Wheat Futures Research Articles

Risk spillovers and diversification benefits between crude oil and agricultural commodity futures markets

This study examines the dependence structure and risk spillovers between crude oil and eight major agricultural futures (wheat, corn, soybean coffee, cotton, lumber, cocoa, and live cattle) markets. It also analyzes the potential conditional diversification benefits using a variety of copula functions and Conditional Value at Risk (CoVaR) measure. The results show significant crisis-sensitive and temporal dependence between oil and agricultural markets. Moreover, crude oil shows a symmetric tail dependence with both wheat, corn, soybeans, and cotton futures, whereas oil exhibits an average dependence with coffee. A strong dependence is observed between oil and cocoa (lumber) during bearish (bullish) market conditions. Oil and Live cattle have a symmetric dependence during bearish and bullish market conditions. On the other hand, we find asymmetric and bidirectional risk spillovers from oil to agricultural markets. Furthermore, the wheat futures contract appears to be the most dominating and vulnerable asset to oil price shocks, followed by lumber and corn futures, respectively, while the live cattle contracts are the least. Finally, an equally weighted portfolio offers the highest diversification benefits at a 5 % expected shortfall.

Time-Varying Deterministic Volatility Model for Options on Wheat Futures

This study introduces a robust model that captures wheat futures’ volatility dynamics, influenced by seasonality, time to maturity, and storage dynamics, with minimal calibratable parameters. Our approach reduces error-proneness and enhances plausibility checks, offering a reliable alternative to models that are difficult to calibrate. Transferring estimated parameters from liquid to illiquid markets is feasible, which is challenging for models with numerous parameters. This is of practical importance as it improves the modeling of volatility in illiquid markets, where price discovery is less efficient. In liquid markets, on the other hand, where speculative activity is high, we find that implied volatility is usually the best measure. Additionally, the introduced volatility model is suitable for pricing options on wheat futures as a risk-neutral measure.

Did grain futures prices overreact to the Russia–Ukraine war due to herding?

We study the impact of the 2022 Russian invasion of Ukraine on grain futures prices. The war allows us to evaluate whether commodity futures markets at the time were driven by investor herding. Using event study methods, we find that wheat futures prices rose by 35 percent above the counterfactual until the EU Solidarity Lanes were announced, more than corn futures prices, which were up 16 percent. This relative price response cannot be explained by herding behavior. Furthermore, prices for control commodities did not respond to the war at all, contradicting the herding theory. There is no statistical evidence of abnormal speculative pressure in the market around the time of the invasion, and we conclude the markets put a fair price on the wartime risk of Black Sea grain shipment disruptions.

Research on Grain Futures Price Prediction Based on a Bi-DSConvLSTM-Attention Model

Grain is a commodity related to the livelihood of the nation’s people, and the volatility of its futures price affects risk management, investment decisions, and policy making. Therefore, it is very necessary to establish an accurate and efficient futures price prediction model. Aiming at improving the accuracy and efficiency of the prediction model, so as to support reasonable decision making, this paper proposes a Bi-DSConvLSTM-Attention model for grain futures price prediction, which is based on the combination of a bidirectional long short-term memory neural network (BiLSTM), a depthwise separable convolutional long short-term memory neural network (DSConvLSTM), and an attention mechanism. Firstly, the mutual information is used to evaluate, sort, and select the features for dimension reduction. Secondly, the lightweight depthwise separable convolution (DSConv) is introduced to replace the standard convolution (SConv) in ConvLSTM without sacrificing its performance. Then, the self-attention mechanism is adopted to improve the accuracy. Finally, taking the wheat futures price prediction as an example, the model is trained and its performance is evaluated. Under the Bi-DSConvLSTM-Attention model, the experimental results of selecting the most relevant 1, 2, 3, 4, 5, 6, and 7 features as the inputs showed that the optimal number of features to be selected was 4. When the four best features were selected as the inputs, the RMSE, MAE, MAPE, and R2 of the prediction result of the Bi-DSConvLSTM-Attention model were 5.61, 3.63, 0.55, and 0.9984, respectively, which is a great improvement compared with the existing price-prediction models. Other experimental results demonstrated that the model also possesses a certain degree of generalization and is capable of obtaining positive returns.

