Open AccessThe empirical research of ARMA-GARCH models based on high frequency data
Author(s) -
Youming Guo,
Tingting Li
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1325/1/012132
Subject(s) - autoregressive conditional heteroskedasticity , econometrics , mathematics , stock exchange , statistics , autoregressive–moving average model , economics , autoregressive model , volatility (finance) , finance
Based on high frequency data (5 minutes) and low frequency data (day), this paper models and analyses the Shanghai Stock Exchange Index (SH1A0001). Through the stationarity test and the ARCH effect test, the ARMA (1,1) – GARCH (1,1) model is established. Under the assumption of the error term obeys the Laplace distribution, the estimation is obtained. By the result of forward prediction, it is found that the result of model prediction in high frequency data is better than those in low frequency data.