance of models incorporating investors' attention to climate change or extreme weather as predictors. While not all exogenous predictors are equally important for volatility forecasts, adopting appropriate variable selection methods to handle different sets of exogenous predictors can lead to better performance than the HAR benchmark. With the inclusion of air pollution or weather factors in the HAR model, a portfolio with an annualized average excess return of 16.2068% or a Sharpe ratio of 10.0431 can be achieved for the wheat futures, respectively.

Research on the Current Situation and Suggestions of China's Agricultural Product Futures Market

The Chinese market for modern agricultural products now includes the agricultural futures market, which is also one of the most significant investment and risk management tools in the country due to the rapid development of China's economy and the globalization of the international market. This paper provides a detailed introduction to the historical history, market development, policy environment, institutional design, trading system, and risk management of China's agricultural commodity futures market. This study also offers some solutions in response to some issues that are now present in the market for agricultural products. The development of China's agricultural future has experienced a tortuous course, despite the efforts of all parties to benefit the country and its people. This research is from the investment subject, market regulation, and market environment analysis and gives detailed suggestions.

Asymmetric Risk Connectedness between Crude Oil and Agricultural Commodity Futures in China before and after the COVID-19 Pandemic: Evidence from High-Frequency Data

Based on the spillover index and an improved spillover asymmetric measure method, this paper studies the volatility spillover and its asymmetric effect between crude oil and agricultural commodity futures in pre- and post-outbreak of COVID-19. We find that the total volatility spillover is higher with pre-outbreak of COVID-19. In addition, the volatility spillover caused by China’s crude oil is more prominent than international crude oil around the COVID-19, which highlights the necessity of risk control through the establishment of an energy financial market in China. Finally, although the asymmetric effect of volatility spillover has always existed, crude oil was less impacted by good news post-outbreak of COVID-19, indicating that the outbreak of COVID-19 makes assets dominated by commodity attributes more sensitive to bad news. These findings are beneficial for investors to establish a cross-sector risk hedging portfolio, and provide empirical evidence for policymakers to ensure energy and food security.

Links between US and Turkish agricultural commodity Markets: Nonlinear dependence and tail risk

In these unprecedented times, marred by the effects of the Covid-19 pandemic, global warming, and the war in Ukraine that began in February 2022, new approaches such as tail dependence have attracted more interest than conventional market dependence methodologies in analyzing time series in order to evaluate market linkage. In this study, we use the tail-restricted integrated regression function (IRF), introduced as a new methodology for nonlinear tail-mean dependence analysis. The IRF approach has several advantages over the existing tail-dependence measures focused solely on the occurrence of single tail events. To examine market dependence and tail risk, we analyze the nexus between the US and Turkish agricultural commodity markets over the period January 5, 2016–May 31, 2022. In addition to the daily prices of barley, corn, and wheat on agricultural commodity markets, we use Brent oil as a representative for the energy market, interest, and foreign exchange rates for financial markets as variables in our study. The results of upside and downside asymmetric risk spillovers show the direction of impact from the US agricultural market to the Turkish agricultural market. The findings suggest that, following the launch of a spot market in Türkiye, the launch of an agricultural commodity futures market will enhance the completeness and the link between the spot and derivatives markets there.

Inflationary Effect of Agricultural Commodity Futures Market in India: Is it There and Should we Care?

The impact of the agricultural commodity futures market on agricultural commodity prices has been a contested issue in India. Regulators have often interfered in this market with the stated objective of controlling inflation. In this article, we examine the relationship between futures trading activity and spot price inflation in the agricultural commodity markets in India by analysing the relationship on the aggregate market level as well as on the individual markets of 12 commodities. We find that the trading volume of AGRIDEX futures contracts has a significant and positive impact on the Consumer Price Index in both the long run and short run. For individual commodities, we find that the futures volume has a significant and positive impact on the spot price in only four out of eleven commodities analysed. The evidence suggests that regulators should monitor the market closely but refrain from repeatedly using stringent measures, like banning trading.

Dependence and risk management of portfolios of metals and agricultural commodity futures

This paper examines the dependence structure and the portfolio allocation characteristics of a main industrial portfolio metals (gold, platinum, palladium, aluminum, silver, copper, zinc, lead, and nickel), and of an agricultural commodities portfolio (wheat, corn, soybeans, coffee, sugar cane, sugar beets, cocoa, cotton, and lumber). Our methodology is based on regular vine copulas and the conditional Value-at-Risk. The motivation to investigate the dependence structure and connectedness between agricultural, and metal commodities is to identify ways in which agricultural and metal commodities can hedge each other and to explore the possibilities of parallel investments. The results indicate that the dependence dynamics of the main metals portfolio are characterized by symmetric features. However, the dependence dynamics of the agricultural commodities portfolio are characterized by symmetric and asymmetric features; symmetric dynamics are predominant. Finally, the metal commodities portfolio is observed to be less risky for financial resource allocation during the global financial crisis.