Is liquidity provision informative? Evidence from agricultural futures markets

AbstractElectronic commodity trading witnesses a massive volume of order messages every trading day, but little is known about their informativeness. We examine limit order dynamics and their role in price discovery in the Chicago Mercantile Exchange (CME) corn, soybean, and wheat futures markets from January 2019 to June 2020, using order‐level data. Between 75% and 79% of the large number of limit orders submitted are then deleted, which contrasts with the much smaller proportion getting executed or revised. Aggressive trades and limit orders substantially contribute to price discovery, whereas nonaggressive trades and limit orders, representing most market events, play a minor role. Following public information releases, there is a shift in trading strategies, with trades contributing more to price discovery and aggressive limit orders contributing less, compared to nonrelease days. Our findings suggest that most limit orders in agricultural futures markets continue to play the traditional role of uninformed liquidity provision.

Grain Futures Market Response to the Black Sea Grain Initiative

This paper assesses the impact of the Black Sea Grain Initiative on the grain futures market. We rely on counterfactual evaluation techniques and detailed futures price series to estimate how corn and wheat futures prices and historical volatility responded to the Grain Deal enforcement, renewals, and termination. Our event study estimates reveal that market participants anticipated the impact of the Black Sea Grain Initiative. This anticipation is evident from the declining trend in grain futures prices aligned with the EU Solidarity Lanes, implying that commodity traders factored in the Grain Deal’s effects on grain futures price dynamics. Our analysis also uncovers a limited market response to the subsequent renewals and termination of the Grain Deal. Commodity traders did not perceive the Russian threat of withdrawing from the Grain Deal as a primary market risk. These findings expand our understanding of commodity trader behavior and market sentiment under the Black Sea Grain Initiative.

Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures

In this paper, we utilize a machine learning model (the convolutional neural network) to analyze aerial images of winter hard red wheat planted areas and cloud coverage over the planted areas as a proxy for future yield forecasts. We trained our model to forecast the futures price 20 days ahead and provide recommendations for either a long or short position on wheat futures. Our method shows that achieving positive alpha within a short time window is possible if the algorithm and data choice are unique. However, the model’s performance can deteriorate quickly if the input data become more easily available and/or the trading strategy becomes crowded, as was the case with the aerial imagery we utilized in this paper.

Strategies for improving food security under the impact of external grain price fluctuations: evidence from China

ABSTRACT The purpose of this study is to explore strategies for improving food security under the impact of external food price fluctuations. Using impulse response function, complex network analysis, and other methods, we studied the linkage between CBOT prices of soybeans, rice, wheat, corn, and Chinese futures prices from 2009 to 2022. This article analyzes the prominent price linkage relationships, network evolution characteristics, and development trends. The results show that CBOT soybean and China soybean futures prices, CBOT wheat and China soybean futures prices, and CBOT corn and China wheat futures prices are three prominent price linkage relationships. The three pairs of price linkage relationships are constantly changing over time and show a gradual weakening trend. The price of domestic soybean futures and CBOT soybean futures have a strong association. The sudden food crisis will strengthen the connection between international and Chinese grain futures prices. The above results demonstrate the importance of strengthening the independence of China’s futures markets. Based on the above analysis results, the paper put forward policy suggestions such as improving soybean yield levels, utilizing the futures market for risk management, and taking measures to respond to possible changes in Chinese wheat prices based on international corn futures prices.

Russia–Ukraine Conflict, Commodities and Stock Market: A Quantile VAR Analysis

The Russia–Ukrainian war, which began in 2014 and exploded with the invasion of the Russian army on 24 February 2022, has profoundly destabilized the political, economic and financial balance of Europe and beyond. To the humanitarian emergency associated with every war has been added the deep crisis generated by the strong energy and food dependence that many European countries, and not only European, have developed over decades on Ukraine (especially for wheat) and Russia (especially for natural gas). The aim of this article is to verify the existence of a link between the performance of the Eurostoxx index and the price of wheat futures and TTF natural gas, from 25 February 2019 to 28 September 2023. Through a quantile VAR analysis, a link is sought between the Eurostoxx 50 index, and wheat and TTF gas futures prices. Furthermore, the analysis intends to understand whether the presence of such relationship only manifested itself following the war events, or whether it was already present in the market. The analysis carried out also shows that the relationship between the stock market and raw material prices was present even before the conflict.

Forecasting Wheat Futures with Convolutional Neural Networks

Forecasting Wheat Futures with Convolutional Neural Networks

Why the Russian Invasion of Ukraine Moved Wheat Futures Prices More in the U.S. than in Europe

Why the Russian Invasion of Ukraine Moved Wheat Futures Prices More in the U.S. than in Europe

Commodity Markets in an Uncertain World: Geopolitical Uncertainty and Wheat Futures Prices

Commodity Markets in an Uncertain World: Geopolitical Uncertainty and Wheat Futures Prices

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.

