The Application of the ARIMA-GARCH Hybrid Model for Forecasting the Apple Stock Price

DOI: https://doi.org/10.62517/jike.202304102

Author(s)

Haoqing Jiang

Affiliation(s)

Wuhan Britain-China School, Wuhan, Hubei, China

Abstract

Modeling and forecasting stock prices is a meaningful task and one of the methods for forecasting is the classic ARIMA models. However, when the data exhibits clustering effects and heteroscedasticity, the generalized auto regressive conditional heteroskedatic (GARCH) model must be used for modeling and forecasting. In this paper, as the object of data analysis, the combination of ARIMA model and GARCH model shows a very good ability to predict the stock price with a very good description of the clustering effect of volatility.

Keywords

Apple Stock Price; Heteroskedatic; Clustering Effects; ARIMA-GARCH Model; Forecasting

References

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