Date Thesis Awarded
12-2022
Access Type
Honors Thesis -- Access Restricted On-Campus Only
Degree Name
Bachelors of Science (BS)
Department
Mathematics
Advisor
Ed Chadraa
Committee Members
Haipeng Chen
Greg Hunt
Abstract
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.
Recommended Citation
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.
https://scholarworks.wm.edu/honorstheses/1897
