Prediction of S&P 500 Index Using HAR-RV Models with Structural Breaks, Day-of-the-Week Effect and Trading Volume

Economic trends are one the crucial research topics. Standard and Poor's 500 index (S&P500) is an indicator of economic change with diversification. In this article, the time series models of forecasting realized volatility, regarding the heterogeneous autoregressive theory, are evaluated. For the day, week, and month predictions, heterogeneous autoregressive models with realized volatility (HAR-RV) type models take structural breaks, the day-of-the-week impact, and trade volume into account. Structure breaks and day-of-the-week effects have positive effects on in-sample prediction, while there is not enough evidence to show that trading volume is significant for in-sample prediction. For out-sample prediction, simple loss functions and the Diebold Mariano test are employed to compare the capability of HAR-RV type models in this article for out-sample performance. The HAR-RV, HAR-RV-VOL, and HAR-RV-WV models are regarded to be accurate for short- and mid-term out-sample prediction. The HAR-RV, HAR-RV-SV, and HAR-RV-SWV models are thought to be significant over the long run.