我国玉米期货价格影响因素与波动特征分析
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摘要
农产品期货市场是我国农业经济的重要组成部分,农产品期货市场良好运行有利于指导农业生产、协调农产品的流通与储备。期货价格是期货市场交易机制的核心,正确认识期货价格特征,有利于交易者与监管者做出恰当的决策。本文以我国主要粮食产品——玉米为例,分析玉米期货价格的影响因素及其波动特征,主要是因为玉米复杂的属性——粮食作物属性、饲料作物属性、能源作物属性(美国大量利用玉米制取生物质燃料乙醇)。在此背景下展开了对玉米期货价格的影响因素及其波动特征的分析,力求能够揭示玉米期货价格的变化特征,为我国农产品期货市场的健康发展服务。
本文的分析思路:综合比较国内外相关文献后,先分析玉米期货价格的影响因素,再分析玉米期货价格的量价时空特征。前者属于基本面分析,由于生物质燃料乙醇市场是一个新兴市场,对玉米期货与现货市场影响较大,本文将其对玉米期货价格的影响单独作为一章进行分析。考虑到基本面因素复杂多变,难以综合构建模型进行定量分析,对此本文主要是进行定性分析,在量价时空方面则主要是进行计量分析,探讨玉米期货价格波动特征。全文共分八章,第一章是绪论,第二章与第三章分析影响玉米期货价格的因素及其一般规律。第四章至第七章分析玉米期货价格的量价时空特征。其中,第四章与第五章分析玉米期货价格在时间上的特征,第六章分析玉米期货价格在空间上的特征,第七章分析玉米期货价格的量价关系。第八章为总结与展望。
全文的主要结论如下:
(1)本文总结了影响玉米期货价格波动的基本面因素主要有玉米现货市场的供需情况、农业产业结构调整、玉米生产成本、库存、进出口情况、气候、相关替代性商品的价格、养殖业发展状况、经济周期、利率与汇率,国家粮食相关政策、生物燃料乙醇的开发利用、其他因素诸如突发事件等。其中玉米现货市场的供需情况是影响期货价格最为基本的因素。随着我国农业产业结构调整的深化,玉米种植面积和产量呈现上升的趋势,这对玉米期货价格形成利空的因素。而玉米生产成本的上升、库存水平的下降、出口增长、不利的气候环境、相关替代性商品价格的上涨、养殖业的繁荣、经济周期处于恢复或繁荣状态、美元的贬值、国家的惠农政策诸如价格补贴等对玉米期货价格形成利多因素。当这些因素处于相反状态时,则可能会形成利空因素,影响玉米期货价格下行。
(2)本文总结了生物质能这一新兴产业的发展背景及其在我国农村地区的发展前景。作为一个农业大国,我国农村地区生物质能发展前途巨大。生物质能的开发利用对玉米的生长环境——传统农业带来了深刻的影响:它促使种植业由“粮经饲”三元结构升级为“粮经饲能”四元结构;改善了农业生产经营管理方式;推动了林业经济的发展;促进农村工业的发展。但同时它也增加了生产资料的竞争性、增加了相关农产品价格的波动性。由于玉米是我国主要的粮食品种之一,为保障粮食安全,我国没有利用玉米大规模生产燃料乙醇,但是美国却利用玉米大量生产燃料乙醇。鉴于期货市场国内外联系紧密,本文以国内玉米期货、美国玉米期货、国际能源产品代表——美国原油期货价格为例,分析我国玉米市场是否具有能源属性。通过协整分析和Granger因果检验,最终发现我国玉米期货不具有能源属性,但国内玉米期货对美过玉米期货价格的连动效应明显,协整实证发现,美玉米期货对国内玉米期货的价格弹性为1.34,远大于国内玉米对美玉米期货的价格弹性0.65,前者是后者的2倍,说明当前国内玉米期货还不是国际期货的主导力量,需要加强期货市场建设,增大国内玉米期货的影响力,以便取得国际玉米的定价权,以保障我国利益。
(3)本文从周日历效应和非线性特征上分析玉米期货价格在时间上的特征。在玉米期货价格周日历效应分析上,本文通过比较ARCH模型、GARCH模型、EGARCH模型,最后发现运用EGARCH-T模型分析玉米期货周日历效应最优。实证结果显示,不同的合约,周日历效应有所不同。交易日近期的合约(交易日后的第一、第二个合约)仅在周二具有负的周日历效应。而交易日远期的合约(交易日后的第三、四个合约)则在周一具有正的、周二具有负的周日历效应。从连续持有三天合约的收益上看,主力合约(交易日后的第四个合约)周四买、周一卖获益效果最佳,有最高的正收益率,而日内各小时的交易没有类似的日历效应。本文也证实了玉米期货不存在显著的杠杆效应。在玉米期货价格非线性特征分析上,通过正态性检验发现玉米期货日收益波动率序列较正态分布更具尖峰、厚尾的分形特性;利用R/S、V/S分析技术分析发现玉米期货合约日收益率与日收益波动率时间序列不是随机游动的,具有持久性,亦即具有长记忆性。实证结果表明,经典R/S分析方法会因受序列短期记忆性的影响而高估Hurst指数,V/S较经典R/S分析方法而言,能得到更合适的Hurst指数。通过V/S分析方法,得到了玉米期货交易日后第四个合约日收益率平均非周期循环长度大约是96天,日收益波动率的平均非周期循环长度大约是181天。这一分析充实了Edgar·E·Peters(1994)在分形理论的基础上提出了分形市场假说,也说明玉米期货市场的收益率并不服从独立同分布的高斯分布,而是服从一种“尖峰厚尾”的有偏的随机游走,具有非线性特征,因此用分形分布代替正态分布来刻画玉米期货市场特征更为合适。