Positive and negative price bubbles of Chinese agricultural commodity futures

This study examines the predictability of positive and negative price bubbles of the Chinese agricultural commodity futures with the log-periodic power law singularity (LPPLS) confidence multi-scale indicators application. To measure the bubble risks of these commodity futures, the duration, the frequency and the magnitude of the price bubble are adopted as the evaluation indicators. The results found that the Chinese agricultural commodity futures with higher bubble risks are soybean, soybean oil, soybean meal, white sugar, cotton and corn futures. Furthermore, the price bubbles of Chinese agricultural commodity futures are affected by the investor sentiment and the overall degree of macroeconomic factors. Economic growth, money supply, and inflation have positive effects on the occurrences of the positive bubbles, while currency appreciation and interest rate have negative effects and vice versa on the occurrences of the negative bubbles. Financialization has positive effects on both occurrences of the positive and negative bubbles. Overall, this study provides new insights for the policy makers and market participants to monitor the occurrence of price bubbles.

Dynamic high-frequency dependence structure of Chinese agricultural commodity futures based on the semi-parametric copula

This paper investigates the dynamic high-frequency dependence structure of Chinese four major agricultural commodity futures by utilizing a semi-parametric copula-based multivariate model with 5-minute high-frequency trading data. The empirical results show that the daily dependence between the agricultural commodity futures is time-varying and slightly asymmetric, and that this dependence and its asymmetry are more pronounced during the world food crisis (2007–2008) and the global financial crisis (2008–2011). Furthermore, the intraday dependence structure exhibits a lopsided inverted U-shaped pattern with relatively lower dependence level around the opening and closing time, and a peak around the mid-trading day.

The Distribution of Commodity Futures: A Test of the Generalized Hyperbolic Process

ABSTRACT Using daily closing price data spreading over 3 April 1990, to 5 May 2020, this study explores the skewness and excess kurtosis behaviour across energy, metals, and agricultural commodity futures. Subsequently, it compares the fitting of an empirical distribution under normal distribution assumption with those under the Generalized Hyperbolic distribution. The generalized hyperbolic distribution includes Hyperbolic distribution, Variance-Gamma distribution, and Normal Inverse Gaussian distribution. The results show that the Normal Inverse Gaussian distribution for natural gas, gold, platinum, copper, sugar, and feeder cattle futures captures skewness as well as excess kurtosis of the daily logarithmic returns. The findings are robust to the sub-sample analysis.

Forecastability of Agricultural Commodity Futures Realised Volatility with Daily Infectious Disease-Related Uncertainty

Given the food supply chain disruption from COVID-19 lockdowns around the world, we examine the predictive power of daily infectious diseases-related uncertainty (EMVID) on commodity traded futures within the agricultural bracket, sometimes known as the softs, using the heterogeneous autoregressive realised variance (HAR-RV) model. Considering the short-, medium-, and long-run recursive out-of-sample estimation approach, we estimate daily realised volatility by using intraday data within the 5 min interval for 15 agricultural commodity futures. During the COVID-19 episode, our results indicated that EMVID plays an important role in predicting the future path of agricultural commodity traded futures in the short, medium, and long run, i.e., h = 1, 5, and 22, respectively. According to the MSE-F test, these results are statistically significant. These results contain important implications for investors, portfolio managers, and speculators when faced with investment risk management and strategic asset allocation during infectious disease-related uncertainty.

“Fast money” around Federal Statistics Releases

AbstractMany public statistics are closely followed by the public as they provide valuable information for decision making. Although economic theory suggests that low‐latency traders (LLTs) can earn excess arbitrage profits from trading on public statistics releases due to their speed advantage, the empirical literature fails to find meaningful LLTs' stock market profits when trading on macroeconomic statistical releases. Here we confirm previous findings with improved techniques, but also show that excess profits can be earned quickly in agricultural commodity futures markets around United States Department of Agriculture (USDA) statistical releases. We attribute differences in the magnitude of the profits to the straightforward nature of the information and to the direct relevance of the reports to traders for the markets considered. Motivated by market concerns, USDA and the United States Department of Labor recently changed their news media prerelease lockup policy to mitigate LLTs' speed advantage. However, we find the policy change had little effect on the speed advantage as no significant reduction in LLTs' profits occurred. Implications of the research are relevant for government agencies concerned with mitigating LLT speed advantages particularly during the release of public statistical information.

