利用农产品期货市场促进农业产业化经营的发展

期货市场是市场经济体系的重要组成部分．在建设社会主义新农村和提高农民组织化程度方面有着广阔的发展空间和不可替代的作用。《中共中央、国务院关于推进社会主义新农村建设的若干意见》指出,推进农业产业化经营需要发展大宗农产品期货市场和订单农业．     

基于农产品期货市场的订单农业问题探讨

我国的订单农业一直难以得到有效的发展，其中一个很直接的原因是由于履约率低，而导致履约率低的原因又主要是订单农业中的价格风险问题，而利用农产品期货市场可以为订单农业的经营主体提供一个风险规避机制和有效的价格信息。而要利用农产品期货市场发展订单农业就必须对我国目前的农产品期货市场加以完善。     

利用农产品期货市场加快农业发展进程

本文针对农产品期货市场现状,对农产品期货市场如何加快农业发展这一问题,运用分析研究的方法,对我国农产品期货市场的经济功能进行分析,解释和描述市场运行中出现的问题,说明该问题出现的原因。此外,通过国内外农产品期货市场对农业问题的影响进行研究,抽取可供借鉴的经验和模式。     

建立农产品期货市场的探讨

期货市场作为商品经济发展的产物，自应用到农产品流通上以来，得到迅速发展，尤其是市场经济发达的国家，期货交易是商品流通的重要方式。目前期货交易在整个世界农产品交易中占有越来越重要的地位，据估计，世界农价格约有80％是根据期货价格而确定的。因此，探索在我国建立农产品期货市场，搞活农产品流通，发展社会主义市场经济，具有重要意义。     

农业产业化应重视利用期货市场

农业产业化的关键是农产品生产经营的市场化,而市场化进程的加快以及市场化范围的扩大,使农业产业化面临着一些新的背景和新的问题:(1)农产品市场价格变化剧烈,“蛛网循环”生成,必然导致农业市场风险的上升,而这种风险正由分散的农户向农业龙头企业汇集;(2)农产品市场对     

现货市场发育与期货市场发展的相关性：———中国农产品期货发展有关问题的探讨

自从１９９５年“三二七”风波①之后,中国暂停了国债期货交易。随后,国家有关部门对当时的期货交易所进行治理整顿,确立了１５家期货交易所为试点单位。１９９８年下半年,为了配合国家宏观政策的调整,再次缩小期货交易所试点范围,保留了３家期货交易所,继续探索期...     

完善农产品期货市场

农产品期货市场具有风险转移和价格发现功能,是运用市场手段调控资源配置的有效手段,能够有效稳定农产品市场。因此,可以借鉴农产品期货市场发达国家的经验,促进中国农产品期货市场健康发展,从而稳定农产品市场,降低市场风险。     

发展农产品期货市场的必要性

期货价格与现货价格的相关性是建立期货市场和发挥期货市场功能的基础。本文论述了期货价格的优点及其对现货市场的重要作用,以及配置资源的重要意义。对农产品期货市场的作用进行了论述。     

Commodity futures markets: a survey

This review article describes the main contributions in the literature on commodity futures markets. It is argued that modern studies have focused primarily on technical questions, with insufficient economic content. More research needs to be directed towards understanding fundamental economic issues such as why so few farmers hedge, the impacts of government farm programs on commodity futures, and the market impacts of commodity pools. The literature has failed to explain the prevalence of inverted markets in grains and oilseeds, and there is unexplainable price volatility in markets such as hogs and orange juice.     

Measuring the functional efficiency of agricultural futures markets

This article presents a method for measuring the functional efficiency of agricultural futures markets in terms of social welfare using a standard futures market structural model. Employing the concept of social surplus, it can be shown that, when futures prices are used to estimate future spot prices, the errors in prediction produce to some degree resource misallocation, which in turn results in welfare losses. Therefore, the social welfare associated with the presence of futures markets can be measured using a Social Loss index. The indicator was calculated for the period 1975–2015 and for several subperiods, which allow us to analyse functional efficiency before and after the 2007–2008 spikes in the prices of agricultural commodities. Futures contracts for 12 products are evaluated. The products are grouped in three different categories: ‘soft products’, ‘livestock’ and ‘grains and oilseeds’. The results indicate that livestock contracts tended to be more efficient than the rest of the contracts during the whole period, but in 2008–2015 their efficiency decreased vis‐á‐vis the rest of the products. Nevertheless, 2008–2015 proved to be the most efficient subperiod, confirming the remarkable development of agricultural futures markets over time.     

Efficiency tests of agricultural commodity futures markets in China

The efficiency of the Chinese wheat and soybean futures markets is studied. Formal statistical tests were conducted based on Johansen's cointegration approach for three different cash markets and six different futures forecasting horizons ranging from 1 week to 4 months. The results suggest a long-term equilibrium relationship between the futures price and cash price for soybeans and weak short-term efficiency in the soybean futures market. The futures market for wheat is inefficient, which may be caused by over-speculation and government intervention.     

More reasons why farmers have so little interest in futures markets

The use by farmers of futures contracts and other hedging instruments has been observed to be low in many situations, and this has sometimes seemed to be considered surprising or even mysterious. We propose that it is, in fact, readily understandable and consistent with rational decision making. Standard models of the decision about optimal hedging show that it is negatively related to basis risk, to quantity risk, and to transaction costs. Farmers who have less uncertainty about prices and those with a diversified portfolio of investments have lower optimal levels of hedging. If a farmer has optimistic price expectations relative to the futures market, the incentive to hedge can be greatly reduced. And finally, farmers who have low levels of risk aversion have little to gain from hedging in terms of risk reduction, in that the certainty‐equivalent payoff at their optimal hedge may be little different than the certainty equivalent under zero hedging. These reasons are additional to the argument of Simmons (2002) who showed that, if capital markets are efficient, farmers can manage their risk exposure through adjusting their leverage, obviating the need for hedging instruments.     

Why do farmers have so little interest in futures markets?

A farm financial model with leverage and investment in two farm enterprises is specified. The model is extended to incorporate futures hedging and the Separation Theorem is used to show that optimal hedging is zero. The assumption of a risk‐free asset is relaxed and, while this leads to a violation of the Separation Theorem, the result that optimal hedging is zero is maintained providing that futures markets are efficient. It is concluded that if capital markets are efficient then farmers will have little interest in futures markets except to speculate.     

Financial activity in agricultural futures markets: evidence from quantile regressions

This study analyses the relationship between financial activity and price returns in 12 US agricultural futures markets. It contributes to the existing research by exploring the forecasting power of trading activity for returns from the perspective of conditional quantiles. Quantile regressions detect Granger‐causal effects from positions of speculators and index traders to price returns in a wide range of commodity markets such as cocoa, coffee, corn, sugar and SRW wheat.