(4)本文将材料变形理论中的弹性与塑性模型引入到玉米期货价格分析中来,分析玉米期货价格波动在空间上(围绕均线上下波动)的特性。通过构建不同的弹性与塑性模型来分析玉米期货价格弹性与塑性的存在与否。在综合比较了弹性(塑性)基本模型、弹性(塑性)基本模型的自回归模型、弹性(塑性)基本模型的分布滞后模型、弹性(塑性)幂指数模型、弹性(塑性)幂指数自回归模型、弹性(塑性)幂指数分布滞后模型来分析玉期货价格的弹性与塑性后,发现玉米期货价格不存在价格弹性,但存在价格塑性。从玉米期货价格塑性的分析模型上看,通过各模型比较分析发现,用10日均线近似替代均衡价格比其它时间长度的均线替代效果好些。在各模型中,塑性幂指数自回归模型比其它模型的估计效果要好,其中,塑性幂指数三阶自回归模型模型估计效果最好。
(5)本文利用塑性模型分析玉米期货价格的量价特征。借助于MDH理论,本文先利用GARCH(1,1)模型说明了原始交易量对玉米期货市场价格波动具备了一定的解释能力,从投资角度来看,也证实了证券期货市场中广为流传的诸如“价走量先行”、“交易量引起价格的变化”、“新手看价、老手看量”等著名谚语,说明通过观察交易量的变化,有可能发掘玉米期货价格的突变。依据此原理,利用塑性模型来构建通过交易量发掘玉米期货价格突变的模型。在塑性模型选择上,塑性幂指数一阶自回归模型模型较塑性幂指数更具有最佳模拟效果,但考虑到幂指数一阶自回归模型中部分信息被自回归项解释,而且一阶自回归模型的塑性系数过小,因此,本文仍选用幂指数模型。根据塑性幂指函数模型,得到塑性系数与价格突变在锁定量上的关系,由此建立了依据价格塑性模型,透过锁定量的异常波动观察价格突变的方法。在整个方法中,时间长度的选择始终是个关键问题。通过比较分析不同时间长度,最终本文选择了用10日平均价格作为均衡价格以及用30日时间长度计算锁定量。通过观察锁定量的异常波动来分析价格突变,最终发现,当锁定量发生异常波动时,在锁定量由异常波动段的最低点重新回归0.8时,之后均会出现一波价格的突变行情,价格变化均在5%以上。从2006年1月4日至2008年10月10日期间,锁定量异常波动时,价格突变的最大变化量为11.5%(如果包括一个小的反弹,最大变化量为12.96%,为2007年7月30至2007年11月23日的这段波动)。最小的变化量为-3.24%。尽管最小波动段2006年7月31至2006年8月14日的价格变化量只有-3.24%,但是在锁定量从异常波动段的最低点回归0.8的过程中,其价格变化量已经达到-5.77%,整个过程的价格变化量实质上达到-8.83%。据此,本文得到的结论“当锁定量发生异常波动时,在锁定量由异常波动段的最低点重新回归0.8时,之后均会出现一波价格的突变行情,价格变化绝对值均在5%以上”。
Agricultural futures market is an important component of China's agricultural economy. Fairly good economic performance of agricultural futures market is conductive to agricultural production and coordinates the flow and reserve. A correct understanding of the characteristics of futures prices, which is the core of futures trading mechanism, is conductive to dealing with traders and regulators to make appropriate decisions. Because of the complexity properties of corn which are grain property, fodder crops property, energy crops attribute (the United States making substantial use of corn ethanol), in this paper, as one of the main food products, corn has been taken as an example to analysis its influencing factors and fluctuation characteristics in order to find out the rule of the changes of corn futures price and stimulate China's agricultural futures market development in a healthy way.
