Open AccessModelling and forecasting S&P 500 stock prices using hybrid Arima-Garch Model
Author(s) -
Farah Hayati Mustapa,
Mohd Tahir Ismail
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 - 10.1088/1742-6596/1366/1/012130
Subject(s) - autoregressive integrated moving average , autoregressive conditional heteroskedasticity , econometrics , economics , stock (firearms) , stock market index , portfolio , index (typography) , financial economics , time series , computer science , volatility (finance) , statistics , stock market , mathematics , engineering , geography , mechanical engineering , context (archaeology) , archaeology , world wide web
The S&P 500 is a bellwether and leading indicator for the economy as well as the default vehicle for passive investors who want exposure to the U.S. economy via index funds. Since 1957, the S&P 500 has performed amazingly, outpacing other leading asset classes such as bonds or commodities. This study seeks to develop an appropriate ARIMA model that best fit the monthly stock price of S&P 500 for a period of 17 years, 2001–2017, thus make a short-term forecast in a way to give an overview and help the investor or portfolio manager in decision making. EViews software was used to run the analysis of the data. Our analysis involved 2-step procedure, which were identifying ARIMA model then fitting GARCH (1,1) into the model. As a result, ARIMA (2,1,2)-GARCH (1,1) model was found to be the best model for forecasting the S&P500 stock prices. The research findings indicate that a dynamic forecast gave a better result compared to a static forecast.