基于异质交易者的期货市场价格动态研究
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摘要
商品期货市场是一国金融体系的重要组成部分,在国际商品定价体系中发挥巨大的作用。随着社会主义市场经济的发展,形成在国际上具有影响力的“中国价格”,为国民经济服务的需求日益显现。商品期货价格波动理论上以相应的现货价格为基础,受到标的资产价格的制约,但经验研究表明,标的资产价格不能完全解释商品期货价格波动。问题之一在于经典金融经济学理论对交易者同质、完全理性的前提假设。本文针对此方面问题展开研究,目的在于完善期货价格形成理论,掌握期货价格波动规律,对期货市场的监管者规章制定提供理论参考。
本文首先回顾了关于有限理性和期货市场价格形成理论两方面的研究成果。从交易者有限理性和异质性的角度,对期货市场价格动态的形成原因进行重新理解,认为是交易者的行为决定了市场价格动态,理解市场动态的一个核心问题在于理解市场中的交易者。本文对计算金融学的基于Agent金融市场仿真研究方法作进行了综合评述。在此基础上,确定了采用计算金融学建模仿真方法,从交易者微观行为的角度来探讨期货市场价格动态的新思路。
本文从分析经济学和社会学中对“理性”和“有限理性”概念的理解入手,借鉴前人的研究成果,并对市场中交易者的投资行为进行深入分析,将期货市场投机交易者的异质性的表现归纳为信念、风险偏好、自信程度和投资期4个方面,归纳出市场中有代表性的交易策略,形成交易者的信念集合。借鉴心理学对人的学习行为的研究成果,对现有的刻画交易者信念演化的学习模型从构建的层面与学习水平两个维度进行归类比较,得出构建个体层面,同时包括个体学习过程和局部信息社会学习过程的学习模型更适合刻画金融市场这一特定情境下交易者的信念演化过程的结论。根据心理学和行为金融学对模仿行为的研究,改进刻画交易者信念演化过程的SBL模型,加入反应交易者从众行为的模仿机制,构建期货市场交易者信念学习模型——FTBL模型。在完成交易者信念演化机制的构建之后,进一步分析期货市场交易机制,包括价格决定机制、保证金制等,并推导交易者需求,构建期货价格决定与信念演化之间的动态反馈模型——HT-AFM模型,并根据真实市场交易过程设定了仿真事件顺序。在HT-AFM模型中,伴随着交易者对不断变化的市场环境的适应和学习过程,市场不断演化,根据密封拍卖机制出清,产生当期市场价格。新的市场价格反过来进一步对市场中的交易者产生影响,如此不断循环形成市场的动态演化过程。
本文对我国期货市场小麦和铜期货连续合约价格时间序列的典型特征进行检验,将结果与HT-AFM模型仿真得到的结果比较,验证模型的有效性。对HT-AFM模型进行仿真,将真实现货市场价格作为外部信息输入,分析典型特征产生的原因,并采用格兰杰因果检验验证了羊群行为与价格波动之间的关系。最后分析个体学习、社会学习与交易者财富之间的联系。
Commodity futures market is one of the most important sections in national financial system, and it has great influence on international commodity pricing system. As our country’s market economy develops, the need for servicing national economy and forming influenced‘China price’become more and more important. In theory, commodity price moves based on related spot price and is restricted to underlying asset price. But empirical evidence demonstrates that underlying asset price cannot explain commodity futures price volatility at all. One of the questions is the homogeneity and rationality assumption about traders in financial market. This paper does research about this question, and it is aimed to perfect futures price formation theory, deeply understand the rules of futures price volatility, and provide reference to market administrators.
This paper firstly review the literature about‘bounded rationality’and‘futures price formation theory’. Then this paper perceives the cause of futures price volatility from the rational and heterogeneous trader point. Trader’s behavior governs the market price dynamics. So the central question is to understand traders in market. This paper comment agent-based financial simulation market research. Based on this work, exploring futures price dynamics from trader’s micro-behavior is proposed. This paper constructs an agent-based model of a futures market to explore the characters of commodity futures markets price dynamics.
First start analyzing the notion‘rationality’and‘bounded rationality’in economics and social science, then using four dimensions, include‘belief’,‘risk preference’,‘confidence degree’and‘time horizon’to characterize heterogeneous traders and summarize the representative trading strategies in market to form‘market belief set’. Classifying present learning models used in agent-based models to describe beliefs evolutionary process according psychology evidence attain the claim that constructing an individual and social learning with local information learning model is a better choice for futures market context. Based on psychology research and behavior finance theory, this paper improve the SBL model through adding imitation machine into model to describe herd behavior and construct futures market trader belief learning model?FTBL model. After this work, this paper moves on to analyze the futures market trading-machine, including price determination machine, marginal requirement, etc. This paper constructs artificial futures market with heterogeneous traders—HT-AFM model and designs the timing of the model according trading process in real futures market. In HT-AFM model, as trader’s learning and adaptive process, market price constantly evaluates. The market price is generated by seal-bid machine.