The logic of this paper is illustrated as follows: first of all, compared the relevant domestic and foreign literature, then analyzed the influencing factors of corn futures prices and their fluctuation characteristics, and finally analyzed characteristics of corn futures prices by time and space as well as the relationship between the volume and the price. As biomass fuel ethanol market is an emerging market which taking greater impact to corn futures prices, this article analyzes its impact on corn futures prices as a separate chapter. Because of these fundamental factors are complex and changeable, it is difficult to build a model of comprehensive quantitative analysis, therefore it is mainly based on qualitative analysis. After that, this article build models from the volume and price、time and space aspects to explore the characteristics of corn futures price volatility. The full text can be divided into eight chapters, the first chapter is the Introduction, ChapterⅡand ChapterⅢanalyzes the impact of corn futures prices and the general rules of factors. ChapterⅦto ChapterⅣanalysis the amount of corn futures prices at time and space characteristics. Among them, ChapterⅣand ChapterⅤgives an analysis of corn futures prices in the time features , while ChapterⅥAnalysis corn futures prices features in the space aspect. ChapterⅦanalyzes the relationship between the volume and price of corn futures. ChapterⅧis the summary and the needs for future researches.
The main conclusions are as follows:
(1) The impact factors of volatility in corn futures prices can be summarized as supply and demand in the spot market , the agricultural structure adjustment, corn production cost, inventory, import and export, climate, price of alternative commodities, aquaculture development, economic cycles, interest rate and exchange rate, national food policy, bio-fuel development and utilization of ethanol, and other factors such as unexpected events. The condition of supply and demand of corn is the most basic factor within all factors. Along with the development of agriculture structure adjustment and corn planting area and output showing an upward trend, they curbed corn futures price increases. But those factors such as the increase in corn production cost, the decline in inventory level, export growth, adverse weather environment, the relevant alternative commodity prices rise, the prosperity of aquaculture, the economic cycle in a state of recovery or prosperity, the dollar's devaluation, the country's agricultural policy benefits such as price subsidies increase corn futures price, while these factors are in the opposite state, they will also curbed corn futures price increase.
(2) This article also summarizes the background of the emerging biomass industry and the prospects for the development of rural areas. As a large agriculture country, China has great potential for developing biomass energy. Biomass energy development and utilization had a profound impact on traditional agriculture, which is the environment of maize growth. It encourages farming by "food crops、cash crops、fodder crops" ternary structure as a "food crops、cash crops、fodder crops、energy crops" quaternary structure, improves agricultural production and operation management, promotes the economic development of forestry and the development of rural industries. However at the same time it also increases the competitiveness of the production materials, and increases the prices volatility of related agricultural products. As corn is one of the main grain varieties, our country did not make use of large-scale production of corn to get fuel ethanol in order to ensure food security, but a large number of U.S. corns are in the use of making fuel ethanol. Because of the futures markets closely connected both at home and abroad, this paper takes domestic corn futures, U.S. corn futures and U.S. crude oil futures prices as examples to analyze the energy attributes of domestic corn futures market. Through co-integration analysis and Granger causality test, it found that China's corn futures do not have the energy properties, but domestic corn futures price obviously affected by U.S. corn futures prices. Empirical co-integration found that the elasticity of U.S. corn futures price on the domestic price which is 1.34 is twice than the elasticity of the domestic price on U.S. corn futures price which is only 0.65. This shows that the current domestic corn futures is not the leading international futures forces, so it needs to strengthen the building of the futures market and increase the influence of domestic corn futures in order to obtain the international corn pricing and protect the interests of our country.
