Stock Closing Price Prediction Based on the ARIMA-GARCH Model
DOI:
https://doi.org/10.54097/qgjpce42Keywords:
ARIMA-GARCH; Stock Prediction; Time Series; Closing Price.Abstract
Amidst a burgeoning stock market, a plethora of predictive models for stock prices have been steadily surfacing, with a wide array of Autoregressive models finding extensive application. This study sets out to evaluate the efficacy of the ARIMA-GARCH model in the domain of stock price prediction, taking as its dataset the stock information of Jinan Hi-Tech Development (600807). This paper begins by transforming the closing prices of Jinan Hi-Tech Development (600807) from July 13, 2023, to July 27, 2023, into a time series for the purpose of model fitting, identifying the ARIMA model parameters, and examining ARCH effects alongside the normality and independence of residuals. Subsequently, GARCH model parameters are discerned and integrated with the ARIMA model to establish the ARIMA-GARCH model, which is then subjected to residual testing. Concluding the study, a comparative error analysis between the predicted and actual closing prices reveals that the ARIMA-GARCH model boasts substantial accuracy in short-term stock price forecasting.
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