Predicting the Volatility of the Russell 3000 Stock Index

Abstract

The forecasting of heteroscedastic models has been a popular subject of research in recent years. The objective of this study is to model and forecast the volatility of the Russell 3000 index during 2000–2015, using various models from the ARCH family. The analysis covers from October 2, 2000 to April 29, 2015 as an in-sample set, and from April 30, 2015 to September 16, 2015 as an out-of-sample set. The measure of the difference between the predicted volatility and the stock’s squared continuously compounded rate of return were estimated by using MAE, MAPE and RMSE. Based on out-of-sample statistical performance, the results reveal that the best estimated model is EGARCH(1,1), and the best model to make dynamic forecasts of volatility is TARCH(1, 1).