Date Thesis Awarded
Honors Thesis -- Access Restricted On-Campus Only
Bachelors of Science (BS)
Volatility in the stock market has increasingly become a target of investigation. Understanding and predicting volatility in the stock market can give one great advantages in investment options. Over the years, many models have been developed, such as the Autoregressive Moving Average (ARMA) and the Autoregressive Conditional Heteroskedasticity (ARCH) models. The ARCH model has served as the foundation for other models like the General ARCH (GARCH) and the Continuous GARCH (COGARCH) models. In this paper, we investigate the effectiveness of the widely used GARCH(1,2) model to the relatively new COGARCH(1,2) model that was conceptualized by Brockwell, Chadraa, and Lindner, in predicting volatility in S&P500 index, namely the VIX index. Specifically, we look at each model’s one step prediction directional accuracy and percent errors in both models.
Hackett, Ethan, "Investigating the Effectiveness of GARCH(1,2) and COGARCH(1,2) Models in Estimating Volatility in the S&P500 Index" (2022). Undergraduate Honors Theses. William & Mary. Paper 1897.
On-Campus Access Only