基于非线性混沌理论的期货市场农产品交易分析
Comparative Analyses of Turnovers of Agricultural Products Futures Based on Nonlinear Chaotic Theories
DOI: 10.12677/ORF.2015.54008, PDF, HTML, XML, 下载: 2,050  浏览: 5,676 
作者: 韩清滨, 朱 喆:武汉理工大学马克思主义学院,湖北 武汉
关键词: 农产品期货交易混沌代替数据法Lyapunov指数非线性Turnovers of Agricultural Products Futures Chaotic Surrogate Data Method Lyapunov Exponents Nonlinear
摘要: 本文应用复杂系统理论研究我国期货价格收益率数据的非线性特性。研究采用基于混沌理论的两种非线性参数估计方法(代替数据法和Lyapunov指数估计法)对大连和郑州期货交易所的2009年到2014年的农产品期货交易额进行分析。文中首先对上述两种非线性方法的具体算法进行介绍,然后对两组期货交易数据进行对比分析。利用代替数据方法对农产品期货价格时序数据进行非线性特性检验。研究结果表明农产品期货价格时序数据中确实存在着非线性成分,在时域波形上直观相似的农产品期货交易额,用上述非线性混沌分析的方法可以有效地加以定量区分。
Abstract: In the paper, two nonlinear estimation methods based on chaotic theory, surrogate data method and Lyapunov exponents, are used to distinguish the difference of non-stationary signals of the turnovers of agricultural products futures between Zhengzhou Commodity Exchange and Dalian Commodity Exchange from 2009 to 2014. After brief introduction of the corresponding algorithms, two typical different signals are compared by using the above two methods respectively. The ob-tained results demonstrate that the apparently similar signals are distinguished effectively in a quantitative way with applying above nonlinear chaotic analyses.
文章引用:韩清滨, 朱喆. 基于非线性混沌理论的期货市场农产品交易分析[J]. 运筹与模糊学, 2015, 5(4): 52-58. http://dx.doi.org/10.12677/ORF.2015.54008

参考文献

[1] 唐衍伟, 陈刚, 张晨宏. 农产品期货价格联动性实证研究——基于中美玉米期货日收盘价数据[J]. 系统科学与数学, 2015, 35(2): 181-192.
[2] Kantz, H. and Schreiber, T. (1999) Nonlinear Time Series Analysis. Cambridge University Press, Cambridge.
[3] 宋佳伟, 徐煜明, 肖贤建. 一种基于小波变换和迭代反向投影的超分辨率算法[J]. 计算机技术与发展, 2015, 25(2): 74-77.
[4] Meltzer, G.S. and Ivanov, Y.Y. (2003) Fault Detection in Gear Drives with Non-Stationary Rotational Speed—Part I: The Time-Frequency Approach. Mechanical Systems and Signal Processing, 17, 1033-1047.
http://dx.doi.org/10.1006/mssp.2002.1530
[5] Katvonik, V. and Stankovic, L. (1998) Instantaneous Frequency Estimation Using the Wigner Distribution with Varying and Data-Driven Widow Length. IEEE Transactions on Signal Processing, 46, 2351-2325.
[6] 张玉波. 基于分形市场理论的大宗商品期货市场风险测度与防范[J]. 统计与决策, 2015(10): 140-143.
[7] 陶慧, 李莹, 马小平. 含噪声多变量混沌时间序列的最大Lyapunov指数计算[J]. 河南理工大学学报, 2014, 33(6): 770-775.
[8] Theiler, J., Eubank, S., Longtin, A., et al. (1992) Testing for Nonlinearity in Time Series: The Method of Surrogate Data. Physica D: Nonlinear Phenomena, 58, 77-94.
http://dx.doi.org/10.1016/0167-2789(92)90102-S
[9] Yonemoto, K. and Yanagawa, T. (2007) Estimating the Lyapunov Exponent from Chaotic Time Series with Dynamic Noise. Statistical Methodology, 4, 461-480.
http://dx.doi.org/10.1016/j.stamet.2007.02.001
[10] 何鹏, 周德云, 黄吉传. 利用互信息确定延迟时间的新算法[J]. 计算机工程与应用, 2013, 49(24): 8-11.
[11] Kantz, H. (1994) A Robust Method to Estimate the Maximal Lyapunov Exponent of a Time Series. Physics Letters A, 185, 77-87.
http://dx.doi.org/10.1016/0375-9601(94)90991-1