Climate policy uncertainty and its impact on major grain futures

It is well known that grain futures markets can be affected by climate change, but there is little literature to explore whether this impact owns asymmetry. Therefore, we study this asymmetry on the basis of climate policy uncertainty. We first find that climate may help to understand the changes in grain commodity prices, especially wheat futures. More importantly, climate shows an asymmetric ability to affecting wheat and corn futures. Besides, the effects of climate on different grain futures vary in the long and short runs, but all unidirectional asymmetric relations seem to be significantly lasting.

Efficiency of Wheat Futures across APMC Mandis

Efficiency of Wheat Futures across APMC Mandis

Cointegration between the Black Sea and Kansas City Wheat Futures: The impact of Russian invasion of Ukraine

We investigate the effect of the Russian invasion of Ukraine on Black Sea Wheat futures. We find that the Black Sea Wheat futures are cointegrated with the Kansas City Wheat futures, the global standard for wheat prices. However, the relationship between these two series significantly changes as a reaction to the main geopolitical events in the region. We also document a significant drop in open interest after the invasion. Our results are relevant to many market participants, such as Ukrainian farmers and consumers in developing countries, including the World Food Program, which buys about forty percent of its wheat supplies from Ukraine.

Evaluation of the operational quality of China's grain futures market based on the comprehensive information weighting method

China's grain prices have witnessed large fluctuations since 2018. Therefore, an accurate evaluation of the impact of major events on the operational quality of the grain futures market can provide a basis for subsequent effective regulation of the grain futures market. This paper evaluates the liquidity, effectiveness, and volatility of the grain futures market with indicators. Based on the genetic algorithm, the Comprehensive Information Weights of grain futures trading are constructed. The above indicators can reflect the quality of market operation more accurately with the CIW. The evaluation of the quality of grain futures market operations shows that the liquidity of cereal futures and bean futures has weakened after December 2019. Meanwhile, the effectiveness of high-gluten wheat futures, japonica rice futures, and soybean 2 futures should be of concern. Finally, the authorities should issue timely market alerts for sharp price fluctuations (such as corn futures and soybean 1 futures).

War and Commodities: An Examination of the Russian-Ukrainian War on CME, Ukrainian and Black Sea Wheat Futures Prices

War and Commodities: An Examination of the Russian-Ukrainian War on CME, Ukrainian and Black Sea Wheat Futures Prices

Wheat futures price prediction based on GA-VMD-CS-LSTM model

Wheat is an important agricultural product. Its price discovery is of great significance to the transaction efficiency of the futures market and even the price fluctuation of the supply and demand relationship in the spot market. Due to the futures price fluctuation itself being a nonlinear relationship, in front of massive data, to achieve a better prediction effect, this paper uses the GA-VMD method to clean the data, and the CS-LSTM model to predict the futures price of wheat in Zhengzhou Commodity Institute. Comparing with the prediction results of SVR, BP and LSTM, it is found that CS-LSTM has lower prediction error and better prediction effect. This paper studies the changes in wheat futures prices and provides theoretical support for government to timely specify appropriate policies to adjust wheat output and prices and improve farmers' lives.

Time-frequency analysis of the comovement between wheat and equity markets

PurposeThis study aims to investigate the time-frequency comovement between wheat futures traded on three US markets (Chicago Board of Trade (CBOT), Kansas City Board of Trade (KCBOT) and Minneapolis Grain Exchange (MGE)) at different maturities and a global equity index.Design/methodology/approachAs they allow to trace transitional shifts over time and across different frequency bands, this paper relies on continuous wavelet tools to investigate the time-frequency comovement among wheat and global stock markets.FindingsThe results show an increase in wheat futures prices at all maturities and a weak integration level within each wheat market during the subprime crisis. Moreover, the wavelet power spectra maps show high wheat and equity price volatility at different time scales and for various subperiods. Furthermore, the continuous wavelet coherence highlights time-frequency-varying comovements between the markets considered, which become particularly high during times of crisis.Practical implicationsThe results provide market participants with a better understanding of the nature as well as the magnitude of the relationship between the global financial market and different wheat markets at different maturities and during tranquil and crisis periods. Indeed, from investors' perspective it is important to understand how markets are segmented or integrated during tranquil and crisis periods in order to better assess risks, diversify portfolios and implement more effective hedging strategies. As for regulators, a better understanding of the level of integration of different markets would further help refine macroprudential policies, and thus strengthen financial stability and resilience.Originality/valueThis paper enriches the existing literature by investigating the time-frequency comovement between wheat and a global equity market. Indeed, the dynamics between stock and wheat markets across different nearest to maturities have not been widely explored by previous studies.