【作者】 赵玉；

【导师】 祁春节；

【作者基本信息】 华中农业大学 ， 农业经济管理， 2010， 博士

【摘要】 我国农产品期货市场起步晚、发展快,从交易规模来看,已经位居世界前列。但是在发展过程中也暴露了诸多问题,其中价格波动的风险问题最近几年表现尤为突出。其所带来的危害正如汉书所言：“籴甚贵,伤民；甚贱,伤农。民伤则离散,农伤则国贫”。大豆是中国市场化程度最高和开放相对较早的农产品之二,中国大豆市场与国际大豆市场整合较好。在国产大豆受到国外转基因大豆不断冲击、国内大豆价格数次出现剧烈波动的背景下,研究大豆期货价格波动的风险管理问题可以为其他农产品提供管理价格波动风险的经验。本项研究围绕两个现实问题即“如何有效防范和化解农产品期货市场价格波动引发的风险?如何建立既符合国际期货市场运行规律,又符合中国期货市场实际的价格波动风险防范和化解机制?”来展开,将农业经济学、期货理论、产业经济学、国际贸易理论、新制度经济学以及现代风险管理理论结合起来,按照从验证已知到探索未知的科学研究范式对我国农产品期货价格波动的风险进行了系统研究。从风险管理的视角,按照风险测度、风险识别和风险控制的逻辑关系,使用较为精确的数量分析方法详细地实证研究了包括大豆1号(豆1)、豆粕和豆油合约在内的大豆期货价格波动风险的大小、诱因以及如何使用保证金制度更好地控制价格波动风险。全文共分为七章。除首尾两章外,其余各章均可独立成篇但又统一于风险管理的基本逻辑框架之中。第1章导论：从我国大豆期货市场运作的实际出发,根据研究目标提出研究所涉及的疑难问题；在对国内外研究文献回顾的基础上设计了一般分析框架：界定了研究中所涉及的概念,凝练并细分了所要解决的科学问题,给出了相应的假说以及验证假说所需的模型；总结了研究中可能的创新、存在的不足以及今后研究需要改进之处。第2章大豆期货价格波动特征研究。选择代表性的大豆期货品种,给出研究中所使用的数据材料；在描述大豆期货价格以及收益率的统计特征的基础上使用多尺度向量自回归(MVAR)模型和多尺度方差分析研究了投资者交易行为与价格波动之间的关系；使用修正的方差和极差重标定方法以及分形维数理论研究了大豆期货市场的分形特征；最后分析了引致大豆期货价格波动的原因以及诱发价格波动风险的原因。研究表明：大豆期货价格以及收益率序列均不服从正态分布；交易行为和价格波动之间存在明显的多尺度因果关系；大豆期货市场具有明显的非线性特征和长期记忆特征；诱发大豆期货价格波动风险的主要因素包括信息因素、投机交易行为等期货市场内部因素并涉及到生产、需求、库存、贸易和经济环境等期货市场外部因素。第3章大豆期货市场有效性检验。分别使用非均衡双因素方差分析和基于市场模型的事件分析方法检验了大豆期货市场上的日历效应和事件效应。研究结果表明：我国大豆期货市场并非有效市场。这为价格波动的风险管理研究提供了支撑,即风险是可以测度、识别和控制的,按照“经验”来管理风险是可行的。第4章大豆期货价格波动风险测度与比较。针对大豆期货市场建立了HS(历史模拟)、MC(蒙特卡洛)、GARCH、IGARCH、TGARCH、EGARCH、PARCH、CARCH以及ACARCH等模型；通过比较这些模型在测度左、右尾部风险时的误判率和对左、右尾部风险的覆盖率找出了风险测度效果较好的模型；分析了诱发大豆期货市场价格波动风险的主要原因。研究表明：在合理的分布假设如t分布下,参数方法对尾部风险的覆盖好于非参数方法,而大样本可以提高这些参数及非参数方法的风险覆盖率并降低误判率；各种方法对风险的右尾部风险覆盖率要高于对左尾部风险的覆盖,即大豆期货市场价格波动风险是有偏的,左尾部风险高于右尾部风险。第5章大豆期货价格波动风险的国际诱因与识别。从国际大豆价格(美国CIF价、巴西和阿根廷FOB价)、国际石油期货价格(NYMEX原油期货价格)以及人民币对美元升值等国际因素着手分析了大豆期货价格波动风险的国际诱因。采用分位数建模理论建立了CAViaR-X模型并实证研究了国际油价和人民币汇率对价格波动左、右尾部风险的不同影响。研究表明：国内大豆现货价格和期货价格之间存在双向因果关系；美国大豆CIF价格、巴西大豆FOB价格对国内大豆现货价格具有引导作用；美国大豆CIF价格与国内大豆期货价格之间存在相互引导关系；巴西大豆FOB价格对国内大豆期货价格具有引导作用；阿根廷大豆FOB价格和国内大豆期货价格具有相互引导的关系；石油价格波动和汇率波动均可诱发国内大豆期货价格波动风险,,并且这两种因素所诱发的左尾部风险高于右尾部风险。第6章基于效率视角的大豆期货风险保证金制度改进。从保证金对风险的控制效果、与风险之间的关系以及对价格波动的影响等方面评价了大豆期货市场上的保证金制度；比较了我国内地期货市场上的保证金制度与成熟和新兴期货市场上保证金制度的优劣；在风险测度的基础上,提出了基于多尺度理论的动态保证金M-IGARCH模型。研究表明：随着波动风险的增加,保证金与价格波动风险线性相关性越来越高,但保证金率的变动并没有很好的起到抑制价格波动风险的作用,而且保证金制度对价格涨跌停板的反应并不敏感；现有保证金制度在设计中更多的考虑了市场稳定而牺牲了市场效率,而使用动态保证金模型M-IGARCH来确定风险保证金水平既能更好地降低价格波动风险又能提高期货市场上资金的使用效率。第7章结论与对策建议。总结了全文主要研究结论；结合研究结论从建立有效市场、创新风险管理技术、应对国际风险以及改进风险保证金制度四个方面给出相应的对策建议。更多还原

【Abstract】 China’s agricultural futures market starts late but develops rapidly. From the perspective of the transaction scale, it has been ranked in the world. But many problems have also been exposed in the development process, in which price volatility risk is particularly prominent in recent years. As stated by Han Story, "When tiaqbie (a kind of rice) is too expensive, the people suffered, while when tiaqbie is too cheap, peasants suffered. It will be riot when people’s interests get harmed, while it leads to poverty when peasants’ interests get harmed"Soybean is one of Chinese agricultural products with the highest degree of marketization and open relatively early. Integration of Chinese soybean market and the international soybean market is good. Under the background that domestic soybeans are continually shocked by foreign genetically modified soybeans and prices of domestic soybean volatile fiercely, study the price volatility risk management of soybean futures may provide managerial experience of price volatility risk for other agricultural products.The paper develops around two questions:How to effectively prevent and resolve price volatility risks in agricultural futures markets? How to create risk prevention and resolve