| 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%.
|
PDF Full Text Request