基于熵算法的股票指数高频数据复杂度测算与评价
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引用本文:温博慧1, 袁 铭2, 侯 笠1.基于熵算法的股票指数高频数据复杂度测算与评价[J].经济数学,2015,(1):19-25
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作者单位
温博慧1, 袁 铭2, 侯 笠1 (1.天津财经大学 经济学院 天津 300222
2.天津财经大学 理工学院 天津 300222) 
中文摘要:在日内高频环境下检验基于兼容法的柯尔莫哥洛夫熵、样本熵和模糊熵等复杂度测算方法对我国沪深300股票指数的测算效率,并运用筛选后的有效算法分阶段研究和比较了序列复杂度的变化过程与变化幅度.结果表明,模糊熵算法是一种更适用于我国沪深300股票指数的有效复杂度测算方法,其对相似容忍度的敏感性更低,测度值连续性更好.随时间推移,我国沪深300股票指数复杂度整体呈上升趋势,而相较于发达市场甚至周边新兴市场其复杂度偏低.
中文关键词:沪深300股票指数  复杂度  kolmogorov熵  样本熵  模糊熵
 
The Study of CSI 300 Index’s Complexity and Comparison of Model Efficiency Based on Entropy Algorithm
Abstract:This paper studied the high frequency data of the CSI 300 index, and examined the efficiency of complexity measures such as Kolmogorov entropy, sample entropy and fuzzy entropy in high frequency environment. By using the effective measurement, it compared the changing process and range of the complexity both before and after the subprime crisis. The results show that, compared with the Kolmogorov entropy based on the compatible method and sample entropy, fuzzy entropy is more suitable for measuring the CSI300 index's complexity, which has the lower sensitivity to the similar tolerance and the better continuity of measure value. The CSI 300 index's complexity is rising during the sample interval. However, the complexity during the crisis is far more less than the two other stages, and the complexity after the crisis is higher than that before the crisis. Compared with the developed markets and even some emerging markets, the CSI 300 index's complexity is much lower.
keywords:the CSI 300 index  complexity  Kolmogorov entropy  sample entropy  fuzzy entropy
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