mechanisms of price volatility in compliance with operating law of international futures market and the actual situation of China’s futures market? Combining agricultural economics, the stock theory, the new system economic with modern risk management theory, the paper systematically studies price volatile risks of Chinese agricultural futures according to research paradigm of exploring the unknown filed by verifying the given information. From the perspective of risk management and according to logical relation among risk measures, risk identification and risk control, the paper empirically studies the size and incentive of price volatility risk of soybean 1, meal and oil as well as how to use margin system to better control price volatility risk by using a more precise method of quantitative analysis.The paper is divided into seven chapters. Except the beginning and the last chapters, the remaining chapters can be separate chapters but all unified in the basic logic framework of risk management.Chapter I is introduction. From the reality of market operation of Chinese soybean futures, the chapter proposes involved difficult problems according to research objectives. Then, it designs the general analytical framework on the basis of literature review of domestic and international researches. It also defines the scope of the study. It summarizes and subdivides the scientific problems to be solved and gives out relevant assumptions and models needed to verify the assumptions. The possible innovation and shortcomings are revealed and the problems needed to be further solved are proposed at last.ChapterⅡis characteristics research about price behavior of soybean futures. It chooses representative soybean futures and gives data used in the study. On the basis of describing statistical characteristics of soybean futures prices and returns, it studies the relationship between investors’transaction behavior and price volatility by using multi-scale VAR model and multi-scale variance analysis, and then uses V/S(2), R/S(2) and fractal dimension theory to study the fractal characteristics in soybean futures market. Finally, it analyzes the reasons caused price volatility and elicited price volatility risk of soybean futures. The study shows that price and returns series of soybean futures are not subject to normal distribution and there is obvious multi-scale causal relation between transaction behavior and price volatility. In addition, market has obvious non-linear and long-term memory characteristics. Main factors elicited price volatility risk of soybean futures can be summarized as internal factors in futures market such as information and speculative transactions, some external factors such as production, demand, stock, trade and economic environment are involved.ChapterⅢis tests of market efficiency of soybean futures, which uses two factors variance analysis of non-equilibrium data and event analysis method based on market model respectively to test calendar effect and event effect in soybean futures market. Results show that Chinese soybean futures market is not efficient market, which provides support for risk management research of price volatility, namely risk can measure, identify and control, thus, defers to "experience" to manage risk is feasible.ChapterⅣis the measurement and comparison of price volatility of soybean futures. It establishes models such as HS, MC, GARCH, IGARCH, TGARCH, EGARCH, PARCH, CARCH and ACARCH for soybean futures market. By comparing these models’ misjudgment rate