超高频数据的日内效应调整方法研究  

The Research of Intra-dayPeriodic Adjustment Based on Ultra High Frequency Data

作　　者：王维国[1] 佘宏俊[1] 

机构地区：[1]东北财经大学数学与数量经济学院,辽宁大连116025

出　　处：《中国管理科学》2015年第6期49-56,共8页Chinese Journal of Management Science

基　　金：国家自然科学基金面上项目(71171035);辽宁省教育厅人文社会科学重点研究基地专项项目(ZJ2013039)

摘　　要：日内效应在金融高频数据研究中已被广泛证实,是一种日内周期性运动的动态效应,它影响了以微观金融指标为参数的计量模型的准确估计。基于金融超高频持续期数据,本文首先论述了日内效应调整的重要性,然后引入自适应映射(SOM)的方法对日内效应进行调整。SOM是一种基于神经网络学习的特征提取方法,能够动态识别高维数据中的结构特征,克服了静态调整方法的不足。最后通过建立基于自回归条件持续期模型(ACD)的蒙特卡罗模拟实验,比较了三种日内效应调整方法的效果。模拟结果表明SOM方法在日内效应调整中更为有效和稳定,特别适合大数据条件下的周期性结构分析。Intra-day periodicity has been widely found in financial high frequency data study. It is a dynamic effect characterized by intra-day periodic motion and it affects the accuracy of econometric model estimation which contains intra-day financial variables. The importance of intra-day periodic adjustment is discussed firstly in this study and then introduces self-organizing maps as a intra-day periodic adjustment solution are introduced based on financial ultra high frequency duration data. The SOM method is a feature extraction on the basis of neural network learning which can recognize the dynamic feature in high-dimensional data in order to overcome the disadvantage of static periodic adjustment. Finally a monte carlo simulation through autoregressive conditional duration model is built to compare the effects of three intra-day periodic adjust- ment methods. The result shows that the SOM method performs more effective and stable. Therefore SOM method can be particularly suited for analysis of periodic structure in big data.

关 键 词：日内效应 自回归条件持续期 SOM 周期性调整 

分 类 号：C931[经济管理—管理学]