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 adjustment 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.
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