(3) This article analyses the characteristics of corn futures prices in time aspect by using the angle of weekday effect and non-linear feature. After compared the ARCH model, GARCH model, EGARCH model, this paper find out that EGARCH-T model is the best model to analysis weekday effect. Empirical results show that different contracts have different effects of the weekday effect. Recent contract of trading day (the first contract、the second contract after trading day) only on Tuesday with a negative weekday effect. The long-term contract of trading day (the third contract、the fourth contract after trading day) is in the positive Monday and negative Tuesday weekday effect. By consecutively three days holding, the main contract (the fourth contract after trading day) benefits most by buying on Thursday and selling on Monday with the highest rate of return. There is no similar weekday effect in the hours of trading days. And it also confirms that there is no significant leverage effect. In the analysis of non-linear characteristic by using the test of normality it is found that corn futures yield volatility is more spike sequence、fat-tail characteristic than the normal distribution. R / S, V / S analysis show that corn futures yield and the volatility of corn futures yield are not random walk but with a persistent feature, that is ,with a long memory. Empirical results show that the classical R / S analysis method will overestimation the impact of Hurst index by the impact of the sequence of short-term memory. Compared with classical R / S analysis method, V / S analysis method can be more appropriate to calculate Hurst index. Through V / S analysis, it finds out that the non-periodic cycle of the fourth contract after trading day is about 96 days and the non-periodic cycle of the yield volatility of the fourth contract after trading day is about 181 days. This analysis enriched Edgar·E·Peters (1994) in the fractal theory is proposed based on the Fractal Market Hypothesis, also shows that the yield of corn futures market is not subject to independent Gaussian distribution, but subject to a kinds of "fat-tail peak" of the biased random walk with non-linear characteristic, so the fractal distribution of corn instead of the normal distribution to describe the characteristics of futures markets is more appropriate.
(4) This paper uses material deformation theory of elasticity and plasticity model to analysis of price volatility of corn futures in space characteristic (fluctuating around the average) . Different models of flexibility and plasticity were built in order to find out whether the flexibility or plasticity of corn futures price is presence or not. Compared the flexibility and plastic model in the basic model、auto regression model of basic model、distributed lag model of basic model、Power exponent model、auto regression Power exponent model、distributed lag Power exponent model, we find that corn futures price elasticity does not exist, but there is the price of plastic. By the different plasticity models, we find that 10-day average price is the best period to substitute equilibrium price than any other length of time. In all the models, the plastic exponential autoregressive model is better to estimate than any other models, in which third-order plastic exponential autoregressive model is the best one.
(5) This paper analyzes the relationship between volume and price in corn futures by plastic model. With MDH theory, this paper first uses GARCH (1,1) model to find out that the original volume influence corn futures price with a certain amount of explanatory power. From an investment point of view, there is confirmed by the securities and futures markets, such as widespread "price volatility after volume changing", "price volatility caused by changes in volume," "newcomers look at price, veterans look at the volume" and other well-known proverb. Thus, we can know that through the observation of changes in trading volume, it is possible to explore the mutant of corn futures price. According to this principle, we build the model by plastic model so we can find out the mutation by observing the changes of trading volume. Choice of the plastic model, plastic power exponent one step autoregressive model is better than power exponent model, but taking into account the power exponent one step autoregressive model in the autoregressive part of information is explained, and the first-order autoregressive coefficient of the plastic model is too small, therefore, this article is still power exponent model selection. Means in accordance with the plastic power function model, it builds the relationship between plastic coefficient and price mutation by the locked volume. This is the method how to explore corn futures price mutation by locked volume. Throughout the method, the choice of the length of time always is a key issue. By comparing the analysis of different length of time, it ultimately chases to use 10-day average price as the equilibrium price ,as well as calculating locked volume by the length of time on 30-day. Analyzing the price mutation by observing the abnormal fluctuations of locked volume, we found that when the locked volume with anomalous fluctuations, after the locked volume go back to 0.8 from the lowest point, there will be tremendous changes in the price which is more than 5%.
