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Introduction: It is commonly observed that the ratio of food prices during the war times had become significantly more important than usual periods within the countries including Turkey, known as the Ottoman Empire that previously defeated in Balkans just before the Great World War. The scope of the study is to analyze increased or decreased wheat prices together with price fluctuations during the war period.

Aim: This study investigates the food pricing progress during The Great World War and its relationship with wheat prices.

Method: A model for the behavior of time series is applied to compare the important days of the war data against the timeline of wheat prices for British, German, and French. The statistical test named Holt–Winters uses exponential smoothing technique to encode the various values from the past and predicts “typical” values for the present and the future.

Findings: As a result, it can be said that wheat prices had anomaly patterns during the specific dates in war for French, British, and German sides. Great Britain’s wheat prices increased significantly on April 1915 when landings began on the Gallipoli Peninsula. Wheat prices in Great Britain and Germany dropped significantly just before on July 1916 when the first Battle of the Somme began. However, it increased in Great Britain whilst decreased considerably in Germany in March 1918 when the Soviet Government signed a separate peace agreement with the Central Powers. A significant increase for France was observed only at the end of this war.

The purpose of this paper is to quantitatively analyse the changes in the functional efficiency of the six Chinese agricultural futures markets and compare the relative behaviour of different futures markets. In addition, this paper analyses the causes of differences in the functional efficiency of agricultural futures markets and advances policy suggestions.

The method used in this paper is the social loss index proposed by Stein (1981, 1986). This method can quantitatively measure the functional efficiency of agricultural futures markets from the perspective of social welfare. The indicator is calculated for the 2009–2017 period and for several sub-periods. The data are from the CSMAR research data services in China.

Preliminary results suggest that the longer it takes for an agricultural futures contract to reach maturity, the lower the functional efficiency of its market. Second, the functional efficiency of the agricultural futures markets in China is improved except for that of the wheat futures market. Finally, the corn futures market is most efficient probably due to the progress of marketization, while the strong wheat futures market is most inefficient probably due to the decrease in futures market liquidity.

This paper uses a more reasonable method to study the functional efficiency of Chinese agricultural futures markets and then analyses the causes of differences in the functional efficiency of agricultural futures markets.

Wheat, which is one of the major staple food grain crops in India, continues to depict occasional fluctuation in the prices though Union government has adopted administered price policy for wheat by intervening in its procurement at assured prices and distribution. Such fluctuations in prices are usually attributed to inefficient functioning of the agricultural markets. Since spatially separated markets also play an important role to determine efficiency of the agricultural markets, the study has used market integration as one of the tools to analyze the price transmission across the spatially separated markets to identify causes of price fluctuations and suggest ways to stabilize wheat prices.

The study utilizes monthly wholesale prices for January, 2006 to May, 2016 for dara wheat. First, the study employs augmented Dickey and Fuller (ADF), Phillips and Perron (PP) and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) tests to check stationarity in wheat prices. Second, Johansen's cointegration test is applied to assess the integration of wholesale prices between selected pairs of wheat markets to determine long-run relationship among them. Third, Granger casualty test is used to find the direction of causality between the wheat market pairs. Finally, threshold vector error correction model (TVECM) and likelihood ratio (LR) tests are employed to examine long-run adjustment of prices towards the equilibrium in selected wheat markets.

Since wheat wholesale prices for the selected markets are found to be integrated of the order one, that is [I(1)], Johansen's test of cointegration is employed and its findings reveal that the selected wheat market pairs exhibit cointegration and show a long-run price association among themselves. There exists a bi-directional causality among the wheat market pairs. Since LR test is in favor of threshold model (except for Etawah–Delhi pair), one and two threshold models were also performed accordingly. Findings show that wholesale prices of wheat in Delhi markets remain higher than the prices of all other regional markets as regional markets are found to adjust their prices towards Delhi market. Distance of the wheat markets from each other is directly associated with threshold parameters, which are analogous to the transaction costs. Geographically dispersed wheat markets incorporate high transaction and vice versa.

The study argues that there is need to improve rural infrastructure and connectivity of the agricultural markets and remove market asymmetries through unified market regulating mechanisms across the states. This will enable price adjustment process from primary wholesale markets (in production regions) to the secondary wholesale markets (in scarcity regions) quickly.

The contribution of the study in the existing literature lies in the fact that there are no empirical evidences in the context of India that use price transmission as a tool of market integration among spatially separated wheat markets using TVCEM as this model examines role of transaction costs in efficient functioning of the agricultural markets.

Estimates the output‐oriented and input‐specific technical efficiency in two samples of Greek, durum wheat farms – organic and conventional ones – using Kalirajan and Obwona’s stochastic varying coefficient regression model. Findings indicate that the organic wheat farms examined are relatively more efficient. Reasons may include lower profit margins and restrictions on inputs permitted, which may force organic farmers to be more cautious with input use. However, technical efficiency scores are still relatively low for both types of wheat farming. Therefore, considerable scope for cost reducing and farm income improvement may exist in both farming modes. This realization could prove crucial for the long‐run viability and the future course of organic wheat farming.