This paper use parameter and non-parameter statistics approach to testify‘stylized facts’of commodity futures price and the results are compared with ones generated by HT-AFM to calibrate the model and to prove the efficiency of the model. With real spot market price as exogenous information, the model simulation can generate most of the stylized facts. At last, this paper proves the relationship between herd behavior and price volatility using Granger causal test, and analyzes the relationship between individual learning and social learning.
本文首先回顾了关于有限理性和期货市场价格形成理论两方面的研究成果。从交易者有限理性和异质性的角度,对期货市场价格动态的形成原因进行重新理解,认为是交易者的行为决定了市场价格动态,理解市场动态的一个核心问题在于理解市场中的交易者。本文对计算金融学的基于Agent金融市场仿真研究方法作进行了综合评述。在此基础上,确定了采用计算金融学建模仿真方法,从交易者微观行为的角度来探讨期货市场价格动态的新思路。
本文从分析经济学和社会学中对“理性”和“有限理性”概念的理解入手,借鉴前人的研究成果,并对市场中交易者的投资行为进行深入分析,将期货市场投机交易者的异质性的表现归纳为信念、风险偏好、自信程度和投资期4个方面,归纳出市场中有代表性的交易策略,形成交易者的信念集合。借鉴心理学对人的学习行为的研究成果,对现有的刻画交易者信念演化的学习模型从构建的层面与学习水平两个维度进行归类比较,得出构建个体层面,同时包括个体学习过程和局部信息社会学习过程的学习模型更适合刻画金融市场这一特定情境下交易者的信念演化过程的结论。根据心理学和行为金融学对模仿行为的研究,改进刻画交易者信念演化过程的SBL模型,加入反应交易者从众行为的模仿机制,构建期货市场交易者信念学习模型——FTBL模型。在完成交易者信念演化机制的构建之后,进一步分析期货市场交易机制,包括价格决定机制、保证金制等,并推导交易者需求,构建期货价格决定与信念演化之间的动态反馈模型——HT-AFM模型,并根据真实市场交易过程设定了仿真事件顺序。在HT-AFM模型中,伴随着交易者对不断变化的市场环境的适应和学习过程,市场不断演化,根据密封拍卖机制出清,产生当期市场价格。新的市场价格反过来进一步对市场中的交易者产生影响,如此不断循环形成市场的动态演化过程。
本文对我国期货市场小麦和铜期货连续合约价格时间序列的典型特征进行检验,将结果与HT-AFM模型仿真得到的结果比较,验证模型的有效性。对HT-AFM模型进行仿真,将真实现货市场价格作为外部信息输入,分析典型特征产生的原因,并采用格兰杰因果检验验证了羊群行为与价格波动之间的关系。最后分析个体学习、社会学习与交易者财富之间的联系。
Commodity futures market is one of the most important sections in national financial system, and it has great influence on international commodity pricing system. As our country’s market economy develops, the need for servicing national economy and forming influenced‘China price’become more and more important. In theory, commodity price moves based on related spot price and is restricted to underlying asset price. But empirical evidence demonstrates that underlying asset price cannot explain commodity futures price volatility at all. One of the questions is the homogeneity and rationality assumption about traders in financial market. This paper does research about this question, and it is aimed to perfect futures price formation theory, deeply understand the rules of futures price volatility, and provide reference to market administrators.
This paper firstly review the literature about‘bounded rationality’and‘futures price formation theory’. Then this paper perceives the cause of futures price volatility from the rational and heterogeneous trader point. Trader’s behavior governs the market price dynamics. So the central question is to understand traders in market. This paper comment agent-based financial simulation market research. Based on this work, exploring futures price dynamics from trader’s micro-behavior is proposed. This paper constructs an agent-based model of a futures market to explore the characters of commodity futures markets price dynamics.