本文的分析思路:综合比较国内外相关文献后,先分析玉米期货价格的影响因素,再分析玉米期货价格的量价时空特征。前者属于基本面分析,由于生物质燃料乙醇市场是一个新兴市场,对玉米期货与现货市场影响较大,本文将其对玉米期货价格的影响单独作为一章进行分析。考虑到基本面因素复杂多变,难以综合构建模型进行定量分析,对此本文主要是进行定性分析,在量价时空方面则主要是进行计量分析,探讨玉米期货价格波动特征。全文共分八章,第一章是绪论,第二章与第三章分析影响玉米期货价格的因素及其一般规律。第四章至第七章分析玉米期货价格的量价时空特征。其中,第四章与第五章分析玉米期货价格在时间上的特征,第六章分析玉米期货价格在空间上的特征,第七章分析玉米期货价格的量价关系。第八章为总结与展望。
全文的主要结论如下:
(1)本文总结了影响玉米期货价格波动的基本面因素主要有玉米现货市场的供需情况、农业产业结构调整、玉米生产成本、库存、进出口情况、气候、相关替代性商品的价格、养殖业发展状况、经济周期、利率与汇率,国家粮食相关政策、生物燃料乙醇的开发利用、其他因素诸如突发事件等。其中玉米现货市场的供需情况是影响期货价格最为基本的因素。随着我国农业产业结构调整的深化,玉米种植面积和产量呈现上升的趋势,这对玉米期货价格形成利空的因素。而玉米生产成本的上升、库存水平的下降、出口增长、不利的气候环境、相关替代性商品价格的上涨、养殖业的繁荣、经济周期处于恢复或繁荣状态、美元的贬值、国家的惠农政策诸如价格补贴等对玉米期货价格形成利多因素。当这些因素处于相反状态时,则可能会形成利空因素,影响玉米期货价格下行。
(2)本文总结了生物质能这一新兴产业的发展背景及其在我国农村地区的发展前景。作为一个农业大国,我国农村地区生物质能发展前途巨大。生物质能的开发利用对玉米的生长环境——传统农业带来了深刻的影响:它促使种植业由“粮经饲”三元结构升级为“粮经饲能”四元结构;改善了农业生产经营管理方式;推动了林业经济的发展;促进农村工业的发展。但同时它也增加了生产资料的竞争性、增加了相关农产品价格的波动性。由于玉米是我国主要的粮食品种之一,为保障粮食安全,我国没有利用玉米大规模生产燃料乙醇,但是美国却利用玉米大量生产燃料乙醇。鉴于期货市场国内外联系紧密,本文以国内玉米期货、美国玉米期货、国际能源产品代表——美国原油期货价格为例,分析我国玉米市场是否具有能源属性。通过协整分析和Granger因果检验,最终发现我国玉米期货不具有能源属性,但国内玉米期货对美过玉米期货价格的连动效应明显,协整实证发现,美玉米期货对国内玉米期货的价格弹性为1.34,远大于国内玉米对美玉米期货的价格弹性0.65,前者是后者的2倍,说明当前国内玉米期货还不是国际期货的主导力量,需要加强期货市场建设,增大国内玉米期货的影响力,以便取得国际玉米的定价权,以保障我国利益。
(3)本文从周日历效应和非线性特征上分析玉米期货价格在时间上的特征。在玉米期货价格周日历效应分析上,本文通过比较ARCH模型、GARCH模型、EGARCH模型,最后发现运用EGARCH-T模型分析玉米期货周日历效应最优。实证结果显示,不同的合约,周日历效应有所不同。交易日近期的合约(交易日后的第一、第二个合约)仅在周二具有负的周日历效应。而交易日远期的合约(交易日后的第三、四个合约)则在周一具有正的、周二具有负的周日历效应。从连续持有三天合约的收益上看,主力合约(交易日后的第四个合约)周四买、周一卖获益效果最佳,有最高的正收益率,而日内各小时的交易没有类似的日历效应。本文也证实了玉米期货不存在显著的杠杆效应。在玉米期货价格非线性特征分析上,通过正态性检验发现玉米期货日收益波动率序列较正态分布更具尖峰、厚尾的分形特性;利用R/S、V/S分析技术分析发现玉米期货合约日收益率与日收益波动率时间序列不是随机游动的,具有持久性,亦即具有长记忆性。实证结果表明,经典R/S分析方法会因受序列短期记忆性的影响而高估Hurst指数,V/S较经典R/S分析方法而言,能得到更合适的Hurst指数。通过V/S分析方法,得到了玉米期货交易日后第四个合约日收益率平均非周期循环长度大约是96天,日收益波动率的平均非周期循环长度大约是181天。这一分析充实了Edgar·E·Peters(1994)在分形理论的基础上提出了分形市场假说,也说明玉米期货市场的收益率并不服从独立同分布的高斯分布,而是服从一种“尖峰厚尾”的有偏的随机游走,具有非线性特征,因此用分形分布代替正态分布来刻画玉米期货市场特征更为合适。
