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The global energy market has been facing lower prices of crude oil in recent years. Lower fuel price leads to lower transport cost and cheaper agricultural inputs (such as pesticides and chemical fertilizer), resulting in lower prices of agricultural commodities in the international markets. On the other hand, lower global oil price reduces the oil revenues of oil exporting countries, resulting in a decrease in government expenditures. Therefore, the purpose of this study is to examine the impacts of lower global oil and agricultural commodity prices and government expenditure on the entire economy and poverty level of Malaysia.

This study used a computable general equilibrium model (CGE) to investigate four simulation scenarios based on the latest Malaysia’s input-output table belonging to 2010. The first scenario is a 30 per cent fall in the export and import prices of agricultural commodity prices, while the second is a 50 per cent decline in the export and import prices of crude oil, and the third combines them. In the fourth scenario, government operating expenditure declines by 4 per cent because of the fall in government’s oil revenues as a result of the decline in global oil prices.

The simulation results suggest that lower international oil price decreases real gross domestic product (GDP) and investment in Malaysia and influences positively the output and employment of some agriculture sectors. However, lower agricultural commodity price increases real GDP and investment in the country and negatively influences the output, employment and exports of all agriculture sectors. The decline in government expenditures also increases the output and the employment in the economy, whereas it decreases household consumption. In conclusion, results show that the agriculture sector losses from the current decline in international agricultural commodity prices, while it benefits from lower oil and government expenditure.

The main contribution of this study is comparing the impacts of recent falls in global oil and agricultural prices on the entire economy and agriculture sector of Malaysia. Investigating the impacts of these issues on the poverty level of Malaysian households is another contribution to the study. Another contribution is analyzing the impact of a reduction in government expenditures because of the decline in global oil price on the economy and welfare of Malaysia. Therefore, this study makes a useful contribution to the small literature of the topic.

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.

This paper aims to conceptually analyse the role of speculation in society to determine whether agricultural commodity index funds, a new form of speculation, contribute to sustainable development.

The theoretical arguments justifying the value of the market economic system for generating sustainable development and the positive contribution speculators make too in this context are elaborated. It is then considered whether the arguments justifying traditional speculation hold for agricultural commodity index funds.

Traditional forms of speculation contribute positively to sustainable development; primarily due to the information they uncover on demand and supply factors which affect prices. Agricultural index funds are a danger to sustainable development, as their transactions are not based on demand and supply factors but simply represent demand for the diversification effect which commodities generate when added to an investment portfolio.

The article offers a new approach to assessing whether agricultural index funds contribute to sustainable development. Empirical research has been conducted on whether speculation via index funds has unjustifiably affected commodity prices. However, results of these investigations have been inconclusive due to stark limitations in data availability. By approaching the issue from a conceptual point of view, the article delivers theoretically sound arguments as to why agricultural commodity index funds are likely to have an unjustifiable effect on prices and, hence, are a danger to sustainable development. This has strong implications for finance practice and regulation.

This paper aims to model the relationship between oil price and six major agricultural commodity prices using monthly data from January 1997 to December 2016.

The authors use both the linear autoregressive distributed lag by Pesaran et al. (2001) and the nonlinear autoregressive distributed lag by Shin et al. (2014), and they also account for structural breaks using the Bai and Perron (2003) test that allows for multiple structural changes in regression models.

These findings are discernible from the authors’ analyses. First, the linear analysis indicates a significant positive effect of oil prices on the agricultural commodity prices, which supports evidence on the non-neutrality hypothesis. Second, oil price asymmetries seem to matter more when dealing with agricultural commodity prices, except for groundnut. Third, it may be necessary to pre-test for structural breaks when modelling the relationship between oil price and agricultural prices regardless of the commodity being analysed. Fourth, the asymmetric effect for the agricultural commodity prices is non-neutral to oil prices, except for rice in the case of structural breaks.