First start analyzing the notion‘rationality’and‘bounded rationality’in economics and social science, then using four dimensions, include‘belief’,‘risk preference’,‘confidence degree’and‘time horizon’to characterize heterogeneous traders and summarize the representative trading strategies in market to form‘market belief set’. Classifying present learning models used in agent-based models to describe beliefs evolutionary process according psychology evidence attain the claim that constructing an individual and social learning with local information learning model is a better choice for futures market context. Based on psychology research and behavior finance theory, this paper improve the SBL model through adding imitation machine into model to describe herd behavior and construct futures market trader belief learning model?FTBL model. After this work, this paper moves on to analyze the futures market trading-machine, including price determination machine, marginal requirement, etc. This paper constructs artificial futures market with heterogeneous traders—HT-AFM model and designs the timing of the model according trading process in real futures market. In HT-AFM model, as trader’s learning and adaptive process, market price constantly evaluates. The market price is generated by seal-bid machine.
This paper use parameter and non-parameter statistics approach to testify‘stylized facts’of commodity futures price and the results are compared with ones generated by HT-AFM to calibrate the model and to prove the efficiency of the model. With real spot market price as exogenous information, the model simulation can generate most of the stylized facts. At last, this paper proves the relationship between herd behavior and price volatility using Granger causal test, and analyzes the relationship between individual learning and social learning.
引文
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102 M. A. Hudson, R. M. Lethold, G. F. Sarasoro. Commodity Futures Price Changes: Recent Evidence for Wheat, Soybean and Live Catle. Journal of Futures Markets. 1987, 5: 149~171
103 R. A. Fujihara, M. Mougoue. Linear Dependence, Nonliner Dependence and Petroleum Futures Market Eficiency. Journal of Futures Markets. 1997, 17 (1): 75~99
104 A. Gao, G. H. K. Wang. Modeling Nonliner Dynamics of Daily Futures Price Changes. Journal of Futures Markets. 1999, 19 (3): 325~351
105 B. P. Helms, R. K. Fred, E. R. Robert. Memory in Commodity Futures Contracts. Journal of Futures Markets. 1984, 4 (4): 559~567
106 R. W. Cornew, D. E. Town, L. D. Crowson. Stable Distributions, Futures Prices, and the Measurement of Trading Performance. Journal of Futures Markets. 1984, 4(4): 531~557
107 H. E. Hurst. The Long-term Storage Capacity of Reservoir. Transactions of the American Society of Civil Engineers, 1951, 116: 770~808
108 B. B. Mandelbrot. A Statistical Methodology for Non-periodic Cycles: From the Covariance to R/S Analysis. Annals of Economic and Social Measurement. 1972, 1 (12): 259~290
109 P. Teles, W. W. S. Wei. The Use of Aggregate Time Series in Testing for Gaussianity. Journal of Time Series Analysis. 2002, 23 (1): 95~116
110 G. Ljung, G. Box. On a Measure of Lack of Fit in Time Series Models. Biometrika, 1978, 65 (2): 265~270
111罗登跃,王玉华.上海股市收益率和波动性长记忆特征实证研究.金融研究. 2005, (11): 109~116
112华仁海,陈百助.我国期货市场期货价格收益及波动方差的长记忆性研究.金融研究. 2004, (2): 52~61
113朱云,吴文锋,吴冲锋等.市场分割下的股价微观行为及其行为金融学解释.系统工程理论与实践. 2006, (10): 24~30
114 M. Parkinson. The Extreme Value Method for Estimating the Variance on the Rate of Return. Journal of Business. 1980, 53 (1): 61~65
115 M. B. Garman, M. Klass. On the Estimation of Security Price Volatilities from Historical Data. Journal of Business. 1980, 53 (1): 67~78
116胡畏.中国期货市场价格波动率的到期效应.预测. 2000, (1): 45~46
117唐衍伟,陈刚,张晨宏.我国期货市场的波动性与有效性—基于三大交易市场的实证分析.财贸研究. 2004, (5): 16~22
118 W. A. Brock, W. D. Dechert, J. A. Scheinkman, et al. A Test for Independence Based on the Correlation Dimension. Econometric Review. 1996, 15 (3): 197~235
119张永东.上海股票市场非线性与混沌的检验.管理工程学报. 2003, 17 (3): 21~26
120王新宇,宋学锋,吴瑞明.中国证券市场的分形分析.管理科学学报. 2004, 7 (5): 67~74
121 M. Corazza, A. G. Malliari, C. Nardelli.Searching for Fractal Structure in Agricultural Futures Markets.The Journal of Futures Markets. 1997, 17 (4): 433~473
122黄光晓.陈国进.基于分形市场理论的期铜价格R/S分析.当代财经. 2006, (3): 60~64
123徐龙炳,陆蓉. R/S分析探索中国股票市场的非线性.预测. 1999, (2):59~62
124郝清民.中国股市收益率长记忆性R/S非线性分析.管理工程学报. 2007, 21 (2): 115~117