The Research of Intra-dayPeriodic Adjustment Based on Ultra High Frequency Data
Wang Wei-gu · Zhongguo guanli kexue · 2015
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.