This paper contributes to the on-going debate on the oil–agricultural commodity nexus using the recent technique of asymmetry and also considering the role structural breaks play in the relationship between oil price and agricultural commodity prices.

Risk aversion is considered as an important factor in predicting asset prices. Many studies have proved that there exists important price information spillover among crude oil, precious metals and agricultural markets. Then there naturally follows the question: Is the risk aversion of investors in crude oil market predictable for the returns of precious metals and agricultural products? The purpose of this paper is to answer this question. For this reason, the authors explore the directional predictability and the cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products.

To better describe the risk aversion of investors, this paper uses high-frequency data and model-free calculation method to obtain variance risk premium of crude oil. Then, this paper uses the cross-quantilogram method to investigate the directional predictability and cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products. Meanwhile, it employs the partial cross-quantilogram (PCQ) method to test the impact of control variables on the empirical results.

Firstly, risk aversion of crude oil market investors has directional predictability for returns of precious metals and agricultural products. Secondly, different degrees of risk aversion of crude oil market investors have different impacts on returns of precious metals and agricultural products. A low (high) degree of crude oil market investors' risk aversion has negative (positive) predictability for returns of precious metals and agricultural products. Finally, during the sample period, the returns of precious metals are more affected by risk aversion of crude oil market investors than returns of agricultural products.

First of all, this paper studies the impact of risk aversion of crude oil market investors on returns of precious metals and agricultural products. It updates previous relevant studies on the factors influencing the prices of precious metals and agricultural products, and provides a new idea for the forecast of those commodity returns. Secondly, this paper provides the evidence that different degrees of risk aversion of investors have different effects on the returns of commodities, and expands the research on the topic of commodity returns prediction. Finally, high-frequency data are employed in this paper to better capture the risk aversion of investors than commonly used daily data.

The purpose of this paper is to apply an analysis of complex networks to empirically research international agricultural commodity trade and countries’ trading relations. The structure of global agricultural commodity trade is quantitatively described and analysed.

Based on statistical physics and graph theory, the research paradigm of a complex network, which has sprung up in the last decade, provides us with new global perspective to discuss the topic of international trade, especially agricultural commodity trade. In this paper, the authors engage in the issue of countries’ positions in international agricultural commodity trade using the latest complex network theories. The authors at first time introduce the improved bootstrap percolation to simulate cascading influences following the breaking down of bilateral agricultural commodity trade relations.

On a mid-level structure, countries are classified into three communities that reflect the structure of the “core/periphery” using the weighted extremal optimisation algorithm and the coarse graining process. On a micro-level, countries’ rankings are provided with the aid of network’s node centralities, which presents world agricultural commodity trade as a closed, imbalanced, diversified and multi-polar development.

The authors at first time introduce the improved bootstrap percolation to simulate cascading influences following the breaking down of bilateral agricultural commodity trade relations.

The cross-quantilogram analysis is employed. The latter can assess the temporal association between two stationary time series at different parts of their joint distribution. Data are daily prices and trading volumes from the futures markets of five agricultural commodities, namely, corn, hard red wheat, oats, rice and soybeans.

The objective to the present work is to investigate for directional predictability between returns and volume (and vice versa) in the futures markets of agricultural commodities.

The empirical results reveal evidence, weak as well as strong, that extreme low values of returns are likely to lead high levels of volume. There is also weak evidence that extreme low values of volume are likely to precede high values of returns, except for the futures markets of oats where there is very strong evidence that low values of volume are likely to lead high values of returns. For the commodity of soybeans, there is very strong evidence that extreme high levels of volume are likely to lead high values of returns, but they are very short lived.

Agricultural futures have been recently characterized by increased volatility leading hedgers to be looking for diversification. The present findings suggest that when price crashes occur, investors who suffer losses wish to sell, increasing this way the trading activity. Concurrently, the results reveal that extreme low levels of trading volume might signal a possible price turn around for traders.

This is the first study that employs the quantilogram approach in order to investigate for potential predictability from returns to volume and from volume to returns, in the futures markets of agricultural commodities.

