چکیده مقاله
The intra day return of high frequency financial data have periodic structure These data have volatilities and existing works assumes it is a stationary process However, there is evidence for the presence of intra day periodicity or seasonality in volatility Due to the inherent periodicity and non linear characteristics of high frequency data, the accurate prediction of these data is critical to the market activity In order to present a model that supports this feature, we introduce a hybrid semi Lévy driven continuous time ARMA SLCARMA gate recurrent unit neural network GRUNN model The hybrid SLCARMA GRUNN model based on the traditional method that assume the linear components and non linear components should be linearly added The proposed hybrid model is applied to 30 minute squared log returns of Dow Jones Industrial Average indices
کلیدواژهها
نویسندگان
Mohammad Mohammadi
Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
Saeid Rezakhah
Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran,
Navideh Modarresi
Faculty of Statistics, Mathematics and Computer, Allameh Tabataba'i University, Tehran, Iran
شیوه ارجاع
Mohammadi, Mohammad and Rezakhah, Saeid and Modarresi, Navideh,1400,A hybrid SLCARMA-GRUNN model for modelling periodic highfrequency data,Fourth International Conference on Soft Computing
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