The purpose of this paper is to analyze the efficiency of temperature-based weather derivatives (WD) in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions.

The objective of this paper is to analyze the efficiency of temperature-based WD in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions.

The results of the efficiency tests show that temperature-based put options are efficient in offsetting yield shortfalls for rice and wheat in China. The weather-yield models have a high prediction power in explaining yield variation by temperature.

The de-trending procedure for the weather-yield model should be improved to distinguish better between technology progress, human activities and influence of weather. Further, more advanced models could be used for the pricing.

The findings of the paper support the launch of WD as an efficient risk management tool for agriculture in China. Compared with traditional damage-based insurance, WD are more flexible, have lower transactions costs and avoid moral hazard or adverse selection.

The efficiency problem of WD has not been analyzed sufficiently worldwide and especially not for developing countries like China where a large proportion of the population works as farmers. This paper supports to fill this gap.

Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

This paper examines the cointegration of wheat market prices in Northern Bangladesh. Results are based on weekly wholesale price data on wheat collected from the Department of Agricultural Marketing (DAM) in eight markets in the Rangpur division of Northern Bangladesh. The data has 208 observations for wheat in each of the eight markets ranging over a period of January 2006 to December 2009. Johansen’s cointegration test reveals that most of the wholesale markets of wheat in the Rangpur division are co-integrated which indicates that price signals and information are transmitted smoothly across the markets. The discovery of the market integration appears to be quite significant for the success of price policy and market liberalization programs which are being undertaken in Bangladesh.

In line with the ongoing global and domestic reforms in agriculture and allied sectors, the Indian Government is reducing its direct market intervention and encouraging private participation based on market forces. This has led to increased exposure of agricultural produce to price and other market risks, which consequently emphasize the importance of futures markets for price discovery and price risk management. The purpose of this paper is to analyze the efficiency of agricultural commodity markets by assessing the relationships between futures prices and spot market prices of major agricultural commodities in India.

The efficiency of the futures market for 12 agricultural commodities, traded at one of the largest commodity exchanges of India, i.e. National Commodity & Derivatives Exchange Ltd, has been explored by using Johansen's cointegration analysis and Granger causality tests. Unit root test procedures such as Augmented Dickey‐Fuller and non‐parametric Phillips‐Perron were initially applied to examine whether futures and spot prices are stationary or not. The hypothesis, that futures prices are unbiased predictors of spot prices has been tested using econometric software package.

Results show that cointegration exists significantly in futures and spot prices for all the selected agricultural commodities except for wheat and rice. This suggest that there is a long‐term relationship between futures and spot prices for most of the agricultural commodities like maize, chickpea, black lentil, pepper, castor seed, soybean and sugar. The causality test further distinguishes and categorizes the commodities based on direction of relationship between futures and spot prices. The analysis of short‐term relationship by causality test indicates that futures markets have stronger ability to predict subsequent spot prices for chickpea, castor seed, soybean and sugar as compared to maize, black lentil and pepper, where bi‐directional relationships exist in the short run.

The results of this study are useful for various stakeholders active in agricultural commodities markets such as producers, traders, commission agents, commodity exchange participants, regulators and policy makers.

There are very few studies that have explored the efficiency of the commodity futures market in India in a detailed manner, especially at individual commodity level.

The purpose of this paper is to study the market efficiency, unbiasedness and the direction of causality among four agricultural commodity futures contracts for a forecasting horizon of 28 days, 56 days and 84 days which are traded at National Commodity and Derivatives Exchange Ltd.

To analyse the efficiency of futures market in Indian scenario, we focus on maize, chickpea, soybean and wheat which are among the most important agricultural commodities traded in India. In the first step, Augmented Dickey-Fuller test and nonparametric Phillips-Perron approaches have been used to examine the stationarity of all futures and spot price series. After testing the presence of cointegration in futures and spot series using Johansen’s Cointegration approach, the joint restrictions of β ₀=0, β ₁=1 and β ₁=1 on the cointegrating vectors were imposed to test whether the futures price is an unbiased predictor of spot at contract maturity. In the next step, linear Toda and Yamamoto (1995) and the nonparametric Diks and Panchenko (2006) causality tests were applied to examine the direction of causality. Finally, nonlinear test were applied on the vector error correction model (VECM) residuals to investigate whether any remaining causality is strictly nonlinear in nature.

The results of cointegration tests between futures and spot prices of the selected agricultural commodities indicated a long term relationship do exist in three out of four futures contracts. However, the Wald tests results on the cointegrating vectors indicate markets as inefficient and biased. Further, analysis of short-term relationship using alternate tests of causality do not give consistent results for same commodity series indicating that results may vary due to alternate measures and specifications. Finally, if we consider the results of Diks-Panchenko test on the filtered VECM-residuals, results provide evidence that if cointegration is taken into account; neither spot nor future leads or lags the other consistently.

The results are based on the sample of four agricultural futures commodity contracts. The study can be extended to a larger sample of contracts and relative efficiency of each contract can be explored.

There are very few studies that have explored the efficiency, unbiasedness and direction of causality using both linear and nonlinear techniques for Indian agriculture commodity futures market for different forecasting horizons.