基于沪深300股指期货高频数据趋势持续期模型的构建与检验

发布时间：2018-03-02 23:30

  本文选题：趋势持续期　切入点：经验模态分解　出处：《统计与决策》2017年20期 　论文类型：期刊论文

【摘要】：文章针对我国沪深300股指期货高频数据时间序列具有趋势运动特性,提出了趋势持续期模型。首先采用泊松过程对趋势持续期的市场微观结构进行建模,得出了趋势持续期在理论上服从Gamma分布;基于经验模态分解算法提取股指期货日内高频交易数据的趋势持续期,采用最大似然估计法,估计趋势持续期的Gamma分布参数,同时通过Kolmogorov-Smirnov检验验证了模型的有效性;最后对不同采样间隔下的趋势持续期进行标准化处理,趋势持续期模型具有很好的稳健性。
[Abstract]:In this paper, a trend duration model is proposed for the time series of high frequency data of CSI 300 stock index futures. Firstly, Poisson process is used to model the market microstructure of the trend duration. Based on empirical mode decomposition algorithm, the trend duration of intraday high frequency trading data of stock index futures is extracted, and the Gamma distribution parameters of trend duration are estimated by using maximum likelihood estimation method. At the same time, the validity of the model is verified by Kolmogorov-Smirnov test. Finally, the trend duration model with different sampling intervals is standardized, and the trend duration model has good robustness.
【作者单位】： 北京大学经济学院;
【分类号】：F224;F724.5
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本文编号：1558602