Market efficiency leads to transparent and fair price discovery of commodity markets, thus enhancing the value chain for competitive benefit. The purpose of this paper is to investigate the market efficiency of Indian agricultural commodities at spot, futures and mandi markets apart from exploring price risk management in these markets.

This study uses Johansen co-integration, vector error correction model and granger causality for analyzing market efficiency of the nine most liquid agricultural commodities across three markets, namely, spot, futures and mandi. All these nine commodities are traded on National Commodity and Derivatives Exchange.

The statistical results indicate price discovery exists in the mandi market and spot market leading to futures prices. Mandi price returns are seen to negatively influence futures returns in the case of cotton seed, guar seed and spot returns in the case of jeera, coriander and chana. For castor seed, the three markets are seen to have no long run relationship. The results of Granger causality reveal short run relationship between all the three markets in the case of soybean seed and coriander. In these commodities, prices in all three markets are capable of predicting the prices in the other markets. For the case of cottonseed, Rape Mustard seed, jeera, guar seed, the results indicate unidirectional causality between the mandi markets and the other two markets.

These results shall facilitate policymakers to explore intervention through integrated agri-platform (IAP) in price discovery and market efficiency.

The results of this study are useful in understanding the price discovery of mandi markets and its role in the spot and futures market. Agricultural commodities price discovery depends upon the integration of all these three markets. Introduction of IAP as described in the paper shall facilitate price risk management apart from improving the efficiency of price discovery.

To the best of the knowledge, this is the first study considering mandi, spot and futures prices in the price discovery process in India. In addition, this study found the role of mandi markets in serving the economic function of price discovery and price risk management. Hence, suggests for policy intervention for Indian agricultural commodities to manage price risk.

The purpose of this paper is to quantify the implications of China's recently adopted agricultural policies on domestic and international commodity markets.

A systematic, quantitative analysis is applied to address whether China's recent trade and production policies distort China's domestic and international commodity markets. The paper provides a clear picture of how trade‐restricting policies affect markets using a 42‐country partial equilibrium global dynamic agricultural simulation model.

The paper shows that recent agricultural policy reforms increase China's production slightly, causing imports to decrease while exports decline because of input subsidies, export taxes and the reduction of export value added tax rebates. Domestic prices to consumers decrease in real terms. The effects on world markets are small as the set of policies adopted partially offset each other in the international arena.

The paper indicates that the adoption of the policy reforms lower price levels domestically and benefit lower income urban and rural households, whose diets are largely based on rice and wheat as staple foods. Future model enhancements should include measures of producer and consumer welfare in order to capture the total impacts of policies and policy changes in China.

The paper quantifies the potential implications of the recent agricultural policy reforms in China. This contributes to the investigation of the effects of these policies implemented by the Chinese Government to achieve the country's policy objectives. Owing to the dynamics of China's policy implementation an in‐depth analysis sheds light and contributes to capturing the impacts of policy reforms on the domestic and international markets.

The study examines herding in the Indian stock and commodity futures market including agricultural, metal and energy commodities. Herding is studied under various market conditions: rising and declining, high and low volatility. The study also examines spillover effects of herding.

The study adapts the cross-sectional absolute deviation model given by Chang et al. (2000) to examine herding in Indian stock and commodity futures markets.

The results of the study indicate absence of herding among commodity futures under all market conditions except for the declining market where herding is present among energy futures. The investors investing in agricultural and energy commodities have a higher tendency to herd during high volatility days as compared to low volatility days. Further, the study of herding spillover effects indicates that the price fluctuations in metal commodities affect herding in agricultural and energy commodities.

The results can help market participants to diversify the risk by investing in agricultural, metal and energy futures along with the stocks.

Majority of the previous studies explore herding among stocks and ignore commodities especially agricultural commodities. This study attempts to fill the gap by studying herding among various commodity futures. To the best of our knowledge this is the first study to explore herding spillover effects in the Indian stock and commodity futures market.