while measure the left and right tail risks and comparing the coverage rate of the extremum of the left and right tail risks, thus discovers the model has better effect to measure risks. The chapter also analyzes the main factors elicited price volatility risk in soybean futures market. Results show that under reasonable distribution assumptions such as the t distribution, parametric method for risk extremum coverage is better than non-parametric method, and large samples can improve the risk coverage rate of these parametric and non-parametric methods and reduce the misjudgment rate. The risk coverage rate of each method in the right tail is higher than in the left tail, that is to say, price volatility risks in soybean futures market are biased, and risk in the left tail is higher than that in the right tail. ChapterⅤis international incentive and recognition of price volatility risk of soybean futures. Start with international soybean prices (CIF US Gulf, FOB Paranaga BRZ and FOB Up River ARG), international petroleum futures prices (NYMEX crude oil futures prices) and the appreciation of the Yuan against the U.S. dollar, the chapter analyzes the international incentive of price volatility risk of soybean futures. In addition, it uses quantile modeling theory to set up CAViaR-X model and empirically study the influence of international oil prices and the exchange rate of RMB on the left and right tail risks of price volatility. Results show that there is bidirectional causal relation between the domestic soybean spot price and the futures price. CIF US Gulf and FOB Paranaga BRZ have guiding function on domestic soybean spot price. There are mutual guiding function between CIF US Gulf and domestic soybean futures price. FOB Paranaga BRZ has guiding function on domestic soybean futures price, but there are mutual guiding function between FOB Up River ARG and domestic soybean futures price. Both petroleum prices and exchange rate fluctuations can induce price volatility risk of domestic soybean futures, and risks induced by the two have the same feature, that is, risk in the left tail is higher than that in the right tail.ChapterⅥis improvement of risk margin institution of soybean futures based on market efficiency. The chapter evaluates margin system in soybean futures market from the three aspects of the effects of risk coverage, relationship between margin and risk, and the influence of margin on price volatility. It compares strengths and weaknesses of margin system in the Mainland of Chinese futures market with the mature and emerging futures markets. On the basis of risk measurement, dynamic margin model M-IGARCH based on multi-scale theory is proposed. Results show, that linear correlation between margin and price volatility risk is getting higher and higher with the increase of volatility risk. But the change of margin rate has not played a good role in inhibiting the risk of price volatility and margin system is not sensitive to price limit. Existing margin system considers more about the stable of market at the expense of market efficiency, but using dynamic margin model M-IGARCH to determine the level of risk margin can ensure market stability but also improve service efficiency of capital.ChapterⅦis the conclusion and countermeasures. The main research results are concluded. And thereby puts forward corresponding countermeasures from the four aspects of building effective market, innovating risk management technology, dealing with international risk and improving risk margin system.更多还原