一类金融系统行为的非线性混沌分析

林勇新; 陈予恕; 曹庆杰

doi:10.3879/j.issn.1000-0887.2010.10.011

一类金融系统行为的非线性混沌分析

doi: 10.3879/j.issn.1000-0887.2010.10.011

哈尔滨工业大学航天学院137信箱,哈尔滨 150001

基金项目: 国家自然科学基金重点资助项目(10632040)

详细信息

作者简介:
林勇新(1974- ),男,哈尔滨人,博士生(联系人.E-mail:linyongxin7406@126.com);陈予恕(1931- ),教授,博士生导师,院士(E-mail:yschen@hit.edu.cn).

中图分类号: O175;O241
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出版历程
- 收稿日期: 1900-01-01
- 修回日期: 2010-09-03
- 刊出日期: 2010-10-15

Nonlinear and Chaotic Analysis of a Financial Complex System

The School of Astronartics, Harbin Institute of Technology, P. O. Box 137, Harbin 150001, P. R. China

摘要

摘要: 利用相位随机化的替代数据方法对中国商品期货市场某些品种特性进行了判定，此方法用于随机时序与非线性混沌时序所得的判据值有明显差异．并应用混沌时序的奇异值分解技术对混沌时序的噪声进行了剥离，将相空间分解为值域空间和虚拟的噪声空间，在值域空间内重构了原混沌时序．进一步采用建立在改进的一般约束随机化方法基础之上强扰动的方法再次判定．根据计算结果对商品期货市场的走势进行了分析，结果表明中国商品期货市场是具有明显非线性混沌特性的一类复杂非线性混沌系统．
- 非线性混沌时序 /
- 随机时序 /
- 相位随机化 /
- 奇异值分解 /
- 一般约束随机化
Abstract: A determination on the characteristics of futures market of commodity of China was presented by the method of the phase-randomized surrogate data.There was a significant difference in critical values obtained when this method was used in random timeseries and nonlinear chaotic timeseries.The technology of the singular value decomposition was used to reduce noise of chaotic timeseries and then the phase space of chaotic timeseries was decomposed to range space and null noise space, and the original chaotic timeseries in range space was restructured.The method of strong disturbance on the basis of the improved general constrained randomized method was further adopted to re-deternine.According to the calculated result an analysis on the trend of futures's market of commodity is made.The results indicate that the Chinese futures's market of commodity is a complicated nonlinear system with obvious nonlinear chaotic characteristic.
- nonlinear chaotic timeseries /
- random timeseries /
- phaserandomized /
- singular value decomposition /
- general constrained randomization

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参考文献(18)

[1]	Abarbanel Henry D I, Brown R, Kadtke J B. Prediction in chaotic nonlinear systems： methods for time series with broadband fourier spectra [J]. Phys Rev A, 1990, 41(4)：1782-1807. doi: 10.1103/PhysRevA.41.1782
[2]	Engle R F. Autoregressive conditional heteroscedasticy with estimates of the variance of UK inflationl[J]. Econometrica, 1982, 50（4）: 987-1008. doi: 10.2307/1912773
[3]	Bollerslev T. Generalized autoregressive conditional heteroscedasticity[J]. Journal of conometrics, 1986, 31（3）: 307-327.
[4]	Tong H. Non-Linear Time Series: A Dynamical Systems Approach[M]. Oxford: Oxford University Press, 1990.
[5]	Tjvstheim D. Non-linear time series: a selective review[J]. Scandinavian Journal of Statistics, 1994, 21（2）: 97-130.
[6]	Yao Q, Tong H. On initial-condition sensitivity and prediction in nonlinear stochastic systems[J]. Bull Int Statist Inst, 1995， 50（4）: 395-412.
[7]	Yao Q, Tong H. On prediction and chaos in stochastic systems[C]Tong H. Chaos and Forecastinged. Singapore: World Scientific, 1994, 348(1688): 357-369.
[8]	Masry E, Fan J. Local polynomial estimation for stationary stable processes[J]. Scandinavian Journal of Statistics, 1997, 24(2): 165-179. doi: 10.1111/1467-9469.00056
[9]	Gourieroux C. ARCH Models and Financial Applications[M]. New York: Springer-Verlag, 1997.
[10]	Cabrera J L, Jzvier F. Numerical analysis of transient behavior in the discrete random Logistic equation with delay[J]. Phys Lett A, 1995, 197(1): 19-24. doi: 10.1016/0375-9601(94)00951-K
[11]	Grassberger Peter. Fininte sample corrections to entropy and dimension estimates[J]. Phys Lett A, 1988, 128(6/7): 369-373. doi: 10.1016/0375-9601(88)90193-4
[12]	马军海，陈予恕，刘曾荣. 不同随机分布的相位随机化对实测数据影响的分析研究[J]. 应用数学和力学，1998, 19(11): 955-964.
[13]	Rapp P E, Albano A M. Phase-randomized surrogates can produce spurious identificztions of non-random structure[J]. Phys Lett A, 1994, 192(1): 27-33. doi: 10.1016/0375-9601(94)91010-3
[14]	陈国华，陈春旺，盛昭瀚，马军海. 经济时序动力系统最佳嵌入维数的选取及应用研究[J]. 天津大学学报（自然科学与工程技术版）, 2001, 34(2):158-162.
[15]	Albano A M, Muench J M, Schwartz C. Singular value decomposition and the Grassberger Procaccia algorithm[J].Phys Rev A, 1988, 38(6): 3017-3026. doi: 10.1103/PhysRevA.38.3017
[16]	马军海，陈予恕. 低维混沌时序非线性动力系统的预测方法及其应用研究[J]. 应用数学和力学，2001, 22(5): 441-448.
[17]	Casdagli Martin, Eubank Stephen, Farmer J Doyne, Gibson J. State space reconstruction in the presence of noise[J]. Phys Ser D, 1991, 51(10): 52-98.
[18]	Scjreiber T. Constrained randomization of time series data[J]. Physical Review Letters, 1998, 80(10): 2105-2108. doi: 10.1103/PhysRevLett.80.2105