(4)本文将材料变形理论中的弹性与塑性模型引入到玉米期货价格分析中来,分析玉米期货价格波动在空间上(围绕均线上下波动)的特性。通过构建不同的弹性与塑性模型来分析玉米期货价格弹性与塑性的存在与否。在综合比较了弹性(塑性)基本模型、弹性(塑性)基本模型的自回归模型、弹性(塑性)基本模型的分布滞后模型、弹性(塑性)幂指数模型、弹性(塑性)幂指数自回归模型、弹性(塑性)幂指数分布滞后模型来分析玉期货价格的弹性与塑性后,发现玉米期货价格不存在价格弹性,但存在价格塑性。从玉米期货价格塑性的分析模型上看,通过各模型比较分析发现,用10日均线近似替代均衡价格比其它时间长度的均线替代效果好些。在各模型中,塑性幂指数自回归模型比其它模型的估计效果要好,其中,塑性幂指数三阶自回归模型模型估计效果最好。
(5)本文利用塑性模型分析玉米期货价格的量价特征。借助于MDH理论,本文先利用GARCH(1,1)模型说明了原始交易量对玉米期货市场价格波动具备了一定的解释能力,从投资角度来看,也证实了证券期货市场中广为流传的诸如“价走量先行”、“交易量引起价格的变化”、“新手看价、老手看量”等著名谚语,说明通过观察交易量的变化,有可能发掘玉米期货价格的突变。依据此原理,利用塑性模型来构建通过交易量发掘玉米期货价格突变的模型。在塑性模型选择上,塑性幂指数一阶自回归模型模型较塑性幂指数更具有最佳模拟效果,但考虑到幂指数一阶自回归模型中部分信息被自回归项解释,而且一阶自回归模型的塑性系数过小,因此,本文仍选用幂指数模型。根据塑性幂指函数模型,得到塑性系数与价格突变在锁定量上的关系,由此建立了依据价格塑性模型,透过锁定量的异常波动观察价格突变的方法。在整个方法中,时间长度的选择始终是个关键问题。通过比较分析不同时间长度,最终本文选择了用10日平均价格作为均衡价格以及用30日时间长度计算锁定量。通过观察锁定量的异常波动来分析价格突变,最终发现,当锁定量发生异常波动时,在锁定量由异常波动段的最低点重新回归0.8时,之后均会出现一波价格的突变行情,价格变化均在5%以上。从2006年1月4日至2008年10月10日期间,锁定量异常波动时,价格突变的最大变化量为11.5%(如果包括一个小的反弹,最大变化量为12.96%,为2007年7月30至2007年11月23日的这段波动)。最小的变化量为-3.24%。尽管最小波动段2006年7月31至2006年8月14日的价格变化量只有-3.24%,但是在锁定量从异常波动段的最低点回归0.8的过程中,其价格变化量已经达到-5.77%,整个过程的价格变化量实质上达到-8.83%。据此,本文得到的结论“当锁定量发生异常波动时,在锁定量由异常波动段的最低点重新回归0.8时,之后均会出现一波价格的突变行情,价格变化绝对值均在5%以上”。
Agricultural futures market is an important component of China's agricultural economy. Fairly good economic performance of agricultural futures market is conductive to agricultural production and coordinates the flow and reserve. A correct understanding of the characteristics of futures prices, which is the core of futures trading mechanism, is conductive to dealing with traders and regulators to make appropriate decisions. Because of the complexity properties of corn which are grain property, fodder crops property, energy crops attribute (the United States making substantial use of corn ethanol), in this paper, as one of the main food products, corn has been taken as an example to analysis its influencing factors and fluctuation characteristics in order to find out the rule of the changes of corn futures price and stimulate China's agricultural futures market development in a healthy way.