On the interdependence between biofuel, fossil fuel and agricultural food prices: Evidence from quantile tests

This study investigates the long- and short-run relationships between fossil fuel, biofuel and agricultural food commodity prices by applying the cointegration and causality analysis. This study employs a quantile approach to consider the non-linearity and asymmetry in the relationship of data. To reduce a possible noise contained in the data, this study uses weekly average data of biofuel (ethanol), fossil fuel (West Texas Intermediate [WTI] oil) and agricultural food commodity (corn) futures prices from April 18, 2005 to October 30, 2020. The main findings are summarised as follows. First, we discover no evidence of cointegration between the quantiles of WTI oil, ethanol and corn prices when all quantiles are considered, although the results of the linear cointegration test are inconclusive. Second, results of the Granger non-causality test in quantiles reveal a significant short-run bidirectional causality between the returns of WTI oil, ethanol and corn prices, for all or most quantiles of the distribution. This implies asymmetric and nonlinear causal linkage between these variables. Moreover, the results of our analysis have several policy implications, including energy price stability, decrease in energy dependence on fossil fuels, promotion of agricultural industry, environmental sustainability, energy conversion and reduction of greenhouse gas emissions.

The Analysis of Causality and Risk Spillover between Crude Oil and China's Agricultural Futures.

This paper aims to apply the time-varying Granger causality test (TVGC) and the DY Spillover Index (Diebold and Yilmaz, 2012) to measure the Granger causality and dynamic risk spillover effects of the international crude oil futures market on China’s agricultural commodity futures market from the perspectives of return and volatility spillovers. Empirical evidence relating to the TVGC test suggests the existence of unidirectional Granger causality between crude oil futures and agricultural product futures. This relationship shows a strong time-varying property, in particular for sudden or extreme events such as financial crises and natural disasters. On the other hand, the volatility spillover in crude oil and agricultural product futures markets responds asymmetrically and bidirectionally according to the result of the DY Spillover index, and the periodicity of total volatility spillover correlates closely with the occurrence of global economic events, which indicates that the spillover effect between crude oil and agricultural commodity futures markets will be exacerbated in turbulent financial and economic times. Such findings are expected to help in formulating policy recommendations, portfolio design, and risk-management decisions.

How connected is the agricultural commodity market to the news-based investor sentiment?

Previous studies indicate a substantial time-variation in the co-movement of commodity futures markets and economic fundamentals. This paper examines the connectedness and directional spillovers for both the agricultural commodity futures markets and the corresponding sentiment indices. We first construct dynamic time-varying connectedness measures both for the agricultural commodity returns and sentiments. Then, we use panel data regressions and time-varying Granger causality tests to evaluate whether the spillovers between these returns and sentiments are influenced by the economic and financial uncertainties, including the global COVID-19 pandemic. In particular, we document that the COVID-19 induced uncertainty influences agricultural commodity returns and sentiments significantly around the first cycle of the pandemic in 2020. Last but not least, economic policy and financial market uncertainty are also found to be significant determinants of the connectedness between agricultural commodity returns and sentiment spillovers.

Statistics Show Value in Old‐School Crop Condition Reports

Statistics Show Value in Old‐School Crop Condition Reports

Price Discovery in the Presence of Embedded Location Options: The Evidence from Chana Contracts in India

The presence of embedded location options in agricultural commodity futures contracts allows for several additional deliverable locations in addition to the reference location for the delivery of the underlying asset using location specific adjustments to futures settlement prices. Price discovery contribution assessment in this context needs to be done simultaneously across futures and cash markets in all these deliverable locations in contrast to present bivariate studies in the Indian context, which only consider futures and the cash market at reference location. We assess price discovery contributions across all the deliverable cash markets and the most liquid futures contract for Chana (Gram/Chickpea) in India. We find that while futures lead price discovery as expected, the cash market at the reference location plays almost no role in price discovery with cash markets at additional deliverable locations playing a larger than expected role. We interpret these results as arising from large values of location options for Chana due to inappropriate specifications of deliverable asset locations and associated premiums and discounts. These results provide evidence that exchanges and regulators need to monitor option values and optimize delivery specifications of futures contracts so that they perform their functions of price discovery and risk transfer well.

Dynamic correlation between crude oil and agricultural futures markets

AbstractIn recent years, the relationship between agricultural commodities and crude oil has become increasingly close with the promotion of biofuel policies. This study examines the dynamic correlation between global crude oil futures and seven agricultural commodity futures by applying the consistent dynamic conditional correlation and dynamic equicorrelation models. The empirical results show that the dynamic correlation between the global crude oil futures market and China's agricultural futures market is weak compared to the global agricultural futures market. In particular, soybean oil has the strongest correlation with crude oil, while Dalian Commodity Exchange (DCE) corn and Zhengzhou Commodity Exchange wheat have the weakest correlation with crude oil. There is an indirect linkage between crude oil futures and DCE soybean meal and DCE soybean oil. Moreover, the dynamic correlation between crude oil and agricultural commodities increased during the financial crisis, the novel coronavirus (COVID‐19) epidemic, and the crude oil crash crisis. Brent crude oil has a stronger co‐movement with China's agricultural commodities than West Texas Intermediate crude oil and can better hedge the risk of agricultural commodities. The findings of this study provide some insights into the contagion risk management of crude oil futures and agricultural futures markets.