| 基于时序模型的股指序列分析 |
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| 引用本文： | 崔玉泉,李培培,李琳琳.基于时序模型的股指序列分析[J].山东大学学报(理学版),2013,48(8):68-77. |
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| 作者姓名： | 崔玉泉 李培培 李琳琳 |
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| 作者单位： | 山东大学数学学院, 山东 济南 250100 |
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| 基金项目： | 山东省自然科学基金资助项目 |
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| 摘 要： | 根据计量经济时序模型,基于2005～2009年沪深两股市的数据和统计软件EVIEWS,将计量模型与分形维数相结合,利用股指的高维混沌特征,以L-P算法确定了分形维数。运用向量自回归VAR模型,对沪深两个股市进行了单位根检验,根据AIC和SC信息准则确定滞后阶数,并对股市的未来趋势进行了有效地动态和静态预测,得出了较为合理的结果。
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| 关 键 词： | 综合指数 分形维数 向量自回归VAR 预测 |
| 收稿时间： | 2012-12-15 |
The analysis of stock index sequence based on timing series model |
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| CUI Yu-quan
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LI Pei-pei
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LI Lin-lin.The analysis of stock index sequence based on timing series model[J].Journal of Shandong University,2013,48(8):68-77. |
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| Authors: | CUI Yu-quan LI Pei-pei LI Lin-lin |
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| Institution: | School of Mathematics, Shandong University, Jinan 250100, Shandong, China |
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| Abstract: | According to econometric time series model, the paper creatively proposed combing econometric model and fractal dimension based on Shanghai and Shenzhen stock market data system of great wisdom and statistical software EVIEWS during 2005-2009.It uses a high-dimensional chaotic characteristics of the stock index, using LP algorithm to determine the fractal dimension, using vector autoregressive VAR model to test unit root on the two stock markets in Shanghai and Shenzhen. According to AIC and SC information criterion, it determine the number of lags, predict the future trend of the stock market in dynamic and static state and obtain more reasonable results. |
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| Keywords: | composite index fractal dimension vector autoregressive VAR forecast |
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