The logic of this paper is illustrated as follows: first of all, compared the relevant domestic and foreign literature, then analyzed the influencing factors of corn futures prices and their fluctuation characteristics, and finally analyzed characteristics of corn futures prices by time and space as well as the relationship between the volume and the price. As biomass fuel ethanol market is an emerging market which taking greater impact to corn futures prices, this article analyzes its impact on corn futures prices as a separate chapter. Because of these fundamental factors are complex and changeable, it is difficult to build a model of comprehensive quantitative analysis, therefore it is mainly based on qualitative analysis. After that, this article build models from the volume and price、time and space aspects to explore the characteristics of corn futures price volatility. The full text can be divided into eight chapters, the first chapter is the Introduction, ChapterⅡand ChapterⅢanalyzes the impact of corn futures prices and the general rules of factors. ChapterⅦto ChapterⅣanalysis the amount of corn futures prices at time and space characteristics. Among them, ChapterⅣand ChapterⅤgives an analysis of corn futures prices in the time features , while ChapterⅥAnalysis corn futures prices features in the space aspect. ChapterⅦanalyzes the relationship between the volume and price of corn futures. ChapterⅧis the summary and the needs for future researches.
The main conclusions are as follows:
(1) The impact factors of volatility in corn futures prices can be summarized as supply and demand in the spot market , the agricultural structure adjustment, corn production cost, inventory, import and export, climate, price of alternative commodities, aquaculture development, economic cycles, interest rate and exchange rate, national food policy, bio-fuel development and utilization of ethanol, and other factors such as unexpected events. The condition of supply and demand of corn is the most basic factor within all factors. Along with the development of agriculture structure adjustment and corn planting area and output showing an upward trend, they curbed corn futures price increases. But those factors such as the increase in corn production cost, the decline in inventory level, export growth, adverse weather environment, the relevant alternative commodity prices rise, the prosperity of aquaculture, the economic cycle in a state of recovery or prosperity, the dollar's devaluation, the country's agricultural policy benefits such as price subsidies increase corn futures price, while these factors are in the opposite state, they will also curbed corn futures price increase.
(2) This article also summarizes the background of the emerging biomass industry and the prospects for the development of rural areas. As a large agriculture country, China has great potential for developing biomass energy. Biomass energy development and utilization had a profound impact on traditional agriculture, which is the environment of maize growth. It encourages farming by "food crops、cash crops、fodder crops" ternary structure as a "food crops、cash crops、fodder crops、energy crops" quaternary structure, improves agricultural production and operation management, promotes the economic development of forestry and the development of rural industries. However at the same time it also increases the competitiveness of the production materials, and increases the prices volatility of related agricultural products. As corn is one of the main grain varieties, our country did not make use of large-scale production of corn to get fuel ethanol in order to ensure food security, but a large number of U.S. corns are in the use of making fuel ethanol. Because of the futures markets closely connected both at home and abroad, this paper takes domestic corn futures, U.S. corn futures and U.S. crude oil futures prices as examples to analyze the energy attributes of domestic corn futures market. Through co-integration analysis and Granger causality test, it found that China's corn futures do not have the energy properties, but domestic corn futures price obviously affected by U.S. corn futures prices. Empirical co-integration found that the elasticity of U.S. corn futures price on the domestic price which is 1.34 is twice than the elasticity of the domestic price on U.S. corn futures price which is only 0.65. This shows that the current domestic corn futures is not the leading international futures forces, so it needs to strengthen the building of the futures market and increase the influence of domestic corn futures in order to obtain the international corn pricing and protect the interests of our country.
