TITLE:
Application of the Improved Generalized Autoregressive Conditional Heteroskedast Model Based on the Autoregressive Integrated Moving Average Model in Data Analysis
AUTHORS:
Qi Yang, Yishu Wang
KEYWORDS:
Forecasting, Outliers, Improved GARCH Model, Partial T-APARCH Model Based on ARIMA Model
JOURNAL NAME:
Open Journal of Statistics,
Vol.9 No.5,
September
6,
2019
ABSTRACT: This study firstly improved the Generalized Autoregressive Conditional Heteroskedast model
for the issue that financial product sales data have singular information when
applying this model, and the improved outlier detection method was used to
detect the location of outliers, which were processed by the iterative method.
Secondly, in order to describe the peak and fat tail of the financial time
series, as well as the leverage effect, this work used the skewed-t Asymmetric Power Autoregressive Conditional
Heteroskedasticity model based on the Autoregressive Integrated Moving
Average Model to analyze the sales data. Empirical analysis showed that the
model considering the skewed distribution is effective.