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).
Highlights
It has become more important for financial institutes to pay attention to the movements of a financial asset
This study focuses on the equity market, It contributes to the existing finance literature by investigating the U.S stock market during the recent period
The main motive of this paper is to investigate the use of ARCH family models for forecasting volatility of the Russell 3000 index by using the daily data
Summary
It has become more important for financial institutes to pay attention to the movements of a financial asset. These movements, seen as the risk of the assets, are estimated by the volatility. The results of research in the model’s performance is conflicting and confusing. In predicting volatility we must ask:Which model captures the volatility better? Could we obtain a more efficient forecast accuracy with a model that better captures volatility? These observations lead the author to determine how well these different models perform in terms of forecasting. This study focuses on the equity market, It contributes to the existing finance literature by investigating the U.S stock market during the recent period
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