(3) This article analyses the characteristics of corn futures prices in time aspect by using the angle of weekday effect and non-linear feature. After compared the ARCH model, GARCH model, EGARCH model, this paper find out that EGARCH-T model is the best model to analysis weekday effect. Empirical results show that different contracts have different effects of the weekday effect. Recent contract of trading day (the first contract、the second contract after trading day) only on Tuesday with a negative weekday effect. The long-term contract of trading day (the third contract、the fourth contract after trading day) is in the positive Monday and negative Tuesday weekday effect. By consecutively three days holding, the main contract (the fourth contract after trading day) benefits most by buying on Thursday and selling on Monday with the highest rate of return. There is no similar weekday effect in the hours of trading days. And it also confirms that there is no significant leverage effect. In the analysis of non-linear characteristic by using the test of normality it is found that corn futures yield volatility is more spike sequence、fat-tail characteristic than the normal distribution. R / S, V / S analysis show that corn futures yield and the volatility of corn futures yield are not random walk but with a persistent feature, that is ,with a long memory. Empirical results show that the classical R / S analysis method will overestimation the impact of Hurst index by the impact of the sequence of short-term memory. Compared with classical R / S analysis method, V / S analysis method can be more appropriate to calculate Hurst index. Through V / S analysis, it finds out that the non-periodic cycle of the fourth contract after trading day is about 96 days and the non-periodic cycle of the yield volatility of the fourth contract after trading day is about 181 days. This analysis enriched Edgar·E·Peters (1994) in the fractal theory is proposed based on the Fractal Market Hypothesis, also shows that the yield of corn futures market is not subject to independent Gaussian distribution, but subject to a kinds of "fat-tail peak" of the biased random walk with non-linear characteristic, so the fractal distribution of corn instead of the normal distribution to describe the characteristics of futures markets is more appropriate.
(4) This paper uses material deformation theory of elasticity and plasticity model to analysis of price volatility of corn futures in space characteristic (fluctuating around the average) . Different models of flexibility and plasticity were built in order to find out whether the flexibility or plasticity of corn futures price is presence or not. Compared the flexibility and plastic model in the basic model、auto regression model of basic model、distributed lag model of basic model、Power exponent model、auto regression Power exponent model、distributed lag Power exponent model, we find that corn futures price elasticity does not exist, but there is the price of plastic. By the different plasticity models, we find that 10-day average price is the best period to substitute equilibrium price than any other length of time. In all the models, the plastic exponential autoregressive model is better to estimate than any other models, in which third-order plastic exponential autoregressive model is the best one.
(5) This paper analyzes the relationship between volume and price in corn futures by plastic model. With MDH theory, this paper first uses GARCH (1,1) model to find out that the original volume influence corn futures price with a certain amount of explanatory power. From an investment point of view, there is confirmed by the securities and futures markets, such as widespread "price volatility after volume changing", "price volatility caused by changes in volume," "newcomers look at price, veterans look at the volume" and other well-known proverb. Thus, we can know that through the observation of changes in trading volume, it is possible to explore the mutant of corn futures price. According to this principle, we build the model by plastic model so we can find out the mutation by observing the changes of trading volume. Choice of the plastic model, plastic power exponent one step autoregressive model is better than power exponent model, but taking into account the power exponent one step autoregressive model in the autoregressive part of information is explained, and the first-order autoregressive coefficient of the plastic model is too small, therefore, this article is still power exponent model selection. Means in accordance with the plastic power function model, it builds the relationship between plastic coefficient and price mutation by the locked volume. This is the method how to explore corn futures price mutation by locked volume. Throughout the method, the choice of the length of time always is a key issue. By comparing the analysis of different length of time, it ultimately chases to use 10-day average price as the equilibrium price ,as well as calculating locked volume by the length of time on 30-day. Analyzing the price mutation by observing the abnormal fluctuations of locked volume, we found that when the locked volume with anomalous fluctuations, after the locked volume go back to 0.8 from the lowest point, there will be tremendous changes in the price which is more than 5%.
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