کنفرانس بین المللی محاسبات نرم

صفحه اصلی / چهارمین کنفرانس بین المللی محاسبات نرم

A hybrid SLCARMA-GRUNN model for modelling periodic high-frequency data

نویسندگان :

Mohammadi Mohammadi¹ Saeid Rezakhah² Navideh Modarresi³

1- دانشگاه صنعتی امیر کبیر تهران 2- دانشگاه صنعتی امیر کبیر تهران 3- دانشگاه علامه طباطبائی

کلمات کلیدی :

CARMA model, Gated recurrent unit neural network, Kalman filter, Periodically correlated process, Recurrent neural networks, Semi-Lévy process.

چکیده :

The intraday 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 intraday 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.